    [From the U.S. Government Printing Office, www.gpo.gov]

               RESOURCE DAMAGE ASSESSMENT

       OF THE T/V PUERTO RICAN OIL SPILL INCIDENT

                      APPENDICES

                            Property of CSC Libraz?

                       U.S. DEPARTMENT OF COMMERCE NOAA
                       COASTAL SERVICES CENTER
                       2234 SOUTH HOBSON AVENUE
                       CHARLESTON , SC 29405-2413

                      April 1986

         United States Department of Commerce
    National Oceanic and Atmospheric Administration
                National Ocean Service
    Office of Ocean and Coastal Resource Management
             Sanctuary Programs Division
               Washington, D.C. 20235

                               TABLE OF CONTENTS

                                                                     Page

    APPENDICES

    1.  List of Species Found in the Gulf
          of  the  Farallones  Region   .................................  1-1

    2. Annotated Species List of Marine Birds Affected
          by the T/V Puerto Rican .......... ...                       2-1

    3. Species Selection of T/V Puerto Rican Oil
          Spill Risk Analysis Canputer Modeling.....................    3-1

    4. Oil Spill Model Description ................................   4-1

    5. Bird Mortality Resulting from Oil Spilled
          by the T/V Puerto Rican ..............     .......... . ...   5-1

    6. Damage to the Plankton of the Gulf of the Farallones
          Caused by T/V Puerto Rican Oil Spills.....................    6-1

    7. Background Documents

         - Biological Hindcast of the Effects of the T/V Puerto
            Rican Oil on Three Seabird Species ...................... 7-1

         - Special Oil Spill Risk Analysis of Tank Vessel
            Puerto Rican Incident ............................  7-29

         - Trajectory Analysis and Modeling Support
            for the Puerto Rican Oil Spill...... 7-41

            The Impacts of the T/V Puerto Rican Oil Spill on
            Marine Bird and Mammal Populations in the Gulf of
            the Farallones, 6-19 November 1984 ........... 7-83

         - Aerial Surveys of Seabirds and Marine Mammals in the
            Gulf of the Farallones Region, 11-20 November 1984 ...... 7-167

         - Aerial Surveys of Seabirds and Marine Mammals in the
            Gulf of the Farallones Region, 30 November 1984 ......... 7-188

         - Aerial Surveys of Seabirds and Marine Mammals in the
            Gulf of the Farallones Region, 12 December 1984 .........  7-191

                                                                         Page

        - The Effects of Oil from the T/V Puerto Rican on
            Recruitment and Recovery of Barnacle Populations ...........  7-199

        - An Analysis of the Inmediate Effect of the Puerto Rican
            Oil Spill on the Intertidal Marine Life of Marin
           County, California ...................   ................... 7-235

        - Toxic Effects of Four Refined Oils Spilled into the Gulf
            of the Farallones on Natural Communities of Plankton
           Under Simulated In Situ Conditions .........................  7-257

        - Necropsy Protocol for Marine Animal Mortality Along
            the Point Reyes National Seashore. November 3, 1984
            (Puerto Rican Oil Tanker Sinking) to March 7, 1985 .........  7-349

        -  Documentation of Administrative Research Costs.............  7-359

    8.  Curriculum Vitae of Participants., ............................    8-1

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                                   APPENDIX 1
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           1  ~~~~~~~~~SPECIES LIST

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    Appendix 1  Marine bird species found in the Point Reyes-Farallon
                 Islands National Marine Sanctuary (source: Ainley, 1976;
                 Winzler and Kelly, 1977; Uduardy, 1977.)

    COMMON NAME                               SCIENTIFIC NAME

    Fork-tailed storm petrel                  Oceanodroma furcata
    Leach's storm-petrel                      Oceanodroma leucorhoa
    Ashy storm-petrel                         Oceanodroma homochroa
    Double-crested cormorant                  Phalacrocorax auritus
    Brandt's cormorant                        Phalacrocorax penicillatus
    Pelagic cormorant                         Phalacrocbrax pelagicus
    Western Gull                              Tarus occidentalis
    Ccmmon murre                              Uria aalge
    Pigeon Guillemot                          Cepphus columba
    Marbled murrelet                          Brachyramphus marmoratus
    Cassin's auklet                           Ptycnoramphus aleuticus
    Tufted puffin                             Fratercula cirrhata
    Brown pelican                             Pelicanus occidentalis
    Arctic loon                                Gavia arctica
    Red-throated loon                         Gavia stellata
    Western grebe                             Aechmophorus occidentalis
    Red-necked grebe                          Podiceps grisegena
    Eared grebe                               Podiceps nigricollis
    Horned grebe                              Podiceps auritus
    Northern fulmar                           Fulmaras glacialis
    Black scoter                              Melanitta nigra
    Surf scoter                               Melanitta perspicillata
    White-winged scoter                        Melanitta fusca
    California gull                           Larus californicus
    Herring gull                              Larus argentatus
    Ancient murrelet                          Synthliboranthus antiguus
    Red phalarope                             Phalaropus fulicarius
    Skua                                      Catharcta maccormicki
    Bonaparte's gull                          Larus philadephia
    Sabine's gull                             Xema sabini
    Common tern                               Sterna hirundo
    Buller's shearwater                       Puffinus bulleri
    Black-footed albatross                    Diomedea nigripes
    Black legged kittiwake                    Rissa tridactyla
    Pink-footed shearwater                    Puffinus creatopus
    Sooty shearwater                          Puffinus griseus
    Elegant tern                              Sterna elegans
    Common loon                               Gavia immer
    Rhinoceros auklet                         Cerorhinca monocerata
    Spotted sandpiper                         Actitis macularia
    Least sandpiper                           Erolia minutilla
    Western sandpiper                         Caladris mauri
    Willet                                    Catoptrophorus semipalmatus
    Long-billed dowitcher                     Timnodromus scolopaceus
    Great blue heron                          Ardea herodias

                                             1-2

    (Cont'd)

    COMMON NAME                           SCIENTIFIC NAME

    Snowy Plover                          Charadrius alexandrinus
    Dunlin                                Erolia alpina
    Killdeer                              Charadrius vociferus
    American Coot                         Fulica americana
    Black brant                           Branta bernicla nigricans
    Diving ducks.                         Anas
    Black-bellied plover                  Squatarola Squstorola
    Semipalmated plover                   Charadrius semipalmatus
    Sanderling                            Crocethia alba
    Whimbrel                              Numenius Phaeopus
    Heermann' s gull                      Larus heermanni
    Forster's tern                        Sterna forsteri
    American white pelican                Pelecanus erythrorynchos
    Pomarine jaeger                       Stercorarius pomarinus
    Glaucous-winged gull                  Larus glaucescens
    Northern pintail                      Anas acuta
    Ruddy duck                            Oxyura jamaicensis
    Greater scaup                         Aythya marila
    Red-tailed hawk                       Buteo jamaicensis

                                             1-3

    Appendix 1.  Marine mammal species found in the Point Reyes/Farallon
                 Islands National Marine Sanctuary (source:  California
                 Department of Fish and Game, 1979. Living Marine resources
                 of the proposed Point Reyes-Farallon Islands Marine Sanctuary).

    IOMMON NAME                                 SCIENTIFIC NAME

    PINNIPEDS:

    California sea lion                          Zalophus californianus
    Steller sea lion                            Eumetopias jubatus
    Harbor seals                                Phoca vitulina
    Northern elephant seal                       Mirounga angustirostris
    Northern fur seal                            Callorphinus ursinus

    Fissiped:

    California sea otter                         Enhydra lutris nereis

    Cetaceans:

    Blue whale                                  Balaenoptera musculus
    Sei whale                                   Balaenoptera borealis
    California gray whale                        Eschrichtius robustus
    Finback whale                               Falaenoptera physalus
    Humpback whale                              Megaptera novaeangliae
    Pacific pilot whale                         Globicephala machorhynchus
    Killer whale                                Orcinus orca
    False killer whale                          Pseudorca crassidens
    Sperm whale                                 Physeter catodon
    Baird's beaked whale                        Berardius bairdi
    Curier's beaked whale                        Ziphius cavirostris
    Ccunon dolphin                              Dellhinus' delphis
    Risso's dolphin                             Grampus griseus
    Pacific white sided dolphin                 Lagenorhynchus Obliquidens
    Northern right whale dolphin                 Lissodeiphis borealis
    Harbor porpoise                             Phocoena phocoena
    Dall porpoise                               Phocoenoides dalli

                                         1-43

    Appendix 1 * Invertebrate species found in the Point Reyes-
                Farallon Islands National Marine Sanctuary
                (source: Jones and Stokes, 1981.)3

    COMMON NAME                        SCIENTIFIC NAME

    Echinodermos

    Giant red sea urchin               Strongylocent-rotus franc iscanus
    Bat star                           Patiria miniata
    Ochre star                         Pisaster ochraceus
    Large- spined sea star    P .         Tgigateus
    Short-spined sea star              P. brevispinus

    Marine Molluscs

    Market squid                       Lolig  opalescens
    Abalone spp.                       Haliotis spp.
    Limpets                            Acmaea spp.
    Periwinkles                        Littorina spp.
    Bay Mussel                         Mytilus edulis
    California mussel                  M.clfrianu
    Falcate date mussel                Adula falcata
    Pacific razor clam                 Siliqua patula
    Conrad's piddock and flat-tipped
      piddock                          Pinitella conradi and P. penita
    California brackish water snail    Tryonia imitator
    Soft-shell clanmy  arenaria3
    Gaper clam                         Tresus nuttallii
    Pismo clan                         Tivela stultorum
    Carnwin Washington clam            Saxidcanus nuttallii
    Purple-hinged rock scallop         Hinnites giganteus
    Shipworm                           Teredoc navalis

    Marine CrustaceansI

    Copepod                            Acartia tonsa
    Copepods                           Calanus spp.
    Acorn barnacle                     Balanus spp.
    opossum shrimp                     Neomnysis mercedis
    Gr ibble                           Limnoria spp.3
    Long-horned beach hopper           Orchestoidea californiana
    Euphaus iids                       Thysanoessa spinifera and
                                        Euphausia pacifica
    Bay shrimp                         Crangon franc iscorum
    Peneid shrimp                      Sergestes similus
    Spot Prawn and pink shrimp         Pandalus platvceros and P. jordani
    Spot Prawn                         P. platyceros
    Lined shore crab                   Pach-ygrapsus crass ipes
    Dungeness crab                     Cancer mnagister
    Rock crab                          C. antennarius3
    MoDle crab                         Emer4i`ta a~naioga

    Appendix 1.  (Cont'd)  Invertebrate species found in the Point Reyes-
                           Farallon Islands National Marine Sanctuary
                           (source: Jones and Stokes, 1981.)

    COMMON NAME                          SCIENTIFIC NAME

    Marine Worms

    Fan Worm                             Eudistylia spp.
    Ornate tube worm                     Diopatro ornata
    Mussel worm                          Nereis vexillosa
    Bloodworm                            Euzonus spp.
    Sand tube worm                       Pharagmatopoma californica

    Coelenterates

    Giant green anemone                  Anthopleura xanthogrammica
    Strawberry anemone                   Corynactis californica
    Orange cup coral                     Balanophyllia elegans
    Brown cup coral                      Paracyathus stearnsii

    Appendix 1. Species of Fish Found in the Point Reyes-Farallon
                Islands National Marine Sanctuary (source: Jones and
                Stokes, Associates, 1981).

    COMMON NAME                           SCIENTIFIC NAME

    Pacific lamprey                       Entosphenus tridentatusI
    Leopard shark                         Triakis semifasciata
    Green sturgeon                        Acipenser medirostris
    White sturgeon                        A. transmontanus
    American shad                         Ailosa sapidissima
    Pacific herring                       Clupea harengus pallasii
    Northern anchovy                      Engraulis mordax
    Chinook salmon                        Oncorhynchus tshawytscha
    Coho salmon                           0. kisutch
    Steelhead rainbow trout               S~almo gairdneri gairdneri
    Longfin smelt                         Sprnchus, thaleichthys
    Eulachon                              T~haleichthys pacificus
    Pacific hake                          Merluccius productus
    Thpsmel t                             Athieri1n-ops affinis
    Threespine stickleback.               Gasterosteus aculeatus
    Striped bass                          Morone saxatil-is
    Barred surfperch                      Amphistichus. argenteus
    Shiner surf perch                     Cymatogaster aggregata
    Pile surfperch                        Rhacochilus vacca
    Monkeyf ace eel                       Cebidichthys violaceus
    Bay Goby                              Lepidogobius leid~us
    Albacore                              Thunnus alalunga
    Bluefin tuna                          T. thynnus
    Swordfish                             Xiphias gladius
    Rockfishes                            Sebastes  M
    Bacacc io                             S. Paucispinis
    Chil ipepper                         S9. goodei
    Yellowtail rockfish          'E. -flavidus
    Canary rockfish                       S. pinniger
    Vermilion rockfish                    S. Mi-ni-atus
    Widow rockfish                        S. entamelas
    Dark-blotched rockfish                S. crameri
    Splitnose rockfish                   'S. diploproa
    Stripetail rockfish          ~.saxicoia
    Shorthelly rockfish                   S.jordani
    Sablefish                             S. Aoplopoma fimnbria
    Kelp Greenling                        Hexagranmns decagraimmus5
    Lingcod                               Ophiodon elongatus
    Pacific staghorn sculpin              Leptocottus armnataus
    Cabezon                               Scorpaenichthys ma rmo ratus
    Sanddabs                              Citharichthys nmarmnoatus
    California halibut                    Paral ichthys cal iforniCUS
    Pacific Halibut                       Hippoglossus stenolepis

    Starry Flounder                       Platichgtys stellatus

                                             1-7

    Fish Species List  (Cont'd)

    Speckled sanddab
    Bat ray
    Brown smoothhound
    White surfperch
    American shad
    California tonguefish
    Northern midshipman
    Pacific pcmpano
    Pacific tomcod
    Red Irish lord
    Saddleback gunnel
    Whitebait smelt
    Angel shark
    Big skate
    Pacific electric ray
    Sevengill shark
    White shark
    Blue shark

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                                                   APPENDIX 2
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    3                              ANNOTATED MARINE BIRD SPECIES LIST

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    Erratum:

    In Appendix 7; Background Documents, Add:

    Qualitative Assessment of Ecological Damage to the
    Point Reyes-Farallones National Marine Sanctuary Resulting
    from the T/V Puerto Rican Oil Spill ........................... Page 7-65

                                      2-1

    Appendix 2:  Annotated species list of selected marine birds affected
                 by the T/V Puerto Rican oil spill (source: Briggs et al,
                 1983).

    ORDER GRAVIIFORMES

    (Common Loon (Gavia inmer)

         This loon is an unccmmon migrant and winter visitor which resides
         primarily on protected coastal waters, bays, and estuaries.  A few
         individuals usually oversummer.

    Arctic Loon (Gavia arctica)

         Nesting on freshwater lakes along the Arctic Circle and wintering
         along the Pacific coast from southeast Alaska to southern Baja California,
         this is the most abundant loon found off California and the most pelagic.
         Though they are present off California in all months, Arctic Loons are
         least numerous in summer, when only a few hundred non-breeders remain.
         Despite wide occurrence in migration, preferred coastal wintering areas
         were relatively few.

    Red-throated Loon (Gavia stellata)

         This loon is an uncommon to canmmon migrant and winter resident
         along the coast; a few nonbreeders remain through summer. They
         prefer waters very near the coast and are the least frequently
         seen loon >5 km offshore.

    ORDER PODICIPEDIFORMES

    Western Grebe (Aechmophorus occidentalis)

         With major nesting colonies on lakes and ponds from northern
         California through the Rocky Mountains into Canada, Western
         Grebes are a prominent component of the seabird fauna wintering
         off central and northern California. Migration to salt water occurs
         from late September through autumn, and return migration occurs
         mainly in April and May. The portion of the wintering population
         occurring off California varies but little each year, though substantial
         shifts occur along the coasts of Washington and British Columbia.

     Red-necked Grebe (Podiceps grisegena)

         Red-necked Grebes are a rare but regular visitor along the coast.
         Most sightings occur during winter and migration from Monterey Bay
         north. They prefer estuaries, harbors, and bays, but can occur
         along the cpen coast, even offshore.

     Horned Grebe (Podiceps auritus)

         Horned Grebes are uncanmon along the coast during migration and
         winter and are rare during surmmer.  They are least numerous south of

                                             2-2

         Point Sur. Preferring protected coastal waters, lagoons, etc.,
         Horned Grebes are rarely found seaward of the littoral zone. They
         are about as abundant coastally as the Eared Grebe and much less so
         than the Western. They exhibit stronger affinity for salt water
         than do Eared Grebes.

    Eared Grebe (Podiceps nigricollis)

         These grebes are uncommon to common along the coast in most seasons,
         but are rare in sunnuer.  Eared Grebes are more gregarious than
         Horned Grebes and often are distributed farther south. This species
         prefers protected bays, estuaries, and harbors, but during winter
         can be quite numerous at offshore islands including the Farallones.

    Northern Fulmar (Fulmarus glacialis)

        Nesting abundantly throughout the Arctic, fulmar populations in
         Alaska are estimated to total about 12 million, including 38%
         non-breeders. After nesting they disperse to California waters,
         where they constitute an important part of the offshore fauna each
        winter. Numbers here are reported to vary considerably from year to
         year.

    Sooty Shearwater (Puttinus griseus)

         Many recent authors regard Sooty Shearwaters to be among the most
         important of avian predators in the North Pacific during spring and
         sumner. Nesting both off South America and in the southwestern
         Pacific, Sooty Shearwaters arrive off California to early spring and
         research peak populations in may through July.  Flocks numbering in
         the tens or hundreds of thousands have been reported. Return migration
        occurs after August, with most birds departing by mid-September.
         Sooties, presumably young birds, may be found off California in any
        month.

    Brandt' s Cormorant (Phalacrocorax penicillatus)

         Brandt's Cormorants are, to a large extent, endemic to California
         Current waters. Their 59,000-bird nesting population is third
         largest among central and northern California species; the Farallon
         Islands harbor the world's largest colonies. Formerly thought to be
         sedentary throughout their range, it now appears that after August,
        Brandt's Cormorants disperse both northward and southward from
        colonies in northern California. This is the most abundant cormorant
        offshore, but usually is found over waters less than 100 m deep.

     Pelagic Cormorant (Phalacrocorax pelagious)

        With a total nesting population of almost 16,000, Pelagic Cormorants
         rank as California's sixth most numerous breeding species. Unlike
        Brandt's Cormorant, the Pelagic nests in low density on precipitous
        cliffs, occupying many hundreds of sites. This species is perhaps the
         least pelagic of the cormorants and appears to undertake no major
        post-nesting population movements.

                                             2-3

    Black Scoter (Melanitta nigra)

         This is a rare to uncommon migrant and winter resident. Though
         they are quite scarce south of central California. Black scoters
         may be locally numerous north of Point Reyes in some years.
         Occasionally, individuals oversummer as far south as San Diego.
         The North American population is thought to be about 0.5 million
         birds.

    Surf Scoter (Melanitta perspicillata)

         The most abundant of marine waterfowl in coastal waters and
         migration, Surf and White-winged Scoters together maintain North
         American nesting populations of over one million birds. All Surf
         Scoters in the world nest in Alaska and Canada. Scoters migrate
         over neritic waters and feed in shallow waters near the shoreline

    White-winged Scoter (Melanitta tusca)

         See Surf Scoter above.

    ORDER CHARADRIIFORMES

    Red Phalarope (Phalaropus tulicarius)

         Red Phalaropes and Red-necked Phalaropes nest in large but unknown
         numbers throughout Arctic North America and migrate through and
         winter off California. Much circumstantial evidence suggests that
         Red-necked Phalaropes migrate earlier and closer to shore than do Reds.
         These two species usually are impossible to distinguish during aerial
         surveys.

    Western Gull (Larus occidentalis)

         Ranking as the state's fourth most numerous nesting species, the
         Western Gull breeding population tops 50,000 birds, with more than
         40 colonies of greater than 50 birds. Western Gulls are ubiquitous
         along the shoreline and in neritic waters, but appear not to venture
         inland or much beyond the continental slope. The world population of
         this California Current endemic is centered on the Farallon Islands.

    Common Murra (Uria aalge)

        The common Murre is California's most abundant nesting seabird,
         occupying about 20 colony sites from Hurricane Point (Monterey
         County) to Castle Rock (Del Norte County). The major California
         colonies are located from Trinidad Head to Castle Rock (Humboldt and
         Del Norte Counties and at the Farallon Islands. Common Murres are
         usually encountered over self waters, where they are abundant; fewer
         are observed offshore. Numbers are highest during winter, when
         immigrants from northern colonies enter California waters. The world
         population has been estimated at 15 million birds, of which, about 10
        million reside in the North Pacific. The Alaskan population numbers
         about 5 million birds.

                                            2-4

    Pigeon Guillemot (Cepphus columba)

        A common nesting resident at hundreds of sites all along the coast,
         the state nesting total is about 14,700 birds. The nesting season
         is April-August. A few birds persist through autumn and winter, but
        most birds are absent from October through February. The winter
         range is virtually unknown, though banding evidence suggests north-
        ward movement out of the state. The population probably is stable.
        Usually Guillemots are found near land, but they may be seen any-
        where over the continental shelf.  The Alaskan nesting population
         numbers around 200,000 birds.

    Marbled Murrelet (Brachyramphus marmoratus)

        This is an uncommon nesting resident of central and northern
         California, where it apparently nests in coastal conifers and forages
         to about 2 km offshore. They are found throughout the year north of
         Monterey but only rarely south of there.  The state's nesting popula-
         tion is about 2,000 birds.

    Ancient Murrelet (Synthliboramphus antiquus)

        A rare to uncommon winter visitor off California, numbers of Ancient
         Murrelets appear to vary widely between years. They usually arrive
         in autumn and depart in January through April (depending on winter
         oceanoographic conditions), but records exist for all seasons. This
         species usually is most numerous from Monterey Bay northward. The
         Alaskan nesting population numbers around 400,000.

    Cassin's Auklet (Ptychoramphus aleuticus)

         This abundant alcid occupies an extensive breeding range in the
         north Pacific.  The Alaskan population numbers about 600,000, and
         there are about 1.06 million nesting in British Columbia. Of the
         131; (-170 Cassin's Auklets nesting in California, 80% (105,000)
         occupy Southeast Farallon Island, 17% (23,000) use the northern
         Channel Islands, and the remainder nest at Castle RDck and Green
         Rock in Del Norte and Humboldt Counties. Cassin's Auklet comnonly
         occurs offshore during winter (year-round near the Farrallones).

    Rhinoceros Auklet (Cerorhinca monocerata)

         Rhinoceros Auklets are seasonally abundant in California waters
         when large numbers of wintering birds enter the region from the
         north. This species is distributed discontinuously around the rim
         of the North Pacific. In Alaska, where predation by introduced
         Arctic foxes probably eliminated many former colonies, the population
         numbers about 200,000 birds. The California nesting population
         totals only about 180 pairs, including 100 pairs at Castle Rock (Del
         Norte County) and 50 pairs at Southeast Farallon Island.

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            I ~~~~~~~~~APPENDIX 3

         1   ~~~~~~             SPECIES SELECTION FOR T/V PUERTO RICAN
                         OIL SPILL RISK ANALYSIS COM4PJJER MO)DELING
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    Appendix 3:  Selection of seabird and marine mamnal species for
                 oil spill risk analysis

         Five seabird species and one marine manmal have been selected for
    inclusion in the oil spill impact analyses. The species have been selected
    on the basis of several criteria relating to the importance of the
    different species within the marine bird and mammal fauna of the Point
    Reyes-Farallon Islands National Marine Sanctuary, their susceptibility to
    adverse impact due to contact with floating oil, and their seasonal status
    on the Farallon Islands.

         The criteria for seabird species are as follows:

        1)  The species having the largest breeding populations within the
             Sanctuary because of their overall importance in consumption of
             oceanic prey, their heavy utilization of the islands for nesting, and
             their visibility to the public.

         2) Seabird species which dive to obtain food because of the increased
             exposure they may have to floating oil.

         3) Species having Federal or State designations as Rare or Endangered
             because of the implications of the Endangered Species Act.

         4) Species for which data exists and which are sufficiently abundant
             at sea within the Sanctuary to facilitate computer modeling of
             populations.

         Under these criteria, the species selected include the largest
    nesting populations on the Farallon Islands (and within the State of
    California), three of which are diving birds, and two additional seasonal
    visitors to the Sanctuary, one of which is listed as Endangered by both
    the State and the Federal Government. Brief descriptions of the status
    of each key species appear below.

    Species                                       Notes

    Common Murre, Uria aalge                      Nesting, diving birds

    Cassin's Auklet, Ptychoramphus                Nesting, diving birds
    aleuticus

    Western Gull, Larus occidentalis              Nesting birds

    Brown Pelican, Pelecanus occidentalis         Seasonal visitor;
                                                 endangered species

    Arctic Loon, Gavia arctica                    Seasonal visitor;
                                                 diving birds

                                             3-2

    COMMON MURRE (Uria aalge)

        Numbering somewhere around 100,000 breeding birds on the Farallones
    in summer 1982, murres also nest at about a half dozen other sites around
    the rim of the Gulf of the Farallones. Following the nesting season,
    adults accompanied by their dependent young move to the waters within a
    few kilameters of the mainland shoreline and disperse north and south to
    make use of nearshore prey such as the northern anchovy.  Numbers decline
    in autumn near the Farallones, then build once again about December, both
    as a result of early visitation to the colony sites and due to an influx
    of visitors from waters as far north as British Columbia. The latter may
    be primarily juvenile birds and may be more common near the mainland than
    near the shelbreak (the vicinity of the tanker tragedy). In general, murres
    occur in densities exceeding 50 birds per sq. km. through the waters of the
    inner and middle continental shelf and in much lower densities seward of
    the 100-meter isobath. This is a species that obtains prey underwater by
    "flying" in the manner of penguins. Murres commonly are found to be
    heavily impacted by floating oil slicks and constituted almost almost a
    third of all birds recovered dead on Gulf of Farallones and adjacent
    beaches in the three weeks after the sinking of the T/V Puerto Rican

    CASSIN'S AUKLET (Ptychoramphus aleuticis)

         These auklets are the second most abundant of the seabirds nesting
    in California and the most abundant breeder on the Farallones. The total
    numbers of birds associated with the islands now are estimated to exceed
    180,000. This is a diving species like its relative the murre, but takes
    primarily euphausiids and other small crustaceans for food. Most birds
    are encountered in the waters of the outer continental shelf and upper
    continental slope, where densities vary from about 10 to greater than 50
    birds sq. km. Areas having the heaviest concentrations of auklets include
    the outer shelf from North Farallon Island to Cordell Bank and from South
    Farallon to about Asension Canyon, 60 km to the southeast. Cassin's
    Auklets appear to move slightly farther offshore during fall and winter
    than in the nesting season (roughly Apil through July) and the numbers of
    locally nesting birds are augmented in fall and winter by visitors from
    Pacific Northwest colonies. Not often encountered in beached bird surveys
    (whether or not oil has been spilled), it is likely that most mortalities
    of this species go undetected at sea, where carcasses sink or are eaten by
    fish or other birds.

    WESTERN GULL (Larus ocidentalis)

         The only gull nesting in large numbers along the coast of California,
    the Western gull is a conspicuous member of the nearshore bird community.
    About a half of the State's 50,000+ total nests on the Farallones. Adult
    birds depart the islands for a few months after conclusion of the nesting
    season in July and August, but return in the thousands by midwinter.
    Both the necessity of establishing territorial claims and the lure of
    carrion associated with winter pupping activities of the northern elephant
    seal may be involved in the return to the islands.  Like other large
    coastal gulls, the Western gull eats a wide variety of naturally occurring

                                      3-3

    have been recorded during beach censuses following oil spills, perhaps
    because as aerial predators they see and avoid oiled waters. A recent
    study campleted under MMS sponsorship in Santa Barbara Channel found,
    hcmTever, that juvenile gulls ware considerably nare apt to come into
    contact with floating oil than were (experienced adults).  Additionally,
    several studies have indicated that transfer of small quantities of
    petroleum to gull eggs during incubation can lead to significant declines
    in hatchability and survival of the young. Gulls of this species occur
    throughout the shelf and slope waters of the Sanctuary and adjacent regions.

    BROWN PELICAN (Pelecanus occidentalis)

         Now rebounding from a severe and prolonged decline in colony
    productivity, the California Brown Pelican is currently under annual
    consideration from change in its status as an Endangered Species. Pelicans
    are abundant and visible visitors to the shores and waters of the Sanctuary
    from about mid-May through December.  Populations found at shoreline
    roosts vary seasonally fram none in January to about 5,000 to 10,000 in
    September (for the area from Point Reyes to Ano Nuevo Island), 100 km
    south of the Farallones).  This species obtains its prey by plunge-diving
    from heights of up to 15 to 20 meters; detection is limited by turbidity
    of the water. Like the gull studies mentioned above, juvenile pelicans
    appear to be more apt to contact floating oil than are adults.  Foraging
    takes place mostly over waters within 15 to 20 km from the nearest shoreline
    roost, but we have encountered feeding birds in densities up to about 2
    birds/sq. km. in the area where the stern of the T/V Puerto Rican now rests.

    ARTIC LOON (Gavia arctica). 1

        The North American population of Arctic Loon (G. a pacifica) breeds
    on freshwater lakes along the Artic Circle in Alaska and Canada, and
    winters on salt water along the Pacific coast from southeast Alaska to
    southern Baja California.  Arctic Loons are found on California waters
    throughout the year, but large numbers occur only during migration; the
    population is smallest during summer when only non-breeders are present.
    The size of the North American population is unknown; data from California
    during migration suggest that the Alaskan population may range into the
    hundreds of thousands. They are most conspicuous during migration when
    large flocks travel near the coast during daylight hours. In winter the
    birds tend to be more sedentary, when scattered individuals and small
    groups inhabit protected inshore areas. Arctic Loons are the most abundant
    gaviiforms in California waters, and the most pelagic in distribution;
    they may be outnumbered by Red-throated Loons (Gavia stellata) within
    0.5 km of shore. Loons obtain all of their small fish prey by diving and
    underwater pursuit; their diet on marine waters is very poorly known.

    1/ From:  Briggs, Tyler, Lewis, and Dettman.  1983.  Seabirds of Central and
      Northern California, 1980-1983 - status, abundance, and distribution.
      August, 1983. Pacific (CS Region, Minerals Management Service, U.S.
      Department of the Interior.

                                             3-4

         Despite their pelagic reputation, we found Arctic Loons to be most
    numerous on waters of the continental shelf in all seasons. They were
    most widely distributed during migration, but even then average density
    was 3 to 40 times higher on shelf waters than over the slope (average for
    November surveys was 4.69 birds/km2 on shelf versus 0.32/km2 on slope).
    Small numbers of Arctic Loons were also observed traveling over waters
    seaward of the slope.

         During the winter, Arctic Loons were even more restricted to protected
    coastal waters; none were observed beyond the slope. Areas preferred by
    wintering loons included the following:  Point Montara to Bodega (including
    Tcmales Bay) accounted for 35.9% of all wintering loons on average;
    Monterey Bay averaged 17.3%; and Point Buchon to Point Piedras Blancas
    averaged 10.5%. In contrast, the 300+ km stretch of coast from Point
    Arena to Oregon accounted for only 15% of the total.

    NORTHERN FUR SEAL (Callorhinus ursinus)

         The criteria for the one marine mammal selected, the northern fur
    seal, Callorhinus ursinus, are as follows:

         1) Northern fur seals are extraordinarily vulnerable to death from
            oiling. This fact results from their need for clean, air-filled
            fur as protection against cold ocean waters. Laboratory studies
            have convincingly demonstrated that oiled fur seals experience
            elevated metabolic rate and hypothermia during immersion in sea
            water at normal environmental temperatures; .depending upon the
            proportion of the body oiled, hypothermia would lead to death.2

         2) Northern fur seals have a large population at sea within the
            Point Reyes-Farallon Islands National Marine Sanctuary, and the
            larger area affected by the T/V Puerto Rican incident. Aerial
            surveys conducted over a three-year period, 1980-1983, indicate
            that the northern fur seal populations within the area defined
            for risk analysis is at a seasonal peak of over 11,000 animals in
            February and March, and averages nearly 8,000 animals from January
            through May (standard error of 1,200)3

         3) The northern fur seal is an economically important species. A
            commercial harvest of young male fur seals occurs annually under
            the regulation of the North Pacific Fur Seal Commission; nearly
            26,000 animals were taken by the U.S. in 1983.4 At present, sale
            of skins produces about $1 million in net revenue.5

    2 Kooyman, G.L., R.L. Gentry, and W.R. McAlister. 1976.
       Physiological Impact of Oil on Pinnipeds. final Report Res. Unit 71
       to OCSEAP, Boulder, Colorado.

    3 Bonnell et al., 1983. Pinnipeds of central and northern California.
       Final report to the MMS 84-044.

    4 National Marine Fisheries Service. 1984. Marine Mammal Protection Act
       of 1972. Annual Report 1983/84.

    5 Gentry, R.L. 1981. Northern Fur Seal in Ridgeway and Harrison, eds.
       Handbook of Marine Mammals, Vol. I. Academic Press, NY.

                                          3-5

     4) The northern fur seal is a candidate species for listing as threatened
        under the Endangered Species Act of 1973 (for purposes of management
        and conservation, candidate species are afforded the same protection
        as listed species). The population of fur seals in the eastern
        North Pacific has been declining at a rate of 4-8% per year;
         studies have implicated entanglement in debris such as trawl nets
        as a factor in this decline.6

    NMFS 1984; Fowler, C.W. 1982. Interactions of northern fur seals and
    commercial fisheries. In K. Sabol, ed. Transactions of the 47th North
    American Wildlife and Niat--ural Resources Conference. PortlandF Oregon.
    Wildlife Management Institute, Washington,, D.C.

    I

    I

    I__                                                  _   _
                                            APPENDIX 4
    I
                                   OIL SPILL MODEL DESCRIPT'ION
    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

    I

                                           4-1
           Appendix 4: Oil spill model description

                Model Description

                The model for estimating bird mortality is divided into

    *      ~~three sections:

                1)   Oil Slick Movement Model:  A detailed description of
                     the history of the slick including the location, size,
      I             ~~~~shape, and percent coverage at different points in
                     time.  Descriptions of the slick are derived either
                     from observational data, or from hindcasts made using
      3             ~~~~HAZMAT's On Scene Spill Model (OSSM).

                2)   Bird Contact Model:  A simulation of bird distribution
                     and movement in the area of and around the slick. This
                     section estimates the number of birds which actually
                     encountered the oil.

     *         ~~~3)   Bird Fate Model:  A model which tracks the fate of
                     birds which encountered the slick. These birds may or
                     may not become seriously ailed. of those which were,
      I            ~~~~some sank or were scavenged, some were carried out to
                     sea, some were washed ashore on beaches which were
                     searched, and some were washed ashore on beaches which
      *             ~~~~were not searched.

                The output of the fate section of the model provides esti-

    I     ~mates of the number of birds which died but were not counted.

    3     ~The overall model structure is diagrammed in  Figure 1.        The

           following are more detailed accounts of the three sections of the

    *     ~model.

           Oil Slick Movement Model

      I         ~~A detailed description of the location, shape and movement

    *     ~of an oil slick is required in order to estimate the number of

           birds encountering spilled oil.  This description was obtained

           using maps of the observed slick at various points in time and

           interpolating between successive observations to describe the

     I     ~behavior of the slick during intervening'time periods.

    ~~I /      Ecological Consulting, 1985.

          2/   See discussion of actual versus observed mortalities in
      I       ~~previous section.

                                         4-2

                                  Figure 1I

        Flow diagram showing the relationship among the three
         sections of the model used in this analysis. ovals
    indicate programs, rectangles indicate input or output data.

             j.Oil Slick
                 Observations                 MOVEMENT MODEL

                                             Digital Maps of
                                             Slick Movement

             * Behavioral Data                  IRD CONTACT
             a Distributional Data                                 MODE

                              i~~~~~~~~~~~~~

                                          i   Locations and
                                            Numbers of Birds
                                          IEncountering Slick

            �Bird Trajectory Model
            *At Sea Loss Rates
            *Beached Bird Data

                                             Estimates of
                                            Total Mortality

                                           4-3

          Description of Oil Slick Data

               observations of the position of the slick from the T/V

    I ~Puerto Rican were summarized from observations by the Coast Guard, PR60

          and Pt.Reyes N.S. The observations consisted of the position and

          shape of the slick at each point in time.  Twenty-one such

          observations were used, spanning the period from the vessel

          breakup on November 6, to the last sighting of a slick north of

    I     ~Bodega Head on November 12.  These data were input to the model as

    *     ~sets of ordered pairs of latitude s and longitudes describing the

          shape and position of the slick at a given time. in addition to

    I     ~these data, we also used estimates of the percentage of the slick

          which was actually covered by oil -- since typically so-called

          slicks contain large areas of open water, and these open areas

          increase in proportion with the age of the slick. Estimates of

          coverage were provided by the MAS group of NOAA, Seattle.

     I         ~~An alternative approach was also used based on a HAZMAT

          hindcast of the spill using the On Scene Spill Model, OSSM. The

          hindcast was based on real time wind data collected during the

          period of November 6-11 at weather stations in and around the

          Gulf of the Farallones. For this analysis, OSSM was conditioned

    I     ~to include the response of the slick to the shelf-edge jet which

           occurred on November 5 and 6, causing the slick to reverse

           direction and move rapidly northward toward the Farallon islands.

           The results of the HAZMAT hindcast were recorded at 6 or 24 hour

           intervals depending on the rate of movement of the slick. These

    I     ~results consisted of estimates of the shape and position of the

           slick at each point in time, and were input to the bird contact

           model in the same way as the real observations.

                                  4-4I

        The results of the hindcast are generally very similar to

    the observations. There are two ways in which the observed and

    hindcast slicks differ. (1) The hindcast shows the main body ofI

    the slick remaining east of the 200 meter depth line (which

    defines the edge of the continental shelf) prior to the reversal

    of November 5 and 6. The observations show the main body moving

    west of the shelf break prior to this time. (2) The sizes of

    the slicks predicted from the HAZMAT results are about 66% higherI

    than those observed. It is unclear whether the hindcast or the

    observations are more accurate in this case, since some portions

    of the actual slick probably went unreported.

    Interpolation of Slick Movement

         in order to completely describe the path of the slick, it

    was necessary to "fill in" the parts of the path between observa-

    tions. This entailed pairing each observed piece of the slick

    with a piece observed at a later time and interpolating betweenI

    the two irregular shapes. Paired observations of the shape of

    the slick were input in the form of two sets of ordered latitudes

    and longitudes defining two closed polygons. Lines forming all

    possible connections between the two sets of vertices were

    constructed, and the two lines containing no intersections withI

    other lines were chosen to represent the boundaries of the path

    of the slick-during the period between the observations. if an

    oil slick was observed at times T-1 and T-2, the area swept out3

    during the period T-1 to T-2 was defined as the area between the

                                          4-5

          two non-intersected lines and the edges of the two polygons

          formed by the slick at T-1 and T-2 plus the area of the slick at

          time T-2. The slick area at time T-I was not included in the
    *     ~estimate because its area would have been included in calculating

          the area swept out between times T-O and T-1. The method used to

    I ~estimate the area swept out by the slick is illustrated in
           Figure 2.

          Bird Contact Model

     I         ~~we assume that birds resting or feeding on the water within

           the region where the passage of the slick was observed or where

          the passage of the slick was interpolated were at risk of oil

           contact. A "risked" bird, however, does not necessarily become

           oiled for several reasons. Areas reported as being part of an

    I     ~oil slick are generally only partially covered,  birds may

           deliberately avoid oil contact, or the oiling of a bird may be so

           light as to have little immediate consequence.  Estimates of

    *     ~mortality are based on the assumption that some percentage of the

           birds on the water in the region of the slick will become oiled

    I     ~sufficiently that they will become debilitated and die.  The

    *     ~method used to estimate this percentage is described in Model

           Calibration.

                          4-6

                    Figure 2

    The interpolation of the passage of an oil
    slick between times T-1 and T-2.  The new
    area affected during the period T-1 to T-2
    would be the stipled region plus the cross-
    hatched region.

                                           SLICK AT

              ~-TIME T-2 I

                                             4-7

    Distributional and Behavioral Data

         For the hindcast, biological input parameters for each species were

    derived frcm survey data for November 1984. Aerial surveys were carried

    out by PRBO biologists under contract to NOAA using the methodology

    developed by UCSC biologists. Surveys were conducted on November 14 and

    on November 17, 1984. Transects are shown in Figure 3. Three biological

    zones in the survey area were identified as follows:

        North Shelf: That area between 380 N and 390 N, west of the California

    coast and east of the 200 meter depth line.  This corresponds generally

    to the area of the continental shelf north of Pt. Reyes and south of

    Point Arena.

         Central Shelf: That area between 370 15'N and 380 N, west of the

    California coast and east of the 200 meter depth line. This corrsponds

    generally to the area of the continental shelf north of Bean Hollow State

    Beach and south of Point Reyes.

         Pelagic: That area within the north-south boundaries of both above

    shelf areas combined, eastward from the 200 meter depth line to the limits

    of our transect data at the 2,000 meter depth line.

         Distributional Data: The distributional maps for each species

    (Figures 4 through 11). The spatial distribution of Common Murres,

    Cassin's Auklets, and Arctic Loons are summarized in Figures 4 through 6.

    Input parameters derived from the UCSC data base and used in the model

    analysis are described below.

                                              4-8

                                       Figure 3

    Aerial transect lines and biological zones used in compiling
    data on seabird distributions.   Dotted  lines denote boundaries  of
    biological zones, dashed lines denote positions of transects.

                       'Fort Bragg

    39-
                          Pt. Arena

                 ~Tra~ecli~:1.......%p

                     :ranset 49  I 1,Bodega Head

                    .    i      : ~~NORTH SHELF
               :Tr'~ ~Transect 5462.

                                    i             P~t. aReyes

                      ZONE  .............    CENTRAL SHELF
                             T  ransect 4 1              Head

                               FaraZlon Is. v     ZONE             Sa  Francisco

                           ;   Transect   4 1 ,         Ree

                                  Transect 39

                     ........        iTrans et . ..........       ...................

                     , ,  ,                ,.    ,    ,    ,   -  Santa Cruz
               3 "  124'                         123'

                                              4-9
                                          3

         Mean Density of Groups:  Mean density of groups of birds was calculated

    of birds was calculated separately for each species in each biological

    zone. Densities are expressed as groups per km2.

         Distribution of Group Sizes: Mean group size and the distribution

    of group sizes was determined separately for each species. These

    distributions did not vary appreciably by biological zone; therefore

    zones were combined for this parameter.

         Daily Activity Pattern: There are two components to the daily

    activity pattern, hours of daylight and percentage of birds on the water

    (as compared with flying) at any given time.  In early November, the

    Farallones region has approximately 11 hours of daylight. All speicies

    considered here are assumed to be active during this time period.

    Percentage of birds on the water during the active period was derived

    from NOAA survey data for November 1984.  Birds are assumed to be resting

    on the water during the hours of darkness.

         Movement Data:  Estimates of movement rates of Cacuon Murres and

    Cassin's Auklets are based on data for radio-tagged Cassin's Auklets

    collected by UCSC biologists. The mean distance moved per three hour

    time interval was 4.3 km.  Arctic Loons are engaged in a southward

    migration at this time of year and periodically stop to rest.  On average

    60% of the Loons observed in the November 14 and 17, 1984 transect surveys

    were on the water, so it is assumed that at this time Loons spent 40% of

    the daylight hours in flight and 60% at rest.

         Values used as input to the Bird Contact Model are given separately

    for each species considered (Tables 1 through 3).

                                                4-10

    Table 1.  Input Values for Common Murre, T/V PUERTO RICAN Hindmast
              Data for November 1984

    Mean Density of Groups:

               Biological Zone                      Groups/km2

               North Shelf                           2.28
               Central Shelf                         5.929
               Pelagic                               0.11

    Distributiion of Group Sizes:

               Group Size                            % Frequency

               1                                     39.5
               2                                    16.1
               3                                      7.3
               4                                      2.2
               5                                      8.0
               6                                      4.4
               7                                      1.5
               8                                      0.7
               9                                      1.5
               10                                     5.1
               11-15                                  5.8
               16-20                                  2.2
               21-30                                  2.1
               31-40                                 0.7
               41-50                                  0.7
               51-60                                 0.7
               61-70                                  1.5

         Mean Group Size: 6.066

    Daily Activity Pattern:
                                          Percent            Percent
                                        on the water        in the air

         11 hours active                   97.4                 2.6
         13 hours quiescent               100.0                  0

    Movement Data:

         Mean distance moved per 3 hour time interval:  4.3 km

                                                   4-11

    Table 2.  Input Values for Cassin's Auklet, T/V PUERTO RICAN Hindcast
              Data for November 1985

    Mean Density of Groups

               Biological Zone                       Groups/km2

               North Shelf                            0.96
               Central Shelf                          0.53
               Pelagic                               1.19

    Distribution of Group Sizes:

               Group Size                             % Frequency

               1                                      64.0
               2                                     18.0
               3                                     10.0
               4                                       6.0
               5                                      2.0

         Mean Group Size:  1.64

    Daily Activity Pattern:
                                           Percent          Percent
                                         on the water       in the air

        11 hours active                      64.2              35.8
        13 hours quiesent                   100.0               0.0

    Movement Data:

         Mean distance moved per 3 hour time interval: 4.3 km

                                              4-12

    Table 3.  Input Values for Arctic Loon, T/V PUERTO RICAN Hindcast
              Data for November 1984

    Mean Density of Groups:

               Biological Zone                       Groups/km2

               North Shelf                            1.456
               Central Shelf                         8.966
               Pelagic                               0.324

    Distribution of Group Sizes:

               Group Size                            % Frequency

               1                                     61.0
               2                                     22.2
               3                                      2.8
               4                                      2.8
               5                                      2.8
               6                                      0.0
               7                                      2.8
               8                                      2.8
               9                                      0.0
               10                                      2.8

         Mean Group Size: 2.083

    Daily Activity Pattern:
                                          Percent            Percent
                                        on the water         in the air

         11 hours active                    60.8               39.2
         13 hours quiescent                100.0                0.0

    Movement Data:

         40% in flight in southward migration at any given time during the
         active period.

                                                                4-13
                                                                                    Figure 4
                                                                                    Density Distribution of
                                                                                    Common Murres During

                                                                                    November 1984

                     382a

    *               ~~~~~~383V.

    I               ~~~~~37 5& -

                     373 -s

                     37 23.                                                  --

    3~ ~ ~~ ~~~l 37 Ili13 21                         1       23 5             122 58      1       23e122

                                  I Birds/sq km                      )0-~ Birds/sq km                      -5 Bird/sq km

                                     5-1Birds/sq 1w                  59-188 Birds/sq km             1       9Srd/qk

                                  Undefined                     A     Farallion Is.                 I     Sunken Stern

                                                               4-14            ~~Figure51

                                                                                 Density Distribution of
                                                                                 Cassin's Aukiet During
                                                                                 November 1984

    38 25

       ---7 -------+-                                                                                +                       .

                                                      4                                                                               1~~~~~~~~~~~~
       38  3- -- --
                                         -------------~ ~         ~         ~ ~ ~

       ---------                                       +                      ++

                       --   a------
              23 5             13--------2----12-3                                                               -         12 2

                   8 Brd/s  k                                     8--Brd/sqkm5-5 Brd/s  k
                    2-58Bidssqkm5818  Brd/s  k                                                                  )18  Brd/s  k

      3   I50        Uneie+ aalnI.I                                                                             Sne   tr

                                               ----------------- ~~~~

                                                                             4-15
                     3                                                                                ~~~~~~~~~~~~~~~~~~~~~Figure 6
                                                                                                   Density Distribution of
                                                                                                   Arctic Loon During

                                                                                                   November 1984    __

                                38a5*..                    -       ---      - -

    3                    ~~~~~~38 3-

                               37587 -..--.-

                3  -     3736                         .4.                    +~~ -: -

    3                    ~~~37 23-                     --.                     '--          -        ----

     1                    37 ~~~~~~123 33             123 29                  123  5                 12 o ~                      36                 1~22 2

                                    J2  Birds/so kw                                     )0-5 Birds/sq km                                5-25 Birds/so kim

         3                                 ~~~~~~~~~~25-58 Birds/sq km                   50-IfiS Birds/sq km                            )188 Birds/sq km

         3  ~ ~ ~ ~ ~ ~ ~~   ~~Undef ined                                          A       Farallon Is.                          I       Sunken Stern

                                                               4-16             ~~Figure   71

                                                                                Density Distribution of
                                                                                Common Murres During                                3
                                                                                Entire Year

    3817

    37 IT       �                                                                                      +                      +4*
               -  - - - -   --

       373&     ---------

    37 23"

    37I3 112                                                 --:--
                     23 35        n                  123  ~~~5               122 58                 12236121                          I

              7 9       Birds/so kx           WS-  Birds/sq kg                                                   5-25 Birds/sq km3

                    25-51 Birds/sq km                            50-188 Birds/sq                    1111     )100 Birds/sq km

                    Undefined                             A       Farallon is.                           z       Sunken Stern         3

                                             4-17
                                                           Figure 8
                                                           Density Distribution of
                                                           Cassin's Auklet During
                                                           Entire Year

                3825

                                           -a
                      3~~_ __=- -- - - - -

                37    __ _

                        U.  ---- - --
                  _=37:   '                       _    _-------------

                   rr tf3   n - - - - -  - - - -
                   - -233            23 2. ' L--- - .....1  21

                  3    8 Birds/so ke                        )8-5 Birds/sqo  5-25 Birdsksq km

                      25-50 Birds/sq km                    58-1s88 Birds/sq km  irdss  m

                  .I~ W    Undefined                   '    Farallon Is.                 I    Sunken Stern
    I~~3 3..._:-'

                                                        4-18
                                                                              Figure 95
                                                                              Density Distribution of
                                                                              Brown Pelican During
                                                                              Entire Year

     325

    38 17-                                                 +                                  +                       +.

    38 3.1--                      +                 -

    37 So.

    37 36                         +                                                                       ++

    37 23-                                                                  --

      137 35                    123 20                  123  5                  122 56                   122 36                  12221       5

              1 d      B irds/sq km                                }8-5 Birds/sq km                    j       ~5-25   Birds/sq km

                    25-50 Birds/sq km                              58-108  Birds/sq km                             )110 Birds/sq km

                    Undefined                              A        FarallIon Is.                           I       Sunken Stern3

                                                            4-19
                                                                              Figure 10
                                                                              Density Distribution of
                                                                              Western Gull During

                                                                              Entire Year

                      31�-- - -

    3                  ~~~-.--------------
                            --------              --- ------
                              -------              ------- d----.                   W        .-:-+

                                        ---------------   ~ ~   ~   ~---
                            383---�--
                                     - - - - - -- - - - -  - - - - -- - - - --
     I~~~~~~~~ -                                                          Pt,   --------
                                    -~~~~~~~~~~ -                                                      --------     -
                              -----------------------
                             ------------------------
                              -�
                             ---------------------
                                  ~~~- - - - ----------
                                  ~~~~~~~~~~-   -    -------------
                                  ~~~~~~~~- - - -----------------
                                  ~~~~~~~~~~-  -  ------------------
                                  ~~~~~~~~-   -   -  -----------------

                                   - - - - - - - - - - --             -/-q-k -             -        -5-B-r-s-s- -
     I~~~~~~~~ -                  -idss k-          -     j33j  )-5 -Bi ----   ----d
     I~~~~~~~~ 5-5 B-dss k-                           -    jj   5-18 Bidss -m              -18 B-dss -km        ---------

     3~~~~~~~~ -~Un   -f -e                       -             -aalo -s              -            -u~e - St---eri----   ---

    I~~~~~~75-

                                                     4-2       0    Figure 11

                                                                    Density Distribution of
                                                                   All Birds During

                                                                    Entire Year

    38  3.

            37 23~~~~~~~~-

               123 35      1~~~3 N                 123  5       s      o        0   ~                  36

                 I Birdsls'q km                                )0-5 Birds/sq km                                3 -25 Birds/so kmu

                   B55   irds/sq km                          51-198 BirdS/sq km                  fjjj  )100 Birds/sq km

                Undefined                              A       Farallon Is.                           I       Sanken Stern3

                                   4-21

         The likelihood that a bird will contact oil depends on

    whether or not it is on the water when in the vicinity of the

    slick.  Members of the species considered here were assumed to be

    resting on the water during the hours of darkness, and to be in

    flight for part of the daylight hours -- about 11 hours in

    November at the latitude of the Farallones. Percentages of birds

    on the water during the daylight hours were calculated from

    unpublished data provided by Dr. Kenneth Briggs.  These data were

    collected during November 1984 in the same geographic area using

    the aerial strip census protocol described previously.  Common

    Murres spent 97.4% of their time on the water, Arctic Loons

    60.8%, and Cassin's Auklets 64.2%.

        Estimates of movement rates for Common Murres and Cassin's

    Auklets are based on data for radiotagged Xantus' MurreletsI/.

    The mean distance moved per three-hour time interval was 4.5 km.

    Arctic Loons are engaged in southward migration at this time of

    year, but periodically stop to rest on the water. No data are

    available describing short term (i.e. on the order of hours)

    movement patterns of migratory loons or any migrating seabird

    species.  However, since migratory distances tend to be very

    large relative to the size of the slick, we assumed that Loons

    either did not move at all -- i.e., were resting -- or moved a

    I/   Hunt, et al., 1976.

                                 4-22

    Estimating Numbers of Birds oiled

         The total area swept out by an-oil slick multiplied by-the

    density of birds in the region through which it passed provides a

    minimal estimate of the number of birds at risk of oil contact.3

    The area swept out by the slick during a given time interval was

    estimated using the methodology described in Interpolation of

    Slick Movement. Bird density estimates were based on the observ-

    ed densities in the three zones described above.  If the slick

    crosspd between zones during a time interval, the average density3

    was computed based on the relative area which lay in one zone or

    the other.3

        Although the density of birds on the water provides a

    partial estimate of the number of birds at risk, the actual
    number at risk may in fact be much greater. Seabirds are highly

    mobile, and although the population of birds present in a parti-

    cular area of ocean might remain relatively constant, the indi-B

    -viduals comprising that population could change completely during

    a given time interval. The turnover rate within the region of an

    oil slick was estimated based on the distribution of distances3

    moved per three-hour time step, assuming that the movement of an

    .individual or group could be modelled as a random-walk process.I

    One thousand simulated bird groups were distributed uniformly

    across a region the shape of the slick. At three-hour intervals

           1                              ~~~~~~~~~~~~4-23

    3     ~each group was moved a random distance in a randomly selected

          direction. The distance moved was selected by sampling from a

          distribution of distances moved per three hours. Each group was
    3 ~moved until it had passed out of the region and the number of

          time steps required to do so was noted. This process is illus-

    3     ~trated in Figure 12     The average number of time steps required

          to leave the region from a random starting position represents an

          estimate of one-half the average time spent in an area of ocean
    *     ~the size and shape of the slick (since groups where already

          within the region when they were placed in motion, this time is

    3     ~multiplied by two).  The turnover rate is therefore the time step

          divided by the mean length of time spent in the region.  For

          example, if a groups spends on average 5 hours in the region

    3     ~before exiting, and the time step is 3 hours, the estimated

          turnover rate would be 0.6 -- i.e., there would be 60% replace-

    I    ~ment of old animals with new animals during one time step.  The

          number of birds encountering the slick would then be 1.6 times

          the number of birds on the water. Note that this is an estimate

    5     ~of the turnover in an unpolluted region of ocean.  We assume that

          the encounter rate would remain the same if the region were

    I    ~covered by the slick, although in some cases flocks might turn

          aside after initially encountering it.

          Bird Fate Model

     *        ~~Birds which were killed or incapacitated by oil are assumed

          to have had one of four fates: either (1) they were carried out

    I    ~to sea by winds and current,s (2) they sank or were scavenged

         before reaching shore, (3) they came ashore along stretches of

                           4-24

                     Figure 12

    Illustration of the method used to estimate
    the length of time a flock of birds would
    spend in a region the size and shape of an
    oil slick.  Circled numbers indicate the
    position of a hypothetical flock at three-
    hour intervals. The distance moved is based
    on the relative frequency of distances moved;
    the turning angle is random.  In this case,
    the flock required about 6.2 intervals or
    18.6 hours to leave the region.

              ,'ii!!!i!!:................iiiiiii

               l: ii:.:: ..........
                             �-::�:::::::ii:~~

           3                              ~~~~~~~~~~~~4-25

    3     ~coastline which were not searched, or (4) they washed up or swam

          ashore on beaches which were regularly enumerated. The purpose

          of the bird fate model is to partition birds among these various

    *     ~fates.

          Trajectories of Oiled Birds

     3         ~~The HAZMAT oil spill trajectory model was used to compute

          families of possible trajectories assuming that dead or incapa-

          citated birds move at 2.2% of the wind velocity. This value is

    5     ~based on published experiments with dead, oiled auks in the Irish

          Sea. 1' The value is lower than the 3.0% typically used for

    3     ~surface films of oil, probably because of the relatively large

          subsurface resistance of the carcasses. Eight typical regions

          were selected from which to launch circular clusters of model

          bird bodies.-' Groups of 100 birds were launched from each

          region and followed until they either came ashore along one of

    3     ~six coastal segments, or were washed out to sea (See Figure 13

          for a map of launch regions and coastal segments). A matrix was

          constructed containing the probability that a bird which was

    3     ~oiled in a given region would come ashore along a given stretch

          of coast and the length of time that would be required to do so

    3     ~~(see Table 4

               The number of birds estimated by the bird contact model to

          have been oiled within a given launch region were partitioned

    3     ~according to the probability that they would come ashore in one

    I     1~/  Hope Jones et al., 1970.

          2/   J. Galt, personal comimunication.

                                                      4-26                                                               5

                                                 Figure  13

                       Simulated Bird Carcass Launch and Recovery
                                 Areas Used in Bird Fate Model
                                                                            OSSM LAUNCH POINTS FOR
                                                                           SIMULATED BIRD CARCASSES
                                                                      ) 37023.3  122057.4    3 Nov 1200
                                                                      () 37018.O  122049.5    5 Nov 1200
                                                                      @37043.4  122053.6    7 Nov 1200
                                                                      ()3756.7  122055.1    9 Nov 1200
                                                                      (  38� 0.7  122055.1    9 Nov 1200
                                                                      )  (37059.6  1230 4.9    9 Nov 1200
                                                                     .(  38014.5  1230 7.0   10 Nov 1200
                                                                      )37016.7  1230 5.2    5 Nov 1200
     390
                                                                              RECOVERY AREAS FOR
                                Pt. Arena                                        SIMULATED BIRD CARCASSES

                                                                      () South of Bolinas
                    ....... *                                                 (4)Bolinas to Pt. Reyes
                                                                       (Pt.  Reyes to Tomales Pt.
                                               !+~~ (~~)~Bodega Area (Tomales Pt. to
                                                                             .Jenner area)
                                                                       Jenner to Pt. Arena
                                                                       North of Pt. Arena
             ........ :....... . (~) Farallones
                                                                       ()Out  to Sea (Never Beached)

                                                         Bodega Head

                                                   �:  O

                                           �3S  '     O    iPt. Reyes

                                     3s:..............            B) -
                                     Farallon Is.                                   an  Francisco

                       374.' 24D                           t3                                       Sana Cr.

    3T'                                                               '4 ' '  ,,Santa Cruz
                                                      14123'

                               4-27

                           Table 4

           Probability (expressed as percent chance)
         that a bird carcass launched in a given area
       will come ashore on a particular stretch of beach
    (for launch areas and recovery areas shown in Exhibit 33.

                   Percent chance of bird carcass recovery
                     if launched from a given launch area

                               Launch Area
    Recovery Area   1    2    3    4    5    6    7    8

          A         0    0    0    0    0    0    0    0
          B         2    0    0    0  100    0    0    0
          C        44    3   21   23    0    0    0    0
          D        53    4    4   17    0    0    0    0
          E         1   93   75   60    0  100  100   29
          F         0    0    0    0    0    0    0    0
          G         0    0    0    0    0    0    0   26
          H         0    0    0    0    0    0    0   45

                                  4-28

    of the six coastal segments. Of these coastal segments, all were3

    carefully searched during the period of the spill except the

    coastline north of Jenner and South of Point Arena. This area is

    composed primarily of steep cliffs with little or no access to3

    the intertidal, and except for one location was never searched

    for beached birds.1'

    Loss of Oiled Birds at Sea

        Although an oiled bird might drift in the direction of a

    particular beach and ultimately come ashore there, it might also3

    be lost to natural processes before reaching shore. of 106 murre

    carcasses released by PRBO biologists in 1980 and 1981 within3

    1,500 m of shore, only 4 were ever recovered (PRBO unpublished

    data). However, a large proportion of the weighted drift bottles

    and gull carcasses released at the same time were eventually3

    recovered, indicating that the disappearance of the Murres did

    not result from being carried out to sea, but rather that the

    carcasses sank or were scavenged along the way. The experiment

    carried out by Hope Jones et al. /. resulted in 20% recovery even

    though bird carcasses were at sea ten or more days and is the3

    source of the loss rate estimate used here.

        We assume that the loss of bird carcasses is a function of

    how long they are at sea, and that the fraction lost each day is

    constant. For example, if one-half of the carcasses were lost

    each day, there would be on-half remaining after one day, one-3

    I/   G. Page, personal communication.

    2/   Hope Jones et al., 1970.

                                   4-29

    quarter remaining after 2 days, one-eighth remaining after 3

    days, etc.  If 20% were still floating after 10 days as Hope

    Jones et al. found,-1 the estimated loss rate would be 15% per

    day.  We use these data to generate a matrix of probabilities

    that a bird oiled in a given region would be recovered along a

    given segment of coastline.  Let L.. be the probability that a

    carcass launched from region i will come ashore in segment j, S

    be the loss rate (sinking or scavenging) per day, and D be the

    number of days required to float from i to j based on the results

    of the HAZMAT trajectory model. In addition, let C. be a vari-
                                                       J
    able which assumes the value 0.0 if a beach was not searched, and

    1.0 if the beach was searched. Then the probability that a dead

    or incapacitated bird launched from i will be recovered in j is:

                         P.. = C. Lj  (1 - S)

    Model Calibration

         The mortality rate of birds encountering the oil slick was

    estimated using the actual observations of oiled birds found on

    the beaches. Let 0. be the number of birds found along coastal

    segment j, let E. be the number of birds which encountered the
                    1
    slick in region i, and let )be the mortality rate.  An estimate

    of ~ based on the observed number of beached birds found in

    coastal segment j,       is given by:

                            A~~~
                            = 0. / 7 (PijEi)

         In other words, the estimated mortality rate is the ratio of

    the number of birds observed in a given segment of coastline to

    1/   Hope Jones et al., 1970.

                                 4-301

    the number which we estimate would have been beached if every3

    bird which passed through the slick died or was incapacitated.

    Note that this estimate includes the loss of oiled birds while in

    transit from the point of oiling to the beac h which is subsumed in        3

    the value of the P j   Since the value of .,')j varies from beach

    to beach, we estimated the overall value of FR as the slope of3

    regression of the 0.- on the  (P. .E.    Because the number of
                      J           ijJ 1
    beached birds must be zero when the number of birds contactingI

    the slick is zero, the regression line was forced through the3

    origin.

    Model Analysis and Results3

        Sensitivity Analysis

        The values of some input parameters and the validity of some
    of the assumptions used in the model involve varying degrees of3

    uncertainty. When the uncertainty is large, the degree to which

    model results might be affected by that uncertainty should be5

    closely examined. Using data for the most common species, Common

    Murres, we examined the sensitivity of the model to the following

    factors:3

        o    The value of the mean distance moved per 3-hour time
             step
        o    The assumption that the bird groups move in a random
             walk fashion, changing direction randomly at each time
             step5

        o    The variability in the distribution of seabirds -
             i.e., the natural randomness involved in how many birds
             were present in an area at the time when it was affect-I
             ed by the slick

        o    The value of the rate at which oiled birds are lost at3
             sea

                                         4-31

     3         a~~~ The assumption that an oiled bird has an equal likeli-
                    hood of being lost each day it is at sea

    3     ~We examined the effect of varying these factors on two model

          outputs:

     1         a~~ The estimate of the total number of birds killed by the
                    spill

     1         o~~ The estimate of the percent of the birds which became
                    debilitated or died as a result of being present in the
                    area of the slick

          Distance Moved Per Time Step

     5        ~~The distribution of distance moved per 3-hour time step was

          extrapolated from data for Xantus' Murreleti/ , and it is not

    U     ~known how appropriate these data are for other alcid species.  We

          examined the sensitivity of the model to this input by making

          model runs using values for the distances moved which were two

    U- ~times as great as those measured for Cassin's Auklet, and one-

          half as great.  Decreasing the movement rate has the effect of

    I     ~increasing the mortality rate, and increasing the movement rate

          has the effect of decreasing the mortality rate.  This occurs

          because the number of birds believed to have encountered the

          slick increases with increasing movement rates, and to account

          for the observed beachings, fewer of them could have been killed

    I    ~or debilitated.  Doubling the movement rate lowered the estimated

          mortality rate by 21%; halving the movement rate increased the

          estimated mortality rate by 19%. Deleting the movement algorithm

    3    ~entirely increased the estimated mortality rate by 44%.  Esti-

          mates of total mortality, however, were only slightly affected by

            1/ Hunt et al., 1976.

                                  4-323

    variation in this parameter, changing by less than 2% in any3

    case.

    Random Turning Angles of Moving Birds

        It has been assumed that the movement path of birds can be3

    described as a random walk process, and that model bird groups

    changed direction randomly at each time step. We examined the3

    effect of this assumption on model results by changing the model

    so that model bird groups continued in the same direction inde-

    finitely.  This change increased the rate at which bird groups3

    encounter the slick, however it had little quantitative effect on

    model results. The estimated mortality rate was unchanged, and3

    the estimated number of birds oiled increased by less than 2%.

    Variability in Bird Distribution

        Although distributional data were collected at the time of3

    the oil spill, we do not know how many birds actually encountered

    the slick.  The distributional data provide a statistical3

    description of the numbers of groups per square kilometer and the

    relative frequency of group sizes in the general area at the time

    of the incident, but these data are averaged over large regions.3

    Even if the mean density of groups in a zone were 0.5 groups per

    square kin, a given square km of slick at a given instant of time3

    might have contained 0, 1, 2, or more groups. Similarly, if the

    mean group size were 1.5 birds, a given group might actually

    contain 1, 2, 3 or more individuals. We estimated the effect of3

    this variability on the model results by permitting densities and

    group sizes to vary randomly based on their observed patterns.3

    Groups were assumed to be distributed uniform random through

                                   4-33

    space, implying that the number of groups within a given area was

    Poisson distributed. Thus, if the area swept out by the slick

    during a given time interval is A, the mean density of birds in

    the region is b, and the number of bird groups in the area which

    passively encounter the slick is np, then np is a random variable

    with the probability density function:

                     P[n pI = (Ab)np exp(-Ab) / np!

         For each time interval, the number of groups making passive

    contact with the slick was simulated by randomly sampling from

    this distribution.  The size of each group was selected by

    randomly sampling from the known distribution of groups sizes.

    The number of groups making active contact -- i.e. flying into

    the slick -- was simulated by assuming that the probability per

    until time of a group entering the slick was constant, so that the

    number of groups actively encountering the slick would also be a

    Poisson process.  If R is the average rate at which new groups

    encounter the slick (the turnover rate), T is the length of a

    time interval, and Na is the number of birds flying into the

    slick, then the na is a random variable with the probability

    density function:

                    P[na] = (RT) na exp(-RT) / na

        The model was run 25 times using different random number

    sequences to evaluate the extent to which model results varied

    due to this form of stochasticity.  The resultant coefficient of

    variation of the estimated mortality rate was 2.1%, and the

    coefficient of variation for the total number of birds killed was

    1.3%.  The  stochastic  nature  of the bird  distributions,

                                  4-341

    therefore, does not appear to have an important ef fect on model

    results.

    At-Sea Loss Rate

        Although data are available which may be used to estimate3

    the rate at which dead or debilitated birds are lost at sea --

    presumably due to sinking or scavenging -- these data are not

    conclusive. The loss rate of oiled auks following the Hamilton

    Trader incident-I provides the best estimate of the loss rate,

    15% per day, but other data suggest that the value could be quite3

    different under other circumstances. For example, Common Murre

    carcasses released nearshore by PRBO biologists resulted in onlyI

    4% recover, implying a much higher loss rate.  We therefore

    examined the sensitivity of model results to the entire range of

    possible at-sea loss rates.3

        The mortality rate of birds encountering a slick and the

    rate at which affected birds are lost at sea both influence theI

    number of beached birds predicted by the model.  An increased

    loss rate results in fewer beached birds predicted; an increased

    mortality rate results in more beached birds predicted.  For a3

    given value of the at-sea loss rate, the model uses observed

    beached bird data to select the corresponding mortality rateI

    which best fits the observed distribution of beached birds. if

    the at-sea loss rate is fixed at 0% per day --i.e., no birds

    sank or were scavenged before -reaching shore --the estimated3

    mortality rate is 23% and the resulting mortality 1,029 birds.

    1/   Hope Jones et al,. 1970.

            1                              ~~~~~~~~~~~~4-35

    3     ~If the at-sea loss rate is fixed at 35%, the corresponding

          mortality -rate must be 100% and the- resultant mortality 3,979

    I     ~birds.   Sinking rates of greater than 35 % per day cannot be

          fitted by the model because they would require mortality rates of

          greater than 100% to account for the observed pattern of

          beachings.

               The loss rate of oiled birds at sea is clearly the most

    I     ~sensitive model parameter.  Based on the range of possible values

          for the at-sea loss rate, estimates of mortality rates for birds

          encountering the slick varied between 23% and 100%.  Our best

    3     ~estimate of the sinking rate, 15%, falls roughly in the midpoint

          of this range.  The PRBO data discussed earlier suggests that

    *     ~this value may err in the direction of underestimating the at-sea

    g ~loss rate, and by implication, underestimating total mortality.

          Exponential Sinking Rate

     3         ~~We have assumed that the loss of oiled birds at sea proceeds

          in an exponential fashion, in other words a constant proportion

    I     ~of them disappear each day.  This is a simple and logical assump-

    *     ~tion, but it is not the only conceivable model.  An alternative

          model is that at sea loss is not time dependent, but that the

    *     ~proportion disappearing is a constant regardless of how long an

          oiled bird is at sea. As with the exponential sinking rate model,

    I     ~estimates of both the mortality rate and the number of birds

    *     ~oiled are highly dependent on the value chosen for the proportion

          lost at sea. If the proportion lost at sea is 0%, these esti-

    3     ~mates are the same as for the exponential loss model-23

          mortality. The greatest loss rate possible for this model is

                                 4-363

    77%, which corresponds to 100% mortality.  This indicates thatU

    the use of a time-dependent loss rate is probably inappropriate

    for this context.  The loss rate of oiled auks in the study

    conducted by Hope Jones et al1I' was 80%, and of Common Murres in

    PRBO's study was 96%.  Thus, when a constant rather than an

    exponential at sea loss model is used, both values of the lossI

    rate are higher than could have occurred in this incident even if

    every bird which encountered the slick died. While this does-not

    "prove" that the loss of oiled birds at sea is time dependent,3

    such an assumption seems more appropriate in this case.

    Model ValidationI

        We tested the accuracy of the model by comparing the observ-

    ed distribution of beached birds with that predicted by the

    model.  The entire model was run 6 times, once for each coastal3

    segment which was searched, each time excluding that segment from

    the calibration process.  The model prediction of the number ofI

    oiled birds in a given coastal segment and the observed number of

    oiled birds were therefore independent, and the predicted number

    of beached birds occurring in a given coastal segment did not3

    entail circular computation.

        The relationship between predicted and observed numbers ofU

    beached birds is shown in Figure 14       Linear regression of the

    predicted number of beached birds on the observed number of

    beached birds for all species indicates that the model accounts3

    for 81% (r=.908) of the variance in the numbers of beached birds.

    I/   Hope Jones et al, 1970.3

                                   4-37

                               Figure 14

    Model predicted and observed numbers of beached birds on six
    segments of coastline.  Predictions of the numbers of beached
    birds in a given segment of coastline were made independently of
    the numbers of beached birds observed in the segment.

        250 .
                                            3~   OBSERVED

        200 -                                   N  PREDICTED

        150 -

     LIJ

      m501 ARCTIC LOMM ON MURRE
                E:  0    o  uo    o   >     >   <: il ii0

         O0 -

         50' ]CASSIN'S AUKLET

     I   50

                 Z        >"        m -  _1Z

                           a. <                     Z o
                             z >-w C3 a0

                                 4-383

    The slope of the relationship is .906 indicating a tendencyI

    toward the underestimation of the numbers of beachings of about

    9%. The tendency toward underestimation is most consistent in

    two areas: south of Bolinas and Bolinas to Point Reyes. These3

    beachings cannot be accounted for by assuming passive transport

    of oiled birds. HAZMAT model results indicated that no beachedI

    birds should have come ashore south of Bolinas, and very few

    between Point Reyes and Bolinas.  This prediction is strongly

    supported by the actual observations of the track of the slick.3

    Although we postulated that oil slicks and oiled birds move at

    somewhat different proportions of the wind speed, 2.2% and 3.0%I

    respectively, these different values' should result in very

    similar patterns of beachings. The fact that no beached oil was

    observed south of Drakes Bay therefore indicates that passively3

    floating birds should not have come ashore either. It is likely

    that this discrepancy resulted from oiled birds flying or swim-I

    ming toward the coast. Even a landward displacement of 5 or 10

    km during the first several days following the spill would have

    been enough to have carried birds into the region influenced by

    the tidal inhalation of San Francisco Bay.  Active movement

    toward land by oiled birds may also have been the cause of theI

    model underestimate of the number of beachings observed on the3

    Farallon Islands.

    Model Estimates of Mortality Rates3

        The mortality rate is the fraction of the birds encountering

    the spilled oil which were injured by the encounter to the extentI

    that, without intervention, they would ultimately have died. The

                                   4-39

    mortality rate for Common Murres was estimated to be 42%, for

    Arctic Loons 10%, and for Cassin's Auklets 30%.  The lower

    estimated mortality rate for Cassin's Auklets than for Common

    Murres probably results from the assumption that both of these

    species are lost at the same rate while at sea. It is probable

    that the small body size of Cassin's Auklets makes them more

    likely to become waterlogged and sink, or to be scavenged while

    floating.  If the at sea loss rate for Cassin's Auklets were in

    fact greater than the rate for Common Murres, the estimated

    mortality rate for Cassin's Auklet would be relatively' higher.

    We cannot demonstrate this at this time because there are no data

    available for at-sea loss rates of small alcids.

    Model Estimates of Numbers and Fates of Oiled Birds

        Model estimates of the number of birds which contacted the

    spill and their subsequent fates are summarized in Table 5

    Based on the observed path of the oil slick and the simulations

    of the distribution and behavior of the various seabird species,

    we estimate that 4,255 Common Murres, 1,670 Arctic Loons, and 210

    Cassin's Auklets encountered the spilled oil from the Puerto

    Rican.  Of these encounters, we estimate that 1,787 Common

    Murres, 167 Arctic Loons, and 63 Cassin's Auklets were injured by

    the oil to the extent that, in the absence of rehabilitation,

    they would have died. The most likely fate of oiled birds was

    probably loss at sea due to sinking or scavenging, accounting for

    48% of the oiled Common Murres, 49% of the Arctic Loons, and 59%

    of the Cassin's Auklets.  The remaining oiled birds washed ashore

    along the central California coast, some areas of which were

                                                                  4-40

                                                              Table5                                                                         3

                               Model Results:  Model Estimates of Numbers of
                            Three Seabird Species Encountering the Oil Slick
                            from the Puerto Rican and Their Subsequent Fates

                                                                                                                               I

                                                                                                                               I
                                                                                Seriously Affected by Oil1

                         Encountering       Not Seriously        Found on         Sunk or       Floating        Beached But        TOTAL DEAD BUT
                           Oil Slick           Affected           Beaches       Scavenged    Out to Sea   Not Recovered             NOT RECOVERED

    Common Murres
      Number of Birds           4,255              2,468               487             861            61               378                 1,300
       Percentage                  100.0%               58.0%            11.4%           20.3%          1.4%              8.9%                  30.6%
    Arctic Loons
       Number of Birds           1,670              1,503                53              82             0                32                    114
       Percentage                  100.0%              90.0%              3.2%            4.9%          0.0%              1.9%                   6.8%
    Cassin's Auklet
       Number of Birds             210                147                16              37             0                10                     47
       Percentage                  100.0%               70.0%             7.6%           17.6%          0.0%              4.8%                  22.4%

                                                                                                                               I
    1 These are birds that would have died without rehabilitation.  Some birds found on beaches were successfully rehabilitated (see
      previous section).

                                   4-41

    searched for oiled birds and some were not. Trajectory simula-

    tions indicate that 21% of the oiled Common Murres, 32% of the

    Arctic Loons, and 16% of the Cassin's Auklets were beached on

    unsearched or inaccessible stretches of coastline between Jenner

    and Point Arena. The total numbers of birds which are believed

    to have died but were never recovered are 1,300 Common Murres,

    114 Arctic Loons, and 47 Cassin's Auklets.

                                           4-42

    Additional Input Data for Hindcast and for Analysis of Chronic Release                   U

    Initial Incident                                                                         3

         In addition to the analyses carried out for Common Murres, Cassin's

    Auklet, and Arctic Loons, estimates were also made of the numbers of

    birds killed by both the spill resulting from the oil released during the                3

    breakup of the T/V Puerto Rican and leakage from the sunken stern section.

         Species of birds found oiled on the beaches were divided into two

    categories based on their general distributional pattern.  Birds typically

    found in nearshore waters were assumed to have swum ashore on nearby

    beaches if oiled, or to have been washed ashore if oiled and dead.  These

    species include:

              Common Loon                       Black Scoter
              Red-throated Loon                 Surf Scoter
             Western Grebe White-winged Scoter
              Hmrned Grebe                      American Coot  -
              Eared Grebe                       Pegeon Guillemot
              Cormorant spp.                    Marbled Murrelet
              Ruddy Duck                        Rhinoceros Auklet
              Scaup spp.                        Ancient Murrelet

         In all cases, these species are much more common in the inshore

    waters near the beaches that were searched than in the waters adjacent to

    beaches that were not searched (primarily north of Jenner). We used

    data fron a Univeristy of California, Santa Cruz aerial survey conducted

    in October, 1980 to examine this assumption in more detail for one of the                I

    more heavily affected species, Western Grebes.  The number of Western                    3

    Grebes along the coast north of Jenner represents about 12% of the Western

    Grebes observed between 370 32' N and 380 53' N.  By the time the slick had              3

    reached as far north as Jenner, its real extent had greatly decreased,

    and the coverage within that region had also decreased from approximately                I

                                           4-43

    10% to approximately 1% (J. Galt, pers. ca, m.).  It is therefore unlikely

    that the estimates of total mortality would be substantially affected by

    the addition of mortality which may have occurred in waters adjacent to

    unsearched beaches.

         Birds with more offshore distributions were modeled as with the

    Ccamon Murres, Cassin's Auklets, and Arctic Loons, but the results and

    methodology for these species are reported in less detail. These species

    include:

                                  Red Phalarope
                                  Sooty Shearwater
                                 Western Gull

    Model results were used to derive correction factors relating the observed

    numbers of beached birds to the true mortality.  Data used in carrying

    out these Analyses are described in Table 6.

    Chronic Release

         The effects of the chronic release on five species were examined.

    Ccmnon Murres, Cassin's Auklets, and Western Gulls were chosed because

    oiled birds of these species were reported on the Farallon Islands during

    the period modeled. The Brown Pelican and the northern fur seal were

    also examined because they are present in the area and are considered

    sensitive species. Biological data used as inputs to the model are shown

    in Tables 7-8 for the bird species and Table 9 for fur seals.  Since the

    number of birds beached on the Farallones was not sufficient to warrant

    the model calibration used for the November, 1984 hindcast, we used

    mortality rate estimates based on that incident in analyzing the effects

    of chronic release. The original estimates of the proportion of the

    birds contacting the slick which became lethally oiled, however, are

                                         4-44I

    probably boo high to be applied to the slicks resulting from the releaseI

    from the sunken stern since those slicks were of much smaller size. We

    therefore assumed that the mortality rate fran these smaller slicks was

    proportional to the amount of time spent in the area of the slick. For

    example, if the estimated time spent in the area of the slick during the

    initial incident were 1 hour and resulted in a mortality rate of 50%, andI

    the estimated time spent in the area of a slick from the sunken stern

    were 15 minutes," the mortality rate parameter for the sunken stern slick

    would be (15/60) X (0.5) = .125. We found it impossible to estimate a3

    mortality rate for fur seals contacting the slick fran the sunken stern

    because very little is known about the behavior of fur seals in theI

    presence of oil. Seals are generally very dependent on their sense of

    smell, and there is little doubt that they canasense the presence of

    spilled oil. Data presented by Geraci and Smith (1982) also suggest that

    if it is possible to avoid the slick-i.e., if there is a nearby area of

    clean water-seals will do so.  it is therefore likely that many or evenI

    all of the seals encountering the slick from the sunken stern successfully

    avoided it; however, it is impossible to rule out the possibility that

    some of the contacts between the fur seals and the slick had fatal

    consequences.

        For seabirds, we carried out these analyses for two seasons, December-I

    February, and March-May.  These seasons were chosen to correspond to3

    seasons used MMS' s Oil Spill Risk Analysis Model which was used to estimate

    the probability that an oil slick originating near the lcoation of the3

    sunken stern section would contact the Farallon islands. We used these

    data as estimates of the probability that an animal encountering oil inI

                                           4-45
                                       4

    this vicinity would be recovered on the Farallones.  The wind drift factor

    used by the MMS model to describe oil slick movement is 3.5% which is

    probably somewhat high for bird carcasses, our best estimate being 2.2%.

    We used the HAZMAT oil slick model to examine the sensitivity of carcass

    trajectories to the value of the wind drift factor using the hindcast of

    the original incident in November, 1984. We tried wind drift factors of

    2.2%, 2.8%, and 3.5% to see if the distribution of beachings varied with

    the value chosen. Although the beaching pattern did vary somewhat, the

    variation was not statistically significant, indicating that the use of

    the MMS model to approximate carcass trajectories is appropriate.

         The bird species which appeared most frequently in oiled condition on

    the Farallones were Common Murres. Biologists from PRBO counted 2 oiled

    murres during the period December-February and 4 oiled murres during the

    period March-May which were either dead or sufficiently oiled that it was

    believed that they subsequently died (G. Page, pers. comm.). Oil samples

    were taken from three of these birds, and all were determined to have

    been affected by oil of the type contained in the sunken stern section.

    MMS estimates of the likelihood of a trajectory launched from the region

    of 'the sunken stern reaching the Farallones was 17% for the winter, and

    3% for the spring. Using model estimates of mortality, we would therefore

    estimate that 20 oiled murres would have arrived on the Farallones during

    the winter, and 4 during the spring which corresponds well with the

    observations. It should be recognized, however, that there are two

    counteracting sources of bias involved in these calculations. Some live

    oiled birds which were not aimed directly at the Farallones undoubtedly

    arrived at least partially under their own power.  This would imply that

                                                4-46

    TABLE  6.  Input values for three seabird species, T/V Puerto Rican
               hindcast.

                                                            Species

                                               Sooty        Western           Red
                                             Shearwater        Gull        Phalarope

    DENSITY (bird/km2)

         Slope (> 200 m depth)                  1.34             .81           0

         Outer Shelf (100-200 m depth)            .05          7.39           3.74

         Inner Shelf -- North (< 100 m)          0             1.87             .95

         Inner Shelf -- South (< 100 m)          0             8.29           1.52

    DAILY ACTIVITY PATTERN
         (% on water)

         11 hours active                         10%            50%             50%

         13 hours quiescent                     100%             0%              0%

    MOVEMENT DATA
                                                                            50% in
         Mean distance moved per                   9             25.4        migra-
         3-hour time step (km)                                               tion
                                                                            at any
                                                                            time

                                                4-47

    TABLE 7. Analysis of chronic release:  Input values for four species of
               seabirds. Winter.

                                                          Species

                                           Common   Cassin's   Western   Brown
                                           Murre     Auklet       Gull   Pelican

    DENSITY (birds/km2)

         Slope (> 200 m depth)              4.22        8.87          .16       0

         Shelf (< 200 m depth)             45.91        5.29         2.80       .05

    DAILY ACTIVITY PATTERN
         (% on water)

         10 hours active                     79%         65%           50%       15%

         14 hours quiescent                 100%        100%            0%        0%

    MOVEMENT DATA

         Mean distance moved per             4.5        4.5           25.4       25
         3-hour time step (km)

    ESTIMATED MORTALITY RATE                   6.4%      4.1%           0.9%      0.7%

                                         4-48

    TABLE  8,  Analysis of chronic release:  Input values for four species of
             seabirds.  Spring.

                                                  Species

                                     Common   Cassin's  Western   Brown
                                     Murre     Auklet       Gull   Pelican

    DENSITY (birds/km2)

        Slope (> 200 m depth)              1.62        7.12          .16      0

        Shelf (< 200 m depth)             16.53        5.55          .97       .05

                                                                                   I
    DAILY ACTIVITY PATTERN
        (% on water)

        12 hours active                     79%         65%           50%       15%

        12 hours quiescent                 100%        100%            0%        0%

    MOVEMENT DATA

        Mean distance moved per             4.5        4.5           25.4       25
        3-hour time step (km)

    ESTIMATED MORTALITY RATE                  10.8%      9.6%           4.5%     0.7%

                                               4-49

    TABLE  9.  Analysis of chronic release:  Input values for northern fur seal.

                                                           Season

                                           December-     February-         April-
                                            January        March           May

    DENSITY (animals/km2

         Slope (> 200 m depth)                .0532          .1038          .1038

         Shelf (< 200 m depth)                .0339          .0631          .0631

    DAILY ACTIVITY PATTERN
         (% on water)

         12 hours active                        100%          100%           100%

         12 hours quiescent                '   100%           100%           100%

    MOVEMENT DATA

         Mean distance moved per                  5             1              5
         3-hour time step (km)

                                       4-51

                       The Oil Spill Risk Analysis Model
                       of the Minerals Management Service
                        U.S. Department of the Interior

    The Oil Spill Risk Analysis (OSRA) model was developed in 1975 by the
    Department of the Interior (DOI) to aid in the estimation of
    environmental hazards of developing oil resources in Outer Continental
    Shelf (OCS) lease areas. The model is maintained and operated by the
    Branch of Environmental Modeling (BEM), with research and data
    collection supported by a multi-million dollar study program.
    Information requests regarding the OSRA Model should be directed to
    Robert LaBelle, Chief SBE. The large, computerized model analyzes the
    probability of spill occurrence, as well as the likely paths or
    trajectories of spills in relation to the locations of recreational and
    biological resources which may be vulnerable to spilled oil.

    The probability of spill occurrence is estimated from historical
    accident rates and information on the anticipated level of oil
    production and method and route of transport. Spill movement is modeled
    in Monte Carlo fashion with a sample of S00 spills per season, each
    transported by seasonal surface-current vectors and wind drift data
    sampled from 3-hour seasonal wind-transition matrices. Transition
    matrices are based on historic wind records grouped in 41 wind velocity
    classes, and are constructed seasonally for up to six wind stations.
    Locations and monthly vulnerabilities of up to 31 categories of
    environmental resources are digitized within the modeled study area.
    Model output includes tables of conditional contact probabilities (that
    is, the probability of hitting a resource, given that a spill has
    occurred), as well as probability distributions for oil spills occurring
    and contacting environmental resources within preselected vulnerability
    time intervals.

    The model provides the DOI with a method for realistically assessing oil
    spill risks associated with OCS development. To date, it has been used
    in oil spill risk assessments for 47 OCS lease sales with the results
    reported in Federal environmental impact statements. Additional runs
    have been performed to analyze OCS development plans in California.
    Hindcasts of actual spills using real-time data have given good results
    in the past (Argo Merchant, Ixtoc, and Santa Barbara Spills, see Amstutz
    and Samuels, 1984)               and have lent credibility to the use
    and projections of the OSRA model. The results of these analyses are
    compiled in a series of the DOI Open File Reports. The OSRA model's
    methodology and results have been published in the scientific literature
    and presented at professional conferences.

                                     4-52

    The DOI performed a series of special runs of the Puerto Rican oil
    spill incident for the U.S. Department of Comnerce, Nationa  Oceanic and
    Atmospheric Administration, Sanctuary Programs Division (SPD). -
    Sensitive areas of concern were supplied by the SPD and were prepared
    as targets for the OSRA. The tow path of the Puerto Rican, and the site
    where the stern sank were analyzed as launch sites (sites where an oil
    spill occurs) and the probabilities of contacting the designated targets
    were assessed. Historical wind transition information from the Farallon
    Islands was used in the analysis rather than real-time wind data.  The
    model results provided probabilities of contact to targets within 30
    days for spills launched during each of four seasons, and 3-, 10- and 30-
    day probabilities of contact on an annual basis.

    The OSRA projections for the Puerto Rican oil spill were by design
    calculated using a stochastic approach with 2,000 hypothetical oil spill
    trajectories being used to represent both the general trend and
    variability of winds and surface currents in the area.  This is in
    contrast to other more deterministic approaches utilizing real-time
    wind, tide and current information. Real-time data input is the most
    likely to provide the optimum sbort-term at-site forecast of where a
    given slick will move in the next few hours. During a spill response,
    such a trajectory analysis should be continuous, with a constant
    modification of results being made available to containment and cleanup
    personnel.  Both approaches to oil spill modeling are specialized tools
    which each have different uses, Limits, and assumptions.  Any comparison
    should take the above factors into account.

                                            4-53

    Marine Environmental Research 13 (1984) 303-319

               Offshore Oil Spills: Analysis of Risks

                David E. Amstutz* & William B. Samuels*

              Minerals Management Service, US Deparutment of the Interior,
                            Washington, DC 20240, USA

                             (Received: I October, 1984)

                                  ABSTRACT

        The methods used by the US Department of the Interior to estimate risks
        of accidental oil spills attendant with offshore oil production and
        transportation are described. Both the likelihood of spill occurrence and
        hypothetical spill trajectories are considered. Five separate applications
        of the risk assessment work are sutqmarized.

                              INTRODUCTION

    The work presented summarizes efforts to quantify risks of accidental oil
    spills within the marine environment. This risk analysis treats both the
    incidence of oil spills, separately from offshore platforms (production
    sites), from pipelines, and from tankers, and the movement (trajectories)
    of these hypothetical spills. The work has been carried out within agencies
    of the US Department of the Interior as a part of the Outer Continental
    Shelf (OCS) oil and gas program. Oil spill analyses are used in the
    preparation of Environmental Impact Statements (required by the
     National Environmental Policy Act) and other Departmental decision
    documents.
       The Outer Continental Shelf Lands Act, as amended in I978, charges
    the Department of the Interior with expeditious development of offshore
    oil and gas resources. The National Environmental Policy Act of 1969, as
    amended (NEPA) and the regulations which implement that Act require
      Present address: Science Applications, Inc., McLean, VA 22102, USA.
                                      303

                                     4-54

    304                David E. Amstutz, William B. Samuels

                             r\  AN  F-MANCM.SCO

                        LUMBER                 ;
       38e

                                           %.   .

      Fig. . Mashowing tractsconsidered for leasing,takrotssaotragadh
                location of the lumber spill site off the California coast.
    quantification of risks posed to the environment through Federal
    undertakings. These laws and the work presented here reflect society's
    goals to protect the environment while pursuing reasonable development
    of non-renewable resources. Oil spill analyses are controversial, reflecting
    not only differences of opinion within the modeling community but also
                           AN\\-    %   %

    the abundance of emotions and intuitive notions surrounding incidents of
    oil pollution in the marine environment.
      The described modeling work was initiated in 1975. Since then, more
    than 30 oil spill risk and trajectory analyses have been completed. All of
    the analyses are contained in the US Geological Survey Open-file Report
    Series. Each analysis has been associated with specific offerings of
    offshore oil leases (OCS lease offerings). For example, Fig. I shows the
    location of tracts considered for a lease offering in central California.
    Nearly all of the US offshore regions (including Alaska) have been
    examined. Each analysis also includes examination of other sources of
              �P-~A C ''F"IC   Ol:EANJ           �

      a  *
                      &           4           .II
                      Nd          N           N            ,-

    Fill. l . Map showing tracts considered for leasing, tanker routes, sea otter range and the
                location of she lumber spill site off the California coast.

    ,quantification of risks posed to the environment through Federal
    undertak.ings. These laws and the work presented here reflect society's
    goals to protect the environment while pursuing reasonable development
    of'non-renewable resources. Oil spill analyses are controversial, reflecting
    not only differences of opinion with~in the modeling community but also
    the abundance of'emotions and intuitive notions surrounding incidents of'
    oil pollution in the marine environment.
      T~he described modeling work: was initiated in 1975. Since then, more
    than 30 oil spill risk and trajectory analyses have been completed. All of
    the analyses are contained in the US Geological Survey Open-File Report
    Series. Each analysis has been associated with specific offerings of
    offshore oil leases (OCS lease offerings). For example, Fig. 1 shows the
    location of tracts considered for a lease offering in central California.
    Nearly all of the US offshore regions (including Alaska) have been
    examined. Each analysis also includes examination of other sources of.

                  ':"'i-ii'~~           4-55

                         Offshore oil spills: analysis of risks         305

    potential oil spills in the area, such as those associated with tankering of
    imported oil.
      Our purposes in presenting this example of applied science are to
    describe techniques and to summarize results and applications. Of
    perhaps equal importance, is our purpose to inform the general scientific
    community of one means by which very large and diverse environmental
    data sets have been put to use to help achieve a societal goal.

                       DESCRIPTION OF MODEL

    The oil spill risk analysis model represents the study area using an
    orthogonal grid. The grid allows input of spatial information from any
    map projection. The study area for each analysis includes potential oil
    production sites as well as tanker and pipeline transportation routes.
    Additionally, the area includes adjacent regions which might be contacted
    by spills within approximately 30 days travel time (see Fig. 1).
      The coastline of the study area is digitized on the model grid and
    divided into segments (usually two sets) which allow examination of
    where marine oil spill trajectories contact the coastline. 'The first set of
    coastal segments are approximately of equal length, thus eliminating any
    length-related bias. Coastal segments of approximately equal length are
    generally 20 to 30 miles long, thus allowing some implicit spatial extent to
    the simulated oil slick. A second set of coastal segments representing
    political boundaries (counties and/or states) is frequently used.
      Various at-sea resources, potentially vulnerable to offshore oil spills,
    are digitized on the model grid in the same manner as the coastline. The
    spatial portrayals of these at-sea resources may be changed each month.
    While some at-sea resources are potentially vulnerable year round, others
    may be present or vulnerable to oil spills only during specific months.
    Thus, the model is capable of addressing resources such as migrating
    birds or whales. The model computations are summarized on a seasonal
    basis to aid in later determinations of environmental impacts. Analysts
    can therefore address potential impacts to summer tourism, seasonal
    fisheries, etc.
      The two main driving forces influencing the advection of oil spills on
    the ocean are surface currents and local wind stress. Although the
    astronomical tides induce horizontal motions in the ocean, these motions
    generally do not contribute to the net advection of oil spills. Instead, the

                                      4-56

                                                                               A~~~
    306                David E. Amstutz, William B. Samuels                                       3

    tidal motions contribute to lateral dispersion of an oil slick. The
    astronomical tides can contribute to net advection in regions of relatively
    shallow water such as found in portions of the Bering Sea (Liu &
    Leendertse, 198 1) and in some relatively confined embayments such as
    Cook Inlet, Alaska. The oil spill risk analysis being discussed treats
    hypothetical oil spills and not actual oil spills. Clearly, those concerned
    with actual oil spill movement in near-shore waters during real time
    circumstances would require knowledge of local tidal conditions to
    establish the approximate time during the day of initial contact and to
    refine estimates of land fall sites to scales less than the length of a coastal
    segment. The oil spill model is designed in a modular fashion such that
    other trajectory movement algorithms (Samuels et al., 1983) can be
    incorporated as needed. In the oil spill trajectory model, surface currents
    and local wind stress are assumed to act independently. This implies that
    the surface water velocity field is free of local wind effects. To satisfy this
    relationship, seasonal or monthly geostrophic surface water velocities
    (Kantha et ae., 1982) are provided to the oil spill trajectory model (see
    Fig. 2). Geostrophic currents are divided from the distribution of sea water
    density. The assumptions in the derivation are that accelerations are
    negligible, that the horizontal pressure gradient and the Coriolis effect are
    in balance, and that other forces are negligible. The contribution of local
    wind stress to oil spill movement is assumed to be 3-5 % of the wind speed
    (Smith, 1968; Stolzenbach et al., 1977) and rotated according to a
    variable wind deflection angle function as described by Samuels et aL
    (1982).
      The oil spill model simulates a large number of oil spill trajectories in a
    Monte Carlo fashion from potential and existing platform locations,
    pipeline routes and tanker lanes in an OCS leasing area. These trajectories
    are summarized as probabilities that if an oilspill occurs at a particular
    location, it will contact a vulnerable resource (e.g. seabird foraging area)
    or section of the coastline within certain defined time limits (usually 3, 10
    and 30 days). The probabilistic nature of the trajectory model stems from
    the treatment of the variation in the wind as a first-order Markov process
    and the assumption that spills occur randomly throughout the year. As a
    first-order Markov process, the wind state (speed and direction) at any
    time step is considered to be solely dependent upon the wind state during
    the prior time step. Time series of wind velocity, collected at coastal as
    well as offshore (buoy) weather stations over several years, are used to
    construct seasonal wind transition matrices (see Fig. 3) (Smith et al,

                                            IS~ ~4-57                            -

                                           Offshore oil spills: analysis of risks          30-7

                                 ~Se~t
                                                 a'e        (.       ........
                                                       ,*      ,   ; 1%   .

    I~ ~~~~Fg2  Mpfesfaceaevlctfedsmesao~fteoutsernU
                                                              ii,7//. , � o
                                                         ~.  '' *, ti, r/ / oIV/-

         I~~~~~~~~-�:: .....

                                     :~'.,.~
                                     s          ;;                     i ......

        1                           U ..t'..~~~~....'"/' "'....*......   *
                                 a              ;.  *  *     S * *  *  ,* t   *
                                          >  e::os~ostK   ' *            .....;. >Q  I

                             Fig.-2.  Map  of-the     sra   oae   veoci #  c   ( u m   � e  .o.   off. -

                                                (Kantha et al., 1982).,    ,  .
                         1982). These stations usually record   o wf         i             eight
                        observations per day; thus, the time step for tracking oil spills in the*

                        model is usually 3 h. At each time step, th   e wind transition matrix is

                    sampled to yield the  wtind velocity imparting motion to the  oil spill.

                    in 3 h: there is a 2 % chance the wind will be calm; a 14 % chance  the wind
                        in~~~~ 3 h :    thr i . a 2 ~  chnc th  win wil be cam a  14 ~ c hane Bhe w i nd X

                    will be from the north at 5 knots; a 21 % chance the wind will persist from
                    the north at 10 knots, etc. In making the trajectory comnputations, the
                    transition matrix is sampled at 3-h intervals in Monte Carlo fashion to
                    yield a 'next wind state' which is in direct proportion to the observed
                    states. After selecting the next wind state, the locally induced wind drift
    I~~ ~~~ape  oyedtewn  eoiyiprigmto  oteolsil

    I~~~~~een oFg ,cnie h rsn idt efo h ot t1

    I~~ ~~~nt.Fo  h esrdtm ere idvcos ehv bevdta

      -*           :' 4-58

    308                 Darid E. Amstutz, William B. Samuels

                           Next wind (direction, speed in knots)

    X0                 N  N  N N N  NE NENENENE   E NE N  E..
    8          calm     5   10  15  20  25        5  10  15  20  25    5
     caim  44   9  1 -  --    6 2  -  - -   5
     N 5   23  26  9 - -                        13  3  -  -    4
     N 10        2     14 21    8   3-           10  13   -   I -         2
      N 15  -    3 26 23 13-   3 -  6 10 -
    o N20   -   -  8 15 23 -   -8 23 15 --
     N25  -                                     - - - - -  - - - -
     XNE 5  25  25  2 -   I -   12  8  I - -    9
       NE lo     4      8   5   2  -   -         15  29  21   3  -        4
     NE 15   -          1   6   5  -   -         4  20  31  24  -         0
      NE20    -       -   -   -    2   1         1   5   11 49  11
    S NE25 -   - - - - -  - - - 48 48
    X- E5  26  9 1--                              I 2 5 - -   17

    Matrix elements in per cent:- indicates <0-5o, � indicates > 99-5'% .
    Fig. 3. A portion of the wind transition matrix (winter season) for the San Nicolas
    island, California, weather station. The matrix is constructed from 27 years of
                        observations taken at 3-h intervals.

    velocity is calculated using the 3-5 % rule and variable deflection angle
    function described above. The locally induced wind drift velocity is then
    added to the surface water velocity to yield the net advection of the center
    of mass of an oil spill at each time step.

                        MODEL APPLICATIONS

    Evidence, justifying the use of the oil spill trajectory model, has been
    obtained by comparing trajectories calculated by the model with the
    paths of actual spills. Wyant & Smith (1978) showed that the model's
    predicted oil spill trajectory agreed quite closely with the path of the Argo
    Merchant spill, which occurred in the coastal waters off Massachusetts
    (see Fig. 4).
      LaBelle et al. (1982) showed that the spatial distribution of
    hypothetical oil spill contacts to the southern California coast, as
    calculated by the oil spill trajectory model, agreed for the most part (see
    Fig. 5), with the spatial distribution of drift bottle land falls. We believe
    that the differences in the distributions are due in part to the fact that not

                                        4-59
                '~:xY                   4 - 5 9:;:'

                         Offshore oil spills: analysis of risks         -309

          M A S                       AT L ANT ::C:  ECA AN

                 I        Hi        AR~GOMR ERCHArT

                              agOBSERVED    -

                                                   MC DCL
      40Z                                           FoEDzCT~s:1iA
                                     |  ,,   ,  , *,.o=~=
                                   I   Jt   L   *  I
                              3NAUIZrCAL  MLQES

    Fig. 4. Comparison of the model's predicted trajectory with the observed movement of
                             the Argo Merchant spill.

    all drift bottles are recovered. Some bottles leak or are broken, some are
    buried on the shore, and some are washed back to sea after a short time
    ashore. To the extent that bottle recovery is related to beach use, one
    would expect a bias towards the California coast and away from the island
    locations (sites 22-30 in Fig. 5). The drift bottle release study was
    conducted - by the California Cooperative Fisheries Investigations
    Program (Schwartzlose & Reid, 1972). Comparisons of this type are valid
    ofcourse only if the drift bottles contain ballast and move with the surface
    waters.
      VanBlaricom & Jameson (1982) reported on the movement of lumber
    spilled off central California on February 12, 1978, and the implications
    of this event for oil spills and sea otters. These authors reported major
    floating patches of lumber within the sea otter range as early as February
    14, 1978 (within 3 days of the spill) and as late as March I11, 1978 (within
    30 days of the spill). We matched the lumber spill site to the nearest tanker
    transportation route (see Fig. 1) considered as a potential source of oil
    spills in an oil spill trajectory analysis (OSTA) for the central California

                                       4-60                                                           1

    310                 Darid E. Amstutz, William B. Samuels

                           s O--  :NCZCATES  LAIJNCH-I S:RE
           30"

                        it II,    : s -3       z,    ,'.,

                       .Jlls I
                   X    I g                                      is
                     ,11,TIS..
                       �     SMl~tTIONS            15t          15

               32    ~10   S20   25  I3~                   S. +
                      SIEGMIN' NUMI1I  I!

                                         N              -
                                             (Y~~~~q

    Fig. 5. Map of the southern California coast showing coincident drift-bottle release
    point and trajectory model launch point. Inserts show distributions of drift-bottle
    landfalls and predicted oil spill contacts to the coastal and island segments (expressed as
                                   per cent chance).

    area (LaBelle et aL., 1983). The sea otter range was considered a target for
    this trajectory analysis. Given that an oil spill could occur anywhere
    along this transportation route and at anytime during the year, the
    probability that it would contact the sea otter range within 3, 10 or 30 days
    was calculated to be 11, 19 and 30 %o, respectively (LaBelle et aL., 1983). A
    further comparison was then made to simulate oil spills from the lumber
    spill site itself. This analysis showed that the probabilities of oil spill
    contact to the sea otter range for 3-, 10- and 30-day travel times increased
    to 19, 29 and 44%, respectively (LaBelle, 1983). Because of the
    uncertainty associated with the time of oil spill occurrence, these
    probabilities are based on the assumption that oil spills could occur
    anytime during the year. We decided to take this investigation one step
    further by simulating oil spill trajectories from the lumber spill site during
    the months of February and March. The oil spill contact probabilities to
    the sea otter range during these months for 3-, 10- and 30-day travel times
    were 15, 51 and 70o%, respectively. The increasing probabilities are a

                      r')~   ~4-61

                         Offshore oil spills: analysis of risks          311

               XND::CATE3  LOCAr=QON  OF.-
               TAP:E'R  S::NI'N{                    a

                                  �t nM'RTH    , " .    Z  ] J f/
                                   = RCL= 14A 5                 -> >2

    as'/                                             X~

                     S                ,x-7lt        P-

                        s~ ~ ~~~~~~~~~~~~~~~o PYIt
        :~~"        . =^    .  .~''"", .            .-.'
        -5          03

                GE ~ ~ A TATCR                             CrEA \1

             -      'a      %       %               .       %
    Fig. 6. Map of the Cape Hatteras, North Carolina, area showing tanker sinking
     locations, lease tracts (PI-P7), transportation routes (TI-TS) and coastal segments.

    consequence of focusing the model spatially and temporally to a specific
    event.
      Since the oil spill modeling work presented in this report is stochastic in
    nature, total verification of the probability distribution of oil spill
    contacts to a coastline requires observations on a number of oil spills in
    order to draw statistical inferences. Such a data base rarely exists for a
    given study area. The Cape Hatteras area of North Carolina (see Fig. 6),
    however, was the site of intense submarine warfare during World War II,
    and tanker sinkings during that period provide a data base for verifying
    the model. Campbell el al. ( 1977) examined tanker sinkings by submarine
    activity during the first six months of 1942, and were able to identify 15
    sinkings of laden tankers just south of Cape Hatteras, between latitudes
    33�N and 35�10'N. During that same period, at least one oil spill was
    confirmed to have washed ashore on the Outer Banks south of Cape
    Hatteras and it is believed that as many as three oil spills may have come
    ashore on this portion of the coastline (Campbell et al., 1977). The
    locations of the 15 tanker sinkings were matched to three oil spill launch

                                       4-62

    312                David E. Amstutz, William B. Samuels                                        3

                                       toa. -A~         RS=NCQN MVr

                      ImAsi i?       R               CAUZft                                        3
                      RmA

                                 NAiAr ZAL. MZ .
                                                                      �                               ~I

    Fig. 7. Map of Santa Barbara Channel showing observed slick outline after 8 days, and
    the sequence of 64 simulated trajectories launched from Platform A at 3-h intervals.

    sites (lease tract group P3, and transportation routes T2 and T5 shown in
    Fig. 6) considered in the OSTA for OCS Lease Sale 56 (Samuels &
    Lanfear, 1980). The model's results were used to predict how many of the
    slicks were likely to come ashore on the Outer Banks (coastal segments 3,
    4 or 5 shown in Fig. 6). Of the 15 spills, the model estimated that there was
    only a 9 % chance that none would come ashore, but that there was a 75 %
    chance that between I and 3 oil spills would contact the Outer Banks                    -
    (Lanfear & Amstutz, 1981). This is in good agreement with the
    observations of Campbell et al. (1977).
      The4ast model application described is the 1969 Santa Barbara oil spill.
    On January 28, 1969, Union Oirs platform A, located in the Santa
    Barbara Channel (see Fig. 7), blew out spilling approximately 5000
    barrels of oil per day over the first 8 days of the spill (Allen, 1969). The size
    and trajectory of this slick were observed at different time intervals using
    aerial photography, and diagrams of the slick outline are reported in
    Allen (1969). To see how well our model would reproduce the slick size
    and trajectory, we simulated the release of oil from platform A over an 8-

                                                                                                      I

                                    4-63

                        Offshore oil spills: analysis of risks    - 313

    day period. We began by selecting the wind records (minimum of eight
    observations per day) for January and February, 1969, from a weather
    station located on San Nicolas Island, California. The San Nicolas Island
    weather station is located approximately 70 nautical miles south of
    platform A; however, it has been shown that these wind measurements
    can be used to represent the offshore wind patterns observed off the
    California coast south of 350 N latitude (Williams et al., 1980). This
    station has been used in all of the OSTAs performed for the California
    OCS. We simulated the release of oil over an 8-day period by calculating a
    sequence of 64 oil trajectories. We began this sequence with the first wind
    measurement made on January 28, 1969. Successive trajectories were
    initiated at 3-h intervals in accordance with the sequence of wind
    measurements. The wind factor and deflection angle were applied, as
    previously mentioned, and this local wind-induced velocity was added to
    the geostrophic surface water velocity calculated by a diagnostic
    circulation model for this region (Blumberg et al., 1983). In one sense,
    then, we were not only comparing our model with an actual event but were
    also investigating whether the winds measured at San Nicolas Island were
    representative of the winds observed in the Santa Barbara Channel. This
    comparison also allowed us to test the appropriateness of the geostrophic
    currents used in this model run. The results of this comparison are shown
    in Fig. 7. The agreement between model results and the slick outline
    reported by Alien (1969) is good except that Allen's observations show a
    tongue of oil moving in the direction of Santa Rosa Island. This
    movement of oil remains so far, unexplained by the model. The
    movement of this slick, as predicted by the model, appears to agree quite
    well with a description of the slick as reported by the California
    Department of Fish and Game (Anon., 1969) as follows:

        'On January 28, 1969, an offshore oil well being drilled offshore
         62 miles south of Santa Barbara, California, ruptured.... The
         next day, aerial surveillance indicated a heavy concentration of
         oil 4 miles wide and 12 miles long stretching in a south-
         southeasterly direction from the site of the rupture, oil
         platform A. A light film of oil tapered off in a northeasterly
         direction and intersected the beach west of Carpinteria, then
         ran south along the beach to Rincon Point. Numerous patches
         of oil stretched 14 miles south of the main concentration and
         east along the coast. On February 1, heavy oil slicks were

         *, 0                  4-64      :

    314,              David E. Amstutz, William B. Samuels

         reported I to 2 miles offshore from Summerland to Port
         Hueneme, and some oil was washing ashore on various beaches
         between Rincon Point and Pitas Point. By February 3, a large
        heavy oil slick surrounded Anacapa Island and was around the
        eastern end of Santa Cruz Island. During the night of February
        4, the oil washed ashore west of Santa Barbara and into Santa
         Barbara Harbor; so that on February 5, oil extended along
         approximately 10 miles of shoreline west and east of the town.
        The rocks and shoreline of Anacapa Island were covered with
         oil. About the same situation existed on February 6. A report
         issued on February 7, indicated oil was on the beaches in
        varying amounts running from light to heavy along a 28 mile
         stretch of coastline from Ventura west to Hope Ranch Beach.'
    It is interesting to note that this description does not report oil contacts to
    Santa Rosa Island, in contrast with Allen's observations. Furthermore,
    diving surveys (conducted by the California Department of Fish and
    Game) were only done at Anacapa Island, and the northeast and southern
    portions of Santa Cruz Island. No diving surveys were conducted at Santa
    Rosa Island.

                 OIL SPILL OCCURRENCE ESTIMATES

    The probabilities that hypothetical oil spills, originating at a particular
    location, will contact various resources at sea and/Qr the coastline are
    conditioned on the assumption that an oil spill has occurred. The two
    remaining steps then in modeling oil spill risk are to determine the
    likelihood of spill occurrence and to calculate the joint probabilities that
    spills will both occur and come in contact with the various resources at sea
    and/or the coastline.
      Spill occurrence is considered to follow a Poisson process (Smith et al.,
    1982). Thus, the probabilities of 0, 1, ..., n spills are given through
    knowledge of the mean (expected) number of spills. The mean number of
    spills is the product of the spill rate (spills per billion barrels) and the
    volumes of oil anticipated to be produced and transported. Thus volume
    of oil produced and/or transported is chosen for the exposure variable.
    Because of the very small numbers of platform (12 since 1964) and
    pipeline (8 since 1967) accidents, analysis of various exposure variables is

                                       4-65

                         Offshore oil spills: analysis of risks         315

    limited. In the case of pipeline-related spills, we do know that volume
    throughput is a better exposure variable than length of pipeline.
    Additionally, many alternative exposure variables (number of wells,
    platform years, etc.) are dependent upon the expected production
    volume. Volume of oil lends itself not only for risk analysis but also for
    benefit analysis.
      Spill rates, for spills _> 1000 barrels (bbls) and a 10 000 bbls, are
    determined through analysis of historical accident, production and
    transportation records. The minimum spill size of 1000 bbls is chosen for
    use in the risk analysis, as a 1I000-bbl spill is considered to be large enough
    to lend itself to trajectory analysis and of sufficient size to affect the
    environment. By using the historical records we assume that past
    experience is a suitable index of future experience. The time span over
    which the oil spill risks are estimated for each offshore lease offering is
    determined by the expected time required to produce the economically
    recoverable volumes of oil contained in the tracts offered for lease.
    Although production lifetimes vary from region to region, they generally
    fall between 20 and 30 years. The historical accident records have been
    subjected to trend analysis (Nakassis, 1982) to assure proper adjustments
    for recent experience.
      Separate oil spill data bases are maintained for platforms, pipelines and
    tankers. Only those platform and pipeline spills associated with US
    Federal offshore activities are used in projecting future spill rates. This
    restriction of the data base implicitly allows for any effects on spill rates
    that might be associated with Federal supervision and regulation of
    offshore exploration and production. We have used spill rates derived
    from platform and pipeline experience on the Federal OCS to project
    spills for Cook Inlet, Alaska (state offshore leases) and Prudhoe Bay,
    Alaska (state onshore leases). The agreement between projections based
    on Federil OCS experience and what has occurred in each of the Alaska
    areas is excellent. For example, as of March 1983, 0.8 billion barrels of oil
    have been produced and transported from Cook Inlet with three spills of
    1000 barrels and greater being observed. Using the rates reported here, we
    would expect 3.45 spills in this size category. Ten spills have occurred in
    producing and transporting 2-8 billion barrels of oil from Prudhoe Bay;
    our oil spill rates predict.the occurrence of 9.1 spills.
      The number of oil spills > 1000 bbls and the volume of oil produced on
    separate areas of the OCS are too small to justify separate spill rates.
    Also, there is no evidence in the available data to support the notion of

                )                    4-66

    316                 David E. Amstutz, William B. Samuels

                                    TABLE I
             Oil Spill Occurrence Rates for Platforms, Pipelines and Tankers

             Source                  Oil spill rates (spills per 109 barrels)'

                                        a 1000 bbls       10 000 bbis

           Platforms           !.00                       0-44
           Pipelines          !             .60           0.67
           Tankers, total                  1-30            0.65
             at sea                        0-90            0.50
             in portb                      0.40            0.15

            Source: Lanfear & Amstutz (1983).
            Includes bays, estuaries, harbors and piers.

    high local variability in the spill rates. Using the assumption that
    accidental oil spill occurrence follows a Poisson process, we have
    addressed the question of local variability in spill rates. Accounting for
    the variation in production from the separate areas, our analysis suggests
    that accident-free (or accident-prone) production in any specific area will
    not be detectable with statistical confidence for another 2 to 5 years.
      Tanker spill rates are projected using the worldwide record of
    accidental spills. We have not been able to reject the hypothesis that
    tanker spill rates for US waters are the same as elsewhere in the world.
    Also, in many instances, the areas included in an oil spill analysis extend
    beyond what might be formally considered as US waters. The oil spill data
    bases and the analyses used for determining spill rates used in the risk
    assessment are presented by Lanfear & Amstutz (1983) and Nakassis
    (1982). The spill rates currently used in our work are presented in Table 1.

                               CONCLUSIONS

    The Department of the Interior oil spill analysis model has been utilized
    for each offshore oil and gas lease offering held in the past 8 years. The
    model is probabilistic and treats hypothetical spills which might result
    from the production and transportation of offshore oil as well as spills
    resulting from tankering of imported oil. The model also serves to analyze
    various deletion alternatives to the total proposed lease offering.
      The model provides a framework for synthesis of enormous amounts of

                                         4-67

                         Ojishore oil spills: analysis of risks        317

    environmental data (winds, currents, and the location and temporal
    sensitivities of various marine resources) as well as historical oil
    production, transportation and accident records. Results from this work
    are utilized in the assessment of environmental impacts and in preparing
    various decision documents associated with the management of offshore
    oil resources. Thus the modeling work conveys technical information in a
    useful way to resource managers.
      Five separate applications of the model have been carried out. Two of
    these applications were hindcasts of actual oil spills: Argo Merchant tanker
    spill and Santa Barbara platform blowout. Three other applications
    compared the moders predicted oil spill contact probabilities with drift
    bottle landfalls, spilled lumber contacts to the sea otter range in central
    California, and oil spillage from World War II tanker sinkings off Cape
    Hatteras. In all cases good agreement between model predictions and
    observations were shown. Because the model has been designed to treat
    hypothetical spills, as opposed to spills in real time, these applications of
    the model are not strictly verifications. The applications do provide
    corroborating evidence which justifies use of the model.

                         ACKNOWLEDGEMENTS

    This work was supported by'the US Geological Survey, Bureau of Land
    Management and Minerals Management Service. The authors wish to
    acknowledge the contributions of R. Davis, J. R. Slack, R. A. Smith, T.
    Wyant, K. J. Lanfear, A. Nakassis and R. P. LaBelle to the oil spill
    modeling efforts. In addition we wish to acknowledge the expert technical
    assistance-of C. Schoen, D. Hopkins, L. Yost, B. McConville and D.
    Banks. We also thank 3. Poczik for typing the manuscript. Appreciation
    is extended to an anonymous referee for helpful suggestions.

                               REFERENCES

    Allen, A. A. (1969). Hearings before the Subcommittee on Minerals, Materials,
        and Fluids of the Committee on Interior and Insular Affairs, United States
        Senate, Washington, D.C., 146-53.
    Anon. (1969). Santa Barbara oil leak. California Department of Fish and Game
        Interim Rep., Sacramento (December).

                                    4-68                 3:

    318                  Darid E. Amstutz, William B. Samuels

    Blumberg, A. F., Herring, H. J., Kantha, L. H. &  Mellor, G. L. (1983).
        California shelf physical oceanography circulation model. First progress
        Report, Rep. No. 67, Dynalysis of Princeton, Princeton.
    Campbell, B., Kern, E. & Horn, A. (1977). Impact of oilspillagefrom World War
        11 tanker sinkings. D. Rep. No. MITSG 77-4, Massachusetts Institute of
        Technology, Cambridge.
    Kantha, L. H., Mellor, G. L. & Blumberg, A. F. (1982). A diagnostic calculation
        of the general circulation in the South Atlantic Bight. J. Phys. Oceanogr.,
        12, 805-19.
    LaBelle, R. P. (1983). Unpublished results.
    La.Belle, R. P., Samuels, W. B. & Amstutz, D. E. (1982). A comparative analysis
        of surface water velocity fields off the Southern California coast--
        implications for oil spill trajectory modelling. Paper presented at the
        American Geophysical Union Conference, San Francisco (December).
    LaBelle, R. P., Lanfear, K. J. & Karpas, R. M. (1983). An oilspill risk analysis
        for the central California outer continental shelf lease offering (October,
        1983), Open-File Rep. US Geological Survey, 83-117.
    Lanfear, K. J. & Amstutz, D. E. (1981). Environmental studies for oil spill
        trajectory modelling in the southeastern U.S. outer continental shelf leasing
        area. Proc. of the 13th Annual Offshore Technology Conference No. 4063,
        Houston, 487-90.
    Lanfear, K. J. & Amstutz, D. A. (1983). A reexamination of occurrence rates for
        accidental oilspills on the U.S. outer continental shelf. Proc. of Eighth
        Conference on the Prevention, Behavior, Control, and Cleanup o' Oilspills,
        San Antonio, 355-9.
    Liu, S. K. & Leendertse, J. J. (1981). A three-dimensional oilspill model with and
        without ice cover. Proc. International Symposium on Mechanics of Oil
        Slicks. International Association of Hydraulic Research, Paris, 249-65.
    Nakassis, A. (1982). Has offshore oil production become safer? Open-File Rep.,
        US Geological Survey, 82-232.
    Samuels, W. B. & Lanfear, K. J. (1980). An analysis for the South Atlantic
        (Proposed Sale 56) outer continental shelf lease area. Open-File Rep., US
        Geological Survey, 80-650.
    Samuels, W. B., Huang, N. E. & Amstutz, D. A. (1982). An oilspill trajectory
        analysis model with a variable wind deflection angle. Ocean. Eng., 9, 347-60.
    Samuels, W. B., LaBelle, R. P. & Amstutz, D. A. (1983). Applications of oilspill
        trajectory models to the Alaskan outer continental shelf. Ocean Manage., 8,
        233-50.
    Schwartzlose, R. A. & Reid, J. L., Jr. (1972). Nearshore circulation in the
        California current. California Cooperative Fisheries Investigation Rep. 16,
        57-65.
    Smith, J. E. (Ed.) (1968). Torrey Canyon pollution and marine life. Cambridge
        University Press, Cambridge.
    Smith, R. A., Slack, J. R., Wyant, T. & Lanfear, K. J. (1982). The oilspill risk
        analysis model of the U.S. Geological Survey. Prof. Pap. US Geological
        Survey, Number 1227.
                             v '~~~~~~~~~~~

                          ?I.>~      ~      4-69

                           Offshore oil spills: analysis of risks              319

    Stolzenbach, K. D., Madsen, O. S., Adams, E. E., Pollack, A. M. & Cooper,
        C. K. (1977). A review and evaluation of basic techniques for predicting the
        behavior of surface oil slicks. Ralph M. Parsons Laboratory, Rep. No. 222,
        Massachusetts Institute of Technology, Cambridge.
    VanBlaricom, G. R. & Jameson, R. J. (1982). Lumber spill in Central California
        waters: implications for oil spills and sea otters. Science, 215, 1503-5.
    Williams, R. G. et al. (1980)..4 A climatology and oceanographic analysis of the
        Califjrnia Pacific outer continental shelf region. US  Department of
        Commerce, NOAA, Washington, DC.
    Wyant, T. & Smith, R. A. (1978). Risk forecasting for the Argo Merchant spill.
        Proc. of a Symposium held January 11-13, at the Center for Ocean
        Management Studies, University of Rhode Island, Providence.

                                         4-70             -3

                                                                                       U
                  List of Publications Related to the Oil Spill
             Risk Analysis Model of the Minerals Management Service

    "The Application of Oceanography to Oil-Spill Modeling for the Outer                  P-ll
    Continental Shelf Oil and Gas Leasing Program," Robert P. LaBelle and
    Cheryl M. Anderson, Marine Technology Society Journal, 1985, Vol. 19(2),
    pp. 19-26.

    "Analysis of the Sensitivity to Variations in Wind Deflection Angle of Oil
    Spill Trajectories Modeled Off the California Coast," Robert P. LaBelle,
    Transactions, American Geophysical Union, EOS 65(45):940, November 6, 1984.

    "Offshore Oil Spills: Analysis of Risks," David E. Amstutz and William B.             P-IO
    Samuels, Marine Environmental Research, 1984, Vol. 13, pp. 303-319.

    "!'An Examination of the Argo Merchant Oil Spill Incident Using a
    Probabilistic Oil Spill Model," Robert P. LaBelle, William B. Samuels, and
    David E. Amstutz, Presented at the 47th Annual Meeting of the American
    Society of Limnology and Oceanography, Vancouver, B.C., June 11-14, 1984.

    "Potential Oil Spill Risks in the Chukchi Sea," William B. Samuels and
    David E. Amstutz, Presented at the 47th Annual Meeting of the American
    Society-of Limnology and Oceanography, Vancouver, B.C., June 11-14, 1984.

    "An Analysis of Surface Winds Off the California Coast," David E. Amstutz,                 I
    William B. Samuels, and Kenneth J. Lanfear, Presented at the 47th Annual
    Meeting of the American Society of Limnology and Oceanography,
    Vancouver, B.C., June 11-14, 1984.

    "An Oil-Spill Risk Analysis for the Gulf of Mexico (Proposed Sales 94, 98,            0-33
    and 102) Outer Continental Shelf Lease Area," Robert P. LaBelle, Anastase
    Nakassis, and A. Doreen Lucas, Minerals Management Service OCS Report MMS-
    84-0066, April 1984, 172 p.

    "An Oil-Spill Risk Analysis for the South Atlantic Lease Sale 90,"  David             0-32
    E. Amstutz, William B. Samuels, and A. Doreen Banks, Minerals Management
    Service OCS Report MMS-84-0037, 1984, 127 p.

    "Comment on 'Steady Wind- and Wave-Induced Currents in the Open Ocean',"              P-9
    David E. Amstutz and William B. Samuels, Journal of Physical Oceanography,
    1984, Vol. 14(2), pp. 484-485.

    "An Oilspill Risk Analysis for the St. George Basin (December 1984) and               0-31
    North Aleutian Basin (April 1985) Lease Offerings," William B. Samuels,
    Minerals Management Service OCS Report MMS-84-0004, February 1984, 71 p.

    "Oilspill Trajectory Modelling in the Beaufort Sea," William B. Samuels,
    Transactions, American Geophysical Union, EOS 64(52):1049, December 27,
    1983.

                                          4-71

    "Coupling of Oil Spill Modeling in the Gulf of Mexico with a Method for
    Ranking Biological Resources," Robert P. LaBelle and J. Kenneth Adams,
    Transactions, American Geophysical Union, EOS 64(52):1056, December 27,
    1983.

    "Oilspill Trajectories and Beaufort Sea Ice," Richard T. Prentki and
    William B. Samuels, Transactions, American Geophysical Union, EOS
    64(52):1049, December 27, 1983.

    "Calculations of Seabird Population Recovery from Potential Oilspills in            P-8
    the Mid-Atlantic Region of the United States," William B. Samuels and
    Anthony Ladino, Ecological Modelling, 1983, Vol. 21, pp. 63-84.

    "An  Oilspill  Risk  Analysis  for  the  Diapir  Field (June 1984) Lease            0-30
    Offering," William B. Samuels, Doreen Banks, Dorothy Hopkins, U.S.
    Geological Survey Open-File Report 83-570, September 1983, 138 p.

    "An Oilspill Risk Analysis for the Gulf of Alaska/Cook Inlet Lease Offering    0-29
    (October 9lg84)," Robert P. LaBelle, Anastase Nakassis, and Kenneth J.
    Lanfear, U.S. Geological Survey Open-File Report 83-882, August 1983, 74 p.

    "Applications  of  Oilspill  Trajectory  Models  to  the  Alaskan  Outer            P-7
    Continental Shelf," William B. Samuels, Robert P. LaBelle and David E.
    Amstutz, Ocean Management, 1983, Vol. 8(3), pp. 233-250.

    "Small Scale Oil Slick Modeling in the Santa Barbara Channel California,"           P-6
    Kenneth J. Lanfear and William B. Samuels, In: Waste Disposal in the
    Oceans: Issues for the 80's (D.F. Soule & D. Walsh, Eds.), Westview Press,
    Boulder, Colo., 1983, pp. 63-75.

    "An Oilspill Risk Analysis for the Southern California Lease Offering               0-28
    (February 1984)," Robert P. LaBelle, Kenneth J. Lanfear, A. Doreen Banks,
    and Robert M. Karpas, U.S. Geological Survey Open-File Report 83-563, July
    1983, 115 p.

    "Simulation  of  Spreading  and  Weathering of Oilspills in the Southern
    California Bight," William B. Samuels, Presented at the 46th Annual
    Meeting of the American Society of Limnology and Oceanography, St. Johns,
    Newfoundland, June 13-16, 1983.

    "An Oilspill Risk Analysis for the North Atlantic (February 1984) Lease             0-27
    Offering,"  David E. Amstutz  and  William B. Samuels,  U.S. Geological
    Survey Open-File Report 83-567, May 1983, 177 p.

    "An Oilspill Risk Analysis for the Navarin Basin Lease Offering (March              0-26
    1984)," William 8. Samuels, Kenneth J. Lanfear, and Doreen Banks, U.S.
    Geological Survey Open-File Report 83-120, April 1983, 34 p.

    "An Oilspill Risk Analysis for the Central California Outer Continental             0-25
    Shelf Lease Offering (October 1983)," Robert P. LaBelle, Kenneth J.
    Lanfear, and Robert M. Karpas, U.S. Geological Survey Open-File Report 83-
    117, March 1983, 67 p.

                                         4-72             -

    "An  Oilspill  Risk  Analysis  for  the  Central Gulf (April 1984) and              0-241
    Western Gulf of Mexico (July 1984) Lease Offerings," Robert P. LaBelle,
    U.S. Geological Survey Open-File Report 83-119, February 1983, 63 p.

    "A  Reexamination  of  Occurrence Rates for Accidental Oilspills on the             P-5
    U.S. Outer Continental Shelf," Kenneth J. Lanfear and David E. Amstutz,
    Proceedings of the Eighth Conference on the Prevention, Behavior, Control,
    and Cleanup of Oil Spills, American Petroleum Institute Publ. No. 4356,
    1983, pp. 355-359.

    "The Relative Contributions of Local Wind Drift and Geostrophic Surface
    Currents to the Movement of Potential Oilspills on the U.S. Pacific Outer
    Continental  Shelf," William B. Samuels,  Robert P. LaBelle, and David E.
                                                                                        m
    Amstutz, Transactions, American Geophysical Union, EOS 63(45):1003,
    November 9, 1982.

    "A Comparative Analysis of Surface Water Velocity Fields off the Southern
    California Coast - Implications for Oil Spill Trajectory Modeling,"
    Robert  P.  LaBelle,  William 8. Samuels,  and  David  E.  Amstutz,
    Transactions, American Geophysical Union, EOS 63(45):1003, November 9,
    1982.

    "An Oilspill Risk Analysis for the South Altantic (Proposed Sale 78) Outer          0-23
    Continental Shelf Lease Area," William B. Samuels, U.S. Geological Survey
    Open File Report 82-807, September, 1982, 161 p.

    "An Application of a Vulnerability Index to Oil Spill Modeling in the Gulf          P-4  I
    of Mexico," Robert P. LaBelle, Gail Rainey, and Kenneth J. Lanfear, U.S.
    Geological Survey Open File Report 82-804, August 1982, 14 p.

    "An Oilspill Risk Analysis  for  the Gulf of Mexico Outer Continental Shelf    0-22
    Lease  Area Regional Environmental Impact Statement,"  Robert P.LaBelle,
    U.S. Geological Survey Open File Report 82-238, April 1982, 210 p.

    "An Oilspill Risk Analysis for the Mid-Atlantic (Proposed Sale 76) Outer            0-21
    Continental Shelf Lease Area," William B. Samuels and Dorothy Hopkins, U.S.
    Geological Survey Open File Report 82-27, April 1982, 163 p.

    "Has Offshore Oil Production become Safer?,"  Anastase Nakassis, U.S.               P-3  I
    Geological Survey Open File Report 82-232, April 1982, 26 p.

    "An Oilspill Trajectory Analysis Model with a Variable Deflection Aigle,"           0-20
    William B. Samuels,  Norden E. Huang  and  David E. Amstutz,  Oce-ni
    Engineering, 1982, Vol. 9, No. 4, pp. 347-360.

    "Oilspill Trajectory Modeling in Alaskan Waters,"  William 3. c  uels,
    Robert P. LaBelle and David E. Amstutz.  Ocean Sciences AGU/ASLC Joint
    Meeting, 16 - 19 February, 1982, San Antonio, Texas.

    "An Oilspill Risk Analysis for the Beaufort Sea, Alaska (Proposed Sale 71)          0-19
    Outer Continental Shelf Lease Area," William B. Samuels, Oorothy Hopkins
    and Kenneth J. Lanfear, U.S. Geological Survey Open File Report 82-13,
    January 1982, 102 p.

                                          4-73

    "The Oilspill Risk Analysis Model of the U.S. Geological Survey,'t  Richard          P-2
    A. Smith, James R. Slack, Timothy Wyant and Kenneth J. Lanfear, U.S.
    Geological Survey Professional Paper 1227, 1982, 40 p.

    "Simulations of Seabird Damage and Recovery from Oilspills in the Northern           P-1
    Gulf of Alaska," William B. Samuels and Kenneth J. Lanfear, Journal of
    Environmental Management, 1982, Vol. 15, pp. 169-182.

    "An  Oilspill Risk Analysis for the  North Atlantic (Proposed Sale 52)               0-18
    Outer Continental Shelf Lease Area," Robert P. LaBelle, U.S. Geological
    Survey Open-File Report 81-865, September, 1981, 120 p.

    "An Oilspill Risk Analysis for the St. George Basin, Alaska, (Proposed Sale    0-17
    70) Outer Continental Shelf Lease Area," William B. Samuels and Dorothy
    Hopkins, U.S. Geological Survey Open-File Report 81-864, 1981, 89 p.

    "An  Oilspill  Risk  Analysis for the Southern California (Proposed Sale             0-16
    68) Outer Continental Shelf Lease Area," William B. Samuels, Dorothy
    Hopkins and Kenneth J. Lanfear, U.S. Geological Survey Open-File Report 81-
    605, May, 1981, 206 p.

    "Documentation and User's Guide to the U.S. Geological Survey Oilspill Risk    D-2
    Analysis  Model:  Oilspill  Trajectories   and   Calculation of Conditional
    Probabilities," Kenneth J. Lanfear and William B. Samuels, U.S. Geological
    Survey Open-File Report 81-316, May 29, 1981, 95 p.
            *~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    "An Oilspill Risk Analysis for the Norton Sound, Alaska, Proposed Sale 57)           0-15
    Outer Continental Shelf Lease Area," William B. Samuels and Kenneth J.
    Lanfear, U.S. Geological Survey Open-File Report 81-320, May 29, 1931, 114
    p.

    "Sensitivity of an Oilspill Trajectory Analysis Model to Variation in Wind
    Deflection Angle," William 8. Samuels, Norden Huang and David E. Amstutz.
    Presented at the 44th annual meeting of the American Society of
    Limnology and Oceanography, Milwaukee, Wisconsin, June 15-18, 1981.

    "Environmental Studies for Oilspill Trajectory Modeling in the Southeastern
    U.S. Outer Continental Shelf Lease Area," Kenneth J. Lanfear and David E.
    Amstutz. Presented at the 13th Annual Offshore Technology Conference,
    Houston, Texas, May 4-7, 1981.

    "An Oilspill Risk Analysis for the Gulf of Mexico (Proposed Sales 67 and             0-14
    69) Outer Continental Shelf Lease Area," Robert P. LaBelle  and Kenneth J.
    Lanfear, U.S. Geological Survey Open-File Report 81-306, 1980, 118 p.

    "An Oilspill Risk Analysis for the Mid-Atlantic (Proposed Sale 59) Outer             0-13
    Continental Shelf Lease Area," William B. Samuels and Kenneth J. Lanfear,
    U.S. Geological Survey Open-File Report 80-2026, October, 1980, 82 p.

    "Documentation and User's Guide to the Spatial Environmental Data                    3-
    Digitizing System, SEDOS," Kenneth J. Lanfear and Anastase Nakassis, U.S.
    Geological Survey Open-File Report 80-871, August, 1980, 129 p.

                       L) U4-74                                                            3

                                                                                        U

    "An  Oilspill  Risk  Analysis  for  the  Cook  Inlet  and  Shelikof Strait          0-12
    (Proposed Sale 60) Outer Continental Shelf Lease Area," Robert P. LaBelle,
    William B. Samuels and Kenneth J. Lanfear, Open-File Report 80-863, August,
    1980, 129 p.

    "An Oilspill Risk Analysis for the South Atlantic (Proposed Sale 56) Outer          0-11
    Continental Shelf Lease Area," William B. Samuels and Kenneth J. Lanfear,
    U.S. Geological Survey Open-File Report 80-650, May, 1980, 76 p.

    "An Oilspill Risk Analysis for the Central and Northern California                  0-10
    (Proposed Sale 53) Outer Continental Shelf Lease Area,"   William B.
    Samuels and Kenneth J. Lanfear, U.S. Geological Survey Open-File Report 80-
    211, January, 1980, 44 p.

    "An Oilspill Risk Analysis for the Kodiak Island (Proposed Sale 46) Outer           0-9
    Continental Shelf Lease Area," William B. Samuels, Kenneth J. Lanfear and
    Anastase Nakassis, U.S. Geological Survey Open-File Report 80-175, January,
    1980, 92 p.

    "Applications of the USGS Oilspill Trajectory Analysis (OSTA) Model to
    Decisions Regarding OCS Oil Development," Kenneth J. Lanfear, Proc. of the
    Workshop on Government Oilspill Modeling, Wallops Island, Virginia,
    November 7-9, 1979, pp. 13-14.

    "The USGS Oilspill Trajectory Analysis Model," William B. Samuels, Proc. of              I
    the Workshop on Government Oilspill Modeling, Wallops Island, Virginia,
    November 7-9, 1979, 20 p.

    "An Oilspill Risk Analysis for the Northern Gulf of Alaska (Proposed Sale           0-8
    55) Outer Continental Shelf Lease Area," Kenneth J. Lanfear, Anastase
    Nakassis, William B. Samuels  and Christina T. Schoen, U.S. Geological
    Survey Open-File Report 79-1284, July, 1979, 79 p.

    "Oilspill Risk Minimization Through Optimal Tract Selection," Richard A.
    Smith, Kenneth J. Lanfear and Ivan C. James, II, presented at the National
    Weather Service Conference, "Physical Behavior of Oil in the Marine
    Environment," at Princeton University, May 8-9, 1979.

    "An Introduction to the Oilspill Risk Analysis Model," K.J. Lanfear, R.A.
    Smith and J.R. Slack, presented at the 11th Annual Offshore Technology
    Conference, Houston, Texas, April 30 - May 3, 1979.

    "An Oilspill Risk Analysis for the Southern California (Proposed Sale 48)           0-7
    Outer Continental Shelf Lease Area," James R. Slack, Timothy Wyant and
    Kenneth J. Lanfear, U.S. Geological Survey Water-Resources Investigation
    78-80, October, 1978.

    "An Oilspill Risk Analysis for the Mid-Atlantic (Proposed Sale 49) Outer            0-6
    Continental Shelf Lease Area," James R. Slack and Timothy Wyant,-U.S.
    Geological Survey Water-Resources Investigations 78-56; 1978; 79 p.

                       I.:/              4-75

    "An Oilspill Risk Analysis for the Eastern Gulf of Mexico (Proposed Sale            0-5
    65) Outer Continental Shelf Lease Area," James R. Slack and Timothy
    Wyant, U.S. Geological Survey Open-File Report 78-132; 1978; 72 p.

    "An Oilspill Risk Analysis for the Western Gulf of Alaska (Kodiak Island)           0-4
    Outer Continental Shelf Lease Area," James R. Slack, Richard A. Smith-and
    Timothy Wyant; U.S. Geological Survey Open-File Report 77-212, 1977, 57 p.

    "An Oilspill Risk Analysis for the South Atlantic Outer Continental Shelf           0-3
    Lease Area," James R. Slack and Richard A. Smith, U.S. Geological Survey
    Open-File Report 76=653, 1976, 54 p.

    "An Oilspill Risk  Analysis  for the Mid-Atlantic Outer Continental Shelf           0-2
    Lease Area," Richard A.e Smith, James R. Slack and Robert K. Davis; U.S.
    Geological Survey Open-File Report 76-451, 1976a, 24 p.

    "An Oilspill Risk Analysis for the North-Atlantic Outer Continental Shelf           0-1
    Lease Area," Richard A. Smith, James R. Slack and Robert K. Davis, U.S.
    Geological Survey Open-File Report 76-620, 1976b, 25 p.

    I

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            I ~~~~~~~~~APPENDIX 5
                   DISCUSSION OF' FACTORS INFLUENCING MARINE BIRD MORTALITY
    I              COUNTS RES.ULTING FROM 'THE T/V PUERTO RICAN OIL SPILL INCIDENT

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    Appendix 5:  Marine bird mortality resulting from oil spilled by T/V Puerto
                 Rican

         Of the many adverse environmental impacts that resulted from the T/V

    Puerto Rican oil spill incident, oiled dead birds observed during beach

    surveys were among the most visible. In their assessment of bird mortality

    caused by the oil spill, the Point Reyes Bird Observatory reported that about

    1,000 oiled birds died during the period 6-19 November 1984.1

         The direct effects of oil contamination on populations of birds are

    determined by counting and examining oiled birds.  Thus estimates of mortality

    and of the effects of sublethal doses of oil on the ability to see, and

    collect affected individuals.  The probability of detecting affects individuals

    is a function of many environmental, behavorial, and physiological factors,

    and as a results of oil contamination are more likely to be underestimated

    for scme species and populations than others.

         For example, different species exhibit differing degrees of buoyancy

    subsequent to having their feathers coated with oil.  Those species that

    remain relatively buoyant after exposure to oil, such as qulls, are more

    likely to remain afloat and wash ashore than are species that are relatively

    non-buoyant, such as the alcids (auks, murres, and puffins). The species

    that are less buoyant after exposure to oil are mrove likely to sink rather

    than wash ashore, thus they are less likely to be counted in beach surveys,

    and the number of birds of that species affected by oil will probably be

    underestimated. Therefore, the probability that the effects of the oil spill

    are underestimated increases from one group to the next in the following list

    of birds, ranked in approximate order of decreasing gulls, shearwaters,

    northern fulmars, grebes, loons, alcids (auks, murres, puffins), and cormorants.2

         I1 Point Reyes Bird Observatory, 1985

                                           5-2

        Probability of detection also decreases as the distance offshore increases

    for the location where the bird is exposed to oil. Birds that inhabit areas

    further offshore and which are farther from the beach at the time of expoosure

    are more likely to sink or suffer predation than those birds that are oiled

    relatively close to shore. Thus the probability of underestimating the

    number of individuals adversely affected by oil increases for the following

    list of birds, ranked in approximate order of increasing distance offshore:

    coots, grebes, ducks, phalarlopes, alcids, scoters, fulmrs, loons.3

         Some species tend to cane ashore soon after being exposed to even

    minimal amounts of oil, while other species treavel relatively great

    distances from the site before finally coming ashore.  For example, a

    murre with only a very small patch of oil on its plumage will sometimes

    ccae ashore, while a-gull will tend to fly some distance, if able, before

    making its way to the beach.4 Thus the bird's behavior affects the probability

    that estimates of ill affects due to oil may be too low. The probabilityof

    underestimating the number of individuals affected increases from one group

    to the next in the following list of birds ranked in approximate order of

    tendency to disperse from the exposure site prior to coming ashore: alcids,

    grebes, gulls, fulmars, loons.

         Other biological factors that affect the determination of the effects of

    an oil spill on bird populations include the overall state of health of the

    population, the response of individuals to sub-lethal doses of oil, and the

    degree of contact with oil. For example, 362 scoters and all 19 northern

    fulmars counted in the initial beach surveys were subtracted from the total

    number of mortalities caused by the Puerto Rican on spill. This was done

         2. Glenn Ford, Pers. Comm., 1985
         3. Sterling Hobe Corporation, 1985
         4. ibid.

              I                                  ~~~~~~~~~~~~5-3

    I    ~because many of these birds were found in poor health  prior to the oil

    I    ~spill incident, thus their deaths could not be attributed strictly to

          exposure to oil. Sane birds, especially gulls, acquired sublethal doses

    I ~or oil. These birds were not systematically recorded during the oil

          spill incident and could not be included in the analysis.

               Environmental factors prevented some affected birds from being

    I    ~included in the total number oiled.  Wind direction and speed and current

          direction and speed acted in conjunction with biological factos (i.e.

    I    ~response to sublethal doses, degree of contact with oil, degree of oil

          saturation, distance from shore, bouyancyl scavenging of carcasses prior

          to reaching shore or being counted on the beach, and behavior) to reduce

    I    ~the probability that affected individuals were detected and included in

          the total number of mortalities.   Furthermore, not all areas where

    I    ~carcasses could have washed ashore were systematically surveyed, due to

          constraints of beach access, time, and personnel. For example, the

          beaches north of Bodega Head were not included in the survey, although

          there were sanme occasional visits, and it is likely that some bird carcasses

          did drift northward and come ashore in these areas.

     I        ~~Thus despite the fact that a total of 378 dead birds was subtracted

          from the initial beach survey count of total mortalities, the factors

          discussed above suggest that the numbers in Figure 17 underestimate the total

          of birds adversely affected by the oil spill of the T/V Puerto Rican. The

          following is a brief discussion of the species most likely most likely to

    I    ~have been underestimated by the beached bird counts.

                                           5-4

         Loons. None of the three loon species normally come ashore, beaching

    only when sick or oiled. Because Arctic Loons remain further offshore than

    Common or Red-throated loons, they would be less likely to reach land, and

    thus an underestimation of this loon species mortality is more likely on

    beach counts. The buoyancy of all loons is probably low compared to gulls

    and shearwaters, thus more oiled loons would sink and remain unreported.

         During the spill, large numbers of Arctic Loons were migrating south.

    Individuals that landed on the oily water may have become lightly to moderately

    oiled before moving on.  Such birds might have flown a considerable distance

    before becoming incapacitated, and would have remained unrecorded. Thus the

    number of Arctic Loons reported as oiled in Figure 17 is likely underestimated

    to a greater degree than the numbers recorded for Ccmmon and Red-throated

    Loons.

         Western Grebes.   Western Grebes are a near-shore species that swim ashore

    when they are lightly to moderately oiled. They are likely less buyoyant than

    alcids and possibly loons.  Because of their proximity to shore, most, if not

    all, of the lightly to moderately oiled individuals probably beached; however,

    a large number were already dead and heavily oiled when found. It is likely

    they died before reaching shore. We expect sane additional oiled Western

    Grebes sank and never rached shore, but cannot estimate the numbers of these

    birds.

         Northern Fulmars. Fulmars tend to occur further out at sea than grebes

    and scoters and do not swim ashore in numbers when lightly to moderately

    oiled. They ar probably more buoyant than loons, grebes, or alcids. Because

    large numbers appeared to be dying of non-oil-related causes during the spill,

    and because most carcasses had relatively small amounts of oil on them (when

    oil could be found at all), we were unable to assign an oil-related impact for

    this species.  It is likely that the impact on northern fulmars was minimal

    compared to the impact on common murres.

         Cormorants. There is little evidence of an impact on cormorants.

    Figure 17 indicates 7 dead of two species. In addition, observers on the

    Farallones reported seeing 8 oiled Brandt's Cormorants and 10 oiled Pelagic

    Cormorants. Aerial surveys reported in the original PRBO report indicate

    cormorants were seen over both the inner and outer shelf. Given the distances

    cormorants range offshore, and their relatively poor buoyancy, it can be

    assumed the numbers shown in Figure 17 substantially underestimate the casualties.

         Brown Pelican. Despite a census total of 2,583 pelicans at the Farallon

    Islands roosts on 6 November 1985, the day the oil first collided with the

    island shoreline, and subsequent polican counts up to 1,000 until 13 November,

    only 2 pelicans with oiled plumage were found in the roots.  Rehabilitation

    centers reported receiving only 1 oiled pelican for treatment during the

    spill period. Thus, in light of this information, it appears that pelicans

    suffered little impact from the spill.

        Waterfowl and Coots. Coots and the species of ducks shown in Figure 17

    reside close to shore.  Most of the waterfowl that were oiled were recovered

    in the Bodega Harbor region where the recovery of beached oiled birds was was

    very complete. Therefore, we suspect the numbers reported in Figure 17 are

    close to the actual numbers of birds affected by the spill.

        Red Phalaropes.   Aerial surveys showed phalaropes migrating over a

    broad front between the shore and the continental slope.  The 3 oiled Red

    Phalarope carcasses found and reported in Figure 17 probably greatly under-

    estimate mortality of this species. There are two reasons for this:

                                           5-6

    phalaropes dying far from shore are likely to be scavenged before beaching

    and after beaching they are difficult to detect because of their small size.

        Gulls.  Figure 17 reports only 5 Western Gull casualties although

    obvservers on the Farallon Islands reported seeing 60 oiled Western and 2

    oiled California gulls. Many additional oiled gulls were seen between Bodega

    Harbor and Point Arena (Page, pers. obs.). Gulls probably disperse farther

    fram the place of oil contact than other species and are therefore less

    likely to be found. However, gull carcasses are highly buoyant, and live

    gulls tend to move to shore. Many moe carcasses would have been found had

    large numbers of gulls died from their initial contact with the oil. Because

    gulls may come ashore and preen the oil fram their plumage, we suspect many

    oiled gulls survived and therefore, assume a limited effect on gulls from the

    spill.

         Alcids.  Comnon Murre and Cassin's Auklet mortalities from the spill

    may be the most underestimated of any species shown in Figure 17. Both

    species are widely distributed from the inner continental shelf seaward

    to the continental slope. Carcass buoyancy of these birds is low compared

    to gulls or shearwaters. The alcids seem to be detrimentally affected by

    even small amounts of oil on their plumage; a murre with only a half-dollar

    sized patch of oil on its breast will sometimes come ashore. Rhinoceros

    Auklets are much less abundant than the other two species and after an absence

    of 100 years, the Rhinoceros Auklet has gradually been re-establishing a

    breeding population on the Farallon Islands. The population was estimated at

    300 birds prior to the spill. The level of reduction from the spill may

    beccme apparent in the 1985 breeding season. Until then, the impact on this

    species will remain unknown.  Only 4 Rhinoceros Auklet casualties were reported.

                                           5-7

    The other species of alcids listed as casualties (Marbled Murrelet and

    Ancient Murrelet) are scarce in the Farallon Islands area and the limited

    casualty estimates reported in Figure 17 reflect this scarcity.

         In summary, the estimates given in Figure 17 indicate that scoters,

    Ccmmon Murres, Cassin's Auklet, Western grebes, and loons were the most

    severely affected species in the T/V Puerto Rican incident. Of these

    species, three were included in the group of species selected for

    computer modeling of the initial spill incident: common murres, Arctic

    loons, and Cassin's Auklet.  Computer modeling of the chronic release of

    oil from the sunken stern was done for: Common Murres, Cassin's Auklet,

    Western Gulls, and the Brown Pelican. The computer model was used to

    derive an estimate of the total number of bird mortalities resulting from

    the initial spill of 35,000 barrels of oil and the chronic release of the

    8,500 barrels of bunker fuel oil in the sunken stern.

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           I ~~~~~~~~~APPENDIX 6
       1  ~~~        DAMAGE TO PLANKTCN CAUSED BY THE T/V PUERTIO RICAN INCIDENT

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                                           6-1

    Appendix 6:  Damage caused to the plankton of the Gulf of the Farallones
                 by the T/V Puerto Rican oil spills with emphasis on the most
                 economically valuable type such as Dungeness crab larvae
                 (prepared by A. Horne, 1986).

         Laboratory studies mimicing the effects of the November spill and the

    year long diesel fuel leak (Horne et. al. 1985) provide a firm scientific

    basis for estimating the damage to the ocean biota.  Literature and field

    observations provide the other data for quantifying the number of organisms

    killed or otherwise damaged.

         The main assumptions are as follows:

         O The toxic water-soluble fraction (WSF) of oil separates frcm the
            surface slick within two days.

         o Vertical diffusion of the WSF (in fact a mixture of tiny droplet
            suspension and true solution) is restricted by droplet buoyancy and
            is much less than would be predicted from vertical eddy diffusion
            in the ocean.
         0 Horizontal eddy diffusion can be predicted from normal ocean coefficients.

         o The numbers of dungeness crab zoeae (young stages) can be estimated
            from sampling near the time of the spill and from long-term averages
            for the Gulf of the Farallones.

         o The oil concentration measured by Woodward-Clyde two days after the
            spill is typical of actual values.

           Alkylbenzene (Alkane 60) levels measured in the afternoon of Novermber

      5, 1984 showed a 0-11 m water column to contain 87 ppb (total alkanls).

      Alkylbenzenes comprised about 40% of spilled so the toxic WFS "disolved"

      2 to 3 days after the spill was 218 ppb.

           All individual oil levels (Alkylbenzene, OLOA, WITCO) at 40-80 ppb

      and mixtures of all three oils at 200 ppb showed severe and statistically

    significant mortality in the most sensitive and economically most valuable

    organisms - shrimp and crab larvae - within 8 days and possibly sooner.

    Also very large and statistically significant declines had occurred at

                                           6-2

    these low oil levels for the most abundant zooplankton group - the nauplii

    or young stages. Mortality became obvious between 4 and 8 days after the

    spill, became severe after 10 days and literature review shows that these

    effects are not necessarily reversible.

         The Woodward-Clyde data on alkane concentrations shows that the WSF

    was underdispersed by about a factor of 60.  This assumes a vertical eddy

    diffusity of 100 cm2 sec -1 and is probably due to the slight buoyancy

    of the minute droplets. Although not part of the outface slick they also

    do not behave like a true solution.

         Horizontal expansion of the toxic WFS can be modelled by assuming

    a patch of water at the end of day 2 (all WSF separated from surface

    slick). Assuming a conservative mixing depth of 12 m by extrapolating

    the Woodward-Clyde data the patch can be -expanded using a horizontal eddy

    diffusion coefficient of 104 cm2 sec-1.  Oil droplet buoyancy will not affect

    horizontal motion.

         The results can be used to dilute the measured 218 ppb total oil

    present at day 2-3.  Two end points are reached.  First after 7-8 days 40

    ppb WSF remains in a volume of 39 km3 (12 m deep). This is close to the

    levels and exposure times used in laboratory toxicity tests.  Second,

    after 48 days, 1 ppb WSF remains in a volume of 3;00 km2 (25 m deep since

    the ocean bed restricts further mixing).  The ppb level is likely near

    the minimum which will kill substantial amounts of the plankton after 10-

    20 days exposure as determined by extrapolation of the bunker oil studies.

    A comparison of bunker oil and alkylbenzene, witco and oloa shows that

    bunker oil is about twice as toxic (determined from median survival times).

    Mortality was detected with bunker oil at 0.4 ppb after 10 days (WSF) and

                                            6-3

    I assume that this is about 50% of the measurable minimum for the other

    oils (see above).

         Laboratory studies using the entire natural plankton community show

    that for the first 7-8 days about two-thirds of the most sensitive plankton

    (including dungeness crab larvae) will die. These studies also show that

    about half of all 300 plankton (including dungeness crab larvae) will die

    in the next forty days.  After this lingering mortality and non-lethal

    damage will persist but is not considered here.

         Measured crab densities in winter 1985 indicate that all larval

    crabs were present at a density of 0.1 per liter. Studies over several

    years by the California Department of Fish and Game (Hatfield et al.,

    1983; Wild and Tasto (eds.) 1983) show that 2.3% of crab zoea are dungeness

    crabs. Thus, the density of dungeness crabs affected by the winter 1984

    oil spill was 0.0023 per liter (2.3 x109 km3).

    For the shorter, higher oil exposure (7-8 days, >1 ppb)

         39 x 2.3 x109 dungeness crab larvae were present in 39 km3 of water.

    2/3 of these were killed (= 6 x 1010 crabs). Assuming the most conservative

    survival rate for a stable population, 2 out of 106 will become adults.

    Thus 1.2 x 105 adults are available. Assuming in this well-fished water

    area (San Francisco - Bodega) 10% are harvested (a conservative assumption),

    1.2 x 104 or 12,000 harvestable adult crabs were potentially killed by

    the Puerto Rican oil spill.

    For the longer, lower oil exposure (48 days > lppb)

         3,700 x 2.3 x 109 dungeness crab larvae were present in 3,700 km3 of

    water 50% of these were killed (=4.3 x 1012 crab larvae). Assuming (as above)

                                           6-4

    that only 2 out of 106 become adults.  This leaves 0.9 x 107 adults

    available.  Assuming a very conservative 1% harvest of the crop (in the

    larger, less populated Bodega-Crescent City coastline) this suggest that

    90,000 potentially harvestable adult crabs were lost by the oil spill.

        The annual catch for the SF-Bodega fishery is about 106 of crab and

    that of the Bodega-Crescent City about 107 lbs.  Thus, the predicted

    losses represent a small fraction (0.2-4%) of the annual yield. Those

    losses due to oil would be thus realistic.

    Other Organisms

         Dungeness crab larvae, although economically valuable, were among

    the least commnon planktonic species. Shrimps were 98 times more ccmmon

    and are bought for aquarium food. Small Zooplankton form the food of all

    ocean water organisms, including young fish of commercial and recreational

    value.  These species were much more numerous and were also killed by the

    oil.  In addition phytoplankton, the "grass of the sea" was destroyed to

    a large extent by the higher oil levels used experimentally. No estimate

    of these losses is given but can easily be calculated using the above

    procedures.
                                                        t~~~~~~~~

    Bunker-Fuel Oil Spills

         The loss of dungeness crabs and other organisms by fuel oil can be

    approximated in the same way as was used for the November 1985 oils.

    Unlike the catastrophic losses in winter, the bunker oil leaked slowly

    using the method described above the volumne of toxic WSF can be estimated

    and the toxic concentration found. Assuming a loss of 8,500 barrels of

    oil (WSF = 1.3 mn3) per year, a mixed depth of 10m and a measured average

                                           6-5

    slick width of 81 m (G. Ford, per. ccmm.), and an average advective water

    flow of 10 anm sec -1 the WSF of bunker oil will be about 0.6 ppb. This

    is greater than the level shown to kill zooplankton after 10 days exposure.

    However, assuming a horizontal eddy diffusion of 104 cm2 sec-1 no organisms

    will be exposed to toxic fuel oil level for long enough to be certain

    that a kill will occur. Thus, the dispersion of the toxic WSF from the

    sunken stern of the Puerto Rican will not produce demonstrable mortality

    in the zooplankton or phytoplankton.

    I
    I

    I
                                 APPENDIX 7
    I

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    I
    I                           BIOLOGICAL HINDCAST MODELING AND SPILL
                     TRAJECTOlRY ANALYSIS OF TH~E TAV PUERTO RICAN. OIL SPILL
    I

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                       7-1

    BIOLOGICAL HINDCAST OF THE EFFECTS OF

      THE T/V PUERTO RICAN OIL SPILL

          ON THREE SEABIRD SPECIES

                R. Glenn Ford
            ECOLOGICAL CONSULTING
          2735 N.E. Weidler Street
           Portland, Oregon 97232

               September 1985

                                  7-21

                            INTRODUCTI ON
        Following the explosion and breakup of the tankship PuertoI
    Rican,, numbers of oiled seabirds were recovered along the coast
    of central California and the Farallon islands.   It is virtually
    certain that these beached birds represented only a part of theU
    total mortality resulting from the spill (see discussion of
    actual versus observed mortalities in previous section). Dead or
    dying birds in unknown numbers were probably lost at sea orU
    beached on inaccessible or unsearched sections of coastline.  A
    biological model originally designed for analyzing oil spill
    risks to seabirds was adapted to carry out a "hindcast" of the
    actual spill events (Ecological Consulting, 1985). This model was
    coupled with a new model which used trajectory simulations of the
    movement of oiled birds to predict the fate of the seabirds which
    had encountered the slick.  The modelling effort focussed onI
    three, species for which observed mortalities were known to under-
    estimate total mortality. These species were: (1) Common Murre,
    (2) Arctic Loon, and (3) Cassin's Auklet.   The methodology and
    results of the analysis are described below.

               MODEL DESCRIPTION AND INPUT DATA SUMMARY
        The model for estimating bird mortality is divided into
    three sections:U
        1) Oil Slick Movement Model:  A detailed description of the
    history of the slick including the location, size, shape, and.
    percent coverage at different points in time. Descriptions of
    the slick are derived either from observational data, or from
    hindcasts made using HAZMAT's On Scene Spill Model (OSSM).
        2) Bird Contact model: A simulation of bird distribution'
    and movement in the area of and around the slick.  This section5
    estimates the number of birds which actually encountered the oil.

        3) Bird Fate Model: A model which tracks the fate of birds
    which encountered the slick.  These birds may or may not becomeU
    seriously oiled. Of those which were, some sank or were sca-
    venged, some were carried out to sea, some were washed ashore on
    beaches which were searched, and some were was hed ashore onI
    beaches which were not searched.

        The output of the f ate section of the model provides esti-3
    mates of the number of birds which died but were not counted.
    The overall model structure is diagrammed in Figure 1. The fol-
    lowing are more detailed accounts of the three sections of the3
    model1.

                                  21

                               0I)  ~     7-3

                 oil Slick
                  I    Observations               OVEMENT MODEL

      I~~~~~~~~~~~~~~~~~~~~~~~

           I ~ ~ ~

                                                 Digital Maps of
                                                 Slick Movement

                 *Behavioral Data                 BIRD CON
                 Distributional Data

                                                  Locations and
                                                 Numbers of Birds

              I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
                                                Encountering Slick

                 e Bird Trajectory Model             IRD FATE
                 *At Sea Loss Rates
                 * Beached Bird Data                  MODEL

                                                   Estimates of
                                                  Total Mortality

    Figure 1.    Flow diagram showing the relationship among the three
                  sections of the model used in this analysis.  Ovals
                  indicate programs, rectangles indicate input or out-
                  put data.

                                           3

                         0       ~~~7-4I

                      Oil Slick Movement Model
        A detailed description of the location, shape, and movement
    of an oil slick is required in order to estimate the number of
    birds encountering spilled oil. This description was obtained
    using maps of the observed slick at various points in time andI
    interpolating between successive observations to describe the
    behavior of the slick during intervening time periods.

    Description of oil Slick Data

       Observations -of the position of the slick from the T/V3
    Puerto Rican were summarized by James Dobbins Associates (see
    Appendix C). The observations consisted of the position and
    shape of the slick at each point in time. Twenty-one such obser-
    vations were used, spanning the period from the vessel breakup onU
    6 November, to the last sighting of a slick north of Bodega Head
    on 12 Novemrber. These data were input to the model as sets of
    ordered pairs of latitudes and longitudes describing the shapeU
    and position of the slick at a given time. in addition to these
    data, we also used estimates of the percentage of the slick which
    was actually covered by oil -- since typically so-called slicks         .
    contain large areas of open water, and these open areas increase
    in proportion with the age of the slick. Estimates of coverage
    were provided by the MAS group of NOAA, Seattle.3

        An alternative approach was als~o used based on a HAZMAT
    hindcast of the spill using the On Scene Spill Model, OSSM. The
    hindcast was based on real time wind data collected during theU
    period of 6-11 November at weather stations in and around the
    Gulf of the Farallones. For this analysis, OSSM was conditioned
    to  include the  response of the slick to the shelf-edge jetU
    which occurred on 5 and'6 November, causing the slick to reverse
    direction and move rapidly northward toward the Farallon islands.
    The results of the HAZMAT hindcast were recorded at 6 or 24 hour3
    intervals depending on the rate of movement of the slick. These
    results consisted of estimates of the shape and position of the
    slick at each point in time, and were input to the bird contact3
    model in the same way as the real observations.

        The results of the hindcast are generally very similar to
    the observations. There are two ways in which the observed and
    hindcast slicks differ. (1) The hindcast shows the main body of
    the slick remaining east of the 200 meter depth line (which
    defines the edge of the continental shelf) prior to the reversalI
    of 5 and 6 November.  The observations show the main body moving
    west of the shelf break prior to this time. (2) The sizes of the
    slicks predicted from the HAZMAT results are about 66% higher3
    than those observed. it is unclear whether the hindcast or the
    observations are more accurate in this case, since some portions
    of the actual slick probably went unreported.5

                                  41

                      0D          7-5

    Interpolation of Slick Movement

         In order to completely describe the path of the slick, it
    was necessary to "fill in" the parts of the path between observa-
    tions. This entailed pairing each observed piece of the slick
    with a piece observed at a later time and interpolating between
    the two irregular shapes.   Paired observations of the shape of
    the slick were input in the form of two sets of ordered latitudes
    and longitudes defining two closed polygons.   Lines forming all
    possible connections between the two sets of vertices were cons-
    tructed, and the two lines containing no intersections with other
    lines were chosen to represent the boundaries of the path of the
    slick during the period between the observations. If an oil slick
    was observed at times T-1 and T-2, the area swept out during the
    period T-1 to T-2 was defined as the area between the two non-
    intersected lines and the edges of the two polygons formed by the
    slick at T-l1 and T-2 plus the area of the slick at time T-2. The
    slick area at time T-1 was not included in the estimate because
    its area would have been included in calculating the area swept
    out between times T-0 and T-1. The method used to estimate the
    area swept out by the slick is illustrated in Figure 2. A map
    showing the passage of the slick is shown in Figure 3.

                                           I ~~CK AT

                    SLICK                  AT

                between times T-1 and T-2.  The new area affected
                                c" INTERPOLATED
                                    "/''�"~ ; acREGION -.".."ii

                    SLICK AT    "
                    TIME T-I

    Figure  2.   The interpolation of the passage of an oil slick
                between times T-l and T-2. The new area affected
                during the period T-l to T-2 would be the stipled
                region plus the crosshatched region.

                                 o .                7-6                    ,]/

           ' '            Fort Bragg

    39' --- ----
                            t.Arena

           . e

               .          ....... . .. .. .......
              * ~     ~ I                                                                                        I   ISnaCu
              � ;

        r            124 123

              Figure 3.   interpolated  passage of the oill slickebased on ober

                         ~~~Factalslic bevtonIs..               Stp% areas.-,  are antesro
                         p~~                   -      oltd ar eas                                             hr h lc sasmd to av

                                 reported cntinuing*northward pa st  Pt. Aren  a

                                 37'  124�
              ~~~~~~Fagraeo 3.    I                                       Snterpltdasageofthcislckbsdonosr

                   reprte c o     t    n i n 2othadpat. Arna

                                       7-7

                             Bird Contact Model

           We assume that birds resting or feeding on the water within
      the region where the passage of the slick was observed or where
    Ithe passage of the slick was interpolated were at risk of oil
      contact. A "risked" bird, however, does not necessarily become
      oiled for several reasons.  Areas reported as being part of an
    Ioil slick are generally only partially covered, birds may delib-
      erately avoid oil contact, or the oiling of a bird may be so
       light as to have little immediate consequence.  Estimates of
    I mortality are based on the assumption that some percentage of the
      birds on the water in the region of the slick will become oiled
      sufficiently that they will become debilitated and die.. The
      method used to estimate this percentage is described in Model
    UCalibration.

      Distributional and Behavioral Data

    U      ~~Bird density estimates were derived f rom data collected by
       personnel of Point Reyes Bird Observatory (PRBO) on 14 and 17
    I November 1984 (PRBO 1984).   Bird counts were made from a fixed
       wing aircraft f lying at 185 km/hr at an altitude of 65 m.  Ob-
       servers scanned a 50 m wide corridor on the glare-free side of
    I the aircraft  and recorded the species (or lowest recognizable
       taxon), the behavior, and the numbers of all birds within the
       strip. On 14 November eight east-west transects totalling about
    U610 km were flown, extending from 37 degrees 30 minutes to 39
       degrees 5 minutes north latitude.   on 17 November  six transects
       totalling about 510 km were flown, extending from 37 degrees 20
       minutes to 38 degrees 25 minutes north latitude.

    I      ~~Three biological zones were identified as being potentially
       different in terms of bird densities (K. Briggs pers. comm.).
    IBiological zones and transect lines are shown in Figure 4. The
       zones were defined as:

           North Shelf: That area between 38'N and 39"N, west of the
       California coast and east of the 200 meter depth line. This
       corresponds generally to the area of the continental shelf north
       of Pt. Reyes and south of Point Arena.

    I      ~~Central Shelf:  That area between 37 015'N and 380N, west of
       the California coast and east of the 200 meter depth line. This
    Icorresponds generally to the area of the continental shelf north
       of Bean Hollow State Beach and south of Pt. Reyes.

            Pelagic: That area south of 39"N and north of 370 15'N and
    I eastward from the 200 m depth line to the limits of our transect
       data at about the 2,000 m depth line.

            mean dens~ity of groups of birds was calculated separately
       for each spe c~ies in each biological zone and was expressed as
    I groups per km .  Mean group size and the distribution of group
       sizes was determined separately for each species. Group size

          I                          ~~~~~~~~~~~~7

                             9S  ~     7-8              )

          ' I        Fort Bragg

                                                                              I

                                                                              I
    39*  -- - ---
                       Pt. Arena                                                 I

                ?T~~~~~~ ..
           .eel ?5"

                                                                              I

                                                                              U,
          :Transect 52

                   Transect 49            odega Head
                            *NORTH SHELF

                         -4 - - - - - ---
                                                                              I
                           T .ansect 46   Pt. Reyes

                           iransect 44                                            I
                  PELAGIC * h
                   ZONE          ...  CENTRAL SHELF
                         - ~~~~~~~OE
                           Farallon Is. *   - ZONE      San Francisco
                           Transect 41I
                                              4~~~~~

                             Transect 39              s,
                                          *                                   ~~~~~I

                     '~~*~9a***e**e4*~~......,......

    37    '    , ,  Santa Cruz                                                     I
             124'                      123'

    Figure  4.   Aerial transect lines and biological zones used in
                 compiling data on seabird distributions. Dotted
                  lines denote boundaries of biological zones, dashed
                                 lines denote positions of transcts.
                  lines denote positions of transects.

                                        7-9

        distributions did not vary between biological zones and zones
         were combined to generate a single value of mean group size and a
    3   ~single group 2size distribution for each species.  Densi~y of
         birds per km  is the product of density of groups per km  and
        mean group size. Results of transect surveys are shown in
    I   ~Figures 5-10.

             The likelihood that a bird will contact oil depends on
    I   ~whether or not it is on the water when in the vicinity of the
         slick. Members of the species considered here were assumed to be
         resting on the water during the hours of darkness, and to be in
    I~ flight for part of the daylight hours -- about I11 hours in Novem-
        ber at the latitude of'the Farallones. Percentages of birds on
         the water during the daylight hours were calculated from un-
         published data provided by Dr. Kenneth Briggs. These data were
    U    ~collected during November 1984 in the same geographic area using
         the aerial strip census protocol described previously. Common
         Murres spent 97.4% of their time on the water, Arctic Loans
    I~60.,8%, and Cassin's Auklets 64.2%

             Estimates of movement rates for Common Murres and Cassin's
        Auklets are based on data for radiotagged Xantus' Murrelets (Hunt
         et. al. 1976).   The mean distance moved per three-hour time
         interval was 4.5 km.  Arctic Loons are engaged in southward
    E ~migration at this time of year, but periodically stop to rest on
         the water. No data are available describing short term (i.e on
         the order of hours) movement patterns of migratory loons or any
         migrating seabird species. However, since migratory distances
    I    tend to be very large-relative to the size of the slick, we
         assumed that Loons either did not move at all -- i.e. were
         resting -- or moved a distance large enough to carry them beyond
         the range of the slick during each three-hour time step.
              input values for the Bird Contact Model are given for each
    I ~species in Table 1.

         Estimating Numbers of Birds oiled

     I~~The   total area swept out by an oil slick multiplied by the
         the density of birds in the region through which it passed
         provides a minimal estimate of the number of birds at risk of oil
    3   ~contact.  The area swept out by the slick during a given time
         interval was estimated using the methodology described in Inter-
         polation of Slick Movement. Bird density estimates were based on
         the observed densities in the three zones describe-! above.  If
         the slick crossed between zones during a time in~terval, the
         average density was computed based on the relative .-rea which lay
    *   ~in one zone or the other.

              Although the density of birds on the water provides a par-
         tial estimate of the numbe'r of birds at risk, the actual number
    I    ~at risk may in fact be much greater. Seabirds are highly mobile,
         and although the population of birds present in a particular area
         of ocean might remain relatively constant, the individuals cont-

         prising that population could change completely during a given

            I                          ~~~~~~~~~~~~~~9

                                      i72             7-10

                         Fort Bragg
                                                                   AERIAL TRANSECT DATA

                                                             For each transect, the top line
                                                             of numbers represents the number
                                  k\~~~~~ ~~of birds sighted per km2 in each
                                                             5' block;  the second line repre-
                                    )\~~~~ ~~sents the number of Iroups of
                                    _ )~~~~ ~~birds sighted per km   in that
    39'                                                                   --- ;--  block.   Numbers are  typically
                     :i     (~Pt. Arena                               fractional because they are coar-
                           :~~~~~~~~~~~~~~~~~~~~~~~~
                 a 0                                                   rected to km   from a narrower
        � * ,,....                                                       effective transect width.

                       ~~~~~~~~~~~P.  R e ye
            ,    o
               *, 4
            -  *          ....... .

               :.   o  o  o  o:a    o        oa  o  o o  )   oea9a
            -  4          49944:  :

                  S~~~ 4

                  ,._

                                   .7
                  4~            S

                                   * 9
                            o   o   a O  �  �  �  �       Bodega Head

                                   :

                               *            13 a na Cru

       38~~~~ ~~: a  ao                      :       a    2m.7 I.

                                              I 0
                                 : o,0  o  O  � ,.  ,.A, }:~                  ~ ''.,            F,
                          ;         .                    t~~~~~~~ Pt. Reyes              _

                                 o o * ��o  o  o z-7 8 .ula8. 16.2    a

                                                                              I~~~o./~
                      ~~~~~~~:       *                  .7 5.4 )S., ,o.8~

                      i, *. .~~'.............. '--),.:   �

                 3:                   Farallon Is. :     .                         S        an Francisco
                                          F   o5.  5.4tibt   o C.r    o  1642  o  O v    1"

                         ,,~~~~~~i. ,.,e.                                     J  ~

                                     2.7 tr  o6      2.9 70II.2    ><

                         ,~~~1                                                         3~,,0e
    37        ,                    .' '                 ,             , ,  ,               ,   ,     Santa Cruz
                         12o                           ~~~~~~~~123�

      Figure 5 . Distribution of Common Murres on l14 November 19 84 .

                                               10

                                                7-11

                          Fort Bragg
                                                                AERIAL TRANSECT DATA

                                                           For each transect, the top line
                                                           of numbers represents the number
                                                           of birds sighted per km2 in each
                                                           5' block; the second line repre-
                                                           sents the number of Broups of
                                                           birds sighted per km  in that
    3980                           ..                     .. block.   Numbers are typically

                 :  ~       / Pt. Arena                        fractional because they are cor-
                                                            rected to km  from a narrower
                             :.... ;                          sreffective transect width.

               *�8a                    O  Oa
           e            c

           a                   ea0 a
           *                   o
           *                   o

                           a 0 - -a    a                aBOdeaa      a
           3I�                                     12

    Fiur O Dst0                                   o   �  b 4
                                   eo2.7 1:a.a 156.6 116.1  24..

                               o   o
                                    e---------e ee-_ �

                                     n o a     0: 0 ~ 8& a.19.)o0.0 2.7 10.O
                                   I            .  oS,.l. 21.6 2 oJ

                                     a       O a  a  a  a g    2 a     a

                            o,            O    a 0    0    e           a  Ha
                                                   2.7 16oa
                  1*Xe@****,���*******,,,O,,^,,,*^^@eZ*::  ......................   ,

                124                         I ,  ,23 i  ,  ,    ,      |            ,SantaCruz
      *          124                               11 36

    Pi~Fae 6.  Distribution of CoIuon Murres on 17 November 1984.

                                                       7-12

                         Fort Bragg
                                                                     AERIAL TRANSECT DATA

                                                               For each transect, the top line
                                                               of numbers represents the number
                                                               of birds sighted per kmZ in each
                                                               5' block; the second line repre-
                                                               sents the number of groups of
                                                               birds sighted per km  in that
                                                               block.   Numbers are typically
                3 li      ~Pt Arena                                 fractional because they are cor-
                                                               rected to km   from a narrower
            ~a  o ,o    o o0 o                                       effective transect width.

          *            4      I

          *                   �
          *        a                   aH

                   - *  .....    ....� 2.7

                          ,�' Q  u 0   2 7       Oa ., O, O  7 0
               *-.                  ,    a 0270

                         � 2.7 �  o, 2.7'] , 5    .    BodegaHead

                                                     i. .   eyes
                                  :   *  C    4 a  a a
                              o o * o o     ���' 4o a                       d'cOX'

                                  I     :                                   c,__�

                                  Farallon Is.! %
                                  Farallon Is. * *  _                        San  Francisco
                                                     2.7

                                                 ,   0 00    a
                                    0   0:  a0  a    Boe H 0  0 e

                 ............ ........ ..........

                       ;3; ~ ,   .  ,  X  .     ,             .      ,     ,        \      ~~~~~Santa Cruz
               124                                     123'

    Figure 7.  Distribution of Arctic Loons on 14 November 1984.

                                               12

                                                  7-13                   ;

                            Fort Bragg
                                     Fort*~~  Bragg ~~AERIAL TRANSECT DATA

                                                          For each transect,  the top line
                                                          of numbers represents the number
                                                          of birds sighted per km  in each
                                                          5' block;  the second line repre-
                                                          sents the number of Iroups of
                                                          birds sighted per km  in that
                  :zg    ,. ,,       Arena                           block.   Numbers are typically
                   : , ~      <Pt. ~Arena                           fractional because they are coar-
              I:     ;                        rected to km2 from a narrower
                    . ...... '    effective transect width.

                        ' * * 6t = o o o ~~~P. Ree
              F eI. Sa
              Gm                         *

           - ......0.. �

                    *o a  a  2 7 * o  al:2  a 0 2 7          Bodega Head

                       ,        �  � . �  a  � �a27 �  i4 10.8.

                                     -  a  a~a   a    O I2 a
                                0  0 :                 07     a   O a    0
                                      ~~~~~2*                     a 2.a

                                    Farallon Is. 1  San  Francisco
                                     a a    � �  *  4    2 7 2      A 2720
                                              �:,.4   242(: .

                                     _  �  �  �  �  �  O  o     0 O  o 0 a  a  a

     Figure 8 .  Distribution of Arctic Loons on l17 November 1984 .

                                             13
                        '~~~~~                            16.2    � 0:0 a     .72
                                                     8.   *          2   .7

    I   7               I....               F .....

                                               F  .                             ,         ~~~~~~~~~Santa Cruz
                  124e                                     123�

     Figure 8o Distribution of Arctic Loons on 17 November 1984.

            I~~~~~~I

                                           :8   ~     7-14                                                        I

                            Fort Bragg
                                                                    AERIAL TRANSECT DATA

                                                               For each transect, the top line
                                                              of numbers represents the number
                                                              of birds sighted per km2 in each
                                                               5' block; the second line repre-
                                                              sents the number of Iroups of
                                                              birds sighted per km  in that
    39  - ------..-..-.-                                            block.  Numbers are typically
                   *o        (\ C1Pt. Arena                       fractional because. they are cor-
              *i �  � i4 �  �rected to km2 from a narrower
                   2:*  ,                                        efFective transect width.

                           * .

                         2.3
                    0540 0.a 0      o. a  0 0

                                     2.7.7
            *                   *

                                     *                                                    I.
                         2 7 �  �  � ''27 �  �  �  �   Bodega Head

                              -  '  o-lt5s.l7l52.7 2.7:    4
                                            2.7 2.7 a    a            a 0  0

                                       2      a 0  0   0  0   0 a   0  0 a

                                     Farallon I                                              an isco
                  124'                                    123*

    Figure 9.  Distribution of Cassin's Auklets on 14 November 1984.

                                                 14

                                             ~~9   ~        7-15

                        Fort Bragg
                                                                       AERIAL TRANSECT DATA

                                                                  For each transect,  the top line
                                                                  of numbers represents the number
                                                                  of birds sighted per km2 in each
                                   �\~~~~~~ ~~5' block;  the second line repre-
                                                                 sents the number of ~roups of
                                                                 birds sighted per km  in that
                                                                 block.   Numbers are typically
                                                                  fractional because they are cor-
                             P.Arena2
                                                                 rected to km   from a narrower
                       oe\~~~  ,   +                                   ~~~~~~~effective transect width.

         I             *
         o             o
         0                    0
         o              e
         0             e
         e                    o

         I   e                c        0e

                            2.7 5.4
                              �j~ ~ 27   ��� BodegaHead

         I                         0:0   0  0  0  0  0

                                 0                   7  Pt. Reyes

                                  5.4   �           O 0 a    OO 0
               e~~~~~~ �

                                 Farallo n Is.                                San  Francisco
                                        i*         4
                                  a a  0  a :  0   a  a  a  a  a   0

                                   a      0.   a 0     0 a  a  a  a   0
                                        �.7

                                  0.1 18.9 5  .1        .
                                  SdY.1 10.82.l 5  0   �  �     0 �  �   O   O

                            1   .  .  B  .      .             ,      ,      |      ,    \   ~~~~~~Santa Cruz
             1124                                      123-

    I Figure 10.  Distribution of Cassin's Auklets on 17 November 1984.

    I

    *                                                     5

                                                      7-16

    TABLE  1.   Input values for three seabird species,  T/V Puerto Rican hindcast.  Data for
                November 1984.

                                Mean Density of Groups (Groups/Km2)

         Biolouical Zone          Common Murre          Arctic Loon         Cassin's Auklet
         North Shelf                  2.28                  1.456                  0.96
         Central Shelf                5.929                8.966                   0.53
         Pelagic                      0.11                 0.324                   1.19

                             Distribution of Group Sizes (% Frequency)

         Group Size             Common Murre               Arctic Loon       Cassin's Auklet
              1                     39.5                      61.0               64.0
              2                     16.1                      22.2               18.0
              3                      7.3                       2.8                10.0
              4                      2.2                        2.8                6.0
              5                      8.0                       2.8                 2.0
              6                      4.4                         0
              7                      1.5                       2.8
              8                      0.7                       2.8
              9'                     1.5                        0
             10                      5.1                        2.8
             11-15                   5.8
             16-20                   2.2
             21-30                   2.1
             31-40                   0.7
             41-50                   0.7
             51-60                   0.7
             61-70                   1.5

       Mean Group Size:              6.066                      2.083              1.64

                  Daily Activity Pattern (Percent On the Water/Percent in the Air)

                                   Common Murre         Arctic Loon          Cassin's Auklet
           11 hours active           97.4 / 2.6          60.8 / 39.2           64.2 / 35.8
           13 hours quiescent       100.0 / 0.0         100.0 /  0.0           100.0 /  0.0

                                            Movement Data

                                    Common Murre             Arctic Loon            Cassin's Auklet
           Mean distance moved                        40% in flight in southward
           per 3-hour time step         4.5 km        migration at any given time         4.5 km
                                                      during the active period

                                                  16

                                   7-17

    time interval. The turnover rate within the region of an oil
    slick was estimated based on the distribution of distances moved
    per three-hour time step, assuming that the movement of an indi-
    vidual or group could be modelled as a random-walk process.  One
    thousand simulated bird groups were distributed uniformly across
    a region the shape of the slick. At three-hour intervals, each
    group was moved a random distance in a randomly selected direc-
    tion. The distance moved was selected by sampling from a distri-
    bution of distances moved per three hours. Each group was moved
    until it had passed out of the region and the number of time
    steps required to do so was noted. This process is illustrated
    in Figure 11. The average number of time steps required to leave
    the region from a random starting position represents an estimate
    of one-half the average time spent in an area of ocean the size
    and shape of the slick (since groups were already within the
    region when they were placed in motion, this time is multiplied
    by two). The turnover rate is therefore the time step divided by
    the mean length of time spent in the region. For example, if a
    group spends on average 5 hours in the region before exiting, and
    the time step is 3 hours, the estimated turnover rate would be
    0.6 -- i.e., there would be 60% replacement of old animals with
    new animals during one time step.   The number of birds encoun-
    tering the slick  would then be 1.6 times the number of birds on
    the water. Note that this is an estimate of the turnover in an
    unpolluted region of ocean.  We assume that the encounter rate
    would remain the same if the region were covered by the slick,
    although in some cases flocks might turn aside after initially
    encountering it.

                                          Figure 11. Illustration
                                          of the method used to
                                          estimate the length of
                                          time a flock of birds
                                          would spend in a region
                                          the size and shape of an
                                          oil slick.  Circled num-
                                          bers indicate the posi-
                                          tion of a hypothetical
                                          flock at three-hour in-
                                          tervals.   The distance
                                          moved is based on the
                                          relative  frequency of
                                          distances  moved;  the
                                          turning angle is random.
                                          In this case, the flock
                                          required about 6.2 inter-
                                          vals or 18.6 hours to
                                          leave the region.

                                  17

                                   7-18

                           Bird Fate Model

        Birds which were killed or incapacitated by oil are assumed
    to have had one of four fates: either (1) they were carried out
    to sea by winds and currents, (2) they sank or were scavenged
    before reaching shore, (3) they came ashore along stretches of
    coastline which were not searched, or (4) they washed up or swam
    ashore on beaches which were regularly searched. Only in the case
    of (4) would the birds have been enumerated.  The purpose of the
    bird fate model is to partition birds among these various fates.

    Trajectories of Oiled Birds

        The HAZMAT oil spill trajectory model was used to compute
    families of possible trajectories assuming that dead or incapaci-
    tated birds move at 2.2% of the wind velocity.  This value is
    based on published experiments with dead, oiled auks in the
    Irish Sea (Hope Jones et al. 1970). The value is lower than the
    3.0% typically used for surface films of oil, probably because of
    the relatively large subsurface resistance of the carcasses.
    Eight typical regions were selected from which to launch circu-
    lar  clusters of model bird bodies (J. Galt pers. comm.).  Groups
    of 100 birds were launched from each region and followed until
    they either came ashore along one of six coastal segments, or
    were washed out to sea (see Figure 12 for a map of launch regions
    and coastal segments).  A matrix was constructed containing the
    probability that a bird which was oiled in a given region would
    come ashore along a given stretch of coast and the length of t.ime
    that would be req-aired to do so (see Table 2).

        TABLE  2.  Probability (expressed as percent chance)
                    that a bird carcass launched in a given
                    area will come ashore on a particular
                    stretch of beach (for launch areas and
                    recovery areas shown in Figure 12).

                       Percent chance of bird carcass recovery
                         if launched from a given launch area

                                  Launch Area
         Recovery Area   1   2   3   4   5   6   7    8

              A         0   0    0   0   0   0   0    0
              B         2   0   0   0 100    0   0   0
              C        44    3  21  23    0   0   0   0
              D        53    4   4  17   0    0    0   0
              E         1  93  75  60    0  100  100  29
              F         0   0   0   0   0   0   0    0
              G         0   0   0   0   0   0   0  26
              H         0   0   0    0   0   0   0   45

                                  18

                                               7-19

                                                                   OSSM LAUNCH POINTS FOR
                                                                  SIMULATED BIRD CARCASSES
                       = Fort Bragg                                     (  37�23.3  122057.4    3 Nov 1200
                                                              �37018.0  122a49.5    5 Nov 1200
                                                              @)37043.4  122053.6    7 Nov 1200
                                                               )37�56.7  122" 55.1    9 Nov 1200
                                                              �  380 0.7  122055.1    9 Nov 1200
                            Q\ �3                        �37059.6   1230 4.9    9 Nov 1200
                                                               )3B0 14.5  1230 7.0   10 Nov 1200
                                                              @37�16.7  1230 5.2    5 Nov 1200

    39                ------           ;rnaRECOVERY AREAS FOR
                 :          <Ft. Arena                                          SIMULATED BIRD CARCASSES

                                                              (Z)Sauth of Bolinas
                                                               �Bolinas to Pt. Reyes
           I             e                                                  � ~)Pto Reyes to Tomales Pt.
                                :e~ :>t~ '~@~~ (Bodega Area (Tomales Pt. to
                      _-~~~~~~~~ e                                               .X~~~eo�X   >   'Jenner area)
                                                                )Jenner to Pt. Arena
           : ,                  *�_ (@)North of Pt. Arena
           : ........ .:  .......  (OFarallones
                                                               Ou t to Sea (Never Beached)

                                                  Bodega Head

                                                      Reyes

                                 '�-�------                      "---  ---
                                 Farallon Is. I               t               San  Francisco
                        I.@*s~' ) I

                                             i ,

                                                        I ,  2   '  t    '  @   * *  t  \Santa Cruz
                1240                                      123'

    Figure 12.  Simulated' bird carcass launch and recovery areas used
                    in Bird Fate Model.

                                              19

                                    7-20

    The number of birds estimated by the bird contact model to have
    been oiled within a given launch region were partitioned ac-
    cording to the probability that they would come ashore in one of
    the six coastal segments. Of these coastal segments, all were
    carefully searched during the period of the spill except the
    coastline north of Jenner and South of Pt. Arena. This area is
    composed primarily of steep cliffs with little or no access to
    the intertidal, and except for one location was,never searched
    for beached birds (G. Page pers. comm.).

    Loss of Oiled Birds at Sea

        Although an oiled bird-might drift in the direction of a
    particular beach and ultimately come ashore there, it might also
    be lost to natural processes before reaching shore. Of 106 murre
    carcasses released by PRBO biologists in 1980 and 1981 within
    1,500 m of shore, only 4 were ever recovered (PRBO unpublished
    data). However, a large proportion of the weighted drift bottles
    and gull carcasses released at the same time were eventually
    recovered, indicating that the disappearance of the Murres did
    not result from being carried out to sea, but rather that the
    carcasses sank or were scavenged along the way.  The experiment
    carried out by Hope Jones et al. (1970) resulted in 20% recovery
    even though bird carcasses were at sea ten or more days and is
    the source of the loss rate estimate used here.

         We assume that the loss of bird carcasses is a function of
    how long they are at sea, and that the fraction lost each day is
    constant. For example, if one-half of the carcasses were lost
    each day, there would be one-half remaining after one day, one-
    quarter remaining after 2 days, one-eighth remaining after 3
    days, etc..  If 20% were still floating after 10 days as Hope
    Jones et al. (1970) found, the estimated loss rate would be  15%
    per day. We use these data to generate a matrix of probabilities
    that a bird oiled in a given region would be recovered, along a
    given segment of coastline.  Let Ll  be the probability that a
    carcass launched from region i will ome ashore in segment j, S
    be the loss rate (sinking or scavenging) per day, andD be the
    number of days required to float from i to j based on the results
    of the HAZMAT trajectory model.  In addition, let C . be a vari-
    able which assumes the value 0.0 if a beach was not searched, and
    1.0 if the beach was searched.   Then the probability that a dead
    or incapacitated bird launched from i will be recovered in j is:

                       Pi j= Cj Lij (1 - S) D

                           Model Calibration

          The mortality rate of birds encountering the oil slick was
    estimated using the actual observations of oiled birds found on
    the beaches.  Let O. be the number of birds found along
    coastal segment j, Iet Ei be the number of birds which en-
    countered the slick in region i, and let 9 be the mortality
    rate. An estimate of ~2 based on the observed number of beached

                                  20

                                     7-21
    birds found in coastal segment j, oi, is given by:
                     i
          j = Oj  /(PijEi)
    In other words, the estimated mortality rate is the ratio of the
    number of birds observed in a given segment of coastline to the
    number which we estimate would have been beached if every bird
    which passed through the slick died or was incapacitated.  Note
    that this estimate includes the loss of oiled birds while in
    transit from the point of oiling to the beach which is subsumed
    in the value of the P. . Since the  value of  0. varies from
    beach to beach, we estiAted the overall value of   as the slope
    of the regression of the 0 .  on the (Pi Ei).  Because the number
    of beached birds must be ze~o when the ndmber of birds contacting
    the slick is zero, the regression line was forced through the
    origin.

                      MODEL ANALYSIS AND RESULTS

                         Sensitivity Analysis

        The values of some input parameters and the validity of some
    of the assumptions used in the model involve varying degrees of
    uncertainty.  When the uncertainty is large, the degree to which
    model results might be affected by that uncertainty should be
    closely examined. Using data for the most common species, Common
    Murres, we examined the sensitivity of the model to the following
    factors:

         *The value of the mean distance moved per 3-hour time step

         �The assumption that the bird groups move in a random walk
           fashion, changing direction randomly at each time step

         �The  variability in the distribution of seabirds --  i.e.
           the natural randomness involved in how many birds were
          present in an  area at the time when it was affected by
           the slick

         a The value of the rate at which oiled birds are lost at sea

         �The assumption that an oiled bird has an equal likelihood
           of being lost each day it is at sea

    We examined the effect of varying these factors on two model
    outputs:

         �The estimate of the total number of birds killed by the
           spill

          �The estimate of the percent of the birds which became
           debilitated or died as a result of being present in the
           area of the slick

                                     7-22

    Distance moved Per Time Step

        The distribution of distance moved per 3-hour time step was
    extrapolated from data for Xantus ,Murrelet (Hunt et al. 1976),
    and it is not known how appropriate these data are for other
    alcid species. We examined the sensitivity of the model to this
    input by making model runs using values for the distances moved
    which were two times as great as those measured for Cassin's
    Aukiet, and one-half as great.  Decreasing the movement rate  hasI
    the effect of increasing the mortality rate, and increasing the
    movement rate has the effect of decreasing the mortality rate.
    This occurs because the number of birds believed to have encoun-
    tered the slick increases with increasing movement rates, and to
    account for the observed beachings, fewer of them could have been
    killed or debilitated. Doubling the movement rate lowered the
    estimated mortality rate by 21%; halving the movement rate in-
    creased the estimated mortality rate by 19%. Deleting the move-
    ment algorithm entirely increased the estimated mortality rate by
    44%.  Estimates of total mortality, however, were only slightlyI
    affected by variation in this parameter, changing by less than 2%
    in. any case.

    Random Turning Angles of Moving Birds

        it has been assumed that the movement path of birds can beI
    described as a random walk process, and that model bird groups
    changed direction randomly at each time step. We examined the
    effect of this assumption on model results by changing the model
    so that model bird groups continued in the same direction indef-
    initely.  This change increased the rate at which bird groups
    encounter the slick,  however it had little quantitative effect
    on model results.  The estimated mortality rate was unchanged,I
    and the estimated number of birds oiled increased by less than
    2%.

    Variability in Bird Distribution

        Although distributional data were collected at the time of
    the oil spill, we do not know how many birds actually encountered
    the slick.   The distributional  data provide a statistical
    description of the numbers of groups per square kilometer and the
    relative frequency of group sizes in the general area at the time
    of the incident, but these data are averaged over large regions.
    Even if the mean density of groups in a zone were 0.5 groups per
    square kin, a given square km of slick at a given instant of timeI
    might have contained 0, 1, 2, or more groups. Similarly, if the
    mean group size were 1.5 birc-11, a given group might actually
    contain 1, 2, 3 or more individuals. We estimated the effect ofI
    this variability on the model results by permitting densities and
    group sizes to vary randomly based on their observed patterns.
    Groups were assumed to be distributed uniform random throughI
    space, implying that the number of groups within a given area was
    Poisson distributed. Thus, if the area swept out by the slick
    during a given time interval is A, the mean density of birds in

                                  22

                       iJ
                                    7-23

    the region is b, and the number of bird groups in the area which
    passively encounter the slick is n , then np is a random variable
    with the probability density function:

                     P[np] = (Ab)np exp(-Ab) / np

    For each time interval, the number of groups making passive
    contact with the slick was simulated by randomly sampling from
    this distribution.  The size of each group was selected by ran-
    domly sampling from the known distribution of group sizes. The
    number of groups making active contact -- i.e. flying into the
    slick -- was simulated by assuming that the probability per unit
    time of a group entering the slick was constant, so that the
    number of groups actively encountering the slick would also be a
    Poisson process.  If R is the average rate at which new groups
    encounter the slick (the turnover rate), T is the length of a
    time interval, and na is the number of birds flying into the
    slick, then the na is a random variable with the probability
    density function:

                    P[na] = (RT) na exp(-RT) / na!

       The model was run 25 times using different random number
    sequences to evaluate the extent to which model results varied
    due to this form of stochasticity.  The resultant coefficient of
    variation of the estimated mortality rate was 2.1%, and the
    coefficient of variation for the total number of birds killed was
    1.3%.   The stochastic nature of the bird distributions,
    therefore, does not appear to hgve an important effect on model
    results.

    At-Sea Loss Rate

        Although data are available which may be used to estimate
    the rate at which dead or debilitated birds are lost at sea--
    presumably due to sinking or scavenging-- these data are not
    conclusive.  The loss rate of oiled auks following the Hamilton
    Trader incident (Hope Jones et al. 1970) provides the best
    estimate of the loss rate, 15% per day, but other data suggest
    that the value could be quite different under other
    circumstances. For example, Common Murre carcasses released
    nearshore by PRBO biologists resulted in only 4% recovery,
    implying a much higher loss rate. We therefore examined the
    sensitivity of model results to the entire range of possible at-
    sea loss rates.

        The mortality rate of birds encountering a slick and the
    rate at which affected birds are lost at sea both influence the
    number of beached birds predicted by the model. An increased loss
    rate results in fewer beached birds predicted; an increased
    mortality rate results in more beached birds predicted.  For a
    given value of the at-sea loss rate, the model uses observed
    beached bird data to select the corresponding mortality rate
    which best fits the observed distribution of beached birds. If
    the at sea loss rate is fixed at 0% per day -- i.e. no birds sank

                                 23

                       /-
       *                             ~~~~~~~~~~~~7-24

    or were scavenged before reaching share -- the estimated mortal-
    ity rate is 23% and the resultant mortality 1,029 birds. Tf the
    at-sea loss rate is fixed at 35%, the corresponding mortalityI
    rate must be 100% and the resultant mortality 3,979 birds.
    Sinking rates of greater than 35% per day cannot be fitted by the
    model because they would require mortality rates of greater than
    100% to account for the observed pattern of beachings.

        The loss rate of oiled birds at sea is clearly the most
    sensitive model parameter.  Based on the range of possible valuesI
    for the at-sea loss rate, estimates of the number of oiled birds
    varied from 1,480 to 3,979, and estimates of mortality rates for
    birds encountering the slick varied between 23% and 100%.  OurI
    best estimate of the sinking rate, 15%, falls roughly in the
    midpoint of this range. The PRBO data discussed earlier suggests
    that this value may err in the direction of underestimating theI
    at-sea loss rate, and by implication, underestimating total mor-
    tality.

    Exponential Sinking Rate

        We have assumed that~the loss of oiled birds at sea proceeds
    in an exponential fashion, in other words a constant proportionI
    of them disappear each day.   This is a simple and logical
    assumption, but it is not the only conceivable model. An
    alternative model is that at sea loss is not time dependent, butI
    that the proportion disappear-ing is a constant regardless of how

    long an oiled bird is at sea. As with the exponential sinking
    of birds oiled are highly-dependent on the value chosen for the
    proportion lost at sea.  if the proportion lost at sea is 0%,
    these estimates are the same as for the exponential loss model
    -- 23% mortality. The greatest loss rate possible for this model
    is 77%, which corresponds to 100% mortality. This indicates that
    the use of a time-independent loss rate is probably inappropriate
    for this context.   The loss rate of oiled auks in the study
    conducted by Hope Jones et al. (1970) was 80%, and of Common
    Murres in PRBO's study was 96%.  Thus, when a constant rather
    than an exponential at sea loss model is used, both values of theI
    loss rate are higher than could have occurred in this 'incident
    even if every bird which encountered the slick died. While this
    does not "prove" that the loss of oiled birds at sea'is time
    dependent, such an assumption seems more appropriate in this
    case.

                           Model Validation

         We tested the accuracy of the model by comparing theI
    observed distribution of beached birds with that predicted by the
    model. The entire model was run 6 times, once for each coastal
    segment which was searched,each time excluding that segment fromI
    the calibration process.  The model prediction bf the number of
    oiled birds in a given coastal segment and the observed number of
    oiled birds were therefore independent, and the predicted number

                                 24

                       l- j7-25

    of beached birds occurring in a given coastal segment did not
    entail circular computation.

        The relationship between predicted and observed numbers of
    beached birds is shown in Figure 13.  Linear regression of the
    predicted number of beached birds on the observed number of
    beached birds for all species indicates that the model accounts
    for 81% (r=.908) of the variance in the numbers of beached birds.
    The slope of the relationship is .906 indicating a tendency
    toward the underestimation of the numbers of beachings of about
    9%. The tendency toward underestimation is most consistent in
    two areas: south of Bolinas and Bolinas to Pt. Reyes. These
    beachings cannot be accounted for by assuming passive transport
    of oiled birds. HAZMAT model results indicated that no beached
    birds should have come ashore south of Bolinas, and very few
    between Pt. Reyes and Bolinas.  This prediction is strongly
    supported by the actual observations of the track of the slick.
    Although we postulated that oil slicks and oiled birds move at
    somewhat different proportions of the wind speed, 2.2% and 3.0%
    respectively, these different values should result in very
    similar patterns of beachings.  The fact that no beached oil was
    observed south of Drakes Bay therefore indicates that passively
    floating birds should not have come ashore either.   It is likely
    that this discrepancy resulted from oiled birds flying or swim-
    ming toward the coast.   Even a landward displacement of 5 or 10
    km during the first several days following the spill would.have
    been enough to have carried birds into the region influenced by
    the tidal inhalation of San Francisco Bay.  Active movement
    toward land by oiled birds may also have been the cause of the
    model underestimate of the number of beachings observed on the
    Farallon Islands.

                  Model Estimates of Mortality Rates

        The mortality rate is the fraction of the birds encountering
    the spilled oil which were injured by the encounter to the extent
    that, without intervention, they would ultimately have died. The
    mortality rate for Common Murres was estimated to be 42%, for
    Arctic Loons 10%, and for Cassin's Auklets 30%.  The lower esti-
    mated mortality rate for Cassin's Auklets than for Common Murres
    probably results from the assumption that both of these species
    are lost at the same rate while at sea.  It is probable that the
    small body size of Cassin's Auklets makes them more likely to
    become waterlogged and sink, or to be scavenged while floating.
    If the at sea loss rate for Cassin's Auklets were in fact greater
    than the rate for Common Murres, the estimated mortality rate for
    Cassin's Auklet  would  be relatively  higher.   We cannot
    demonstrate this at this time because there are no data available
    for at-sea loss rates of small alcids.

          Model Estimates of Numbers and Fates of Oiled Birds

        Model estimates of the number of birds which contacted the
    spill and their subsequent fates are summarized in Table 3.
    Based on the observed path of the oil slick and the simulations

                                 25

                       J        7~~~-26         2

        250- .....
                                           OBSERVED

             200-         -   ....~~ PREDICTED

         150 -

      5  1oo - COMMON MURRE     .........

         50 -     .     ..
          LLJLz.

        �0

            50 CASSIN'S AUKLET

                   2     >-�0UL                            ..4Z
                              W (n     4I-

                       �0 iZa.U

    Figure 13.  Model predicted and observed numbers of beached birds
              on six segments of coastline.  Predictions of theI
              numbers of beached birds in a given segment of coast-
              line were made independently of the numbers of
              beached birds observed in the segment.3

                              26

    TABLE 3.   Model results:  Model estimates of numbers of three seabird species encountering the oil slick from the Puerto Rican
               and their subsequent fates.

                                                                                    Seriously Affected by Oill

                          Encountering    Not Seriously      Found on      Sunk or      Floating     Beached But      TOTAL DEAD BUT
                            Oil Slick        Affected         Beaches     Scavenged   Out to Sea   Not Recovered       NOT RECOVERED

    Common Murres
       Number of Birds       4,255            2,468             487          861           61            378               1,300
       Percentage               100.0%           58.0%           11.4%        20.3%         1.4%           8.9%               30.6%

    Arctic Loons
       Number of Birds        1,670           1,503              53           82            0             32                 114
       Percentage               100.0%           90.0%            302%         4.9%         0.0%           1.9X                6.8%

    Cassin's Auklet
       Number of Birds         210              147              16           37            0             10                  47
       Percentage               100.0%           70.0%            7.6%        17.6%         0.0%           4.8%               22.4%

     These are birds that would have died without rehabilitation. Some birds found on beaches were successfully rehabilitated (see
     previous section).

                         � ~!~.7-28               1

    of the distribution and behavior of the various seabird species,
    we estimate that 4,255 Common Murres, 1,670 Arctic Loons, and 210
    Cassin's Auklets encountered the spilled oil from the Puerto
    Rican.   Of these encounters,  we estimate that 1,787 Common
    Murres, 167 Arctic Loons, and 63 Cassin's Auklets were injured by
    the oil to the extent that, in the absence of rehabilitation,
    they would have died.  The most likely fate of oiled birds was
    probably loss at sea due to sinking or scavenging, accounting for
    48% of the oiled Common Murres, 49% of the Arctic Loons, and 59%
    of the Cassin's Auklets. The remaining oiled birds washed ashore
    along the central California coast, some areas of which were
    searched for oiled birds and some were not. Trajectory simula-
    tions indicate that 21% of the oiled Common Murres, 32% of the
    Arctic Loons, and 16% of the Cassin's Auklets were beached on
    unsearched or inaccessible stretches of coastline between Jenner
    and Pt. Arena. The total numbers of birds which are believed to
    have died but were never recovered are 1,300 Common Murres, 114
    Arctic Loons, and 47 Cassin's Auklets.

                              REFERENCES

    Ecological Consulting.  1985.  A Risk Analysis Model for Marine
        Mammals and Seabirds: A Southern California Bight Scenario.
        Final Report. Prepared for Minerals Management Service,
        Pacific OCS Region. Contract No. 14-12-0001-30224.

    Hope Jones, P., G. Howells, E.I.S. Reese and J. Wilson.  1970.
        Effect of 'Hamilton Trader' oil on birds in the Irish Sea in
        May 1969. British Birds 63(3): 97-110.

    Hunt, G.L.,Jr., M. Quammen, and K.T. Briggs.  1976.  Foods and
        foraging range of nesting seabirds. pp. 681-726. Vol. 3,
        Book 2. Marine mammal and seabird survey of the Southern
        California Bight.  Univ. of California, Santa Cruz.

                                  28

                            7-29

     SPECIAL OIL SPILL RISK ANAYLSIS (OSRA) OF
         TANK VESSEL PUERTO RICAN INCIDENT

    by Lawrence J. Hannon and Cheryl M. Anderson

            Minerals Management Service
         Branch of Environmental Modeling

                   November 1985

                                        7-30

              PT. ARENA

                                                                                     I

               Pi1  PI:
                         ' PT. REYES

                   P$

       Figr .     MI  to
                             I                               .

                                                                                     II

                                                                BAY                       3

                             N                              N                                t
    Figure 1. -- Map showing the location of oil spill launch points P1 to P19 and
                launch point S (S = sunken stern of PUERTO RICAN).

                                 7-31

               rPT. ARENAt

    I

    I                     WT. REYE

                 AI                     AN FRANCISCO BAY

    *            Dg
                 F

    I
                 H
                   I 2  3'4 5  6 7

               i~~ 37-                          ~~~MONTEREY
               l*~~~~ ~~BAY

    I
    I

       I       "                            N                N0

          Figure 2.--Target grid matrix for special OSRA run of PUERTO RICAN oil spill
                 incident.
    U

                     <?'p     7-32     -

           'PT. ARENA

                              A~~~~~~~~~~

                            .REYES

                           \g    AN FRANCISCO BAY

                                       ~~~~~11

    -S7~
                                             MONTEREY
                                             BAY

                  -~~~~~~~~~~~~~~

                        _                N
       Figure 3.--Map showing the location of the target "Farallon Islands":
              crosshatching indicates areal extent.

                     TABLE I1.-   PROOABILITIES (EXPRESSED AS PERCENT CHANCE) TIJAT AN oil. SPILL STARTING
                                   AT A PARTICULAR L-OCATION WILL CONTACT A CERTAIN TARGET WITHIN 3 DAYS.

                 TARGET STEM H~~~~~~~~YPOTHIETICAL. SPILL LOCATION
                               IS)  PI   P2   P3   P4   PS   P6   P7   Pe   p9  pi(  p11  p12~ jp3  P14  Pis  P16  pi?  pie  PIS
    LAND                        1    4   14  I              5   34    N    S            7   67    N    N   22   59    N    N    3   27
    A-1                         N   16  a       40  se    19    N   20    1    2    N    N  I            N
    A-2                         1   25   20   11   41   63   19    3  it           1    3    N  I        N
    A-3             I              I11   21    1    8   34   49    N   *A          N  a       N    N       m
    A-4                         1    2    5    N    1    7   22    N  la           N   10  I       N    N    2    1      N     N     N    N
    A-S                         N    N    N      N N      I            N  a        N    3    5    N    N    2    2       N     N     N    N
    0-1                         N    6    5   33   30   10    N   33    N    4    N    N  I              N    N  a    I        N    N     N
    9-2                         1   12    7   13   34   35   11    6  it           3    3    N  I    I           N    N        N N        N
    11-3                        2    8   12    2   11   29   28    1   95  I           is    N    H  I              N  I       N  I       N
    6-4                         1    3    6    N    2   11   22    N   SO    N   20    2    N    N  A    I               N    N  I        N
    B-S                         N                                      H     4    N    4  a        N    N    4    3    N    N    N    N
    9-6                         N  I                                               N    N   13    N    N    3    4    N    N    N    N
            C-i                 N   ~~~~~~~~3    2   24   16    7    N   39    1       NN          3    1    N    N    2    1    N    N
    C-2                         2    5    3   15   25   19  7         12   11   11    3    N    2    3    N    H    2    1           N    N
    C-3                         3    4    5    3   12   20   15    1   75    2                N    N     1    2    N  I    I    I
    C-4                         3    2    5    N    3   12  la         N   70    N  ~'        2    N    N    7    2    N    N        2    N
    C-S             I                 N  I       N    1    2    6   *N   14  9           914       N    N   13    7    N    N  I          1
    C-6                         N    N    N    N    N    N  I          N    2    N      I   *'     N    N  a       13    N  I    I
    D-1             I               I      1   12    6    3    N   31    N   99    N    N   *a  a             N           3    1       NN
    D-2                         4    1    1   10   12    7    4   14    8   33    2    N           4  It*           N    3    2    1    N
    D-3                         6    1    1    3    8   10    6    2   33    4   48    N    1    2    1    N    2    1    2    N
    D-4                         5  I    I        N    3  a        9    N  se       1   72    1    N  I       11    2    N    N    3    1
    D-S                         2    N    N    N    N    2    4    N   20    N  la    19    N            N   11   12    N    N    2    1
           D-6                 ~~ ~~~~~N    N    N    N     1    1    N    4  3         390        ff    N   14          H     N    2    1
    E-1                         N    N     N    6    3    2    N 20    N   03           N     N          9    N     N    5     2     N    N
                   3-2          4    N    N    7    6    3    2~~~ ~~~~ ~~~~214  6  67  1    N   13  At          N       6    3    1    N
    3-3                        12    N    N    2    5    4    3    3   19   10   26    N    2    7    1    N    3    2    3    N
    3-4                        10    N  I              2    5    4    N   44    2   58  I          N    1.  20    1    1    1    6    1
    E-S                         2    N     N     N    N    2    2    N   22    N   24   17    N    N   **   20    N                       3
    E-6             I                                  N    N  I       N    6    N    5   67    N    N   28    -~              N    2    2
    F-I             I                            3    2    1    N   12    N   53        N N       90   10    N    N   12    5          NN
    F-2                         S    N    N    4    3    1    1   10    s   68    2    N 52   98    N    N   19    7    1                 N
    F-3                        A           N 1                    1  3 13191   laN  6 31   1  N6  3  4  N
    F-4                        41    N    N    N       1    2    1    1 29         5 43       1    1    621              1    12          1
           F-S                 ~~~~ ~~ ~~ ~~ ~~ ~~ ~~~~~ ~~~~~~~~ ~~ ~ ~~4    N      N  I    I  N   22    N   28   14    N    1   79   21 N N 9 4
    F-6                         1    N             N     N        N N        7    N    9   42    N    N   45   77    N    N    3    7
    G-I                         N    N  I        1                m    4    N   20    N    N   73    9    N    N   38    9    N    N
    0- 2                         4       N II             N     I      5    3   49    2    N   68  so         N    N   **   14    1       N
    0-3         B       e                N       N  I       N    N    2    6   24   11    N   12   56    N    N   10    5    4    N
    v- 4                       59    N    N      N    N    N    N    1   12    6   22    N    2   1 2   15  I    I             1I'        2
    j- 5                        0                H           N    N    N   13    1   22    9    N    2   40   15    N    N   22    7
    0-6                          1    N    N     N    N    N    N    N    S        N    9   14    N    N   39   33    N        N    5   16
           6-7                  N   ~~ ~~ ~~N    N    N  N N      N    N    I    N    1    4       N     N    9   12     N    N  I   a
    H-1                            N    N    N    N         N    N  I        N   11    N    N   42  a               N   30   38      N    N
    11-2                        3    N     N     N     N    N    N    2    2   36    3    N   64   56    N    N   87   *4    N            N
    11-3                       53    14   N              N        N    1    3   25    7    N   22   59    N    N   26   12    4    N
    H1-4                       66    N    N    N    N    N    N    N    5              12    1    6   22   11    N        4    2          1
          51-5                ~~~ ~~~20    N    N    N    N    N N    N    7    1   16    6    1    4   29   11    N    N   *
    11-6                        2    N    N      N     N    N     N    N    4    N  a         9    N H2 2
           If-?                 N    N    N~~~~~ N    N    N    N    N  I         N    2    4    N    N   14   1 3    N    N    1   22
    FARALLON ISLANDS            3    2    2    1    5  10         6    N   32    1 49         N                        N11 N 1 1     2    N

     NOTE.'     GREATER THAN 99.5 PERCENT; N =LESS MhAN 0.5 PERCE21.

                    TABLE 2. --  PROBABILITIES (EXPRESSED AS PERCENT CHANCE) THAT AN OIL SPILL STARTING
                                 AT A PARTICULAR LOCATION WILL CONTACT A CERTAIN TARGET WITHIN 10 DAYS.

                                                       HYPOTHETICAL SPILL LOCATION
    TARGET                  STERN
                             (S)  P1   P2   P3   P4   PS   P6   P7   P8   P9  P10  P11  P12  P13  P14  P15  P16  P17  PIO  P19
    LAND                       1    4   14    H    1    S   34    H    5    N    7   67    N    H   22   59    N    N    3   27
    A-i                        N   16    8   40   58   19    N   20    1    2    N    H    1    N    H    N    N H             N H    H
    A-2                        1   25   20   11   41   63   19    3   11  ' 1    3    N    1    N    H    N    N    N    N    H
    A-3                        1   11   21    1    8   34   49    N   **    N    8    N    H    H    1    N    N    N    N    N
    A-4                        1    2    S    N    1    7   22    N   18    H   10    1    N    H    2    1    N    N    N    N
    A-5                       NH  NH    H H                    1  H  2  H  3  5  HN                       2  2  H  H  N N
    8-1                        N    6    5   33   30   10    N   33   .N    4    H    H    1    N    N    N    1    N    N    N
    B-2                        1   12    7   13   34   35   11    5   11.   3    3    NH    1             H    N    N    N    N    N
    8-3                        2    8   12    2   11   29   28    1   95    1   18    N    N    N    1    N    1    N    1    N
    B-4                        1    3    6    N    2   11   22    N   50    H.  20    2    N    N    4    1    N    N    1    N
    B-5                        N  H  H  N  N   1  3  N  4  HN   4  8  N                                   4  3            H H  H
    8-6                        N    N    N    N    H    H    N    N    1    N    N   13    N    N    3    4    N    N    N    NH
    C-1                        N    3    2   24   16    7    N   39    1   **    N  3    1    N  2    1    N    N
    C-2                        2    5    3   15   25   19    7   12   11   11    3    N    2    3    H    H    2    1    H    N
    C-3                        3    4    5    3   12   20   15    1   75    2   **        H    HN    1    2    H    1    1    1    N
    C-4                        3    2    5    H    3   12   18    N   70    H   **    2    N    H    7    2    H    N    2    N
    C-5                        1    N    1    N    1    2    6    N   14    N    9   14    N    N   13    7    N    H    1    1
    C-6                        N    H    N    H    H    H    I    H    2    N    1    a**    N    N    8   13    N    N    1    1
    D-1                        1    1    1   12    6    3    N   31    N   98    H    N   **  a           N    H    3    1    N    N
    0-2                        4    1    1   10   12    7    4   14    8   33    2    N    4   **    N    N    3    2    1    N
    D-3                        6    1    1    3    8   10    6    2   33    4  48    N    1    2    1    H    2    1   2    N
    D-4                        S    1    1    H    3    8    9    N   58    1   72    1    N    1   11    2    N    N    3    1
    D-5                        2    H    N    N    H    2    4    N   20    N   18   19    N    N   **   12    N    N    2    1
    0-6                        N     H    H    N    H    1    1    N    4    N    3   90    N    N   14   **    H    N    2    1                             -
    E-1                        N    N    N    6    3    2    N   20    N   83    N    N   *^    9    N    N    5    2    N    NH
    E-2                        4       H H    7    6    3    2   14    6   67    1    N   13    *    N    H    6    3    1    N                             4
    E-3 '                     12    H    H    2    S    4    3    3   19   10   26    N    2    7    1    N    3    2    3    N
    E-4                       10    H    1    H    2    S    4    N   44    2   58    1    N    1   20    1    1    1    6    1
    E-5                        2    H    N    N    H    2    2    H   22    N   24   17    N    N   **   20    N    H    5    3
    E-6                        1    N    N    N    N    N    1    NH    6    N    5   67    N    N   28   **    H    H    2    2
    F-1                        1    N    N    3    2    1    H   12    H   53    N    H   98   10    N    H   12    5    H    N
    P-2                        5    H    N    4    3    1    1   10    5   68    2    H   52   98    N    H   19    7    1    N
    F-3           H*    N    H    1    2    1    1    3   13   19                    18    H    6   31    1    H    6    3    4    N
    F-4                       41    H    H    N    1    2    1    1   29    5   43    1    1    6   21    1    1    1   12    1
    F-5                        4    N    N    H    N    1    1    N   22    N   28  14    N    1   79   21    N    N    9    4
    F-6                        1    H    H    H    H    H    H    N    7    N    9   42    H    N   45   77    N    H    3    7
    G-1                           H N    N    1    1    N    N    4    N   20    N    N   73    9    N    N   38    9    H    N
    G-2                        4          H    1    H    N    1    5, 3 49    2    N   68   80    H    N   ^*   14    1    N
    6-3                       80    H    N    N    1    N    N    2    6   24   11    N   12   56    N    N   10    5    4    H
    G-4                       69    N    N    N    NH       H       1   12    6   22    N    2   12   15    1    1    1              2
    G-5                        8    H    N    H    N    N    N    N   13    1   22    9    N    2   48   15    N    N   22    7
    G-6                        1    N    N    H    N    N    N    N    5    N    9   14    N    N   39   33    H    N    5   16
    G-7                        N    N    N    N    N    N    N    N    1    N    1    4    N    N    9   12    N    H    1    8
    H-l                        N    N    N    N    H    H    N    1    H   11    N    H   42    8    N    H   30   38    N    N
    H11-2                      3    N    N    N    N    N    N    2    2   36    3    N   64   56    N    H   897   **    H    N
    11-3                      53    N    H     N  NH    H    N    N    1    3   25    7    N   22   59    N    N   26   12    4    N
    11-4                      66    N    N    N    N    N        H        5    8   12    1    6   22   11    N    4    2   **    I
    H-5                       20    N    N    N    N    N    N    N    7    1   16    6    1    4   29   11    N    N   **   11
    11-6                       2   N    N    NH    N    H    H    H    4    H    8    9    N    1   32   21    H    H    9   ^^
    H-7                        N    H    N    H    N    N    N    N    1    N    2    4    N    H   14   13    H    N    1   22
    FARALLON ISLANDS           3    2    2    1    5   10    6    N   32    1   49    N    N    1    1    N    1    1    2    H

     WOTE:  = GREATER THAN 99.5 PERCENT; N = LESS THAN 0.5 PERCENT.

                                        NROBARPIES RR SS") P~ERCENIT CHANCE) IIIAT AN OIL SPIM~~TN
                                   AT A PARTICULAR LOCATION WILL CONTACT A CERTAIN TARGET WITHIN 30 DAYS.

                                                           HYPOTHETICAL SPILL LOCATION
    TARGET                   STERM
                               is)  Pi   P2   P3   P4   PS   P6   P7   Ps   P9  Pie  P11  P12  P13  P14  PIS  P16  PI?  P19  P19
    LAND                        1    4  It       N  I       5   34    N    5    N    7   67    N         N   22   59    N      N     3   27
    A-i                         N   16    3   40   58   19    N   20    1    2             N I           N    N    N    N
    A-?                         1   25   20   11   41   63   19    3   11    1    3    N  I              N    N    N    N    N    N    N
    A-3                          1   it   21    1    a   34   49    N   **              8    N    N    N  I         11    N    N    N     N
    A-4             I                 2    5  I             7   22    N   is    N   10  I          N    11    2    1    N    N    N       N
    A-S             a                 N                     N  I       Nf    2    N    3    5    N    N    2    2    N    N    N          N
    0-1                         N     6    5   33   30   10    N   33    N         4    N  I                N       N  I       N    N     N
    9-2                         1   12    7   13   34   35   11    S  .11    3    3    N  I    I                 N        N    N    N    N
    9-3                         2    9   12    2   11   29   28    1   95    1    toN                    N  I    I             N  I       N
    9-4                         1    3    6    N    2   11   22    N150           N   20    2    N    N    4    1    N    N          I    N
    B-S                         N4    N    N    N           1    3    N    4    N    4    8    N    N    4    3    N    N    N    N                                  ,FA
    9-6                         N    N        N N              N I                N    N   13    N    N    3  A          Nf    N          N
    C-i                               3    2 24 16          7    N   39    1   a*    N             3    1    N            2    1    N    N                          k
    C-2                         2    S    3   Is   25   19    7   12   11   11    3    N           2    3    N    m    2    1    N
    c-3                         3    4    5    3   12   20   15    1   75    2                N    N     1    2    N  I    I    I         N
    C-4                         3    2    5    N    3   12   19    N   70    N   "            2    N    N    7    2    N    N    2    N
    C-S             I                 N    1    N      1    2    6    N   14    N    9   14    N    N   13    7    N  I                   1
    C-6                         N    Nf    N          N    N  I        N    2    N    I            N   N    9   13    N        N  I   I
    D-1             I               I      1   12    6    3    N1  31    N   90   Nf    N                B  aN    N    3    1    N        N
    D-2                         4    1    1   10   12    7    4   14    S   33    2    N           4   it     N    N    3    2    1    N
    D-3                         6    1    1    3  la   10    6    2   33  A            48    N    1    2    1    N    2    1    2         N
    D-4                         S  I    I        N    3  a        9    N   so    1   72    1    N  I         I11    2    N    N    3    1
    D-5                         2    N    N    N      N     2    4    N   20    N   is   19    N    N              12    N    N    2    1
    D-6                         N    N        N       N  I    I        N  A       Nf    3 90       N    N   14   **       NN         2    1
    E-1                         N     N    N    6    3    2    N   20    N   83    N    N   *            9    N           5    2    N    N
    9-2                         4    N     N     7    6    3    2 14         6  67   I        N   13          N     N    6    3    1    N
          E-3   ~~~           12    N     N    2    5  A         3    3   19   10   26    N       2    7  I        N    3    2    3    N
          E-4                 ~~~10    N  I     N    2    5    4    N   44    2   58    1    N    1   20    1    1    1    6    1
    E-S                         2        N       m           2    2    N   22    N   24   17    N    N   **   20    N    N    5    3
    E-6                         1    11   N         N I     N     1    N    6    N           67    N    N   28            N    N    2    2
    F-I                         I    N    Nf    3    2    1    N   12    N   53    N    N   99   10   Nf    N   12    5    N    N
    P-2                         S    N    N    4    3    1    1   10    5   68    2    N   52   98    N    N   19    7  I                 N
    P-3                         *     I N        1    2    1    1    3   13   19   18              6   31    1    N    6    3    4    N
    F-4                        41    N           N    1    2    1    1   29    5   43    1    1    6   21    1    1    1   12    1
    F-S                         4    N     N     N    N    I    I    N   22    1120 14             N    1 79 21           N    N    9    4
    F-6                         1    N     N     N    N     N          N     7    N    9   42    N    N   45   77    N    N    3    7
    G-IN                              N    N     1    1 N      N       4    N   20    N    N   73    9    N         N   38    9    N
    G-2                          4    N    N  I       Nf    N  I       5    3   49    2    N  fie so                N   **   14    1    N
    0- 3           a      s         I      N    N      1    N     N    2    6   24   11    N   12   56    N    N  la           5    4    N
    0- 4                       69    N    N    N    N       N     N    1   12    6   22    N       2   12   15  I    I    I1 0            2
    0- S                        0     N    N    N              N       N   13    1   22    9    N    2   48  IS                N   22    7
             0-6~~~~~~~~~~~ 1f N                           N    N    N    S    N    9 14          11    1139 33          N    N    5 16
    G-7                         N    N           N    N    N    N    Nf  I         N    1    4    N    N    9   12    N    N  I   a
    "-IN                              N N        N    N    N  I              N   11    N    N   42    B    N    N   30   38    N    N
    H1-2                        3    N    N    N      N    N    N    2    2   36    3    N   64   56    N    N   87   t*             N    N
    11-3                       53        N N     N    N     N    N    1    3   25    7    N   22   59    N    N   26   12    4    N
    11-4                       66    N    N    N            N    N    N    5    9   12    1    6   22   It    N    4    2                 1kI
    11-5                       20    N     N     NN         N    N    N    7    1   16    6    1    4   29   11    N    N                11 i
    11-6                        2    N    N    N                             4    N           9    N     1   32   21    11    N    9   it,
    11-7                           N N           N          N I                         2    4     N    N   14   13    N    N    1   22
    FARALON ISLANDS             3    2    2    1    5   10    6    N   32    1   49    N  I    I    I                          1    2     N

     NOTE:       GREATER THAN 99.5 PERCENT,'  N =LESS THAN 0.5 PERCENT.

                   TABLE 4. --  PROBABILITIES (EXPRESSED AS PERCENT CHANCE) THAT AN OIL SPILL STARTING
                                In THE WINTER SEASON AT A PARTICULAR IfLCATION WILL CONTACT A CERTAIN
                                TARGET WITHIN 30 DAYS.

                                                     HYPOTHETICAL SPILL LOCATION
    TARGET                  STERN
                         (S)  P1   P2  P3  P4  PS  P6  P7  PS  P9 P10 P11 P12 P13 P14 PIS P16 PO  P18 P19
    LAND                       36  48  65  31  43  52  69  24  58  25  61  92  21  26  69  86  22  23  47  61
    A-I                         4  39  25  40  61  43  1I  27   9  12   8   1   8   7   2   1   6   4   4   1
    A-2                         9  40  39  23  43  64  40  14  36  13  17   2   8  10   5   2   9   6   5   1
    A-3                        10  22  30  13  21  35  51  10  *6  10  26   4   7   8   6   3   8   6   6 I
    A-4                        10   9  13   8   9  13  24   6  32   7  28  10   5   8  11   6   6   5   5   2
    A-S                         6 3 3  3 3  7 41 11 3 15 172410 9 3 3 5 3
    8-1                         5  29  19  41  45  32  14  32  10  17   7   1  11   9   3   1   9   5   5   1
    0-2                         9  33  31  25  41  50  27  16  36  15  19   2  12  11   5   3  12   8   5   1
    B-3                        13  22  28  13  23  35  370 11  95  10  46   5. 16  12   8   4  11   8   7   2
    0-4                        12  12  15  10  13  17  27   8  54   8  42  12   7  10  16   9   8   6   7   3
    0-5                         7444  5 6 9 3 1441  15 27 3 4 1413 4 4 6 3
    8-6                         3 2 N 1 3 2 3 16 2 430 1 2 12 14 1 2 4 4
    C-I                         8  26  17  41  35  28  12  40  11  **               8   1  15  12   3   1  14   9   5   1
    C-2                        16  32  27  32  39  44  23  22  33  26  22   3  18  18   6   3  18  14   7   1
    C-3                        18 24 28 17 26 38 30 14 79 16  *  6 13 17 11  5 15 14 11 3
    C-4                        161718 13I15 24 29  9 63 11  *1410 14 26 11 10  913  5
    C-S                        10   6   6   6   8  10  14   4  20   8  23  41   5   7  30  23   5   4   9   7
    C-6                         6 3 2 1 4  5 4   9  5 8 *f 4 3 22 29 3 3 9 7
    D-1                        10  21  14  36  28  22  10  42  11  98   8   1  **  29   2   1  19  13   4    1
    D-2                        19  26  22  33  34  36  19  24  26  47  17   3  22  A*   6   2  22  18   9   2
    D-3                        24 24 23 16 24 34 25 16 48 18 54 5 17 20 11 4  18 16 14 3
    D-4                        22  18  18  11  18  24  27  11  57  12  66  12  11  16  36  13  12  11  17    7
    D-5                        15   7   7   7   9  12  13   5  24  10  20  33    5   9  1*  36   7   7  16  10
    D-6                         8  3 4         3   7    6  3 11  5 11 80                      4        427*         14  11
    E-1                        10  16  13  32  27  19   8  39   8  87   0   1  *6  34   3   1  25  16   5   1
    8-2                        22 24 20 31 32 30 16 29 25 67 14  2 31 ** 6 1 29 21112
    E-3                        35  24  20  18  25  31  21  17  38  23  37   4  20  25  10   4  23  19  18   4
    E-4                        27  19  17  11  20  27  23  11  51  15  56  12  14  20  43  12  16  14  24   S
    R-5                        15   9   9   9  10  14  14   6  30  11  29 25    9  11  **  39   9   8  23  13
    E-6                         84           4   5  9  8  3 15 7 14 52  5  5 30 **  S  7 15 12
    P-I                        12  18  12  31  25  15   7  39   8  65   9   1  97  36   3   1  43  25   5   1
    F-2                        28 24 17 30 30 25 14 34 22 69 16  2 58 97 6 1 47 32 11 2
    F-3                        9   23  18  22  26  29  19  21  34  33  30   5               26 41  11  4  28  26  24    4
    F-4                        45  21  10  13  22  29  22  12  43  22  49  11  17  27  35  11  18  21  40  11
    F-5                        21  11  10   9  13  17  15   7  34  13  35  19  11  15  70  30  10  12  32  20
    F-6                        13  5  7  7 7110 10  4 19  9 17 36  7  9 41 63  8  9 20 27
    r--I                       13  16  11  26  20  11   5  31   6  43   6   1  79  32   2   1  64  33   4   1
    0-2                        30  20  15  26  24  21  12  32  17  58  13   1  67  82    5   1                   4* 41  11   2
    0-3                        83  19  16  21  24  22  16  21  26  36  24   3  28  57   9   3  30  29  27   5
    G-4                        63  20  18  13  20  26  19  14  38  25  40   9  18  32  27   9  20  25  **  13
    0-S                        24 13  11   8  15  18I15    7 33   14 35 14   11  19  48  21  11  11 41   26
    0-6                        13   6   6   6   9  10  10   5  19  10  19  17   6   9  37  34   7   9  23  39
    G-7                         5 2   3 3 3 3 2 6 4   7 74 1  5 10 55 3 410 18
    H-1                        12  14   8  22  21  10   6  28   6  39   5  I   55  28   2   1  49  61   4   2
    H-2                        26 18 13 27 23 19 9 31 16 53 11 1 69 67 5  178  46 11  4
    H-3                        65  18  13  22  22  21  15  24  23  43  20   3  35  60   0   3  37  36  27   6
    11-4                       65  18  15  IS  20  24  19  16  33  29  33   7  23  39  26   8  26  27 1*  14
    11-5                       30  15  12  10  15  20  16   8  34  19  36  13  16  23  39  16  15  18  *9  37
    H-6                        15   7   7   6  11  12   9   5  21  12  22  15  10  13  38  26   9  12  26  *9
    H-7                        10 4 3 4  5 5 441  10    6 9 10 6 7 1717 5  5 15 30
    FARALLON ISLANDS    17 19 19 10 18 28 21 12 44 12 5t 4411 16 10 4 12 11 11 3

     NOTE: **   GREATER ThAN 99.5 PERCENT; N = LESS THAN 0.5 PERCENT.

                   TABLE S. --  PRODABILITIES (EXPRESSED AS PERCENT CHANCE) THAT AN OIL SPILL STARTING
                                IN THE SPRING SEASON AT A PARTICULAR LOCATIOM WILL CONTACr A CERTAIN
                               TARGET WITHIN 30 DAYS.

                                                     HYP(IRTICAL SPILL LOCATION
    TARGRT                 STERN
                         (S)  P1  P2  P3  P4  PS  P6  P7  PS  P9 PIO P1l P12 P13 P14  PIS P16 PI7 P19 P19
    LAND                       21 57 70 28 40 54 79 17 54 16 55 92 6 12 68 90 7  4298 57
    A-I                        H25  9 65 67 13  N 31  N  2  N  N  N  N  N  N  M I   N  N
    A-2                         2 58 34 34 6476 13 15 5 21 N  NI  N I Ni  I N
    A-3                        2 44 49 13 26f61S55  6** 27  N 112  N N IlIKN
    A-4                        1 21 22  7 12 22 36  3 19 2    NIt    N 3 11  Ni  N
    A-S                        N   3   3   1   3   5   5   N A           N   5 3       N   N   3   1   N   N   N   N
    0-1                        H IS  7 5938?  HS1  N A  N  N  MN   N  N                                          IHI  N  N
    8-2                         2 46520 42 62 49 10 21 5 4    NIl  N NIlIN N
    a-3                         2 42 40 19 33 59 34 10 96  2 13 M. 1  1 2   H I                                      I   I N
    0-4                        2  27  30  10  18  34  38   4  65   2  19   N   1   1  A    N   I   N   I M
    B-s                        179 8 3 6 10 12 2 8175  N MN6 2 1 H                                                                  I M
    8-6                        N 1 3 1 2 3 2 1  3 M 1I10 N N 2 2  N N I  N
    C-i                        N 12  5 51 27  5  NM66  N **  N  N  2 1  N  M  2                                    N  N
    C-2                        2 36 14 54 54 32  8 35  5 IS M N 12  NH i    1 2  N N
    C-3                         3  42  33  30  45  53  21  15  77   6  **   N   1   2   1   N   2   1   1   N
    C-4                         3 36 33 17 27 51 34 8 81 3*N* NIl  5 N  2      2 1
    C-5                         2  14  16   6  12  20  21   4  24   1  18   7   N   1  12   3   1   N   2   1
    C-6                         1 3 8 3 4  8 9 2    H M 5 ** N M 996 M NI    N
    D-1                        N  7 4 34 174  NM58  SM98  N  N ** 4 NHN  3 1     NH
    D-2                        3 27  9 50 38 22  5 45  4 35  M N  4 *4N H  4 2  1 M
    D-3                        6  33  21  34  44  39  15  20  26   9  43           N   2   4   N   N   3   1   2   N
    D-4                        6  37  31  20  33  50  27  12  71   4  84  M    1   3   6  M    2   1   3   1
    D-S                        4 19  18  10  17  28  2,   6  36   1  30   7   1   2  **   4  A    N   2   2
    0-6                        298 11 4   8 It112 3  14  l  993  Ni3e I IS   I  M 11                                    2
    E-1            M                6 3 25 IS 3   H49  N 93  N n**  4 MH 9 4 1  NHN
    E-2                        2  20   8  47  30  15   4  52   4  82  M    N  15                N   N   6   2   H   N
    E-3                        9  31  18  39  40  28  13  26  20  20  21   N   5  10   N   N   4   2   2   H
    E-4                       11  38  32  28  36  47  23  16  63   9  74   H   3   5   9   N   4.  2   4   1
    E-s                        6  25  22  13  19  34  23, 8  47   3  42  11   2   3 A*   7   2   1   5   2
    E-6                        3  13  13   7  10  16  14   3  24   2  16  73   1   2  36  *6   1  M                3   2
    F-i            M                S 2 21 13 2   H 38 N 50 N  HM97  4 NH? 7   3 M M
    F-2                        2  17   5  38  24  12   4  54   3  83  M    N  62  99   N   N  16   5   H   N
    PF3                       *  27 IS 4236 23 11 34 18 3716  M 13 37 1  N 10  4 2  H
    F-4                       44  36  30  36  41  43  21  22  53  17  63   N   6  13  12   N   6   2   9   1
    F-S                        9  28  24  20  26  41  24  12  55   6  59  10   3   6  83  13   A   2  10   3
    F-6                        6  19  16   9  16  23  19   6  35   3  30  52   2   3  63  83   2   1   6   6
    G-1                        N  4 2 16I10n2  HN25  N 1B  H  N 75  3 N N27  S N  N
    G-2                         1  12   4  30  17   8   3  45   2  67   1   H  79  83   N MH**  10  M    N
    G-3                       91  20  10  35  26  17   7  36  12  41  10   N  22  73   1   N  16   7   1   N
    G-4                       79  33  25  37  38  34  IS  26  35  25  39  M    9  22  10   N   7  4   *4 I
    G-5                       16  28  23  23  28  39  21  15  48  10  52   7   5  10  53  12   5   2  22   4
    0-6                       10  20  15  12  18  27  18   7  37   4  36 p19   3   5  58  36   3   2   9  15
    G-7                        3 8 4 3 5 8 6 2 13  1 12 8 2 2 20 18   1 2  9
    11-1                       N  4 2 13  9                H N 20  N 13      N 40  3 N N 21 27  NMH
    U-2                        1  10   3  25  15   6   2  38   2  57   H   N  79  61   N   N  94  "    N   N
    U-3                       53  17   8  32  24  15   6  42   0  59   8   N  41  78   1   N  39  16   1   H
    11-4                      79  28  20  38  38  30  13  34  25  36  29   1  18  38   8   N  IA              a A*   N
    1U-5                      35  30  22  28  34  36  18  19  43  17  49   7   7  15  34   9   7   4  at  4
    11-6                      13  25  17  16  23  30  17   9  42   6  41  16   4   8  56  26   4   2  17  *'
    H-7                        8  14   9   6  12  16  10   S  23   3  20  11   2   4  34  21   3   1   8  28
    FARALLON ISLANDS           3  23  18  20  30  33  13  10  23   5  42   N   1   2   1   N   1 I                 I   M

     NOTE:  14 x GREATER THAN 99.5 PERCENT; MLESMS "IAN 0.5 PERCENT.

                    TABLE 6. -- PROBABILITIES (EXPRESSED AS PERCENT CHANCE) THIAT AN OIL SPILL STARTING
                                 IN THE SUMMERf SEASON AT A PARTICULAR LOCATION WILL CONTACT A CERTAIN
                                 TARGET WITHIN 30 DAYS.

                                                       HYP(foTITICAL SPILL LOCATION
    TARGET               STERN
                          (S)  P1  P2  P3  P4  P5  P6  P7  P8  P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19
    LAND                    20  47  67  19  34  52  71  17  58  12  52  90   7  12  68  83   5   2  29  61
    A-i                      N  26  14  66  63  17   N 2        N   N   N N   N   N   N    N    N   N   N       N
    A-2                      N  57  32  30  69  77 17   7  2   N   N   N   N   N   N   N   N   N   N   N
    A-3                      N  45  47  8 27  58  55   2           **   N   N   N   N   N   N   N   N   N   N   N
    A-4                      N  19  22   3   7  23  32   N  19   N  2   N   N   N   N   N   N   N   N   N
    A-S                      N   3   3   1   1   2   3  N1 2   N   1   N    N   N   N H                N   N   NH
    0-1                      N  18  13  61  34  12   N  52   N   N   N   N               N    N    N N    H   N   N   N
    B-2                      N  40  20 41  62 46  15 16    3            N    N   N    N   N   N   N    N   N   N   N
    B-3                      N  43  34  13  39 58  36   4  S95   1   3   N   N   N   N   N   N   N   N   N
    B-4                      N  29  29   5  15  38  37  1  56   N   9   N            N   N   i   N   N   N   N   N
    0-5                      N   8   9   1  4  9    11   N          8   N   3   N   N   NH      N   N   N      N   NH
    9-6                      N   2   1   N   1   1   1   N   2   HN    1   2   N  H    1   N   N   N   N   N
    C-l                      N  18  12 48  25  13   N  64   N  **   N            N   N   N   N   N   N N   N   N.
    C-2                      N  32  17  52  52  33  14  35   6  10   N   N   N   N   N   N   N   N   N   N
    C-3                      N  41  27  28  47  50  22  11  72   2  **           N    N    N    N   N   N   N   N   14
    C-4                      N  37  34  12  29  50  36   4  83   1  **   N   N   N   N   N   N   N   N   N
    C-5                      N  16  17   3   9 23  22   1  30   1  13   N   N   N   3   N   N   N   N   N
    C-6                      N   5   6   1 3   8   8   N   8   N   5  **   N              N   2   N   N        N   N
    D-1                      N  14  10  31  19  12   1  53   N  99   N   N  **   3   N   N   N   N   N   N
    0-2                      N  25  14  46  36  21  12  45  6 41   N   N   1  **   N   N   N   N   N   N
    D-3                      N  32  20  33   44 34  15  18  32   6  45   N   1   1   N            N   N   N   N   N
    D-4                      N  35  29  18  37  49  28   8  69   3  82   N   N   1   N   N   N   N   N   N
    D-5                      N  21  19   5  15  30  25   2  43   1  36  16   N   N  **   N   N   N   N   N
    D-6                      N  10  10   2   5  14 14    N         18   N   9  97  1 N  11 0       N N     N   N
    E-1                      N  13  10  25  16  11   1  39   N  79   N   N  **   3   N            N   N   N   N   N                 co
    E-2                      N  22  12  40  28  10  12  50   7  79   N   N  11  **           N   H N   N   N   N
    E-3                      1  27  16  35  37  271  13  28  21  20 26   N   2   6   N   N   1   N   N   N
    E-4                      2  35  27  23  42  44  23  13  55   5  69   N   N   1  16   N   N   N   N   N
    E-5                      1  24  22   9  21  34  25   4  50   2  49  20   N   1           A 414   N   N   N   N
    2-6                      N  16  13   3   9  21  10   1  27   1  21  75   N   N  39  **   N   N   1   N
    F-I                      N  11   8  23  16   9   2  34   N 49   N   N  99  4   N   N   N   N   N   N
    P-2                      N  20  11  34  26  16  10  49   8  90   2   N  60  99   N   N   8   N   N   N
    F-3  .                   *  26  15  37  33  21  13  36  19  40  24   N  10  43   H   N   4             N   N   N
    F-4                     46  36  23  31  43  39  19  22  40  16  49   N   3   8  22   N   1   N   1   N
    F-5                       4  29  23  15  30  37  24   8  51   5  56  18   1   2  87  21   N   N   2   N
    P-6                      1  20  16   7  15  27  20   4  41   2  37  41   N   1  62  90   N   N   2   1
    G-1                      N   8   6  20  11  7   2  25   N  1B   N   N  69   5   N   N  21   N   N   N
    G-2                      N  17  10  27  19  12   9  42   7  61   6   N  83  79   N   N  **   2   N   N
    s-3                     94  21  12  33  27 17  12  35  16  52  19   N  24  66             N   N  12   2   N   N
    -4                      82  26  19  34  37  30  14  27  28  24  30   N   5  23  20   N   3   N  *           H N
    G-S                     12  28  19  20  32  32  19  12  41   8  47  15   2   4  51  20   1   N  15   N
    G-6                      3  18  14   8  18  26  15   5  38   2  39  15   1   1  53  35   N   N   3   4
    G-7                      1   6   5   2   6  10   5   2  16   1 12   5   N   N  22  15              N   H   1  4
    11-1                     N  -7   5  19  11   5   2  18   1  14  N   N  37   5   N   N  23  24   N   N
    11-2                     N  15  10  26  17  11   8  37   0  52   7   N  79  51   N   N  97  **   N   N
    11-3                    50  19  12  31  24  18  11  36  14  56  18   N  47  69   N   N  44  11   N   N
    11-4          03  25  17  38  36  2H  13  32  25  39  24   2  21  44  18   N  11   3  **   N
    H-5                     40  29  20  29  31  30  17  18  33  15  38  13   5  15  33  10   3   1  **   N
    11-6                     8  21  15  12  22  28  16   B  39   4  42  13   2   4  47  27   1   N  10  **
    H-7                      3  11   9   6  13  18  10   4  26   2  25   8   1   2  37  18   N   N   3  22
    FARALN ISLANDS          N  24  16  19  30  20  12   6  30   2 43   N   N            N    N   N   N   N   N   N

     NOTE: *    GREATER THAN 99_5 PERCEN  T -            AW
           m  so  w   M  ift ow~~w"PI~~E

    m    OW LE AO PRJILITIMXPREI  AS M6 CM, JRB oiL,  STARTING W
                                 IN  llE AtUIM  SEASON AT A PARTICULAR LOICATION   ILL  WMACTI  A CETAIN
                                 TARGET WITUIN 30 DAYS.

                                                        HYPOlIHETICAL SPILL. LOCATION
    TARGET                STERN
                          (S)  P1  P2  P3  P4  PS  P6  P7  PO  P9 P10 Pll P12 P13 P14 P1S P16 P17 P1  P19
    LAND                      12  27  46  12  18  a27  53   7  31   7  30  81   3   9  42  73   5   4  19  30
    A-1                        1  46  20  50  7633    4  24  2  3    1   N   2   1   N   N   1   N   N   N
    A-2                        1  54  52  22  49  79  35  10  13   4   3   N   3   1   N   N   1   N   N   N
    A-3                        3 23        47   0 17  43  62   6  **   2   9   N   2   a   1   N   2   1   N   N
    A-4                        3  1019   5   7  13  27  3 20   2  14 1   1   1                     3 N  I   I   N   N
    A-S                        1   3   5   2   1   2   5   N   2   1   7   5   1   1   5   2   N   N   1   N
    9-1                        1  34  17  51  57  24  4 35 2   9   1   N   32    N                        N     N   N
    8-2                        2  51  40  27  57  66  30  12  15   7   4   N   3   2   N   N   a   1   N   N
    9-3                        3  27  45  14  21  51  49   8  96   4  23   1   3   2   1   N   2   1   N   N
    9-4                        3 12  21  7    9  19  34   5  43   a  29   2.2 a               5   1   1   I   1  1
    0-5                        1   4   5  2          3   a3   N   5  2   10   9   N   1   8    5           N  2   N
    0-6                        N 2   1   1  1 N 1 N 21216  N I  6 6  N N I  N
    C-1                        1  33  16  50 48  21   4 49   3   h   1  N    711  N     2  1    N   N
    C-2                        2  51  37  34  60  55  24  19  17  16   5   N   7   6   N   N   4   2   N   N
    C-3                        5  31  43  18  32  52  40  10  85   7               1 4   4   2   N   4   2   1   N
    C-4                        7  16  27  11  13  29  40   6  71   5  **   3   3   3   9   2   2   2   3   1
    c-5                        3   8   9 4       5   6  12   2  12 2    16  162    1  19  11   1   2   3   1
    C-6                        2   3   4   2   2   3   4   N   4           1   4 **   N   1  11   1B   N   N   2   1
    D-1                        N  26  13  42  37 17   5  51   3  99   1   N  **    7  NN               3   2   N   N
    D-2                        3  44  30  35  53  41  20  25  15  34   5   1  11  **   1   N   6   4   N   N
    D-3                        8  33  36  19  37  47  30  12  52  11' 63   1   7   5   2   N   6   3   2   N
    D-4                        9  20  29  12  15  36  37   8  71   6  78   3   4   4  13   3   3   2   5   1
    D-5                        6  11  12   5   6  11  17   3  22   4  24  26   3   3  **  13   1   1   4   2
    D-6                        2   4   4    3  2   4      6   1   7   1   6  92   2   2  20**   N   N   2  2
    E-1                        1  23  12 41  32  16   4  51   3  91   1   N  *k    8   N  N    5   4   N   N
    E-2                        3  41  28  38  47  34  18  34  16  61   6   1  21  **   1   N  10   6   N   N
    E,3                       16  35  33  23  38  44  27  13  37  15  40   1  11  12   3   1   9   5   3   N
    e-4                       17  22  29  14  19  41  35   9  69   8  74   2   6   7  26   3   4   3   7   a
    E-5                        8  13  14   5   7  15  20   4  32   4  32  26   3   3  **  27   1   1   7   3
    E-6                        4 5   6  4    3   6 8          3   9   2  10  752    2  29 I'           1   N   4  3
    F-i                        1  21  10  43  31  15   4  52   3  73   1   N  99  11   N   1  13   7   N   N
    P-2                        7  37  25  39  45  32  16  40  14  76   8   1  52  99   N   1  23  13   N   N
    F-3                       *  36  31  23  42  39  27  17  33  25  34   1  16  33   3   1  13   8   4   1
    P-4.                      50  25  31  17  26  43  35  11  63  12  70   2   9  12  33   5   8   5  16   3
    P-5                       12  15  18   8  11  20  23   5  42   4  41  23   4   7  88  30   2   3   s15   7
    F-6                        6   8   9   4   5   0  12   4  15   3  18  57   2   5  50  82   1   1  10  10
    G01                        1  19   9  34  26  13   4  44   3  47   1   N  84  11   N   N  45  12   N   N
    G-2                        7  31  21  37  40  27  12  42  11  70   8   N  71  91   N   1  **  23   N   N
    G-3                       92  31  27  25  37  35  20  21  27  32  27   N  19  57   3   1  19  12   3   1
    G-4                       75  27  29  18 .29  39  32  13  51  14  57   3  11  10  26   5  11   6**   3
    0-5                       18  16  19  10  13  25  23   6  47   7  46  17   4   8  69  24   5   4  33   9
    0-6                        8  10  11   5   6  10  11   4  21    4  21  28   3   5  51  46   3   2  15  19
    G-7                        2 5   3   2   2 2         4    1   6   1  8    9   1   1  12  18   N  5  11
    11H-1                      2  17   9  31  25  11   4  42   2  37   1   N  62  14   N   N  39  4e   N   N
    11-2                      10  30  19  38  40  24  11  46  10  67   8   N  77  73   N   N  93  aA   N   N
    H-3                       65  34  24  29  39  34  19  26  23  42  24   1  31  70   2   1  33  20   5   1
    11-4                      76  29  29  23  34  41  30  16  45  21  50  -3  15  28  24   6  16  10  *    3
    H-5                       27  17  21  14  17  28  24   9  48   9  49  15   8  11  57  20   8   5   *   4
    H-6                       11  11  13   7   7  13  12   5  27   6  25  25   3   7  54  38   3   2  23  **
    14-7                       3   7  7    3   3   6   6   2  12   2 1 3  13         2   3 23    23  2    1 8    26
    FARALLON ISLANDS           6  23  29  13  23  37  27   8  51   5  63   N   4   3   2   N   3   2   2   N

     NOTE: *  = GREATER THAN 99.5 PERCENT; N   LESS TIAN 0.5 PERCENT.

                                      7-41

                  Trajectory Anal7sis and Modeling Support for
                           the Puerto Rican Oil Spill

                                   J. A. Gait
                               Seattle, Washington
                                December 6, 1984

    INTRODUCTION

         The maritime -ilosion on :he 7essei ?Puerto li=n, :he ;ubseauen:

    release of cargo, breakup of the ship, and the resulting pollutant

    release problems have provided a subject of considerable discussion.

    These discussions have taken place both in and out of the press, and

    have spanned the community of spill responders, responsible parties,

    special interest groups, and interested bystanders. The wide variety of

    opinions on both the event itself and the adequacy of various response

    efforts is a clear testimony to-the complex and many-faceted nature of

    major environmental spills.

         This will be a review of the trajectory analysis support for the

    2uerto Rican spill event.  This task began on October 31, 1984, and

    required continuous trajectory analysis and forecasting efforts until

    November 10.  After that time, occasional consultations have been

    initiated due to the intermittent leaks from the sunken stern secticn of.

    ithe vessel.

         During this period, the coastal currents off the San Francisco

    3ight went through a major reversal associated with the onset of a

    typical winter regime, or the so-called "Davidson Current:'.  The details

    of this frontal passage and the comiLes eddy pat:er= in :he San

    Francisco 3ight contributed to the difficulty in carrying out =rajectl'r'

    anal7sis and making acc4orace forecasts of pollucant movement.  The

    objec:ive of this discussion is to outline :he avaliabie information

                                      7-42

    INTRODUCTION, cont.

    that was used for the forecasts and to discuss the controlling

    oceanographic processes as they are understood.

         The second section of this paper presents a chronology of the

    trajectory analysis response to the spill evenc.  T-he third section

    covers a discussion of oceanographic processes and how rno3wledge :

    these processes was used to make specific recommendations. The final

    section of this paper presents conclusions and a discussion of alternate

    trajectory analysis, or forecasting, procedures that could have been

    used for support during the Puerto Rican pollution event.

    CHRONOLOGY

    October 31, 1984

         0600 Initial notification of the Puerto Rican explosion and ship

    casualty was reported to Jerry Gait of the Modeling and Simulation

    Studies (A.SS) group of the NOAA Eazardous Materials Response Branch

    (HMRB), Seattle.  At that time, a partial list of the ship's cargo was

    provided, and an approximate location of the ship was given.  An

    immediate check was made of available forecasted tidal infor-ation and a

    trajectory response was initiated.

         Debra Payton and Glen Watabayashi were called to the Seattle wIMRB

    office for immediate trajectory analysis support.

         06J0  MASS personnel called the National weather Ser-ice (NW-S)

    office in San Francisco (Bill Hackle) and identified :wo offshore

    weather data buoys, (buov Ii26, just off the Goiden gace, and buov -!12,

    just off Half Moon Bay), as concinuous real-ti- e wind inrcr-mation sit:s

    which would be available during the crajec:or-7 analysis resoonse.  At

    :hat time, 3uoy ,256 repor:ed norvhwest winds at four k:nosc, and 3uoys  :1-

                                       7-43

    October 31, 1984, cont.

    reported north winds at ten to twelve knots. Afternoon and evening

    �winds for October 31 were forecasted for speeds of 15 to 25 knots from

    the northwest. The extended forecast for November i was for light and

    variable winds.  Based on these data and expected tidai currents, :he

    initial trajectory  f any oil that may liave spilled from The 3hi s

    present location (37045'N, 122�50'W) would have been to the south and

    nearly parallel to the coast. This information was passed to the

    Scientific Support Coordinator (SSC) on-scene at the U.S. Coast Guard

    Marine Safety Office in San Francisco.

         0700 MASS personnel in Seattle immediately began to compile and

    enter data into the computerized trajectory analysis routines used for

    spill support.  This information included tidal current data, forecasted

    wind data, digitized trajectory maps for the San Francisco Bight region,

    and a hydrodynamic circulation model for the San Francisco 3ight area

    which included geostrophic and Ekman dynamics to represent the surface

    flow.

         0820  A call was placed to the National Marine Fisheries ?acific

    Environmental Group (PEG) group, located in Monterey. This group

    studies nearshore winds and upwelling processes along the California

    coast.  A discussion with PEG researchers indicated that their best

    information showed that upwelling had ceased in the Monterev area and

    that northerly flow was expected in the Monterey Bay region (i.2., the

    Davidson current regime had advanced as far north as this region).

         0835  Discussions were carried out with the NOAA contract chemist

    (Dr. Ed Overton) at Louisiana State Universit-  (LSU) co ascertain :he

    nature of the ?uerto Rican's cargo componenrs. From these discussions,

                                      7-44

    October 31, 1984, cont.

    it was decided that the major components of the cargo were hydrocarbon

    additives and should be treated as hydrocarbons with regard to

    trajectory analysis.

         0840 MASS personnel in Seattle obtained satellite analysis data of

    infrared sea surface temperature mans for the Califoarnia zoast. This

    imagery clearly delineated a band of lower temperature water along the

    California coast.  This band of water indicated a residual of the

    coastal California system and highlighted the fact that the Davidson

    Current was not yet strongly established in the San Francisco Bight

    region.  Based on this satellite imagery, the results of the                           3

    hydrodynamic circulation modeling for the region, discussions -with the

    PEG group, and on-scene reports of southerly currents, it appeared that

    the advance of the.Davidson Current front was located somewhere between

    Monterey and the San Francisco Bight region itself.   This meant that

    flow in the San Francisco Bight region itself was to the south and that

    flow in the Monterey, and possibly Santa Cruz area, was to the north.

         0845 Based on the available current information and wind

    forecasts, MASS passed a recommendation to the SSC on-scene that the

    ship should be towed offshore and south of the Farallon Islands. This

    recommendation was based on three specific factors.  The first factor in

    this decision was the reported condition of the ship was extremely

    unstable with an active fire in progress. This suggested that a perhaps

    catascrophic release of oil was imminenc and that a large fraction of

    the cargo could be spilled at any Moment.  All available initial

    trajector/  estimates indicated a southerly flow and the oniv aiternarive

    to taking the ship offshore and south of the Farallon Islands -would have

                                     7-45

    October 31, 1984, cont.

    been to leave it in place or to try and route the ship north of the

    Farallon Island. In either case, the potential of the spill would have

    been significantly more hazardous for the Farallon Islands region and

    the mainland coast area of the San Francisco Bight.

         The second major factor in :his iecision was that :he  vydrcdynan-_

    flow modeling of the San Francisco Bight area, literature, discussions,

    and personal experience indicated that a region of extremely complex

    current eddies lay inshore of the Farallon Islands. This region

    extended over a major section of the Bight region. A second area of

    eddy current formation was south of the Pt. Reyves area. It was felt

    that in it would be extremely difficult both to estimate trajectory

    movement in either of these areas and to determine the the ultimate

    region of impact if cargo were spilled in this region.

         The third factor in this decision was the convergence, or coming

    together, of the southerly-flowing currents which were present in the

    San Francisco Bight area, and the northerly-flowing currents which were

    suggested off the Monterey-Santa Cruz area. The joining of these

    currents would force a general offshore flow to the water, and this

    would tend to carry floating pollutants offshore away from the coast in

    the area south of the Farallon Islands.

         0859   MASS received a follow-up call from the chemists at LSU.

    LSU had previously contacted toxicological snecialists at Chevron and

    obtained an updated, corrected description of the shiD's zargo.  This

    description confirmed the previous conclusicr that the materials

    involved should be treated as various classes of hydrocarbons with

    regard to trajectory analysis.

                                      7-46

    October 31, 1984, cant.

         0925   MASS was informed by the SSC on-scene that the fire on the

    Puerto Rican appeared to be somewhat better controlled, and that the

    ship was being towed on a course of 2680 at 3.3 knots.  After receiving

    this information, MASS reiterated the suggestion that the ship's track

    be moved south of the Tarallon :siands, and pointed out :hat :he ihin '3

    present course did not fulfill those requirements. At that time, MASS

    suggested that the ship fcllow a course between 2300 and 240� to

    maximize the distance between the ship and the Farallon Islands as the

    vessel passed out of the San Francisco Bight region.

         1120   MASS personnel contacted the Pt. Reyes Bird Observatory

    (Sarah Aikin) and requested that hourly wind data observations, taken

    routinely by the biologists on the Farallon islands, be oassed to MASS

    on a regular basis.  To accomplish this, MASS set up a three times daily

    transmission schedule from the Farallon Islands directly to MASS

    personnel in Seattle.  MASS also discussed present circulation

    conditions around the Farallon Islands with observers there. These

    observers indicated that the general trend of the currents in the region

    was still south, and that the extension of the Davidson Current had

    apparently not reached the San Francisco Bight region. Once again, this

    indicated that the Davidson Current advance and its subsequent expected

    reversal was stalled somewhere between the Monterey B3a  region and the

    San Francisco Sight area.

         1211   The SSC on-scene asked for a specific recommendation of how

    far the ship would have to be towed.  Since the ship was on fire and

    being towed direct.r  away :rom firefigh:ing support euipment and

    logistic bases, there vas some concern about how :ar -c would have to  0

                                     7-47

    October 31, 1984, cont.

    before it would outrun all possible relief efforts. MASS personnel

    recommended that the ship should be towed towards the dump site located

    at the continental shelf break (37�32'N, 122�59'W) at a minimum, and

    that this course would optimize its movement away from the Farallon

    Islands -nd the California mainland with regard to anv  threacs

    associated with catastrophic spills during the next few hours. During

    this discussion, contact by other MASS personnel was made with

    biologists on the Farallon Islands and updated on-scene wind information

    was collected and factored into the analysis.

         1500 Computer maps and printouts of trajectory analysis estimates

    were forwarded to the U.S. Coast Guard On-Scene Coordinator (OSC) via

    NOAA's electronic mail system.  These estimates indicated that spills

    that occurred during the initial explosion would move south with no

    landfall anticipated for the initial forecast period.  At this time, the

    N-WS Office in San Francisco presented an updated weather forecast which

    basically extended and corroborated the morning's forecast. This

    forecast information was used to extend and update the computer analysis

    trajectory estimates. At this time, on-scene modeling and crajectory

    analysis support was requested, and Jerry Gait prepared to depart for

    San Francisco with an estimated arrival time on-scene of 2200.

         2210 At this time, the final evening radio contact was made with

    the Farallon Islands to receive the upated on-scene weather.  These

    observations, forecasted wind information received from NWS, and the

    observations from the offshore data buovs were used to updace the

    trajectory analysis forecast.

                                       7-48

    October 31, 1984, cont.

         2300  Jerry Galt arrived at the U.S. Coast Guard Marine Safety

    Office (MSO) in Oakland, obtained a briefing of the current situation

    for the Puerto Rican, and presented MASS's latest trajectory analysis

    estimates that had been prepared by Debra Payton and Glen Watabayashi in

    :he Seattle office.  These estimates indicatad :hac am- pocancfai sni`ls

    would have a southerly and slightly onshore movement, but that no

    landfall would be anticipated during a two-day forecast period.

    November 1, 1984

         0800 On-scene MASS personnel forwarded the morning's briefing to

    the MASS group in Seattle, and provided an updated position of

    37"21.1'N, 122�56.9'W for continuous trajectory analysis. This position

    put the ship at the outer edge of the submarine operation area, some 20

    miles south of the Farallon Islands.  A4SS recommended that the Puerto

    Rican remain in this area or move slightly cifshore, but that it should

    not return north, or closer, to the Farallonl Islands.  This

    recommendation was based on the persistent northerly winds and the fact

    that the advance of the Davidson Current had been stalled south of the

    ship's location.  Wind forecasts predicted weak or variable winds, with

    occasional stronger winds from the north or northwest. In addition, the

    ship was positioned over the mouth of a large submarine canyon, and thus

    over particularly deep water. This meant that if the ship were to sink

    at its present location, it would be below the region of trawl

    fisheries, and it would also provide some additional protection for the

    Farallon Islands because currents in the region tended :o follow

    bathymetric contours.  Taus, northerlv or southerly flcw would follow

                                      7-49

    November 1, 1984, cont.

    the slope of thle submarine canyon and tend to be offshore, regardless in

    which alongshore direction the flow of the spill initially moved.

         All of these arguments indicated that the present ship position

    would optimize spill threats, or sunken vessel threats, to 1)  the

    .arallon Islands; 2) the mainland coast area of :he San 7rancisco 3ay

    region; and 3) the fisheries potential of the shelf region.

         0910  MASS personnel in Seattle were requested to put in additional

    digitized computer maps to cover the larger area of the extended San

    Francisco Bay Bight region.

         Throughout the day of November 1, additional wind information was

    gathered from the NWS office in San Francisco, and from direct contact

    with observers on the Farallon Islands.  This data and on-scene reports

    were used to provide continual updates for trajectory estimates, and

    these were forwarded to the U.S. Coast Guard through the-SSC.  These

    trajectory analysis results indicated a southerly flow for any spilled

    pollutants during this period. In addition, on-scene MASS personnel

     worked with other HMRB personnel to gather background information to

    factor into planning for the possible use of chemical dispersants if a

    major oil release occurred.

         1700  A general debriefing meeting was held at the U.S. Coast Guard

    MSO in Oakland, and at that time, MASS recommended that the present

    position of the ship should be maintained in the outer segment of :he

    submarine operation area, over the submarine canyon. Thlis

    recommendation was mutually agreed upon between everyone participating

    in the meeting, including :he U. S. Coast Guard and 'TOAA scientrflc

    support personnel.

                                     7-50

    November 2, 1984

         During the morning, estimates were made of the area covered by

    spilled oil from the previous day.  This information was incorporated

    into the possible dispersant use plan. MASS and U.S. Coast Guard

    personnel participated in an overflight of the ship's site, and mapped

    the position of spilled oil.   n add.i-on, '-L4SS perlormed iffaranr:ai

    oil-water velocity movements for the yellow, or light brown, component

    of the spilled oil (tentatively identified as lube oil additives).

    These measurements indicated that the radiation wave pressure on this

    component of the oil was minimal, and, as a result this fraction of the

    spilled cargo may have anomalous results with regard to typical

    hydrocarbon spills. In particular, there should be less of a tendency

    for wind drift to affect the movement of this component, and there would

    be less of a tendency for this material to beach under the influence of

    wind.

         Throughout the day, continual weather information was gathered from

    both the Farallon Islands and NWS sources. Trajectory analyses were

    continued, and hindcasts were made to comoare co the previous day's

    sightings, and to upgrade and correct the hydrodynamic flow model

    estimates. All of the observations and trajectory analysis information,

    combined with available wind data, indicated the presence of southerly

    flowing currents in the area of the ship and the spilled material from

    the previous day. This information also indicated the absence at that

    time of the advancing Davidson Current front for the same area.

    November 3, 1984

         0100   On-scene !4ASS personnel vera informed that :he shiD had

    broken in tcwo, and that three floating pieces had been identified.  :n

                                      7-51

    November 3, 1984, cont.

    addition, during the night the ship had been moved off-station and was

    north of the recommended area over the submarine canyon. Personnel in

    Seattle were immediately alerted to begin work on trajectory analysis

    updates, and both Seattle and on-scene MSS personnel worked on updacing

    the computer informacion and trajecror- inalysis estimatas.  'hese

    estimates were routinely passed on to the U.S. Coast Guard via the SSC.

         During the morning of November 3, oil was reported offshore of the

    Golden Gate region, and during a morning overflight by U.S. Coast Guard

    and MASS personnel, these locations were checked and it was dtermeined

    that no oil was present in these areas. During the overflight, a

    detailed map of the spilled oil was made with the distribution of oil

    plotted from both the sunken stern section and the bow section which was

    under tow.

         Throughout the day, continual weather updates and trajectory

    analysis results were presented, and indicated that the spilled oil was

    predicted to move nearer to shore and to the south over the next two-day

    period.

    November 4, 1984

         Overflights and mapping of the oil continued with U.S. Coast Guard

    personnel and members of the NOAA scientific support team. Trajectory

    analysis results were presented at a press briefing with an estimate

     hat the oil would continue to move east during the day, and then south.

    Throughout the forecast period, the oil was not expected =o make anv

    landfalls.  On the afternoon of November 4, Jerer Gait, the on-scene

    MASS group member, returned to Seactte.

                                     7-52

    November 5, 1984

         Throughout the day, overflight information, trajectory analysis

    estimates, and additional weather information were used to forecast oil

    movement. A continued southerly path was predicted, and observations

    indicated that the oil was in fact moving south and slightly offshore.

         During :he morning of November 5, on-scene rports or  il on                   3

    Montaro Beach were investigated and determined to be negative sightings.

    Also on the morning of November 5, strong northwesterly winds were

    reported, with the afternoon forecast indicating a shift to strong winds

    from the south. Trajectory analysis results anticipated that the strong

    southerly winds would slow down the south-flowing currents with the

    possibility of causing a weak current reversal. Trajectory analysis

    estimates based on this data indicated that the oil could stop its

    southerly motion and could possibly move slightly north.

    November 6, 1984

         On the morning of November 6, oil was reported around the Farallon

    Islands, some 20 miles north of where it was reported the previous

    evening.  This indicated that a current reversal and extremely strong

    current event had occurred during the night of November 5-6. This had

    the effect of moving a major portion of the spilled oil to the vicinity

    of the Farallon Islands and into the San Francisco Bight area itself.

    By the afternoon of November 6, the oil had moved beyond the southern

    Farallon Islands and into the region inshore from the Farallon Islands

    where the current patterns were dominated by intermediate-si:ed eddies,

    and :he complex nature of the flow made it extramely difficuit to

    predict detailed motion.

                                     7-53

    November 7-November 9, 1984

         During this period, a weak northerly flow was present throughout

    the San Francisco Bight region, and the floating oil between the

    Farallon Islands and Pt. Reyes was subject to a weak and complex current

    pattern that moved it generally north and northwest up around the Pt.

    Reyes region.  The major components of the floaing pollutant appeared

    to be the lube oil additive which had previously been observed to have a

    minimal wind drift factor.  This material also showed a reduced tendencyv

    to come ashore, and as such, the shoreline impacts were considerably

    less than could have been anticipated from a heavier form of

    hydrocarbon.  During this period, Jerry Galt returned on-scene to the

    U.S. Coast Guard MSO, San Francisco.

    November 10-November 11, 1984

         After the floating oil residual from the spill moved north around

    Pt. Reyes, it continued to move north under the influence of the wind

    and the advancing Davidson Current.  Once beyond the Farallon Islands

    and Pt. Reyes region, this current formed a more persistent flow and the

    remainder of the oil moved north past Bodega Bay, where some oil -vent

    ashore, on to the Russian River area, and eventually off Pt. Arenas.

    During this period, trajectory analysis once again was able to indicate

    the expected movement of the oil and the slight offshore tendency

    associated with the convergence seen along the advancing Davidson

    Current front.

    After November !2. 1984

        After the initial major spill was tracked to the ?'. Arenas reg�on,

    only occasional trajectory anal-sis support -was requested for

    intcerittent spills that were released from the sunken stern sec:tion

                                      7-54

    After November 12, 1984, cont.

    south of the Farallon Islands. Each of these relatively small releases

    were subject to a weak northerly flow that tended to follow isobathic,

    or constant depth, contours. These current movements and local wind

    effects in each case tended to move the spilled oil to the northwest for

    several miles before it was dissipated to :he point -here  -; was not

    recognizable as an observable surface sheen, and was below the level of

    concentration where it could be cleaned up.

    OCEANOGRAPHIC PROCESSES AFFECTING TRAJECTORY ANALYSIS ESTIMATES DURING
    THE PUERTO RICAN SPILL EVENT

         The California coastal current system is notoriously complex and

    has been the subject of intense study for many years. Particular

    long-term studies have been carried out by Scripps Institute of

    Oceanography and the California Coastal Fisheries Project. A

    comprehensive review is presented by Dr. Hickey in Progress of

    Oceanogravhv, Volume 8, published by Pergamon Press.  SMore recent

    detailed data has been available from various satellite imagery projects

    and National Ocean Survey and National Weather Service investigations.

         In a general sense, the coastal currents off San Francisco can be

    described as falling into one of two general regimes. The first regime

    is southerly flow, or the so-called "upwelling" period which is

    typically present during summer months. The second regime is northerly

    flow, or the so-called "Davidson Current" which is typically present

    during the late fall and winter.  Based on this simpliscic view of the

    offshore currents, one might conclude that during the Puerto Rican spill

    event, a persistent northerly trajectorv would be expected.  During the

    first few hours of the ?uerto Rican spill event, it was established that

                                      7-55

    OCEANOGRAPHIC PROCESSES, cont.

    this was not a typical or climatologically average fall period, and that

    the Davidson Current had not vet established itself as dominant in the

    San Francisco Bight area. Rather than a dominant Davidson Current

    period, it appeared that the frontal advance of the Davidson Current was

    slowly working izs way up the coast and was .siuared somewhere between

    the Monterey Bay area and the San Francisco Bay region as of October 31.

         In coastal regions where a seasonal reversal of the currents is

    present, it is typical that the reversal does not occur simultaneously

    along the entire coast. Mfore commonly, the current reversal works its

    way up the coast as a progressive front. Behind this front, currents

    will be in one direction; ahead of the front, currents will be in the

    other direction.  The progress of this front does not move uniformly; it

    will typically be affected by the local winds and can be seen to advance

    under favorable wind conditions, stop or stagnate during contrary winds,

    and under extreme wind events, may actually reverse and move back down

    the coast in the opposite direction of its advance for short periods.

    This was the situation that was anticipated for the Puerto Rican spill

    period. It was assumed that the Davidson Current front would continue

    to move north, and at some stage the south-flowing regime in the Bight

    area would stop and then show a reversal with possibly some period of

    ambiguity and oscillatory currents as the front passed through the San

    Francisco Bight region. Based on this conception, the computer

    analysis, the observational data that was available, and the theoretical

    descriptions of the seasonal nature of the currents, :rajectory analysis

    correctly described the pollutant movement -rom the initial accident on

    October 31 through the evening of November ;.

                                     7-56

    OCEANOGRAPHIC PROCESSES, cont.

         During the late afternoon and evening of November 5, an ex:r-emei-

    rapid northerly movement of the pollutant indicated the passage of the

    Davidson Current front as a very energetic event which is atypical of

    the vast majority of the available observations for this type o frontal

    passage.  To tCr- and explain this unusually energetic fr-ontal system, a

    number of factors must be considered.  First of all, there were strong

    sustained winds during the week previous to the frontal passage in the

    region of the Farallon Islands. These winds appeared to actually retard

    the advance of the Davidson front more than usual.  In addition, the

    northerly and northwesterly winds tended to actually move water out of

    the San Francisco Bight region and the shelf area just south of the

    Farallon Islands.  During the afternoon of November 5, the reversal of

    .the wind direction and the intensity of the flow not only caused the

    reversal of the current, but also forced a significant on-shelf movement

    of offshore water. As this water moved onto the shelf and encountered

    the rapid variations in depth along the shelf break, an intense current

    jet was formed. This jet-like phenomenon was a strong and narrow

    current that was present right along the edge of the shelf break, which

    happened to be where the major patch of oil was on the afternoon of

    November S.  The duration of this current was between 12 and 18 hours,

    and it was probably no wider than a few kilometers.  Following this

    et-1like relaxation and readjustment of the frontal movement, the

    Davidson Current advance continued in a more or less predictable manner

    into the San Francisco Bight and Farallon islands region.  From this

    point on, the area was dominated by a relatively weak northerly flow

    throughout the region.

                                                  7-57

                OCEANOGRAPHIC PROCESSES, cont.

                     A complete understanding and description of the relaxation and

                jet-type current that appears to be responsible for the movement of oil

                to the vicinity of the Farallon Islands cannot be obtained.  Some

                theoretical discussions of this type of flow are presented in the

                oceanographic !iteratur3,  ': no detailed observational documentation

                has been available from preazious studies.

                     The effects of one suca jet, however, have been observed during an

                intense 10-month observational program that was carried out during the

                IXTOC 1 well blowout in the Gulf of Mexico. 'At that time, a seasonal

                reversal along the Texas coast was being tracked using a number of

                radio-tracked buoys deployed in a line perpendicular to the shore.

                During this jet relaxation event, one of the buovs in the line was

                displaced some 20 miles over a 12 to 18 hour period during which time

                the remaining buoys in a. line perpendicular to the shore moved only

                slightly in the same direction. This type of phenomenon is extremely

                complex and beyond the ability of hydrodynamic modeling to predict at

                the present time.  Frontal passages are always difficult to predict and

                small-scale rapid movement such as this Jet cannot be properly included

                in any preseutly available forecasting techniques.  Since they are rare,

                this is generally not a problem.

                     Within the San Francisco Bight region itself, the general

                alongshore pattern of the currents is disrupted by the complex geometry7.

                Modeling studies indicated that the Farallon islands'cau.se a major

                disrurcion of :he alongshore flow with the developmenc of strong eddy

                patterns or small-scale :ircualtion features behind and inshore from the

                islands.  Similar small-scale features are seen to the south of Pt.

    I)

                                       7-58

    OCEANOGRAPHIC PROCESSES, cont.

    Reyes. Pollutants spilled in this inner area are expected to generally

    mill around and move slowly in the direction of the dominant current

    regime. Once again, during the first few hours of the spill event, this

    region was recognized and identified as one where decailed trajector-

    analysis would be difficult or impossible.

         As was noticed early in the observational studies of the spill, the

    differential oil-water motion associated with the lube oil additives

    appeared to be much Xess than would typically be expected for

    hydrocarbon products. This meant that the spilled pollutant had a

    tendency to follow the water more closely and not be affected by the

    local winds as much as a normal oil. This had the advantageous effect

    that the oil would not be as likely to go ashore, and this was in fact

    observed as the oil went around the Farallon Islands and later as it

    rounded Pt. Reyes and moved north towards Bodega Bay. This would

    definitely be considered as a positive factor with regard to the

    beaching of the oil. However, it had a disadvantage with regard to the

    dispersive effects that the wind can have on an oil slick.  The patches

    of lube oil were seen to remain together as consistent, coherent patches

    well beyond the period that would normally be expected, and that has

    been observed in many previous spills.

         The movement of the major spilled oil along with the front of the

    Davidson Current had an additional advantageous effec:t. In particular,

    as the Davidson Current moved north it encountered the residual of the

    southerly flowing currents along the coastline.  Where these two

    currents net, the water was forced offshore.  This presented a weaK

    offshore tendency as the flow moved north along the coast, and had :he

                                     7-59

    OCEANOGRAPHIC PROCESSES, cont.

    effect of holding the spilled pollutant somewhat away from the

    coastline, thus reducing potential beach impacts. This effect was

    recognized during the initial trajectory analysis studies and, in fact,

    was considered as one of the factors in initially requesting that the

    ship move south towards this suspected convergence and of-snor  flow

    condition.

    CONCLUSIONS

         The Puerto Rican spill event required continuous spill trajector7

    analysis for a period of about 12 days. Within the first two to three

    hours of notification, the trajectory analysis team located evidence

    that the typical Davidson Current, or winter current regime, was not

    present.  This resulted in correct trajectory estimates which were

    counter to the climatologically-averaged expectations or "conventional

    wisdom" for the region during this time of the year.

         Initial dynamic modeling results also available within the first

    few hours indicated troublesome and uncertain flow regimes inshore from

    the Farallon Islands and south of Pt. Reves.  The advancing front of the

    Davidson Current regime was estimated to be north of the Monterey Bay

    area, and its arrival in the San Francisco Bight region was anticipated,

    but short of actual observations of the frontal passage, no detailed

    theoretical procedures were available to predict its arrival at any

    particular site.  Prior to the frontal arrival (six days into the

    spill), standard trajectory analysis procedures gave basically correct

    descriptions of the direction and speed of spilled pollutant movement.

    The derailed arrival time of the front associated with the Davidson

    Current, and more specifically, the intense nature of the shelf break

                                     7-60

    CONCLUSIONS, cont.

    jet, were not anticipated; thus, for the overnight period of November

    5-6, the trajectory analysis predictions were thus wrong. 3v the

    evening of November 6, the frontal passage of the Davidson Current was

    recognized, modeling procedures were updated, and once again =rajectory

    techniques were :esolving generally  correct  stimatas of oollutant

    movement. This situation then continued throughout the remainder of the

    spill incident.

         Reviewing this performance of the modeling and trajectory analysis

    techniques that were used, we might consider improvements or alternate

    procedures that might have been possible. First of all, we can

    categorically state that the use of climatological data to estimate

    pollutant movement would have been a significant error. In particular,

    this would have led to incorrect results for almost exactly half of the

    spill event duration.  More significantly, this error would have

    occurred during the early part of the spill when the largest amounts of

    undispersed oil were being found in concentrated areas.

         As a second point, we may note chat the early observations of

    southerly flow and the evidence of a stalled Davidson Current front were

    critical for obtaining accurate trajectory estimates during the first

    six days of the spill. An obvious question would be, "'What kind of

    additional observations would be necessary to track the advance of the

    Davidson Current front as it approached the San Francisco Bight region?"

    Some experience during the IXTOC spill and studies of current reversals

    along the Texas coast indicate that frontal regimes can be cracked using

    air-deplyvable current probes and helicopter mapping techniques.  ln

    order to suppor: this kind of activity, two or more specially trained

                                     7-61

    CONCLUSIONS, cont.

    personnel and the logistics support for multiple helicopter overfligh:s

    must be available. With this kind of added support, it might be

    possible to track the advance of the frontal system itself and be able

    to more closely predict the time of the reversal at any particular

    location along the coast.

         As a final point, one might consider what would be necessary to

    observe the intense current jet which was seen along the shelf break

    during the night and morning of November 5-6.  In order to delineate

    this feature, it would be necessary to have in place many hundreds of

    thousands of dollars' worth of experimental.current imagery equipment.

    Under these conditions, it might be possible to actually document the

    details of the jet. One might note, however, that even under optimum

    conditions when this current could be documented, there is still no

    theoretical basis on which to base predictions for a trajectory. The

    only advantage that could be obtained by this kind of data is that its

    passage and currents could be documented.  There would still be no -wa,'r

    to incorporate this into ant a priori forecast.

         It is hoped that this chronology and discussion will provide useful

    information and details for the ongoing review and critique of the

    response efforts associated with the Puerto Rican oil spil.

    R47
    1986