[From the U.S. Government Printing Office, www.gpo.gov]
State of Alaska Division of Governmental Coordination Assessment of Stormwater Controls in Coastal Alaska June 1995 6575D MONTGOMERY WATSON .W38 1995 Assessment of Stormwater Controls in Coastal Alaska Prepared for: State of Alaska Division of Governmental Coordination U. S. DEPARTMENT OF COMMERCE NOAA COASTAL SERVICES CENTER 2234 SOUTH HOBSON AVENUE CHARLESTON, SC 29405-2413 Prepared by: Montgomery Watson 4100 Spenard Road Anchorage, Alaska 99517 Property of CSC Library June 1995 U ~TABLE OF CONTENTS I ~~~1.0 Executive Summary ....... .....I..........................1-1 1.1I Background and Objectives...................................1-1 1.2 Projections of TSS loadings ..................................1-1 1. 3 Best Management Practices...................................1-2 1.4 Costs and Economic Impact ..................................1-2 2.0 Introduction ...............................................2-1 2.1 Study Objectives.........................................2-1 2.2 Background............................................2-3 I ~~3.0 Baseline Conditions of Indicator Municipalities............................3-1 3.1 Introduction ............................................3-1 3.1.1 Typical Year ......................................3-1 I ~~~~~~3.1.2 Rainfall .........................................3-1 3.1.3 Runoff ..........................................3-2 3.1.3.1 Rainfall Runoff ..............................3-2 I ~~~~~~~~3.1.3.2 Snowmelt Runoff.............................3-3 3.1.4 TSS Loadings......................................3-4 3.1.4.1 Pre-Development TSS Loadings ....................3-5 3.1.4.2 Post-Development TSS Loadings....................3-6 3.1.5 Summary of Derivation Methods ..........................3-7 3.1.6 Land Development Scenarios.............................3-8 3.2 Anchorage.............................................3-9 3.2.1 Rainfall .........................................3-9 3.2.2 Runoff.........................................3-10 3.2.3 Soils and Drainage Conditions...........................3-10 3.2.4 TSS ..........................................3-10 3.2.5 Expected Site Development Types.........................3-10 3.2.6 Typical Year .....................................3-11 3.2.7 LocaliRegulations ..................................3-13 3 ~~~~3.3 Bethel...............................................3-13 3.3.1 Rainfall ........................................3-13 3.3.2 Runoff.........................................3-14 3 ~~~~~3.3.3 Soils and Drainage Conditions...........................3-14 3.3.4 TSS ..........................................3-14 3.3.5 Expected Site Development Types.........................3-14 I ~~~~~3.3.6 Typical Year .....................................3-15 3.3.7 Local Storm Drainage Regulations.........................3-17 3.4 Juneau ..............................................3-17 I ~~~~~3.4.1 Rainfall.......................I............... .3-17 3.4.2 Runoff.........................................3-18 3.4.3 Soils..........................................3-18 3.4.4 TSS ..........................................3-18 Siormwater Controls in Coastal Alaska ,~page i IJune, 1995 3.4.5 Expected Site Development Types ...............................................3-19 3.4.6 Typical Year ........................................................................3-19 3.4.7 Local Storm Drainage Regulations ...............................................3-21 3.5 Local Economic Conditions ................................................................3-21 4.0 Management Practices ................................................................................4-1 4.1 Survey of Applicable Best Management Practices ..........................................4-1 4.2 Type of Development and BMP for Each Land Use .......................................4-8 5.0 Cost Estimates ........................................................................................5-1 5.1 Design Considerations for Selected BMP Construction and Maintenance ..............5-1 5.2 Cost Estimate for Selected BMPS ...........................................................5-2 5.3 Measures of Economic Impact ...............................................................5-2 6.0 Conclusions ...........................................................................................6-1 7.0 References .............................................................................................7-1 Stormwater Controls in Coastal Alaska page ii June, 1995 LIST OF FIGURES 1 Location Map ..........................................................................................2-2 2 Anchorage Mean Monthly Precipitation Distribution - 1923-1984 and 1991 ..................3-9 3 Anchorage Monthly Rainfall-Runoff Distribution for Typical Year ..........................3-11 4 Cumulative Pollutograph for Anchorage for Typical Year .....................................3-12 5 Bethel Mean Monthly Precipitation Distribution - 1923-1984 and 1991 .....................3-13 6 Bethel Monthly Rainfall-Runoff Distribution for Typical Year ...............................3-15 7 Cumulative Pollutograph for Bethel for Typical Year ..........................................3-16 8 Juneau Mean Monthly Precipitation Distribution - 1949-1984 and 1987 ....................3-18 9 Juneau Monthly Rainfall-Runoff Distribution for Typical Year ...............................3-19 10 Cumulative Pollutograph for Juneau for Typical Year .........................................3-20 1E1 Particle Size Distribution Analyses for Suspended Sediment in Stormwater ................4-10 LIST OF TABLES I Summary of Derivation Methods for Runoff and TSS Loadings ...............................3-8 2 Hydrologic Characteristics of Each Land Development Scenario for Anchorage ...........3-12 3 Hydrologic Characteristics of Each Land Development Scenario for Bethel ................3-16 4 Hydrologic Characteristics of Each Land Development Scenario for Juneau ...............3-21 5 Economic Features of Indicator Municipalities ..................................................3-22 6 Non-structural Best Management Practices ........................................................4-3 7 Structural Best Management Practices .............................................................4-6 8 Summary of Target TSS Removal Percentages .................................................4-11 9 Summary Pond Sizes ................................................................................5-1 i 10 Estimated Stormwater Control Costs ...............................................................5-2 11 Measures of Economic Impact ......................................................................5-3 12 Unit Costs for Stormwater Controls ...............................................................5-4 APPENDICES Appendix A Runoff and TSS Calculations Appendix B Cost Estimates Stormwater Controls in Coastal Alaska J page iii June, 1995 1.0 EXECUTIVE SUMMARY 1.1 BACKGROUND AND OBJECTIVES The US Environmental Protection Agency (EPA) has established "Management Measures" for control of Nonpoint Pollution in the Coastal Zone, in conjunction with the National Oceanic and Atmospheric Administration (NOAA), the agency responsible for regulations of the Coastal Zone Management Act. The Management Measures have been devised for a variety of land development activities, including resource extraction, roadways, and urban development. Management Measures cover a variety of pollutants. Of particular note is the requirement to control Total Suspended Solids (TSS) in community development. Specifically, the Management Measure calls for coastal communities to: (a) Reduce the average annual TSS loadings by 80% after construction has been completed and the site is permanently stabilized; and/or (b) Reduce the postdevelopment loadings of TSS so that the average annual TSS loadings are no greater than pre-development loadings. Previous research by Montgomery Watson on behalf of the Municipality of Anchorage (Montgomery Watson, 1994) suggests that few "best management practices" (BMPs) have documented performance sufficient to reliably meet these measures. This is particularly true where Alaska's sub-arctic and arctic conditions complicate the effectiveness of such practices. Montgomery Watson prepared this assessment of storm water controls for the State of Alaska, Division of Governmental Coordination, Coastal Management Program. The work focuses on Anchorage, Bethel, and Juneau, cities selected to represent the range of conditions typical in Alaskan coastal communities. This assessment has been undertaken to accomplish several objectives, as follows: Quantify annual pre-development and post-development loadings of TSS � Determine target load reductions to meet the management measures * Determine appropriate best management practices * Estimate costs to implement BMP's * Determine the economic impacts of such costs 1.2 PROJECTIONS OF TSS LOADINGS Development scenarios were derived for each city, on scales ranging from 5 acre residential development to 20 acre industrial development. Total annual combined rainfall and snowmelt runoff in Anchorage was estimated to range from less than 1.4 inches before development to approximately 10 inches for commercial development. Similar ranges were 0.27 to 2.52 inches for Stormwater Controls in Coastal Alaska ~ page 1-1 June. 1995 Bethel, and 1.45 to 20.54 inches for Juneau. Typical runoff TSS concentrations were estimated to range from SI mg/L (for Bethel) to 224 mg/L (for Anchorage commercial development).I Loadings were estimated by multiplying TSS concentrations times projected runoff on a daily basis through the year. Estimates of TSS loadings range from 48 to 56 pounds per acre per year for "predevelopment" Anchorage, and 140 to 333 pounds per acre per year after development. Estimates were higher for Juneau, due to more effective mobilization of TSS during runoff, up to over one-half ton of TSS per acre per year for commercial sites after development. BethelI estimates were much lower, due to low intensity rainfall, flat slopes, and well established vegetation. 1.3 BEST MANAGEMENT PRACTICES Maintenance of urban runoff facilities was judged to be the best non-structural BMP for implementation, although costs and benefits were not directly quantifiable. Wet pond type sedimentation basins were judged to be the best structural controls for Anchorage and Juneau. These ponds are impractical for Bethel due to permafrost and shallow groundwater. Vegetative slope protection for embankments appears to provide the best pollution prevention function in low lying tundra areas, although the effectiveness has not been reliably quantified.5 Sedimentation ponds are not viewed as effective in capturing fine particulates (<10 microns effective diameter) from runoff. This fraction of TSS typically accounts for more than 20% of theI TSS load in Alaska's low intensity storms. Therefore, it was concluded that the 80% removal management measure is not attainable even with the BMP judged most cost effective for Alaska's commnunities. 1A COSTS AND ECONOMIC IMPACT In most instances, reduction in loadings to predevelopment conditions was judged to be less stringent than the 80% reduction level. Costs were estimated for 3 Anchorage and 2 Juneau development scenarios based on minimum sizing criteria for effective sedimentation pond development. Annual costs for sedimentation ponds range from $490 per developed industrial acre to over SI1640 per developed residential acre. This represents approximately 0.5 to 0.75 % of the annual cost of an industrial or commercial enterprise, or nearly 5% of annual household income for a residence.3 Another measure is on the basis of total cost per pound of pollutant removed. For a twenty acre industrial development, this can be as low as $3.00 per pound of TSS. Smaller commercial and3 residential developments are limited b~y sizing criteria, forcing costs up to as much as $26.00 per pound of TSS for a 5 acre residential development in Anchorage. Stormwater Controls in Coastal Alaska 0 page 1-2I June, 199S 2.0 INTRODUCTION 2.1 STUDY OBJECTIVES The purpose of this study is to determine the costs of stormwater quality controls to meet federal management measures for the reduction of suspended sediments from new urban development. Suspended sediment from stormwater runoff in urban areas constitute the largest mass of pollutant loading to surface waters. NOAA and EPA have established management measures for total suspended sediment (TSS) for new development in urban areas. The goal of this report is to present an economic analysis of TSS controls for stormwater in coastal Alaska consistent with EPA guidelines and to provide useful information to Alaskan communities for management of TSS in urban stormwater. Objectives of the study: * Quantify TSS pre and post development loadings a Determine target TSS load reductions for two specified management measures: - 80% removal - removal to predevelopment conditions Determine appropriate best management practices (BMPs) to meet both management measures and to meet current local stormwater quality standard a Estimate the costs to implement appropriate BMP o Determine the economic impacts of these costs Each objective is carried out for each of three municipalities, Juneau, Anchorage, and Bethel for new development. The communities are located on the map in Figure 1. New development is characterized by three scenarios for each municipality: residential, commercial, and industrial land use. For each scenario, one structural BMP was to be chosen for each of the two TSS reduction goals. Although this study describes non-structural controls for TSS, there is not enough data to determine if the controls are sufficient to meet the management measures for new development or to estimate -the costs associated with them, especially if they are implemented on a site-specific basis. Stormwater Controls in Coastal Alaska 3 page 2-1 June, 1995 (C~~~~~~~~~~~~~~ 4-~~~~~~~~~~~~~~~~~~~~~~~~~~~~s A:-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~P * I *~~~~~~~~~-'- Ovv '1*~~~~~~~~~~~BT ~~~~~~~~~~~~GULFO L , FIGURE I MONTGOMERY WATSON W ~~~Anchorage, Alaska LOCATION MAP 22 BACKGROUND The NOAA and EPA Coastal Nonpoint Pollution Control Program Management Measure for new urban development, which includes urban redevelopment, new or relocated roads, highways and bridges, requires: (1) By design or performance: (a) After construction has been complete and the site is permanently stabilized, reduce the average annual total suspended solid (TSS) loadings by 80 percent. For the purposes of this measure, and 80 percent TSS reduction is to be determined on an average annual basis,* or (b) Reduce the post-development loadings of TSS so that the average annual TSS loadings are no greater than redevelopment loadings, and (2) To the extent practicable, maintain post-development peak runoff rate and average volume at levels that are similar to pre-development levels. * Based on the average annual TSS loadings from all storm less than are equal to the 2- year/24-hour storm. TSS loadings from storms greater than the 2-year/24-hour storm are not expected to be included in the calculation of the average annual TSS loadings." (in Section II. A. New Development Management Measure (EPA, 1993)) These guidelines do not explicitly included snowmelt TSS loading in the calculation for average annual TSS loading. However, they don't explicitly exclude it, either. In order to limit the scope of this study, the following procedure has been adopted. TSS loading from snowmelt is quantified in Section 3 of the report, in order to present a complete picture of the annual TSS loading. The TSS removal of the chosen BMP for snow melt runoff is estimated, but the BMP is not sized to treat snow melt runoff to the (a) and (b) criteria. The BMPs'are selected and sized to meet the (a) and (b) criteria based on their ability to meet treat the annual TSS loading for rainfall events up to the 2-year/24-hour storm (May through September for Anchorage and Bethel; February through October for Juneau). Stormwater Controls in Coastal Alaska 0 page 2-3 June, 1995 ~3.0 BASELINE CONDITIONS OF INDICATOR MUNICIPALITIES3 3.1 INTRODUCTIONI The purpose of this section is to define the hydrologic and TSS loading conditions in each indicator municipality. These conditions will provide the bases for BMP selection and cost analyses in sections 4 and S. TSS loadings for urban basins are caused by runoff events. Runoff events, in turn, are caused by rainfall and by snowmelt. Annual timing and amounts of runoff and TSS loading are variable because of the influence of local meteorological conditions. In the following sub-sections, the rainfall, runoff, soils conditions and TSS loadings are described3 in general and then in particular for each municipality. Local drainage conditions are described and scenarios are developed that characterize expected site development sizes and conditions for the three land use categories (residential, commercial and industrial). The typical year's runoff and3 TSS loads for each scenario are quantified. finally, local stormwater quality regulations for each community are discussed and a summary of local economic conditions is presented. 3..1 Typical Year In order to obtain annual TSS loadings, a "typical" year, in terms of precipitation, was identified3 from available weather service records for each municipality. A daily runoff rate was estimated based on the daily rainfall or snowmelt and, from these runoff rates, daily TSS loadings were generated. Because of the variability of precipitation events and the short record period of readilyI available data, the "typical" year may vary considerably in individual months from the long term record. In spite of this discrepancy, the use of actual rainfall records was assumed to be more representative of actual conditions than a simulated series would have been. The typical year forI Juneau and Bethel were determined by analysis of annual climatological summaries for years with complete records during the period 1980 through 1993. A typical year for Anchorage was suggested by the Municipality of Anchorage (MOA). 3.1.2 Rainfall3 Rainfall events greater than 0.1I inches were identified in the rainfall records for the typical year. For all three municipalities, no daily rainfall in the chosen typical year exceeded the 2-year 24.-hour event determined for the location by the U.S. Weather Bureau in Technical Paper 47 (TP 47) (Miller, 1963). Professional experience in Alaska has found that TP 47 consistently overestimates rainfall intensities for any recurrence interval. As a consequence, the use of this document often leads to an overestimate of the number of rainfall-runoff events. This will consequently lead to an overestimation of the TSS loadings for rainfall events that would be subject to management measures. Before management measures are implemented, a more refined estimation of the 2-year 24-hour event should be made for specific localities.I Storm water Controls in Coastal Alaska Z1 page 3-1 June, 1995 3.1.3 Runoff TSS loadings from urban basins is mobilized from the ground by runoff events. Coastal Alaska's runoff events fall in three general categories: summer/fall rainfall events, winter thaws, and spring snow melt. 3.1.3.1 Rainfall Runoff Runoff due to rainfall is influenced by a number of factors, the primary ones being the soil types and percent imperviousness of the site, rainfall intensity, and antecedent moisture conditions. In developing a rainfall-runoff relationship, site specific data is the most reliable. For ungaged locations, other methods have been developed. For Anchorage, some site specific rainfall-runoff data was available for developed urban basins. An equation, developed by the USGS (Brabets, 1987) based on data from three basins in the Anchorage area, was used to model the rainfall-runoff relationship in the Anchorage area. The equation has the following form: VOL = 0.39 * (RF)1-10 (DA)0.14 (PEIA)0.38 (1) where VOL is volume of runoff, in inches RF is total storm rainfall, in inches DA is drainage area in acres PEIA is percent effective impervious area This equation has been calibrated for basins of less than 38 acres that have effective imperviousness less than 70%, for storm rainfall events that are less than 0.5 inches. Because this equation was calibrated for Anchorage, it was used to determine rainfall runoff for Anchorage only. For Bethel and Juneau, no site specific data was available. For these two municipalities, the method described in the USDA Soil Conservation Service's (SCS) Technical Release 55 (TR-55) was used to estimate runoff response to rainfall. TR-55 presents a simplified procedure to calculate storm runoff volume and is applicable to small urbanizing watersheds. This method estimates the runoff volume for a 24 hour storm event, based on two parameters: a factor, or curve number (CN), that reflects the soil type and imperviousness of the site, and the depth of rainfall. (P-.2*S)2 Q-(P+.8*S) where P=rainfall in inches Q=runoff in inches CN I S~~~~~10o010 There are limitations on the use of this equation; both with respect to precipitation and the CN. Stormwater Controls in Coastal Alaska C page 3-2 June, 1995 SCS suggests that this equation is less accurate when runoff is less than 0.50 inches. This is the case particularly in Bethel, and for a majority of the rainfall events in Juneau. The TR-55 method predicts lower flows than does another standard method, the Rational method. The Rational method, which predicts flow as the product of rainfall, basin area, and percent impervious, wasI developed to estimate peak flows (Sheaffer, 1982). It was not developed for the study of runoff volume, but approximations can be made by dividing the flow by the basin area. However, it was used here to serve as a check on the results from the TR-55 method. The TR 55 method accountsI for two factors that the Rational method does not: antecedent moisture conditions and initial abstraction. Consideration of these factors tends to more fairly represent actual conditions than does the Rational method. The SCS has mapped soils throughout the lower 48 United States and developed a system of soil types, ranked A through D, that relate to the CN in this equation. A review of the soil surveys of the Juneau and Bethel areas was made. The soil types in these areas have not been classified within this system. CN numbers were estimated, based on soils descriptions and their distributions in the developable areas. The CN is site specific and will vary from location to location within theI municipality. This is especially true in Juneau; Bethel area soils are more homogenous. The soil type variability within the Juneau area will cause site specific runoff to be more variable than in Bethel. Antecedent moisture conditions are taken into account by assigning a higher CN; the higher CN is prescribed by the SCS and based on the CN for average conditions. Despite these limitations regarding precipitation and CN values, we felt that TR-55 was the bestI available method to estimate the runoff from rainfall events. These limitations should be kept in mind, and the results from this method taken as relative rather than absolute values. 3.1.3.2 Snowmelt Runoff Snow melt runoff is variable from year to year. Within a year, snow melt is highly variable in duration and volume. The length of the snow melt period varies, depending on daily and hourly temperatures, wind speed and direction, and the amount of snow on the ground. Although the3 amount of snow on the ground may influence the length of the snow melt period, it is not directly correlated to the amount of runoff, either over the snow melt period or on a given day, because of infiltration. If the ground beneath the snow is frozen, the amount of runoff will be greater. If freezing temperatures precede snow fall in the fall, the ground will freeze and stay frozen through the winter. Under these conditions, snow melt runs off rather than infiltrates, because the ground thaws after the snow melt. These factors influence snow melt runoff in each of the indicatorI communities to a different extent. Snow melt runoff data was available for five urban basins in the Anchorage area, but none wasI available for Juneau or Bethel. The data for Anchorage (Brabets, 1987, and Billman and Bacon, 1990), collected during spring breakup periods, indicate that daily runoff rate lies generally in the range of 0.01I to 0.20 inches, but is variable from day to day, due to changes in temperatures, windI velocity, insolation, and other heat transfer components. The rate of runoff is also influenced by the amount of impervious area (including frozen ground as well as pavement and buildings), but this relationship has not been quantified. Snowmelt runoff does not occur until the snowpack is Stormwater Controls in Coastal Alaska -- page 3-3I June, 1995 saturated. Saturation, or snow pack ripening, is generated by melting snow or rain trickling through the snowpack. Ripening may take a week or more, depending on the initial condition of the snowpack and the rate of snowmelt. Rainfall on a snow pack will accelerate the ripening process. Since the day-to-day variability in temperatures during spring breakup is similar in all three municipalities, runoff rates for a specific series of days can be reasonably approximated using Anchorage data. A sequence of daily snow melt rates was derived from the Anchorage data, using a 30% impervious residential area, and applied to the land development scenarios for Anchorage, Bethel and Juneau. The length of the breakup period was determined by a combination of daily average temperatures above 32� F and the daily snow on the ground record for Bethel and Juneau. Both sets of snowmelt data (Billman and Bacon, 1990, and Brabets, 1987) showed an increase in snowmelt runoff from developed areas with higher imperviousness. A factor was applied to the assumed snowmelt rate from the 30% impervious area to account for this increase. This results in an equation of the form: VOL = VOL30 *(1 + (PEIA - 30)*.03) (3) where VOL = runoff, inches VOL30 = runoff from 30% impervious site, inches PEIA = percent effective impervious area, expressed as a percent This adjustment factor was based on basins varying from 30% to 70% impervious. Use of the factor for areas with imperviousness greater than 70% may overestimate the runoff; and for areas with less than 30% imperviousness, it may tend to underestimate the runoff. Days of snow melt for winter months were defined based on the number of days the maximum temperature exceeded 32� Fahrenheit. No data were available for runoff from winter thaw events; but the initial spring snowmelt may be comparable to winter thaws. During the early part of the spring snowmelt, flow rates are in the range of 0.01 to 0.04 inches. These values were estimated from 1988 data (Billman and Bacon, 1990). Therefore, a constant snow melt rate was assumed on winter thaw days. Some winters may have extremely warm periods, causing greater snow melt runoff than this assumption covers, leading to an underestimation of snow melt. Conversely, thaw days with no runoff may also occur if there is little or no snowpack, and the constant rate assumption would overestimate runoff in that case. 3.1.4 TSS Loadings TSS data is sparse in these areas of Alaska. Where it has been collected, it has rarely been correlated to antecedent rainfall conditions or to basin area. No daily data is available for an entire year at one site. The TSS data is most often collected in streams, which are not representative of developed conditions. Where it has been collected, sampling has occurred in the summer, or rainfall, months. Winter thaws and spring snow melt data are very limited. TSS sampling data is expressed as a concentration of suspended particles per unit volume of water, generally, milligrams per liter (mg/l). TSS loadings represent the mass of suspended particles, Stormwater Controls in Coastal Alaska Z page 3-4 June, 1995 I generally represented by pounds per day or pounds per year. TSS loadings are obtained by multiplying the TSS concentration times the flow (times appropriate conversions factors for disparate units). Thus, a low flow with a high concentration can yield a similar load to a high flow with a low concentration. 3.1.4.1 Pre-Development TSS Loadings Pre-development conditions in the three indicator municipalities span the spectrum from bare ground to natural undisturbed vegetation. The guidance manual specifying the New Development Management Measure (EPA, 1993) describes pre-development it as follows: "...the term pre-development refers to the sediment loadings and runoff volumes/velocities that exist onsite immediately before the planned land disturbance and development activities occur. Predevelopment is not intended to be interpreted as that period before any human- induced land disturbance activity has occurred." It goes on to say that %... management measure option II.A.(1)(b) is not intended to be used as alternative to achieving an adequate level of control in cases where high sediment loadings are the result of poor management of developed sites e.g. ... sites where land disturbed by previous development was not permanently stabilized." From this, it appears that management measure II.A.(1)(a), the 80% removal measure, is applicable to bare or unstabilized sites and that management measure II.A.(1)(b) is more likely to be applied to sites that were stabilized or are in a naturally vegetated state before development. Therefore, pre-development TSS was estimated for natural or stabilized sites only. TSS loadings for undeveloped conditions with natural vegetative cover were based on the Universal Soil Loss Equation (USLE). This equation takes the form: A R x K x LS x C x P (4) where A = soil loss, tons/(acre)(year) R = rainfall erosion index, in 100 ft - tons/acre x in/hr K = soil erodibility factor, tons/acre per unit of R LS = slope length and steepness factor, dimensionless C = vegetative cover factor, dimensionless P = erosion control practice factor, dimensionless This method was originally developed to estimate the annual sediment yield from small cropland areas. It calculates annual soil loss in tons per acre, based on rainfall, soil erodibility, site slope and length, and cover and erosion control practices. Because this method is empirical and the parameters have been calibrated for agricultural conditions in the lower 48 United States, this method is not directly applicable for developed urban areas in Alaska. It is somewhat applicable for the "pre-developed" condition, assuming the effects of natural vegetation on soil loss in these Stormwater Controls in Coastal Alaska ~2 page 3-5 June, 1995 indicator municipalities is similar to effects in the lower 48 states. Another drawback of the USLE is that it does not differentiate soil losses attributable to rainfall from those due to snow melt I ~ ~runoff. Since the equation is being used to estimate the annual load from soils with natural vegetative cover, it is reasonable to assume that snowmelt would not cause soil loss. Thus, the loads predicted by the USLE in this application represent pre-development TSS from rainfall events only but could reasonably approximate annual loads as well. This equation does not predict TSS concentrations or daily loads. 3.1.4.2 Post-Development TSS Loadings TSS data from urban rainfall and snow melt runoff has been collected in the Anchorage area, but not for the same basins. This data were used to generate two relationships; one for rainfall and one for snowmelt. The rainfall-runoff-TSS load relationship is based on a regression equation using the parameters of runoff, drainage area, and percent effective imperviousness as independent variables. The snowmelt-TSS loading relationship uses consecutive thaw day as the independent variable. The relationship between stormwater runoff and TSS concentrations is based on data from three urban basins in Anchorage and shows two distinct patterns. The first pattern is an initial peak of 3 ~~sediment concentration at the beginning of the storm and then a rapid decrease. The other pattern shows sediment concentrations following the fluctuations of the storm's runoff. These patterns I ~reflect two TSS mobilization mechanisms. An initially high intensity storm mobilizes loose sediment readily. This observation follows from the USLE theory. A low intensity storm mobilizes sediment at a lower but more constant rate as the sediments are wetted and loosened over the course of the storm. It is reasonable to assume that the high intensity storm mobilizes particles Iof larger diameter, but it is not known whether the distribution of particle size in the TSS between the two storm types is significantly different. IRecognizing these limitations, a relationship was established between total storm runoff and TSS load. Regression techniques applied to data from these three basins were used to calibrate an equation that calculates estimated TSS loads based on the runoff volume, drainage area, and percent of effective imperviousness for a given basin (Brabets, 1987). The equation is of the form: SSED = 42.6 * (VOL)(-90 (DA) I 0 I (pEIA)0.71 (5) where SSED is suspended sediment load, in pounds VOL is volume of runoff, in inches DA is drainage area in acres PEIA is percent effective impervious area This equation is considered to have a high standard error of estimation. However, it is used here, where no other information is available. It has been calibrated for basins of less than 38 acres that I have effective imperviousness less than 70%, for storm rainfall events that are less than 0.5 inches. I Since rainfall patterns are expected to be quite similar for Anchorage and Bethel, the calibrated equation was used for predicting TSS loads in Bethel. This equation is limited to use on rainfall I Stonmwater Controls in Coastal Ala.ska J page 3-6 June, 1995 events of less than 0.5 inches. Even though this limitation is exceeded in Juneau, the application of this equation led to fairly reasonable TSS loadings for Juneau, so it was used for Juneau as well. There is no data with which to judge the accuracy of these estimates. During snowmelt, mean TSS concentrations are typically higher than for rainfall runoff. Data from Chester Creek (Brabets, 1987) indicates that TSS concentrations in urban snowmelt can be 16% to 400% higher than in rainfall runoff. Spring thaw TSS concentrations for two urban basins showed two concomitant patterns: a diurnal fluctuation and a trend through the snow melt period (Billman and Bacon, 1990). On a daily basis, suspended sediment concentrations peak in the afternoon with peak discharge (Brabets, 1989). Through the month (more or less) of the snow melt period, the daily concentrations are initially quite high and then decrease. Therefore, a relationship between day of snowmelt and runoff was developed based on 1988 data from two basins. It is of the form: VOL = 215 - 5.48(DAY) (6) where VOL= runoff, in DAY = day of snowmelt period The constants in this equation are calibrated to 1988 data only. These constants vary from location to location and year to year, but the downward trend was verified by the Chester Creek data (Brabets, 1987). The relationship between concentration and day of the snowmelt period was assumed to be the same for thaw periods during winter months. The magnitude of the concentrations, however, was assumed to vary over the winter. Because the snowpack tends to accumulate sand and precipitated airborne materials over the course of the winter, TSS concentrations are expected to be highest in the spring and lower during an early winter thaw. Thus, for example, November thaw was assumed to exhibit TSS concentrations similar to those on day 25 of the spring thaw. The concentrations were multiplied times the flow to obtain TSS loads. These snow melt patterns were considered to be similar in all three municipalities, although the magnitudes of concentrations vary. In Bethel where there is little street sanding, the snow melt concentrations were assumed to be half of those in Anchorage. In Juneau, the Anchorage concentrations were used. 3.1.5 Summary of Derivation Methods A summary of the methods used for each location is shown in Table 1. Details regarding the development of the snow melt and rainfall runoff and TSS loading for each community are given in the following descriptions. Stormwater Controls in Coastal Alaska 0 page 3-7 June, 1995 Table 1 Summary of Derivation Methods for Runoff and TSS Loadings Variable Rainfall Spring Breakup Winter Thaw I I Snowmelt Snowmelt Anchorage Runoff Equation (1) Snowmelt runoff rates flat 0.03' rate from Anchorage basins with Equation (3) Pre Development TSS Equation (4) none none Loading Post Development TSS Equation (5) Equation (6) for Equation (6) for Loading concentration; concentration; concentration x flow for concentration x flow for load load Bethel Runoff Equation (2); CNs for D Snowmelt runoff rates flat 0.03" rate soils from Anchorage basins with Equation (3) Pre Development TSS Equation (4) none none Loading Post Development TSS Equation (5) Equation (6) for Equation (6) for Loading concentration; concentration; concentration x flow for concentration x flow for load load Juneau Runoff Equation (2); CNs for C flat 0.03" rate flat 0.03" rate soils Pre Development TSS Equation (4) none none Loading Post Development TSS Equation (5) Equation (6) for Equation (6) for Loading concentration; concentration; concentration x flow for concentration x flow for load load Equation 1 VOL = 0.39 * (RF)1'10 (DA)0'14 (PEIA)0-38 _(P-.2*S)2 Equation 2 Q-(P.*S) Q~(P+.8*S) Equation 3 VOL = VOL.3 *(1 + (PEIA - 30)*.03) Equation4 A=RxKxLSxCxP Equation 5 SSED = 42.6 * (VOL)0-90 (DA)1-01 (PEIA)0-71 Equation 6 VOL = 215 - 5.48(DAY) 3.1.6 Land Development Scenarios The development scenarios outlined for each municipality are those that can reasonably be expected to occur. An implicit assumption is that there is no runoff into these sites that must be treated. It is assumed that the stormwater control practices will be implemented by the developer of the site as part of site development. These construction costs and the annual and periodic maintenance costs will be passed along to the buyers or leaseholders. Although there may be some component of municipal involvement for maintenance, we assumed that the municipality would recoup the cost of this from the property owners. Stormwater Controls in Coastal Alaska D page 3-8 June, 1995 For single family residential development, density was taken as four houses per acre. Of the land available, 90 percent would be used for housing and 10 percent for roads and other infrastructure, not including the stormwater control. Thus for a 5-acre residential development size, 18 houses are expected. Commercial development was assumed to be retail stores. The building size was assumed to be one-third of the impervious area of the site. The other two-thirds would be paved. Industrial development was assumed to be equipment yards and warehouses. The building size was assumed to be one-half of the impervious area of the site. The other one-half would have equipment or covered storage. 3.2 ANCHORAGE 3.21 Rainfall Anchorage precipitation averages 15.3 inches. TP 47 gives the 2-year/24-hour storm for Anchorage as 1.5 inches (Miller, 1963). The Municipality of Anchorage (MOA) uses 0.66 inches for a 2-year/6-hour event. MOA has not established a 24-hour event for any return period. Based on the depth of the 2-year/6 hour storm, however, the 2-year/24 hour storm event would likely be less than 1 inch. The monthly rainfall distribution is shown in Figure 2. This figure shows that the peak precipitation period is in the months of July through September. Rainfall greater than 0.5 inches occurs approximately 5 days a year. Figure 2 Anchorage Mean Monthly Precipitation Distribution - 1923-1984 and 1991 7 - - -- -- - --- - --- - -- - - ----- -------- ------ - -- - -- 0V4 4: ! / /\--C-Mean Monthly Total (in); '--Me,/ an No of days > 0.1 In I ** r /I~ ] I X~~ o,---Mean No of days > 0.5 in ! 1965 Month Total tin) 2 0- - -_ c i - >, = B a, 2i 8 Month Source: Leslie, 1986 and NOAA, 1965 1 Stormwater Controls in Coastal Alaska Q page 3-9 June, 1995 3.2.2 Runoff The Anchorage spring break up period is generally from mid March through mid April. Summer rains occur from the end of April through the middle to end of October. A daily runoff relationship for snow melt and for rainfall was developed for Anchorage, on a depth per unit area basis. The rainfall runoff relationship was developed on Chester Creek by the USGS (Brabets, 1987). The snow melt relationship was based on data from two residential basins and adjusted for percent imperviousness. 3.2.3 Soils and Drainage Conditions Anchorage lies in a gently sloping bowl, although some developable land is located up stream and river valleys. The soils in the Anchorage area are glacial till. Some sites are on gravel or sand where the soils are highly permeable, but the majority of developable sites will be on relatively impermeable soils or near surface bedrock. The developable areas are drained by well defined creeks. 3.2.4 TSS Total suspended solids data has been collected by the United States Geologic Survey (USGS) from 6 creeks in the Anchorage area. Most of the Anchorage area USGS data is based on stream sampling, which includes base flow, and generally represents runoff from several land use categories. One USGS report (Brabets, 1987), however, presents rainfall and snow melt runoff data from one commercial and one residential basin, and some in-stream data from an undeveloped basin. Snow melt data has been collected from two residential basins by the Municipality of Anchorage (Billman and Bacon, 1990). Pre-development TSS loading for Anchorage was based on the Universal Soil loss equation. Post-development TSS loading for Anchorage was based on the TSS-runoff relationship developed by the USGS (Brabets, 1987). 3.25 Expected Site Development Types According to the MOA Department of Community Planning and Development (Weaver, 1995), Anchorage residential development is generally in the 2.5 to 5 acres range; a 40 acre site is considered large. Commercial site sizes are dictated by the amount of parking and percentage of landscaping required. Industrial sites are generally graveled. Assumed land uses and types are as follows: Residential 5 ac 4 houses per acre 38% impervious Commercial 10 ac 123,000 sf retail store 85% impervious Industrial 10 ac 109,000 sf warehouse/office 50% impervious Stormwater Controls in Coastal Alaska D page 3-10 June. 1995 3.2.6 Typical Year The Municipality of Anchorage has identified 1965 as its typical rainfall year (Wheaton, 1995). The snow melt runoff pattern for March and April, 1988, were used to simulate runoff. Winter thaw periods in the months of November through February were based on the number of days that, on a long-term average basis, the maximum daily temperature exceeded 32� F. During the winter thaw days, the number of thaw days per month was reduced by two, to account for the time it would take for the snowpack to ripen before runoff occurs. The rainfall-runoff pattern for Anchorage for the typical rainfall year is shown in Figure 3. Two runoff peaks, one in April and one in August, illustrate the bimodal runoff, from snowmelt and rainfall. Figure 3 Anchorage Monthly Rainfall-Runoff Distribution for Typical Year 4.5I 3.5 __ 3- --- Rainfall 2--- Residential Runbff x" \ /~\ ~ r--Commercial Runoffi i1v".5 ~/~,~ ~i, I ! X Industrial Runoff 0 LLO 'M <.0 0) z.~ month The cumulative TSS loading for the typical Anchorage year is shown in Figure 4. This figure shows the loadings due to runoff from development for each land use category. It also shows the total annual predevelopment load from each of the land use categories on the right side of the graph. Stormwater Controls in Coastal Alaska 'l page 3-11 June, 1995 Figure 4 Cumulative Pollutograph for Anchorage for Typical Year 3500 - 3000 - 2500 2000 ---- ---Residential 2000 =,---- Industrial a . -_.___Commercial - f......... _-ANNUAL PreDev Res 1500 So . ANNUAL PreDev Ind & Comrn 1000 Soo C - _. rC 0 � ._, __ _ . _- 5 =0 0 -' CP---'~-d 7~~~~~~~~~ ' .---, .___ . _ o ~'--"'*/---- (sC e - _ _ --_N_6 date A summary of the hydrologic characteristics of each land development scenario is shown in Table 2. Table 2 Hydrologic Characteristics of Each Land Development Scenario for Anchorage Land Use Type Variable Condition Units Residential Industrial Commercial ~Arem~a | acres 5 10 10 % Impervious % 38 50 85 Rainfall (May - Sept) inches 9.45 9.45 9.45 Rainfall Runoff Pre Development inches 1.01 1.01 1.01 Depth Post Development inches 2.81 3.43 4.20 Snowmelt Runoff Pre Development inches 0.35 0.35 0.35 Depth Post Development inches 2.74 3.53 5.85 TSS Loadings Annual Pre lbs 240 560 560 Development Annual Post lbs 699 1942 3322 Development Summer Post lbs 338 992 1734 Development Removal Required for Pre=Post % 29% 44% 68% Conditions - Summer Median TSS Annual Post mg/l 128 148 187 Concentrations Development Summer Post mg/I 131 157 224 Development Maximum 6-hr Summer Post cfs 0.17 0.43 0.52 flow Development Median 24-hr flow Summer Post cfs 0.01 0.02 0.03 Development ~~~~~~~~~WIN_ Stormnwater Controls in Coastal Alaska 0 page 3-12 June, 1995 3.2.7 Local Regulations The population of Anchorage is greater than 100,000 so the MOA must comply with the National Pollution Discharge Elimination System permit requirements for stormwater runoff. In the course of applying for this permit, the MOA has modified its Municipal Code to implement regulatory control over stormwater discharge. In particular, the MOA has identified TSS as a pollutant for which it can require treatment or removal. The MOA has not established performance objectives for stormwater control and currently defers to the Alaska Department of Environmental Conservation (ADEC),which is the agency that can legally enforce its own performance objectives. In the interim, until the MOA establishes performance criteria, it will not issue a developer the authority to proceed without review by the state. 3.3 BETHEL 3.3.1 Rainfall Bethel's annual precipitation is 16.9 inches. The 2-year/24-hour storm for Bethel is 1.5 inches (Miller, 1963). The rainfall distribution is shown in Figure 5. The highest precipitation occurs in August, and less than 5 days a year have rainfall depths greater than 0.5. Figure 5 Bethel Mean Monthly Precipitation Distribution - 1923-1984 and 1991 8 I 71 '=l-Mean Monthly Total (in) ?'--Mean No of days 0.1 In i---Mean No of days 0.5 In 4 4.199 Month Total (in) 34 I 2 ci I 0- Month Source: Leslie, 1986 and NOAA, 1991 Stormwater Controls in Coastal Alaska 0 page 3-13 June, 1995 3.3.2 Runoff The TR-55 method was used to generate runoff from rainfall events in Bethel. Since the majority of rainfall is of low intensity, this method predicts very low runoff. In Bethel, total snowfall is somewhat less than Anchorage. Snowpack is also smaller than Anchorage, due to wind effects. Both of these factors lead to a shorter snow melt runoff period than Anchorage in general. Colder temperatures in April cause the snow melt period to occur later than in Anchorage. 3.3.3 Soils and Drainage Conditions Bethel is located on the banks of the Kuskokwim River in southwestern Alaska. Bethel's soils are predominantly silts underlain by permafrost and are generally impermeable. This, and the lack of relief in area, create standing water following rainfall and snow melt events. Consideration for permafrost conditions has necessitated the construction of elevated roadways and above ground utilities. Scraping and grading of sites is generally limited to work on the constructed pads. Only one five mile road is paved; the rest are gravel or native soil. Very little, if any, sand is applied to the streets in the winter. Consequently, the primary source for sediment loading is erosion of the roadways and embankments. The primary stormwater structures are ditches and culverts. Most of the drainage is diffuse, with only one well defined creek running through the town. 3.3.4 TSS There is no suspended sediment data for the Bethel urban area. Suspended sediment data is available for the Kuskokwim River, but this data is not representative of urban runoff TSS. Pre-development conditions were estimated based on the USLE. A generalized regional analysis indicates that non glacial streams in the region probably do not normally exceed 100 mg/I in suspended sediment in the summer (Feulner, 1972). The post development TSS loading for the Bethel area was assumed to be half the rate of the Anchorage area for snow melt runoff. In Bethel, roads are not typically sanded in the winter and streets and parking lots are not typically paved. 3.3.5 Expected Site Development Types Bethel residential development is generally in the 2.5 to 5 acres range. The minimum lot size is 9,000 square feet. Commercial site sizes are small, generally accommodating such individual enterprises as a store or a bed-and-breakfast. No new industrial sites are likely to be developed; most industry is maritime and operates off-shore, on the Kuskokwim River. No street or parking lot paving is required, so the percent impervious is lower than that in more urban communities. Residential 5 ac 4 houses per acre 25% impervious Commercial 2 ac 40% impervious Industrial not anticipated *Str mwater Controls in Coastal Alaska 0 page 3-14 June, 1995 3.3.6 Typical Year 1991 was identified because of its near normal annual precipitation and average March 31 snowpack. The March 31 snowpack was used as an indicator of the snow melt season, and to evaluate if the chosen year were typical or not. Rainfall and thaw events were taken from the climatological record for the year. The 2-year/24 hour rainfall was not exceeded on any day in 1991. The runoff pattern for Bethel is shown in Figure 6. Two peaks, one in April and a smaller one in September, illustrate the runoff from snowmelt and rainfall. Figure 6 Bethel Monthly Rainfall-Runoff Distribution for Typical Year 1.80 1.60 1.40 - 1.20 - 10o - Rainfall t/ \ : A, Residential 0./0 ---- Commercial : 0.60A/\ 0.40-. 0.203 0.00 - month The cumulative TSS loadings for the typical Bethel year are shown in Figure 7. This figure shows the loadings due to runoff from development for each land use category. It also shows the total annual predevelopment load from each of the land use categories on the right side of the graph. !~~~~~~~~~~~~~~~ Stormrwater Controls in Coastal Alaska 0 page 3-S5 June, 1995 Figure 7 Cumulative Pollutograph for Bethel for Typical Year 1.40 - - 1~~~~~.d.O .. ... . ............. ... ........................................ 1 00 ~ ~ ~ ~ ~ .- r_ or~~~1~ _~~~~~~~~~~~~~~~~~~~~~~~~~~ 8 0 - R|icental. --- __ _ComnerclSl / ~~~~~~~~~~-....ANNUAL PfDev ARes 6 0 -- --.- ANN--J-,-eDev -nmm 4 0 d. '--H 20 0 -- - a a g- = = - - - - - - - date A summary of the hydrologic characteristics of each land development scenario is shown in Table 3. Table 3 Hydrologic Characteristics of Each Land Development Scenario for Bethel Land Use Type Variable I Condition Units Residential Commercial Area I acres 5 2 % Impervious I % 25 40 Rainfall (May - Sept) inches 6.67 6.67 Rainfall Runoff Pre Development inches 0.03 0.03 Depth Post Development inches 0.39 0.55 Snowmelt Runoff Pre Development inches 0.24 0.24 Depth Post Development inches 1.29 1.97 TSS Loadings Annual Pre lbs 85 15 Development Annual Post lbs 140 45 Development Summer Post lbs 42 16 Development Removal Required for Pre=Post % -100% 8% Conditions - Rainfall TSS Annual Post mg/l 81 81 Concentrations Development Summer Post mg/l 107 140 Development Maximum 6-hr Rainfall Post cfs 0.13 0.03 flow Development Average 24-hr flow Summer Post cfs 0.003 0.001 Development I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Stormwater Controls in Coastal Alaska 3 page 3-16 June, 1995 Calculated TSS loadings in Bethel decreased under developed conditions. We believe this wouldI not be the case, for two reasons. A good cover of natural vegetation in the predevelopment conditions limit sediment loss. Developed conditions generally involve pad or elevated road construction, on which both the side slopes and horizontal surfaces are generally more vulnerableI to erosion than predevelopment conditions. The predevelopment loads are most likely lower than those predicted by the USLE, which is especially sensitive to rainfall energy and the slope of the site. The post development loads are probably underestimated. Even though the sites have lowI percentages of imperviousness, the native soils are also highly impervious, as well. Because of the lack of data for Bethel with which to verify these results, they should be considered with skepticism. They do not provide a strong basis for development of target removal levels of TSS.I However, because of other site specific conditions, no BMPs that can be designed to meet targeted removal levels are practical for Bethel. 3.3.7 Local Storm Drainage Regulations Bethel has a Coastal Management Plan, which requires a review of subdivision plats. TheI municipal ordinance requires that drainage channels on private property be preserved and requires the installation of culverts where these channels are crossed by driveways or roads. There are no minimum landscaping requirements for commercial or industrial development, although the lots have minimum setbacks. 34 JUNEAU 3.41 Rainfall Annual rainfall in southeast Alaska is much greater than in south-central or western Alaska. Juneau's climate is typically much rainier than either Bethel or Anchorage, but is highly variable even within the developed area of the City and Borough of Juneau (CBJ). The average annual rainfall in downtown Juneau (90 inches) is nearly twice that at the airport (52 inches). Data from the weather station at the airport were used in this study, because published records were moreI complete in recent years. In addition, new development is more liely to occur north of town than in the town proper. Use of the airport data will lead to an underestimation of the runoff, and therefore TSS, in some parts of Juneau. The 2-year/24-hour storm for Juneau is 3.0 inches (Miller, 1963). The rainfall pattern for the airport weather station is shown in Figure 8. The maximumI precipitation occurs in October. Precipitation exceeds 0.5 inches on 28 days a year. Although the shape of these curves is similar for the downtown weather station, the magnitude, both in inches and in days of exceedence is higher. There are 61 days a year when precipitation exceeds 0.5 inches. Sic rmwater Controls in Coastal Alaska C2 page 3-17I June, 1995 Figure 8 Juneau Mean Monthly Precipitation Distribution - 1949-1984 and 1987 18 -, 14 12 a 10 "-- -Mean Monthly Total (in) -4i--Mean No of days 0.1 in -a-mean No of days 0.5 in a ,,-m-,-1987 Month Total (in) 4 . -IC C _ Month Source: Leslie, 1986 and NOAA. 1987 3.42 Runoff The TR-55 method was used to generate runoff from rainfall events in Juneau. Rainfall tends to persist over consecutive days; so adjustments were made (to the assumed CN) to account for antecedent moisture conditions, which generally result in higher runoff. Juneau's snow melt events include more frequent winter thaw events, including winter rains and earlier spring snow melt events than south-central or western Alaska. Some Juneau winters are dominated by rainfall runoff events, rather than snow and thaw events.. 3A3 Soils The high relief of the Juneau area has led to development along the coast and up stream and river valleys. The soils in the flood plains of these streams is silty. Soils on the uplands are either thin, underlain by bedrock or thicker glacial till deposits, which are firm and compact. Although there are tracts of well drained soil, the soil conditions generally impermeable. Storm runoff in developed areas is handled by a combination of underground storm sewers, ditches, and culverts. The developed areas are drained by creeks. 3A4 TSS Total suspended solids data has been collected by the United States Geologic Survey (USGS) from creeks in the vicinity of Juneau. The TSS data collected from these streams is associated with mining activity and is not applicable to this study because the sites are much higher in elevation than the area where development may occur. Rainfall and snowmelt runoff conditions in Stormwater Controls in Coastal Alaska J page 3-18 June. 1995 southeastern Alaska are strongly affected by elevation, which reflects both orographic and temperature effects. The Alaska Department of Environmental Conservation (Richards, 1993) presents stream water quality data for 15 streams and rivers in the Juneau area. This data does not include the drainage area above the sampling point, instantaneous stream flow, or antecedent rainfall or snow melt conditions. This data can provide general ranges for the summer months. The rainfall period was taken as the months of February through October. A TSS loading based on the Anchorage area runoff relationship was used. The snow melt runoff and TSS loading developed for Anchorage was used for winter thaw periods. 3A5 Expected Site Development Types According to the CBJ's Department of Planning, Juneau's new development is generally characterized as in-filling. Its residential development is generally in the range of 5 acres range. A typical commercial site size is 15 acres. Industrial sites are generally graveled. Residential 5 ac 4 houses per acre 40% impervious Commercial 15 ac retail store 85% impervious Industrial 20 ac 218,000 sf warehouse/office 50% impervious 3A.6 Typical Year For Juneau, 1987 was identified as the year with total rainfall closest to the long term average. However, the winter snowfall was below average this year, and the winter temperatures above average. This led to a higher percentage of the runoff due to rainfall, with consequently lower TSS concentrations through the winter. The runoff pattern for Juneau is shown in Figure 9. Two runoff peaks, in June and October, illustrate the runoff from rainfall. Figure 9 Juneau Monthly Rainfall-Runoff Distribution for Typical Year 1o.oo - 1.00 - 8.00 - 7.00 -- / \ 6.00-- / ' i i-Raintall S 5.00 - / A / A XWN~ ii-- Residential Runoff e -0 / \ J -Tin \ A- ; --lndustrial Runoff ago4.00 4-- Commercial Runoff 3.00- I.00 I 0.00 - month Stormwater Controls in Coastal Alaska Q page 3-19 June, 1995 The cumulative TSS loadings for the typical Juneau year are shown in Figure 8. This figure shows the loadings due to runoff from development for each land use category. It also shows the total annual predevelopment load from each of the land use categories on the right side of the graph. Figure 10 Cumulative Pollutograph for Juneau for Typical Year 16000 14000 12000 - f ....- Residential n 10000 ----Industrial F J -Commercial 8O~~~~~~~~~~ .............00 ,ANNUAL PreDev Res 6000- - ''ANNUAL Pre Dev Ind 8 000- -:~. omeca __ ', ~ -ANNUAL Pre Dev Comm 4000 - _- - 2000- - _ 0-,-.- .- ... ... . ..... C C .0.0D - ~- ~- > CC - - 0)0)0. 0. , > > C.) C.) n n IL tL L < M;nnn8 <i )t z z D C N _U NC CU -.. N 00_00000N date A summary of the hydrologic characteristics of each land development scenario is shown in Table 4. Stormwater Controls in Coastal Alaska O page 3-20 June, 1995 Table 4 Hydrologic Characteristics of Each Land Development Scenario for Juneau Land Use Type Variable I Condition Units Residential Industrial Commercial Area I acres 5 20 15 % Impervious % 40 50 85 Rainfall (Feb-Oct) inches 38.54 38.54 38.54 Rainfall Runoff Pre Development inches 1.41 1.41 1.41 Depth Post inches 6.59 8.17 19.18 Development Snowmelt Runoff Pre Development inches 0.04 0.04 0.04 Depth Post inches 0.67 0.82 1.36 Development TSS Loadings Annual Pre lbs 480 2500 1785 Development Annual Post lbs 1285 7351 17782 Development Summer Post lbs 879 5106 12544 Development Removal Required for Pre-Post % 45% 51% 86% Conditions - Summer Median TSS Annual Post mg/l 127 157 214 Concentrations Development Summer Post mg/I 133 163 222 Development Maximum 6-hr flow Summer Post cfs 0.53 2.40 3.10 Development Median 24-hr flow Summer Post cfs 0.01 0.03 0.05 Development 3.4.7 Local Storm Drainage Regulations Juneau has a Coastal Management Plan which includes stream setbacks. The CBJ is currently working with the ADEC on two streams in the borough that have been identified as impaired. Developers in the CBJ have been required by ADEC to install stormwater controls on their project, after site specific review. 3.5 LOCAL ECONOMIC CONDITIONS The economic indicators for each community are summarized in the Table 5. The figures that were available included population, municipal full value determination, total municipal revenue, median annual household income, and median owned-house value. Population and tax base extend over several orders of magnitude, although household income and median home price indicators are comparable. Stormwater Controls in Coastal Alaska C page 3-21 June, 1995 Table 5 Economic Features of Indicator Municipalities Feature Anchorage Bethel Juneau Incorporation Type Unified Home Rule Second Class City Unified Home Rule Municipality Municipality Population 248,296 2,009 29,078 Area (sq mi) 1,698 44 2,594 Population Density (per sq mi) 146 46 11 Property Tax (mils) 16.23 none 14.02 Total Municipal Revenue $790,239,935 $9,729,980 $121.312,436 Municipal Full Value Determination (tax $12,295,898,030 $184,121,800 $1,765,984,100 base) Median Household Income $43,946 $45,203 $47,924 Median Owned Home Price $109,700 $82,000 $113.500 Source: Alaska Department of Community and Regional Affairs, 1995 Stormwater Controls in Coastal Alaska -7 page 3-22 June. 1995 4.0 MANAGEMENT PRACTICES 41 SURVEY OF APPLICABLE BEST MANAGEMENT PRACTICES In the previous section, typical annual pre and post development TSS loads for coastal Alaska were estimated. In this section, methods for reducing the TSS loadings, known as best management practices (BMPs), in coastal Alaska are presented. Although scores of best management practices have been recommended and used throughout the lower 48 states, Alaska's climatological conditions limit the applicability of many of them. We have completed a draft survey of potential BMPs for stormwater pollution prevention, with an extensive and thorough summary of their applicability to Anchorage conditions, for the Municipality of Anchorage (MW, 1994). That document and three sources (Scheuler, 1987, Scheuler, 1992, EPA, 1993) were reviewed for applicability to the municipalities and land development types targeted in this study. Twenty best management practices (BMPs) are outlined on Tables 6 and 7. Table 6 includes 11 non-structural practices. Table 7 includes 9 structural practices This list has been developed to aid in the selection of Best Management Practices (BMPs) for new development projects in coastal Alaska, particularly for the scenarios used for the cost analysis in this study. In the first column, a code indicating the function of the BMP is listed. The BMPs are arranged in the following categories: Source BMPs Which Reduce Pollution at Their Source Erosion Erosion, Sedimentation and Drainage BMPs Vegetative Vegetative BMPs Retention Retention/Detention and Flow Regulation BMPs Filtration Filtration and Infiltration BMPs The second and third columns gives the name and a description of the BMP. The fourth column describes site specific constraints, clarifies how the BMP may be applied and may mention unusual maintenance conditions (e.g. a BMP has a very short life even with proper maintenance). The fifth through seventh columns gives a ranking for each municipality. The identified BMPs are ranked for their applicability to each of the three indicator municipalities and the land use scenarios developed for the cost analysis. The rankings are based on professional judgment, weighing such factors as: Stormwater Controls in Coastal Alaska D page 4-1 June, 1995 * site size � soil type * slopes less than 5% * maintenance requirements * climatic conditions * community acceptance * constructibility in given community * existing storm drainage infrastructure The ranking for non-structural (NS) and structural (S) BMPs are separate, with 1 being the most effective in the given category for the given municipality. Entries of N/A indicate that the BMP would not be applicable to the municipality. Stormwater Controls in Coastal Alaska 3 page 4-2 June, 1995 Table 6 Non-structural Best Management Practices Non-structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of Function OPNm Function BMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Source Maintenance of Ensure that all urban runoff facilities are urban runoff operated and maintained properly. Maintenance facilities should occur at regular intervals, be performed by one or more individuals trained in proper inspection and maintenance of urban runoff facilities, and be performed in accordance with the adopted standards of the State or local government (EPA, 1993). Source Setback Setback distances should be determined on a In level or gently sloping terrain, a general 8 4 7 distances near site-specific basis since several variables may rule of thumb is to establish a setback of 50 wetlands, be involved such as topography, soils, to 100 feet from the edge of the wetland or waterbodies, and floodplains, cut-and-fill slopes, and design riparian area and the right-of-way. In areas riparian areas geometry (EPA, 1993). of steeply sloping terrain (20 percent or greater), setbacks of 100 feet or more are recommended. Right-of-way setbacks from major waterbodies (oceans, lakes, estuaries, rivers) should be in excess of 100 to 1,000 feet (EPA, 1993). Source Residential road Plan residential roads and streets in accordance Narrower streets would reduce the quantity 10 6 6 and street with local subdivision regulations, zoning of runoff and accompanying pollutants. planning ordinances and other local site planning requirements. Table 6 Non-structural Best Management Practices (cont.) Non-structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of Function BMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Source Retain existing Do not alter wetlands or riparian areas to In general, the location of surface water I I 2 I functions of improve their water quality function at the runoff ponds or sediment retention basins in wetlands and expense of their other functions (EPA, 1993). healthy wetland systems should be avoided riparian areas (EPA, 1993). Source Sweep, vacuum, Sweeper technologies used in conjunction with Equipment types commonly used for street 2 N/A 2 and wash other BMPs that are effective in trapping fine sweeping include abrasive brush and vacuum parking lots solids could improve downstream water quality device sweepers. A newly developed helical (NVPDC, 1987). brush sweeper that incorporates a steel brush with vacuum has been shown to be more effective at removing fine solids and is currently being evaluated (NVPDC, 1987). Source Preserve natural Natural drainage features infiltrate and attenuate 3 3 3 drainage flows and filter pollutants. Depressional storage features and areas reduce runoff volumes and trap pollutants natural (EPA, 1993). depressional storage areas Source Snow storage Sites designated to keep melt water runoff from Prevent dumping of accumulated snow into 5 7 5 overloading streams with pollutants. New sites surface waters (EPA, 1993). should provide containment and appropriate treatment (HDR and CH2M Hill, 1993). Table 6 Non-structural Best Management Practices (cont.) Non-structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of Function IBMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Source Alternative Apply sand in controlled amounts based on 4 N/A 4 sanding temperature and road conditions. practices Erosion Minimize Restrict paving and the use of non-porous cover 9 N/A 10 imperviousness materials in recharge areas (EPA, 1993). Erosion Reduce the Pollutant loading from impervious surfaces may 7 N/A 9 hydraulic be reduced if the impervious area does not connectivity of connect directly to an impervious conveyance impervious system (EPA, 1993). surfaces Vegetative Retain existing Clear only those areas that are essential for 6 5 8 vegetation completing site construction. Avoid disturbing wherever vegetation on steep slopes or other critical areas. feasible Route construction traffic to avoid existing or newly planted vegetation. Protect natural vegetation with fencing, tree armoring, retaining walls, or tree walls (EPA, 1993). Table 7 Structural Best Management Practices Structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of Function BMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Vegetative Vegetated filter Low gradient area of land with vegetative cover In coastal Alaska, vegetated filter strips will 2 4 2 strip that is designed to intercept runoff as overland be limited by a fairly short growing season sheet flow from upstream development (EPA, and will not be effective during initial 1993). snowmelt. Vegetative Grassed swale An earthen conveyance system in which In coastal Alaska, grassed swales will be 4 3 4 pollutants are removed from urban stormwater limited by a fairly short growing season and by filtration through grass and infiltration will not be effective during initial snowmelt. through soil (Schueler, Kumble, and Heraty, 1992). Vegetative Seeding and Seeding with erosion protection blankets 7 1 7 mulch/mats for protects road and pad side slopes while the side slope vegetation becomes established (EPA, 1993). protection Erosion protection blankets are tacked in place and can be made of straw, jute netting or nylon fiber. Seeds can be incorporated into the blanket to provide the necessary ground cover to curb erosion and aid plant establishment. Vegetative Vehicle surface On roads and in parking and storage areas Gravel caps are the prime example of this 6 2 6 preparation where asphalt and concrete are too expensive, method. Permazyne, a chemical soil an alternative soil cap is beneficial to counter additive, is in the research stage in rural wind and water erosion. Alaska. Soil cement is an older technology that may serve this function. Table 7 Structural Best Management Practices (cont.) Function Structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of BMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Detention Extended A pond which temporarily detains a portion of 5 N/A 5 detention pond urban runoff for up to 24 hours after a storm, using a fixed orifice to regulate outflow at a specified rate, allowing solids and associated pollutants the required time to settle out. Normally dry between storm events and does not have any permanent standing water. Provides greater flexibility in achieving target detention times (EPA, 1993). Detention Wet pond (also A basin designed to maintain a permanent pool I N/A called of water and temporarily store urban runoff sedimentation until it is released at a controlled rate. (EPA, basin) 1993). Detention Catch basin In its simplest form, a catch basin is a single- 3 N/A 3 (water quality chambered urban runoff inlet in which the inlet) bottom has been lowered to provide 2 to 4 feet of additional space between the outlet pipe and the structure bottom for collection of sediment. Several designs exist (EPA, 1993). Detention Catch basin with A water quality inlet with a second chamber 8 N/A 8 sand filter (water containing a sand filter to provide additional quality inlet) removal of finer suspended solids by filtration. The first chamber provides effective removal of coarse particles and helps prevent premature clogging of the filter media (EPA, 1993). Table 7 Structural Best Management Practices (cont.) Function Structural BMP Description Constraints, Applications, and Unusual Rank of Rank of Rank of BMP Name Maintenance Conditions Applicability Applicability Applicability to Anchorage to Bethel to Juneau Infiltration Porous A porous asphalt through which runoff is 9 N/A 9 pavement and diverted into an underground stone reservoir, permeable gradually exfiltrating out of the stone reservoir surfaces into the subsoil (EPA, 1993). 4.2 TYPE OF DEVELOPMENT AND BMP FOR EACH LAND USE3 The II.A.(l)(a) management measures for controlling TSS in runoff from new development is expressed as 80% removal of TSS. The second management measure, prescribing that post3 development TSS load equal predevelopment loads, can also be expressed as a percentage, when the pre and post development loads are known. The percentage efficiency of the BMP is calculated by dividing the mass of settled TSS by the mass of the total incoming TSS. These percentages3 establish target levels of TSS removal. Non-structural BMPs have proved effective in removing TSS, but cannot be managed to meet3 targeted removal levels. Vegetative structural BMPs have also proved effective, even in northern climates (Marshall, 199 1), but cannot be designed to remove a targeted level of TSS. This is due both to lack of information to aid in developing design methods as well as the variability of performance in the field. .Performance is highly dependent on proper construction and maintenance. The only structural BMPs that can be designed to targeted reduction levels include detention and infiltration methods. Infiltration methods, which include retention facilities and infiltration structures, are not applicable in areas where soils are relatively impervious. This is always the case in Bethel, which hasI uniformly silty soils. It is the general case in Anchorage and Juneau. In Anchorage and Bethel, and to a lesser extent in Juneau, infiltration methods are only functional for the times of the year when they are neither covered by snow nor frozen. Because of these limitations they were notI considered to be effective. Detention methods detain storm water. While the water is detained, sedimentation occurs, whichI lowers the TSS concentration in the outflow. Gravity detention structures (those not requiring mechanical equipment such as pumps) require excavation in order for water to flow by gravity. In Bethel, construction requiring excavation is not feasible due to the high groundwater table andI permafrost conditions. In Juneau and Anchorage, detention facilities, either water quality inlets or sedimentation basins, have been used on site specific bases. Since these are considered to prove more effective than infiltration methods, they were chosen for the cost analysis rather than infiltration methods. Detention BMPs remove TSS by settling suspended particles. Under passive treatments (that is, with no chemical or physical controls), settling occurs by precipitation. Particle set'tling is influenced by three factors: settling velocity, flow rate and surface area of the detention facility. These factors are related by the following equation: Vs where Q = flow rate, cfs3 Vs = particle settling velocity, ft/sec A = basin surface area, sq ft Storm water Controls in Coastal Alaska Jpage 4-8 I June, 1995 Settling velocity is dependent on water temperature and particle shape and diameter. The colder the water, the smaller the particle diameter, and the less spherical the particle, the slower the particle settles. The suspended particles that make up TSS vary in diameter and shape. Clay particles settle very slowly, if at all, because of their planar shape. Turbulence and wind action 3 ~~create conditions under which smaller particles do not follow this equation, because the lift forces counteract gravity and they cannot settle. Experience has shown that it is usually physically practical to design for removal of sands, but removal of silts and clays is likely to be physically 3 ~~prohibitive (Walesh, 1989). Clays and silts have particle diameters in the range of <z2 microns and 2 to 50 microns, respectively. For purposes of this analysis, 10 microns was taken as the minimum diameter of a settleable particle. Distribution of particle size within the TSS varies, depending on the sources of the TSS, such as local soils and road maintenance practices. The distribution also varies based on storm intensity; I ~ ~higher intensity rainfalls can mobilize larger particle sizes. (This follows from the Universal Soil Loss Equation). If all of the TSS particles are greater than 10 microns, a high removal efficiency can theoretically be achieved. Conversely, a large fraction less than 10 microns will place a lower I ~ ~limit on the sedimentation efficiency. It follows that the percentage of the TSS particles, by mass, greater than IO microns, defines the upper level of removal efficiency that can be achieved. I ~~Sediment sampling results are available from stormwater in the Anchorage area (JMM, 1992) and are shown in Figure I11. The Basin Inlet Composite #1 in Figure I11 represents the particle range of a number of composited samples. The percent of suspended sediment greater than 10 microns I ~for Basin Inlet Composite #1 is 72%. Although the other samples show a higher percentage of particles greater than 10 microns, Basin Inlet Composite #1 represents the lower bound on the 3 ~~distribution. This 72% value, and the particle size distribution for these small diameter particles, compare favorably with the particle size distribution found in stormwater from nationwide sources (Pitt, 1985), where 78% of the particles were greater than 10 microns. As mentioned previously, rainfall intensity is one factor that determines TSS loading and it follows that higher intensity storms mobilize particles of larger diameter. Since rainfall in the Anchorage area is generally of lower intensity than the nationwide average, the slightly greater percentage of smaller diameter particles is reasonable. Therefore, this distribution was used in evaluating the expe cted efficiency of sedimentation basins in Anchorage. This distribution was also used to evaluate the efficiency of sedimentation basins in Juneau, because, even though the Juneau area experiences higher annual Irainfall, its rainfall intensities are still lower than the nationwide norm. Because Bethel has uniformly silty soils, we would expect an even smaller percentage of particles greater than 10 microns. Based on the particle size distribution, the best removal efficiency that can be expected in Juneau and Anchorage is 72%; and even lower in Bethel. Therefore, sedimentation basins will not meet 3the 80% target of management measure in II.A.(l1)(a) in these locations in coastal Alaska. However, for five of the land development scenarios, reducing pre development loads to post development levels entails removal rates lower than 72%. For these scenarios, sedimentation Ibasins were sized to meet the percent removal rates, and prototype sedimentation basins were designed. Cost figures have been calculated for these prototype basins. Stormwater Controls in Coastal Alaska D page 4-9 IJune. J99S Figure 11. Particle Size Distribution Analyses for Suspended Sediment in Storm Water 100% 75% Percent ---- 100th Ave. Grab, Q=0.3 cfs -- It- 100th Ave. Grab, Q=0.6 cfs Passing 50% PassBy Weight 5 -11- 100th Ave. Grab, Q=0.3 cfs By Weight --El-- Basin Inlet Composite #1 -U--1- Basin Inlet Composite #2 25% Source: (JMM, 1992) 0% . 0 10 20 30 40 50 60 Particle Diameter, microns II - m m mI -3 m m m - m - m - Quantifiable structural BMPs are not feasible for the residential or commercial land development scenarios in Bethel. The recommended control methods include gravel capping of parking areas and erosion protection on the side slope of pads. There is not enough data to determine whether these BMPs will achieve the targeted removal rates, but it is reasonable to assume a 50% removal rate. Table 8 summarizes the target removal efficiency for each municipality and land use scenario under management measure II.A.(1)(b) for rainfall runoff events. There was no municipality in which 80% removal efficiency (management measure II.A.(1)(a)) could be achieved. The scenarios in which these target percentage removal levels were less than 72% were carried forward for cost estimates in Section 5. Table 8 Summary of Target TSS Removal Percentages Target Removal Efficiency (%) Required for Pre=Post Development (II.A.(1)(b) Municipality Land Use Anchorage Bethel j Juneau Residential 29 I -100 I 45 Industrial 44 [ 8 ] 51 Commercial 68 [ NA ] 86 Costs were not developed for other removal scenarios for various reasons. Since none of the municipalities have specific local ordinances addressing TSS removal levels, no cost estimates were developed for meeting existing municipal ordinances. As mentioned previously, the effectiveness of non-structural measures cannot be quantified. Since non-structural measures cannot be recommended to meet the management measures, no cost analyses was performed. No industrial development scenario for Bethel was considered, because a new industrial site that could reasonably be expected to be developed could not be characterized. No cost estimates were developed for residential and commercial land development in Bethel, since there are no quantifiable BMPs that will work there. As discussed in Section 3.3.6, the TSS loading estimates made for pre and post development loads for Bethel are highly uncertain, so any costs developed based on the loading estimates would be ambiguous. I Stormwater Controls in Coastal Alaska Z page 4-11 June, 1995 5.0 COST ESTIMATES 5.1 DESIGN CONSIDERATIONS FOR SELECTED BMP CONSTRUCTION AND MAINTENANCE Sedimentation basins sizes were estimated for five of the scenarios based on rainfall runoff flows and TSS loading. The minimum pond surface area was calculated by an iterative technique. A pond surface area was assumed, and the mass of TSS removed by the pond for each storm in the typical year was calculated. The total mass removed from all rainfall runoff was divided by the total TSS for the rainfall season to obtain a summer removal percentage. When the removal percentage matched the level prescribed in Table 8 (the pre=post management measure), the pond surface area was established. In all cases, the calculated pond surface areas were too large to be incorporated into underground facilities, such as water quality inlets. Therefore, sedimentation ponds were chosen as the BMP for each scenario. Other design considerations, such as maximum side slopes and minimum storage volume for retained sediment, dictated a larger pond size in three out of the five cases. These considerations were included in the design on which cost estimates were based. Appendix B gives details of the assumptions and methodology used to determine the pond design for each scenario. Table 9 Summary Pond Sizes Land Use Municipality Anchorage Juneau Minimum Sedimentation Estimated Removal Minimum Sedimentation Estimated Removal Pond Size Efficiency Pond Size Efficiency Theoretical Practical Summer Annual Theoretical Practical Summer Annual Surface Area Surface Area Surface Area Surface Area sq ft % % sq ft sq ft % % Residential 90 1,300 72 44 450 1.300 66 55 Industrial 400 1,300 67 43 2,600 2.600 51 43 Commercial 1,600 1,600 65 42 NA NA NA NA Table 9 shows a summary of minimum pond sizes. The theoretical minimum pond surface area was calculated by the iterative technique described above. The practical pond surface area was determined by the geometry of the pond design criteria. The summer and annual percentage removal rates for the practical pond surface areas are also shown. The annual percentage removal rates were based on the assumption that the pond would be effective during 25% of the snowmelt runoff events in Anchorage and 50% of the snowmelt runoff events in Juneau. Although we feel these are reasonably conservative assumptions, there are no data to support them. Stormwater Controls in Coastal Alaska Z? page 5-1 June, 1995 52 COST ESTIMATE FOR SELECTED BMPS Cost estimates for storm water controls are presented in Table 10. The costs for stormwater controls included land costs and building and site development costs. The sum of these is the total capital cost (TCC). The costs for construction of the controls were based on a prototype sedimentation design, and unit prices for construction from Means Heavy Construction Cost Data. In addition, annual and periodic maintenance costs were estimated. The maintenance tasks were itemized and unit prices for these were taken from Means Heavy Construction Cost Data. The annual cost for development was estimated by annualizing the capital costs over 25 years at 10 percent interest rate. The total annualized cost (TAC) of the project includes both the annual maintenance costs and the annualized capital cost. For prices taken from the Means Cost Data, the City Cost Index for Anchorage was used to adjust the unit prices for Anchorage. For Juneau, the 105 percent of the Anchorage City Cost Index was used. These methods are consistent with the method used by the EPA in its economic analysis of coastal nonpoint source pollution controls. (EPA, 1992). Table 10 Estimated Stormwater Control Costs Type of Land Use Storm Water Controls Municipality Project Size Project Type Total O&M Cost Total Acres Annual Cost (ac) Capital ($) Annualized Required per Developed Cost ($) Cost ($) Acre ($) Anchorage 5 Residenutial(38%) 38,231 3,754 7,966 0.34 1,593 10 Industrial(50%) 33,695 3,754 7,466 0.34 747 10 Commercial(85%) 68,720 4,095 11.666 0.36 1,167 Juneau 5 Residential(40%) 38,782 3,936 8,208 0.34 1,642 20 Industrial(50%) 39,472 5,402 9.751 0.43 488 5.3 MEASURES OF ECONOMIC IMPACT To measure the control practices' economic impact on development activities, ratios of stormwater control costs to development costs without stormwater controls were computed, based on costs derived in Section 5.2. These ratios, consistent with the method used by the EPA (EPA, 1992), are described as follows: Residential development TCC/total land price TCC/number of housing units / median home price TAC/number of housing units / median annual mortgage TAC/number of housing units / median household income Stormwater Controls in Coastal Alaska a page 5-2 June, 1995 Commercial and Industrial development I TCC I Total development cost TAC / Annualized development cost Two costs were used to estimate capital development costs for commercial and industrial development, land costs and building and site development costs. Land prices were based on local knowledge. Building and site development costs were obtained from Means Building Construction Cost Data. The annual cost for development was estimated by annualizing the capital | costs over 25 years at 10 percent interest rate. Residential housing costs were based on tabulated data from the State of Alaska Department of Community and Regional Affairs (1995). This source reports median household income and the median value of owned homes. The annual mortgage payment was calculated from the owned home value, assuming a 15% down payment, an 8%, thirty-year note, and 10% for insurance and 3 taxes. The storm water control to development costs are shown in Table 11. Also included in Table 11 I are the range of values for similar ratios as reported by EPA for control costs meeting both management measures. As pointed out in Section 5.1, no BMP controls are expected to treat storm water to the 80% removal level. Therefore, the costs and ratios presented here are for meeting the pre=post management measure only. Table 11 3 Measures of Economic Impact Single Family Residential Municipality Project Type TCC/House/Annual TCC/Land TAC/House/ TCC/House/ Mortgage Price Household House Price Income (%) (%) (%) (%) Anchorage Residential 1.94 2.93 4.86 1.01 Juneau Residential 1.90 2.97 4.84 0.95 National Range for Single Family .31 - .93 % 3.7 - 8.6 % .45 - 1.3 % .16 - .32 % Commercial and Industrial Municipality Project Type Capital Development Annualized TCC/Capital TAC/ Annualized Cost Capital Cost Cost Cost ( $) ~~~~~~~I ($) ~~~($) (%) (%) Anchorage Industrial 9,090,613 1,001,495 0.37 0.75 Commercial 15,219,444 1,676,697 0.45 0.70 Juneau Industrial 18,654,687 2,055,151 0.21 0.47 National Range for Commercial Only .49 - .67 % .70 - .95 % TCC - total capital cost for storm water control I TAC - total annualized cost. including O&M, for storm water control I Stormwater Controls in Coastal Alaska ~ page 5-3 - ~Jau~ne, ~1995 As can be seen in Table 11, the measures of economic impact for stormwater controls on residential development are consistently high compared to the national range, except in the comparison with land values alone. For commercial land development, the economic impact ratios are within the national range. The residential economic indicators use the annual household income and mortgage expense of the eventual owners of the property. The commercial economic indicators only represent the cost of controls as a portion of the total development cost. The residential method more accurately reflects the market's willingness to pay than does the commercial method. In the commercial method, there is no way to determine if the incremental costs will still make the development an attractive one for investors or buyers. Therefore, even though the commercial economic indicators in Table 11 compare favorably with national averages (EPA, 1992), they do not reflect the true conditions that would determine whether the control measures are economically achievable. Table 12 Unit Costs for Stormwater Controls Municipality Development Area TAC Annual Removal of Load Cost per Cost per Type [Load Annual Removed Acre per Pound Load Year Removed ac $ lbs % lbs $ $ Anchorage Residential 5 7,966 699 44 308 1,593 25.90 Industrial 10 7,466 1,942 43 835 747 8.94 Commercial 10 11,666 3,322 42 1,395 1,167 8.36 Juneau Residential 5 8,208 1,287 55 708 1,642 11.60 Industrial 20 9,751 7,403 43 3,183 488 3.06 Table 12 summarizes the annualized unit costs of stormwater controls in cost per developed acre and cost per pound of sediment removed. Stormwater Controls in Coastal Alaska 0I page 5-4 June, 1995 6.0 CONCLUSIONS The 80% TSS removal standard cannot be reliably met in any of the three indicator communities by any BMP whose performance can be quantified. Since the only quantifiable BMPs that will work rely on settling and the fraction of settleable solids is less than 80%, there is no way to improve the removal rate by BMPs. The methods for removing the remaining unsettleable fraction involve chemical or physical treatment, such as employed for drinking water supplies. These methods are much more expensive than BMPs and would fail the economic indicator tests for developments of the size presented in this analysis. The pre=post removal standard can be met in Anchorage and for residential and industrial development in Juneau. Meeting this standard comes at annualized costs, including O&M, ranging from $490 per developed acre for industrial development to $1640 per developed residential acre. Stormwater Controls in Coastal Alaska page 6-1 June, 1995 I7.0 REFERENCES Alaska Department of Community and Regional Affairs Community Database. June 1995. Community Profile - Juneau. Research and Analysis Section Alaska Department of Community and Regional Affairs Community Database. June 1995. Community Profile - Bethel. Research and Analysis Section Alaska Department of Community and Regional Affairs Community Database. June 1995. Community Profile - Anchorage. Research and Analysis Section Berglund, E.R. 1978. Seeding to Control Erosion Along Forest Roads. Oregon State University Extension Service, Extension Circular 885. In: Guidance Specifying Management Measures for Sources of Nonpoint Pollution in Coastal Waters. EPA 840-B-92-002. January 1993. Billman, Daniel and Thomas R. Bacon. 1990. Spring Breakup Flows in Anchorage Storm Drains. Cold Regions Hydrology and Hydraulics. ASCE Technical Council on Cold Regions Engineering Monograph. 669-693. Brabets, Timothy P. 1987. Quantity and Quality of Urban Runoff from the Chester Creek Basin Anchorage, Alaska . United States Department of the Interior Geological Survey. Water- Resources Investigations Report 86-4312. City of Bethel. June 1983 Bethel Coastal Management Plan Conceptually Approved Draft Feulner, Alvin J., Joseph .M Childers, Vernon W. Norman. 1972. Water Resources of Alaska. United States Department of the Interior Geological Survey . Water Resources Division. Alaska District Goldman, Steven J., Katharine Jackson, Taras A. Bursztynsky. 1986. Erosion and Sediment Control Handbook. McGraw Hill. HDR and CH2M Hill. 1992. National Pollutant Discharge Elimination System Storm Water Discharge Permit Application, Part 1. Prepared for the Municipality of Anchorage and Alaska Department of Transportation and Public Facilities. May. HDR and CH2M Hill. 1993. National Pollutant Discharge Elimination System Storm Water Discharge Permit Application, Part 2. Prepared for Municipality of Anchorage and Alaska Department of Transportation and Public Facilities. May. Hinton, Robert B. and Charles L. Girdner, Jr. 1968. Soils of the Bethel Area, Alaska .:United States Department of Agriculture, Soil Conservation Service. Stormwater Controls in Coastal Alaska J page 7-1 June, 1995 James. M. Montgomery Engineers. 1986. Sedimentation Basin BI-] Decisional Documents. Prepared for the Municipality of Anchorage Department of Public Works. December. Jokela, J. Brett and Thomas R. Bacon. 1990. Design of Urban Sedimentation Basins in Anchorage. Cold Regions Hydrology and Hydraulics. ASCE Technical Council on Cold Regions Engineering Monograph. 761-789. Leslie, Lynn D. 1986. Alaska Climate Summaries Alaska Climate Center Technical Note No. 3. Arctic Environmental Information and Data Center. September. Marshall Macklin Monaghan Limited. 1991. Stormwater Quality Best Management Practices. Prepared for: Environmental Sciences & Standards/Water Resources, Ontario Ministry of the Environment. June. Miller, John F. 1963. Probable Maximum Precipitation and Rainfall-Frequency Data for Alaska. Technical Paper No. 47. U.S. Department of Commerce Weather Bureau. Montgomery-Watson. 1993. Design Storm Investigation . Prepared for the Municipality of Anchorage, Department of Public Works. December. Montgomery-Watson. 1993. Areawide Water Quality Monitoring Program 1992-1993 Sedimentation Basin Performance Monitoring Report. Prepared for the Municipality of Anchorage, Department of Public Works. July. Montgomery-Watson. 1994. Potential Best Management Practices for Stormwater Pollution Prevention.. Phase 2 Draft. Prepared for the Municipality of Anchorage, Department of Public Works. November. National Oceanographic and Atmospheric Administration. 1987. Climatological Summary for Alaska. National Oceanographic and Atmospheric Administration. 1991. Climatological Summary for Alaska. North Virginia Planning District Commission (NVPDC). 1987. BMP Handbook for the Occoquan Watershed. Annadale, VA. In: Guidance Specifying Management Measures for Sources of Nonpoint Pollution in Coastal Waters. EPA 840-B-92-002. January 1993. Pitt, R. 1985. Summarized Guidelines for Wet Detention Pond Design. Wisconsin Department of Natural Resources. Nonpoint Source and Land Management Section. Schoephorster, Dale B. and Clarence E. Furbush. 1974 . Soils of the Juneau Area, Alaska. United States Department of Agriculture, Soil Conservation Service. Palmer, Alaska. June. Stormnwater Controls in Coastal Alaska I page 7-2 June, 1995 Scheuler, Thomas R. 1987. Controlling Urban Runoff: A Practical Manual for Planning and Designing Urban BMPs. prepared for Washington Metropolitan Water Resources Planning Board. July 1987. Scheuler, Thomas R., Peter A. Kumble, and Maureen A. Heraty. 1992. A Current Assessment of Urban Best Management Practices, Techniques for Reducing Non-Point Source Pollution in the Coastal Zones. Prepared for USEPA, Office of Wetlands, Oceans, and Watersheds. March. Sheaffer, John R and Kenneth R. Wright. 1982. Urban Storm Drainage Management. Marcel Dekker, Inc. New York. U.S. Army Corps of Engineers Alaska District. 1979. Soils of the Anchorage Area, Alaska. Volume 7 Metropolitan Anchorage Urban Study Final Report. U.S. Department of Agriculture. 1985. National Engineering Handbook, Section 4, Hydrology Soil Conservation Service U.S. Department of Agriculture. 1986. Urban Hydrology for Small Watersheds Technical Release 55. Soil Conservation Service Engineering Division US EPA. January 1993. Guidance Specifying Management Measures for Sources of Nonpoint Pollution in Coastal Waters. EPA 840-B-92-002. US EPA. December 1992. Economic Analysis of Coastal Nonpoint Source Pollution Controls: Urban Areas, Hydromodifications and Wetlands. Walesh, Stuart G. 1989. Urban Surface Water Management. John Wiley and Sons, Inc. Weaver, Jerry. June 1995. Personal Communication Wheaton, Scott. May 1995 Personal Communication Wiegand, C., T. Scheuler, W. Chittenden, D. Jellick. 1986. Cost of Urban Runoff Quality Controls. Urban Runoff Quality - Impact and Quality Enhancement Technology. Proceedings of an Engineering Foundation Conference. June, 1986. 366-350. Williams, Richard. 1993. Juneau Streams - A Water Quality Study. Alaska Department of Environmental Conservation July. Stormwater Controls in Coastal Alaska C page 7-3 June, 1995 I I I I I i I I I I I Appendix A I I I I I I I --TOIMIO I Appendix A Daily Runoff and TSS Load from Rainfall and Snowmelt Events for Typical Year � Anchorage * Bethel * Juneau Derivation of Snowmelt Runoff and TSS Loading from North Arctic/Orbit Data Derivation of Annual Predevelopment TSS based on Universal Soil Loss Equation . Stormwater Controls in Coastal Alaska Zi page A-l June, 1995 I I I Daily Runoff and TSS Load from Rainfall and Snowmelt Events For Typical Year a Anchorage I * Bethel * Juneau I I I I I I I I ~~~~~~~~II I - ~ I I I I ANCHORAGE Residential Industrial Area: 5 ac Area: 10 ac Au imp: 38 % imp: 50 Assumed TSS Snowmelt Snowmelt Rainfall concentr Assumed for TSS conc Runoff Snowmelt Runoff alion Rainfall Snowmelt Runoff Day of Melt Date Precip imp=30 (mo/I) in Runoff TSS lbs cts mall Runoff in Runoff TSS lbs cfs 11 12-Jan 0.03 157 0.04 7 0.01 157 0.05 17 0.02 12 13-Jan 0.03 151 0.04 6 0.01 151 0.05 16 0.02 13 14-Jan 0.03 146 0.04 6 0.01 146 0.05 16 0.02 422-Feb 0.03 195 0.04 8 0.01 195- 0.05 21 0.02 523-Feb 0.03 190 0.04 8 0.01 190 0.05 21 0.02 6 24-Feb 0.03 184 0.04 8 0.01 184 0.05 20 0.02 7 25-Feb 0.03 179 0.04 8 0.01 179 0.05 19 0.02 26-Feb 0.03 173 0.04 7 0.01 173 0.05 19 0.02 -1 .10-Mar 0 -046799 212 0.06 14 0.01 212. 0.07 36 0.03 -2 11-Mar -0.02291 206 -.03 7 0.01_ 206 0.04 17 0.02 -12-Mar- 0.04679 201 _2I 0.06 13 0.01 201 0.07 34 0.03 4 3-Mar 0.03002 195 0.04 8 0.01 195 0.05 21 0.02 514-Mar 0.01967 190 0.02 5 0.01 190 0.03 14 0.01 6 15-Mar 0.01967 184 0.02 5 0.01 184 0.03 13 0.01 7 _16-Mar 0.01967 179 0.02 5 0.01 179 0.03 13 0.01 8- 17-Mar 0.01967 173 0.02 5 0.01 173 0.03 12 0.01 9 18-Mar 0.0352. 168 0.04 8 0.01 168 0.06 21 0.02 10 19-Mar 0.04037 162 0.05 9 0.01 162 0.06 24 0.03 1_-20-Mar 0.04058 157 0.05 9 0.01 157 0.06 23 0.03 12 21-Mar_ 0.0383 151 0.05 8 0.01 151 0.06 21 0.03 1 3 22-Mar 0.03023 1-46 0.04 .-. 0.01. 146. 0.05 16i 0.02 14 23-Mar _0.0383 140 0.05 8 0.01 140 0.06 20 0.03 15 24-Mar -0.03727 135 ___ 0.05 7 0.01 135 0.06 18 0.03 16 25-Mar 0.02588- 129 _ 0.03 5. 0.01 129 0.04 12 0.02 17 26-Mar 0.0383 124 __ 0.05 _ 7 0.01 124 0.06 17 0.03 18 -27-Mar 0.03106 __118 _ 0.04_ 5 0.01 118 0.05 13 0.02 1 928-Ma-r - 0.03002 113 _ 0.04 5 0.01 113 0.05 12 0.02 20 -29-Mar 0.02588 1 07 0.03- 4 0.01 107 0.04 10 0.02 21 30-Mar _0,04017 102 -(0.05 6 (.01 102 0.06 15 0.03 22 31-Mar__ 0.04617 96 0.06: 6 0.01 96 0.07 16 0.03 23 1-Apr 0.04617 91 0.06 _ 6 0.01 91 0.07 15 0.03 24. ?-Apr. 0.06336 85 0.08 8 0.02 85 0.10 20 0.04 25_- 3-Apr - 0.0499 80 _ 0.06 6 0.01 80 0.08 14 0.03 26 _4-Apr - 00_ 354 75 0.04 4 0.01 75 0.06 10 0.02 27 5-Apr 0.04679 69 0.06: 5 0.01 69 0.07 12 0.03 28 6-Apr 0.05176 64 0.06 5 0.01 64 0.08 12 0.03 29 7-Apr . 0.05176 58 __ 0.06 4 0.01 58 0.08 11 0.03 30. 8-Apr. 0.03894. 53 _ 0.05 3 0.01 _ 53 0.06 7 0.03 31 9-Apr_ 0.05073 47 ___ 0.06 3 0.01 47 0.08 9 0.03 32 10-Apr ._ 0.06522 42_ _ 0.08__ 4 0.02 42 -.10 10 0.04 33. 11-Apr. 0.0-5176- 36 -. 0.06 3 0.01 36 0.08 7 0.03 34. 12-Apr._ 0.08157 _31 0.10 4 0.02 31 0.13 9 0.05 35_ 13-Apr_ __ 0.0793 25 0.10 3 0.02_ _25 0.13 7 _ 0.05 36 14-Apr 0.08758 20 0.11, 2 0.02 20 0.14 6 0.06 37__15-Apr __ _0.11346 14 0.14 2 0.03 14 0.18 6 0.08 22-May_ k0.9 _ _ 0.02- 40.01.139' 0.03 11 0.01 24-May 0.09 __ 0_ _0___ _4 .01139. 0.03 11 0.01 31-May 0.15 0.04 6 0.01 132 0.05 18 0.02 6-Jun 0.14 __ 0.04 6 0.01 133 0.05 17 0.02 19-Jun__ 0.16 _ 0.05 7 0.01 131 0.06 20 0.02 20-Jun 0.26 0.08 11 0.02 124 0.09 32 0.04 22-Jun 0.1 0.03 4 0.01 138 0.03 12 0.01 30-Jun 0.12 0.03 5 0.01 135 0.04 15 0.02 I-Jul 0.28 0.08 12 0.02 123 0.10 34 0.04 9-Jul 0.18 _ 0.05 8 0.01 129 0.06 22 0.03 1-3-Jul _0.1 _ 00.03 5 0.01 135 0.04 15 0.02 l8-Jul 0.55 0.18 23 0.04 114 0.21 67 0.09 24-Jul _ 0.09 ___ 0.02 4 0.01 139 0.03 11 0.01 25-Jul 0.12 _ 0.03_ 5 0.01 135, 0.04 15 0.02 3-Aug_ 0.5 _ 6.04 _ _ 6 0.01 132 _ 0.05 18 0.02 9-Aug 0.2 0.06 a 8 0.01 .128. - 0,07 24 0.03 -12-Aug 0.11 0.03 ----5 0.01- 1 36.----.--0.04 14 0.02 13-Aua 0.3 0.09 12 0.02 122 0.11 37 0.05 Page 1 of 4 ANCHORAGEI Residential Industrial Area: -5_ec Area: I 0 ac % imp: 3 8 _ % imp: 50. Assumed TSS Snowmell Snowmelt Rainfall concentrI Assumed for TSS conc Runoff Snowmelt Runoff ation Rainfall Snowmelt Runoff Day of Melt Date Precig) imv30 (moll in Runoff TSS lbs cts moll Runoff in Runoff TSS lbs cis5 14-Aug 0.26 0.08. 11 0.02 124 0.09. 32 0.04 16-Aug 0.1. 0.03. 4_ 0.01 138 0.03. 12 0.01 23-Aug 0.24 0.07. 10 0.01 125 0.09 29 0.04 4-Sep 0.16 -0.05. 7 0.01_ 131- 0.06 2 0 0.02 6-Sep 0.17 0.05. 7 0.01 130 0-.06 '21 0.02 8-Sep 0.27. 0.08- 11_ 0.021 124 Q. -1 0 33. 0.04 iI-Sep 0.14 0.04. _ 6. 0.01 1 33.- 0.05. 1 7 0.02 14-Sep 0.16- 0.5_ _ _7. 0.01_ 131 __ 0.06 -2 0_ 10.02 I15-.Sep_ 0.18 0._05. - 8 0._01 129 0.06 . 22 ' 0.03 17-Sep 0.51 -0. 16. 21 -0.03 11 5 0.20 62- 0.08 18-Sep- 0:12 -- 0.03 _ 5 0.01 135 0.04 1-5 0.02 19-Sep 0.64 0.21- .26__ 0.04 112, 0.25 77 0.11 20-Sep. 0.14 0.04 6_ _ 6 0.01. 133 0.05 17 0.02 23-Sep. 0.34 . 0.10- 14 0.02 120 _ 0.13 41. 0.05 26-Sep 0.39. 0.12 _ 16__ 0.03 119 0.15 - 47. 0.06 27-Sep_ -947 0.5 19_ 0.03. 116~ 0.118 5 7. 0.08 29-Sep 0.34 -0.1I0- 14 0.02 1 20. 0.13 a 41 0.05 30-Sep 0.29 0.09 __ 12_ 0,02. 123 0.11 a s 0.04 6-Oct 0.44 0.14 I 8 0.03 _117 0.17 . 53 0.07 a-Oct 0.12 . . 0.03 _ 5 0 .01 135 0.04 .15 0.02 10-Oct 0.42 01 17 0.03 _118 0.16 51 0.07 13-Oct_ 0.09 _ 0.02 4. 0.01 139 0.03 11 0.01 24-Oct 0.14 _0.04. 6 0.01 133 - 0.95 17 0.02 -25-Oct _ 0.11 - -- -- 0.03- _____ 5 -0.01 136 0.04- 1 4 'D.02 26 11-Nov_ 0.03 - 75 0.04 3 0.01 75 0.05 8 0.02 27 12-Nov-003 6 0.04 3 0.01 69 0.5 a 0.02 2 8.13-Nov-__ 0.03. 64 0.04 3 0.01 64 0.05 7 0.02 289 14-Nov. 0.0.3 58 a 0.04 2 0.01 58 0.05 6 0.0 2 3 0 1I5--Nov__ _ 0.03. 53 _ 0.04 2 0. 1 300 6_ 0.02 3 1 16-Nov 0.03 4 7 0.04; 2 0___ _47 ______ _0 02 321__1-Nov _ _ 0.03 42 0.04 2 0.01 427 0.05 5 0.02 33 18-Nov _ 0.03 36 . 0.04 2 0.01 36. 0.05. 4 0.02 1-8 15-Dec 0-.03- 1-18 0.04 5: 0.01 _118: 0.05 _ 13 0.02 I19__16-Dec 0.03 113 0.04 ~ 5 0.01 113 0.05 12 0.02 26 17-Dec 0.03 107 0.04: 5 0.01 107 0.05 12 0.02 Total 9.45. 2.8 2.7 699 1.2 3.4 3.5 1942 2.9 Median Day 0.16 0 0.1 0.0 7.1. 0.0 128 0.1 0.1 19.8 0.02 Rain 9. 4 5 ~~~ 2.4 338 0.01 130 3.0 ___ 992 0.02 Snowmelt -.2.7 361 0.01 113 -.3.5 950 0.02 Maximur _0`64 0.2 0.1 26.4_ 0.04 _ 0.3. 0.2_ 77.4 _ 0.11 Minimum 0.09 -0.0- 0.0t 1.5 0.0 _ 0.0 0.0 3.9 0.0 Winter % of Total 52% 49% Page 2 of 4 ANCHORAGE Commercial Pre-development Area: 1 0 ac Area. 1 0Oac Assumed TSS Snowmelt Snowmelt concentr TSS Assumed for TSS cornc ation Rainfall Snowmelt Runoff concentra Rainfall SnowmeII Runoff Dav of Melt Date Precip imp=30 (mu/I) ma/I Runoff in Runoff TSS lbs cfs lion ma/I Runoff in Runoff CfS 11 12-Jan 0.03 157 157 0.08 28 0.03 157 0.00 0.002 12 13-Jan 0.03 151 151 0.08 27 0.03 151 0.00 0.002 13 14-Jan 0.03 146 146 0.08- 26 0.03 146 0.00 0.002 422-Feb. _ 0.03 195- 1-95. _ 0. 08 - 3-5 0.03- -_195 0.00 0.002 5 23-Feb 0.03 190- 190 - 0.08 34 --0.03. ~190 - 0.00 0.002 6 24-Feb _ 0.03 184 184 0.08 33 0.03i 184 - 0.00 0.002 725-Feb 0.03 179 179 0 .08 32 0.03 179 0.00 0.002 826-Feb 0.03 173 173 0.08 _ 31 0.03 173 0.00 0.002 1 10-Mar 0.04679 212 212 0 .12 60 0.05 212 0.01 0.003 21 11-Mar_ 0.02298 206 206 0.06 28 0.03- 206 0.00 0.002 3_12-Mar _0.04679 201 201 0.12 _56 0.05 201 00 .0 413-Mar 0.03002 195 __195 0.08 35 0.03 195 0.00 0.002 514-Mar -0.01-96-7 190 190o 0.05. 22 _0.02_ 190 0.00 -0.001 6 15-Mar 0.01967 184 184 _ 0.05 22 . 0.02 184 0.00 0.001 7 16-Mar 0.01967 179 179 0.05 21 0.02 179 0.00 0.001 8 17-Mar 0.01967 173 173 __0.05 20 0.02 173 0.00 0.001 918-Mar 0.0352, 168 168 _ 0.09 35 0.04 168 0.01 0.002 10 19-Mar 0.04037 162 162 _ 0.11 39 0.04 _162 0.01 0.003 11 20-Mar -0.04058 157 157 0.11 38 0.05 157 0.01 0.003 12 21-Mar - --0.0383 151 1I5 1 _ 0.10 315 0.04. 151 0.01 0.003 13 22-Mar _ 0.03023 146 146 0.08 26 0.03 146 0.00 0.002 14 23-Mar 0.0383 14 0 140 __ 0.10 - 3 2 0.0 4 _ 140 -0.01 _ 0.003 15 24-Mar 0.32 135 _135 0.10__s 304 667 0.0 135' 0.01 0.003 16 -25-Mar 0,02588. 129_ 12 9 0.07 .20. 0.03 129 0.00 0.002 17 26-Mar 0.0383 124 -124 0.1I0 _29 0.04 124 0.0 1 0.003 -18 27-Mar 0.03106 11 8 118 ___0.08 22 0.03 _ 118 0.00 0.002 19 28- Mar 0.03002 113 _113 _ 0.08 20 0.03 113 0.00 0.002 2-0 29-Mar 0.02588 -107 107 ___ 0.07 17 0.03 107 0.00 0.002 21I 30-Mar_ 0.04017 102 102 0.11 25 0.04. 102 0.01 __0.003 22__31-Mar 0.04617 96 960.12 27 0.05~ 96 0.01- 0.003 23 1-Apr 0.04617 __ 91 ~~~~~9-1- 0.-12 25 0.5. 911 0.01 0.003 24 -pr0.06336_ 8 5 _85' 017 33 __0.07 - 85 0.0 1 _ 0.004 26___4Ap 0.0354 75 75 0.09 16; 0.041 75 0.0 1 0.002 2 7 5-Apr _____0.04679 69__69 _ 0.12 19 0.05 69 _ 0.01- 0.003 28 6-Apr 0.05_176 64 64 _____ 0.14 20 0.6 64 0.01 0.003 29 7-Apr _ 0.05176 58 58 0.14 18 0.06 _58 0.01 0.003 30 8-Apr. 0.03894 53 5 3 ____ - 0.1 12 0.04. 53 0.01 0.003 31 9-Apr, 0.05073 47 47 --0.13- 14 -0.06 47 0.01. 0.003 32 10-Apr 0.06522 _ 42 - 42 _ - -0.1-7- -1-6 --0.07 42 0.01 0.004 33 1 1-Apr . _ 0.051-76 36 3 6- 0. 14 II_ 0.06 -3 6 0.01. 0.003 34 12-Apr. 0.08157 31 - 31 ... 0.22 1 5 0.09- 31 0..01 -0.005 __ 35 13-Apr009 25 __ 25 __ 021- 12 0.09 25- 0.01 0.005 36__14-Apr _ 0.087-58 20 2 0 0.2 10 0.10 20 -0.01__ 0.006 37 15-Apr_- - - - 0. 11346 14 ___I4 - 0.30 10 0.13 14 0.02 __ 0.0OB __2-M~y- 0f.09 ___167 0.04' 19 0.02. 23-9 0.01I--- 0.004 2__4-May- O.09 ____ 167 0.04' 19 0.02; 239. 0.01- 0.004 --1-May_ 0.15 158 0.06 32 0.03' 226i 0.02 70.006 6un 0.14 ___1-59, 0.06. 30 0.02 227' 0.01-- _ 0.006 19-Jun -0.16 ___17 0.07 34 0.03 224 0.02 0.007 20-Jun 0.,26 __ 149 0.12 55 0.05 _213 _ 0.03 _ 0.012 22-Jun -0.1 15 0.04 - -22 0.02' 236 _0.01 _ 0.004 30-Jun 0.12 __ _162_ 0.05 26 0.02 231 0.01 0.005 1--Jul- -0.28 1-4-8 0.12- A 60 0.0-5 211 0,03. 0.013 9-Jul 0.18 ----155 0.08 ___39 -0.03 221 0.02- 0.008 13-Jul_ 0.12 ___ __162 0.05 ______26 0.02 231 0.01 0.005 I8-Jul 0.55 ___137 0.26 116 _0.11 196 0.06 0.027 24jl 0.0-9 _16 0.04 1 9 0.02- -239, 0.01- -0.004 25-Jul 0.12 _162 0.05 26 0.02_ _231 0.01 0.005 3.-Au. -_0. 15 _ _158 0.06 32 0.03. 226 0.02 0.006 0.2c _ 153- 0.09 43 0.04.- 219 --0.02 _ 0.00 I27AU9-- _ ___164 .0 24 0.02' 234_ -0.01 __ 0.005 13-Aua 0.3 146 0.13s 64 0.06. 209 0.03 0.014 Page 3 of 4 ANCHORAGE Commercial Pre-development Area : 1 0ac Area: I O acI 0/oimp: 8 5 % imp: Assumed TSS Snowmelt Snowmelt concentr TSS Day of Melt Date Precip imD=30 (mo/i) m/Il Runoff in Runoff TSS lbs efs l ion mo/I Runoff in Runoff cfssue o S ocaln Rifl nwet uof cnetaRifl nwetRnf 14-Aug 0.26 149 0.12 55 0. 05 21-3 0.03 0.012 16-Aug 0.1 165 0.04 22 0 .02 236 0.01 0.004 23-Aug 0.24 150 0.11 -51 0.04 214 0.03 0.011 4-Sep. 0.16, 157 0.07 34 0.03 224' 0.02 0.007 6-Sep 0.17 156 0.07 36. 0.03 223 0.02 01.007 8P-Sep. 0.27 - 148, 0~.12 58 0.035 212. 0.03. 0.012 11-Sep. 0.14 159 0.06 - 30D 0.0O2_ 22-7 0.01 0.006 _ 14Sep 0,16 _157 __0.07 34 0.03 224 0.02 0.007 15-Sep. 0.18 _155_ 0.08 _ 39 0.0-3 221 _ 0.02 0.008 17-Sep 0.51 138 0.24 1 - -108 0.10 D- 1-97. 0,06 0.024 18-Sep 0.12 162 0.05 26 0.02 231- 0.01 0.005 19Sp 0.64 I35 0.31- 13-5 0.13- 192- 0.07 0.031 20-Seq. 0.14 159 0.06 30 0.02- 227. 0.01. 0.006 2_3-sep_ 0.34 - 144 0.1 5 72 0.06 206 0.04. 0.016 26-Sep. 0.39 142 0.18 83. 0.08 203. 0.04. 0.018 _27-Sep. 0.4 7 _- -- 1,39 _0.22. 100 0.09 -199 0,05 -0,022 29-Sep 0.3 4 ~ 144 0.15 72 _0.06 206: 0.04 -0.01_6 *30-Sep_ 0.29 147 0.1 3 6 0.5 210 D 0.03 .p.1 6-Oct 0.44 140 0.21 93, -0.09 201 0'05 0.021 B -Oct 0.12 162 0.05 26 0.02. 231. 0.01 . 0.005 10-Oct 0.42 - 141 0.19 ___- 89 COS8 202 0.05 _0.020 13-O-ct. 0.09' 167 -0.04 1 9 0.02 239_ 0.01 __ 0.004 -24-Oct 0.14 -i 159 0 6.06-- - _ __30' 0.02 227. 0.01 .0.006 2625-DOt, 0.11 - 164 0.04 24 0.02 234 _ 0.01 OPOS5 211I-Nov_ _ -0.03. 75 75 0.08 13 0.03 _75 0.00 0-.002 27 12-Nov ~~~0.03 69 69 0.08 120.O03 _ 69 0.00 0.002 __ 28 13-Nov 0.03 64 64 0.08 11 0.03 64 0.00- 0.002 2 9-14-Nov 0.03- 58 58 - 0.08 10 0.03 58 0.00' 0.002 30 15-Nov ~~~0.03 53 30.08 9 0.03 53 0.00 0.002 30 15No ___ _ __ 5 3 I 31 16-Nov_ 0.03 47 47 0.08 9 0.03 47 0.00 0.002 32 17-Nov- 0.03__ 42 42__ 0.08-_ 8 __0.0-3 4 2_ 0.00- 0.002 33 18-Nov 0.03 36 36 0.08 7 0.03 ___36 0.00' 0.002 18 15-Dec 0.03 -118 -118 0.08' 21 0.03 118 0.00, 0.002 19 16-Dec 0.03 113 113 0.08 20O 0.03 1 1 3! 0.00 0.002 20 17-Dec_ 0.03 107 107 0.08' 19 0.03 107 0.00 0.002 T-otal -9.4-5- __4-.2 5.8 3322 4.2 0 687' 0.35 0.01 Median Day 0.16 _ 148 0.1 01- -a.9 _ 0.0 187 an 9.45 _156 __ 3.6- 1734 0.03 223 .0.87 . 0.01 Snowmelt ___ 113' _ 5.8 1588 0.03 113 0.35. 0.00 -Maximun- 0.64 0.3 0.3- 135.3. 0.13 Minimum 0.0 9 0.0 0.1 6.5 _ 0.0 Winter % of Total 48% .-- - Page 4 of 4 JUNEAU Residential Industrial Area : 5ac -Area ~ 20 ac % imp : _ 40 S %mp 50 S CN ACIVI I _83 __2.0 CN AM It: 8 6 - 1.6 CN AMC III: 93 0.8 CN AMC III: 9 4 0.6 1 2 3 4 6 7 8 9 10 1 12 1 3 I 1 5 Snormel Day0o f Snowmelt TSS, cmo Rainfall Snownmell Concentral Rainfall Snowniell Concenlrat Melt Date PreciD for mro=30 (moll I Runoff in Runoff TSS lbs Runofi els ton mowl Runoff in Runoff TSS lbs Runoff cis ion milo, 1/5/87 0.13. 0 In/a n/a n/a ni-a nWa n/a fl/a n/a 1/7/87 0.23-, 0.01- 1 000O 1~62- -0.01- 12 0.0O1. 181 111/8/87 0.9. 0. I11 1 6 _0.02 124 0.14 90 0.12 144 1/9/8 7 0.52_ 0.12 17._ 0.0.3 123. 0.19 97 0.13 14 3 -1/15/87- 0.1I5- W n/a - _n/a Wen/na n/a -n/a - fla /a na 1/16/87- 0.98 0.43 , 93 0.09 108 0 49 281 0.41 12 7 1/178 0.4-6 0.09 I3. 0.0-2 1-27 0.11 - 76 0.1 0 147 2 2 1/1 8/87 0.O03- 98.44 n/ae 0.04 _ 4 0.01, 85,n/a -0.0 5 0. 0. 01 0 2 3, 1/19/87- 0.62- 0.03 90.96 0.18a 0.04 _ 28__ 0.05 _ 1-13 0.~211 0.09 1 54 0.22 1 29 2/2/8 7 0.15 W e a n/a n/a n/a n/a W /a n/a n/a 2/4/8 7 0.1 3 n/a ___ n/ a n la - --n/a 0.00 0- 0.00. 380 2/15/847- -0-.22 0.0 1- I __ 1 0.00- 166 0.01 I 10 __0.01 -.185 2/18/87 0.16 n/a 0 n/a( 246 __0 2/ n/0.na0 f 0a~~ 2/20/871a 0.352 0.04 i s 0.01 1372 0.06. o i- 0.09 1972 2A/21/87 0.47 -0.00 09 0.00 2131 0.0 5 6 0.00 521 2/29/8 7 0.29 n/ W e n/a W n /a - / n/a We n_ na n/a n/ 2_ /169/87 O.15 n/a n/a n/a n/a n/a n/a I /A n/a 3/262/87 O0.99 0.13' 18 0.03 1227 0.19G 12 0.16 1397 3292 /87 _ 0.217 - 0.02- 3- _ 0.00 2130 0.003 _ 2 _ 0.02 . 170 2/208 -- 0.29 n/a a1/a n/a n/a- - -n/a n/a n/a a/al 431 /8-7 O.8 -s n/a n/a na nan/a n/a n/ n/_ 4/9/87 0.18 n/a nia n/a n/a n/a _ n/a n/a _ n/a~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-11a.9 A 412 4/i8 7 0.21na _ na na na n/a n/a n/ _ na 4/16/87 0.99 0.04 6- 0.01. -137 0.069 41 22 0.069 1397 4/139/87 _ 02n/a na n/ na . We/eaW n/a n/a n/a 4/20/7 _ 0.11. n/a n/a n/a n/a n/a W e n/ n/a n/a 4/27/87 0.12 n/a n/a nWa n/a n/a n/a nla n/va 4/1418 0.211 We _ We na / n/a _ n/a n/a _ /a n/a 5/12/87 O.1 n/la n/a n/a n/a n/a n/a n/a n/a 9/2/8 7 We1 n/a e / n/a W na-n/a 0.00n/ 0 0.0 - 27 9 /4/8 7 0.15 n/a n/a- ae/a n/a 0.00 11 We0 243 9/8/87. 0.13 _____ n/a n/a -na n/a - 0.00- 0 _0.00 238 -5/23/87 0.5 3 - 0.00 1I 0.00 16240.02 .. 0.(01 178 !5_ /2687_ 0.28 n/a n/a n/a Ala Wea I / n/a 24a 0.5/271871 0.0-12 7 ___ n/a n/ I / 7 / 3 .00 0.09 143 __ 6//8 7 0.134 n/a n/a We/a n/ 0.00 0 -0.00 3 80 65/2387 0.5 0.00 1I 0.00: 174' 0.02 14. 0.01. 1 78 516/1 /7 0.28 n/a n/a n/a n/a n/a W e a n/a n/a 6/127/87 0.15 / n/a -nta- -We 0.00 I- 0.00 243 6/513/67 0.4 . .5 0.06 1 00: 14 __ .1 17 state 7. -0.9-- -0.09- 14~~~ 0.02 _ 131 6 0.08 58 7 _ 0.07 3 1.4 3 _6/16/87 0.27 n/a nla l/ Ia - -nra -n/a nia n/a n/a 6/I 2/87 0.96 0.42 52 W W 0.09 I 09 0723 0.40 128 6/116/87- 0.31 0.03 9 0.01 14L2 0.04. 30 0.03 163 fill 9187 0.26 0.01, 2 0.00 1 53 - 002 i s 0.02. 173 6/21/87 0.75 0.27 34 0.06 114 0.31 I 8 0.2 133 6/22/87 0.15 __ n/a n~a n/a n/a 0.00 I 0.00- 243 6/24/-87 0.114. We_ n/ n/a n/a n/a .___ 0.00 0 0.00 273 6/25187 0.54 0.13 1 8: 0.03 122 0 16 1 04 0.14 1 42 7/11/8167 0.36; n/a n/a n/e n/a 000- I 0.00 2-44- 7/112/87' 0.13 n/a n/a n/a n/a __0.00 0 0.00' 380 7/113/8 7 0.26 1 ___ 0.01' 2. 0.00 1 53 0.02 is 0.02: 1 73 7/14/87 .0.611 0.17 2 3, 0.04 11 9 0.21 130 _0.17 138S 7/26/87-- 0.14. ,_ n/a We____ / n/a W /a Wea W e n n/a n/e 7/27/87 0.26: 0.01 2 0.00 15 3 0.02' 1 8 0.02 1 73 7/28/87 0.2: _____0.00 1I 0.00- 178 0.01 6 0.01 -193 7/29/87 0.52. 0.12 17 0.03 123 0.1 5 97 0.13 1 43 8/16/87 019, n/a n/a /la n/a n/a We./a n/a n/a 8/1 4/87, 0.1, 5 n/a n/a n/a _n/!a n/a -nia nI/ a -/a 8/1 5/87 0.14 n/a n/a n/a Ala 0.00 0 0.00 273 8/16/87 0.17 0.00 0 -0.00 21 3 0.00. 3 0.006 21 4 all 7/87 0.29 0.02. 4 0.00 146 0.03 25 0.03 166 8/21/87. 0.24 n/a n/e Wea n/e We n/a ril n/a l n/ 8/26/8 7 1.11 0.18 - 2 4 0.04. 118 0.26 19 7 0.21 __136 __ 8/3087. 1.2-0.98: 89 .0.12 10 5 . 0.72 - 397 0.80 122 ____8/31/8 7 0.41 0.07 10 -0.01- 13 1- 0 09- S 59 0.07' 151 ___ 9/2/87 0.21 __________ ~~~~~~~ ~~~~0.00' - - 0.0 17 .1 .1' 8 9/3/8 7 0.21 -_ __ 0.00i I-10.00 1 71 0.01 8 0.OV ISO 9/4/8-7 -----0.74 __ 0.26' 33 0.05 1 14 0.30 1182, 0.25. 133 9/.7/8 7 0.5- 0.00 I 0.0 174 0.02 1 4 0.01 1 78 9/9/87 0.34 ___ 0.04. 6 0.01 138 0.05 38 0.04 159 91/7 1.22 _ _ 0.63 74 0.13 1 04 0869 384!, 0.58___ 123 9/1 1/87 0.44 0.08 12 0.02 I128 O.10 69 0.08 I ZS * i~~~~~~~~~~~~4L JUNFAU Area: - cArea: 2 O-ac % imp : 40 . %imp: 5O CN AMC If: a 63 2.0- CN ANII: a 86 1 6 CN AMC III: 93 ~ 0.8 CN AMC III: 9 4 0.6 1 2 3 4 6 7 S 9 10 11 12 - 13 14 is Snowmell Day of Snowmerl TSS conc Rainfall Snowmnelt Concentrafl Rainfall Snowmielt Concentrat Melt Date Precioto IrMD=30 [Mo/I) Runoff in Runoff TSS lbs Runoff crs ion m/Il Runoff in FRunoff TSS lbs Runoff cis ion moail 9112/9 7 0.17 0.00 _ 0. 0.00 _ 21 3 0.00 3 _ 0.00 21 4 911418 7 0.t2 In/a - 11 niana n/a Ala A l a nWe Ala 9/1 618 7 0.14 6/ / / / 0.00 0 0k - .00. 273 9/16/87 0.16 0.00 0~~~~~~~~~~~~~~~~~~~' 0.00 24 6 0.00 2 0.00 226 9/1 7/87.__ 0.43 0.08 I 1 0.02 129 0.066 De0 149 9/1 -8/8-7 0 13 Me _/9 na _ N/a n/a 0.00 _ 0 0.00 380 9/119/ 87 0.45 0.09. 12. _ 0.02 -127 0.11- 7 3 0.09 1 48 9/21/I8 7 .0.-11- n/ __ n/a n/a n/Aa -- n/a _ na n/a n/a 9/2 7/ 87 0.3 --n/a _ __ nia. Ala n/a Ala __ na n/a n/~a 92/7 0.69 0.23 30 0.05 11 6 0.26 1 62 0.22 135 9-/2 9/87 .502 04.0 11 02146 0.20 137 9/30/87 0.63 0.19 S . __ 25 0.04 11 8 0.22 -138 0.19. 138 10/1/87S i 0.9 0.37 _____47 0.08 11I0 09.42 247 0.36 129 110/2/87~ 0.46. 0.09 12 0.02 1 27 0.11 7 6 O.10o 14 7 0._/35 0.0 6 _0.01X 1 37 D.06 ..41 0.05 157 104/87 0.69 0.23-_ 30 0.05 116 0.26 . 16T2 0.22 135 10/5/87 0.53 0.13 18 0.03 .-122 0.16 101 _ 0.1 3 _14 2 10/6/67 01-9 . .00 0.00 186 0.01 5 0.00 199 10/101/87 1.6 -- 0.16- 2 1 0-.03 120 0.2 12 0.9_ 13 10/1_1/187 0.67- 0. 21I 28 0.04 1 116 0.25 1 54 0.21 136 10/28 0.1 8 i7 A 0.00 0 0.00 1 97 0.00 4 0.003 206 10/3/8 0.5004 _ 6. 0.01 1 37 00 410.05 s 17 01014/87 _0.31 0.35 0.01 1 42 0,043 .0A6 10/15/87 ~~0.2 0.00 I-0.00 1 78 0~01 - 6 0.01. 193 I10/1 7/8 70.04. Alna n/a n/a n/a 0.00 0.00 273 10/18/987 0.29 __ 0.02- 4 0.00 1 46 0.03' 25 .03 166 iO/19!87 ~~~~~~~~~~~~~~0.5 014 20 0.03 121 0.17 112 ___5 141 1 0/22/67 0.26 n/-a _n/a n/a Ala Ala -n/a n/Aa n/a 10/23/87 05 01 20_ 0Q.03_ 120 0.18 11 5 0.15 _ 140 1 0/24187 - 0.73 - -- 0.25 33 0.05 11 4 0.29 177 0.25 134 1 0/26/87 0.56 ____0.14 2 0. 0.03 121' 0.17 ___ 112. 0.15 141 1 0/27/8 7 ~037 0.05 8 0.01 134! 0.07 47 0.08 5 10/28/87 0.31 0.03 5 0.01 142 0.04; -30 0.03. 163 10/30/67' Al na A l a n/a Ala Ala __ _ n/a n/a "Is 1 0/31/67 0.19 0.00 0 0.00 1 86' 0.01' _ 5 0.00' 199 11/1/87 0.46 0.09 I 13 0.02 1 27 0.11 76 0.10 147 111/2/87 0.57 0.19 5 _ 20. 0.03 1201 0.18 115 0.15 140 11/3/87 0.17 C I A O 0 0.00 2133 0.00 0.0_ 214 1115/87 0.53 0.13 1_ 18 0.03 122 0.16 101 0.13. 142 11I/6/`87- 0.23- 0.01 1 0.00 __Lk2 L 0.01. ___ 12 0.01; 1I81 11/8/87 0.38 0.05 a 0.01 1 33 0.07 5 0.6: 15 1//7 0.5 0.1 I 1 I6 0.02 124: 0.14: s o 0.12 144 -111/1 0/87 0.24 0.01 _ 2 0.00 159, 0.02. 1 4. 0.01' 178 _ _ _/1 8 _ _ _ _ _ _ _ _ _ _ _ A l a _ A l a Al nI eAa l 11/187 0,1 n/a /A l na A n/a n/e We_ Ala n/a 11D28 A~l na _ r/ / / / / / 11/114/8-7 0.25_ 0.01 - 2. 0.00 --155 0.02 16: 0.-02' 175 I1tisis87 n/a n/ na n/a -/0i/ /a n/a Al na 11/117/87- A l a n/a n/a n/a Ala We Ala Ala n/ 3 0 I11/18/87.. - 0.03 52.6 rA/a . . 0.04 2 0.01l 46,n/a ___0.05 _ 0 0.01 0 31 11/19/87 ~~0.11 0.03 471 / .4 2 0.01 42fl/8 0.05 00 0.01 . 0 .3 2 11/2 0/87 0.03 41.64 Ala 0.04 2 0.1 7n/ - 005_ 0 .1_ 0 11/2 4187 0.47 ___ 0.00'; 0 0.00 lee 0.0~1 10 0.01: 184 11/2 5/87 0.18 .0.00: Oi 0.00 246' 0.007 2 0.00- -226 11/26/87 0.5 A l a A l a n/a flS0.00,-1) .0 4 11/27/8k7 0.52 0.12 1 7' 0.03 123' 0.15 97 0.13 143 11/28/8 7 __ 0.62 0.18 24 0.0 18 0.21 134. 01 13 111/29/8.7 0.5 0.11 I 16' 0.02~ 124i G 01 4 901 0.12' 144 -1 2/2/87 0.24 W e /a n/a "la Ala Ala A l a Ala _ l/a 1 2/3/67 0.12 N /O /A l n/a Ala Ala n/a Ala fl/a__ -_ 1 2/4/87 0.19s 0.000 0 0.00 Iasi 0.01 5 O.00 O199T! 12/ 1 CIA87 _ 0.36 W e / n/a A l/a MIE /Aa 0.00 . 1I 0.001 244 12/12/8 7 - 0.63 0.19S 25 0.04 118 S0.22 138 0.1,9 I138 12/I 13/87' 0.6 0.01 2 0.00 I153 0.02 1 8 0.02 1 73 1 2/16/87 ____ n/a A l/a A l/a Ala Ala ___ na Ala Ala 26. 12/17/87 0.44 0.3 74.52 0.08' 0.04. 1 5 0.03 1 11; 0.10 0.05 85; 0.13 1 125 2 7 0.211947 0.03_ 69.04 0.01__ 0.04' 5 0.01 8 7 0 0 .5 29 .-5 9 7 -28 -12/20/87 0.27 0.03 63.56 0.2 04 6 001 8 0.03 0.0 34 .6 11 12238 - -. -1/ n/a /af n/a We/a- n/a Ala Ala 12124/87 1.13 0.55 66 0.12 1 06 0.81 34. 0.51 124 12/25/87 0.45 __ 0.09 1 2 0.02 127 0.11 73 0.09, 148 Total __ 2.74 9.46 D. 31 12835 11.63 0.38 7351 Medan Day 0.27 0.06I .4 8.69, 0.01 12 7 0.05. .5 2.9. 00 1 57 Rpain Ffpeb-C 38.54 __ 6.591 879 0.01 1 33 8.17 5106, 0.03 1635 Maximuam 1.2 se _ 0.63; 0.04, 74. 0.13 246.04 0.72: 0.05 397' 0610, 3.79.86 Minimum 0 1 , 0.00 0.04 0 0.00 39.70 0.00. 0.05 0 0.00 0.01 JUNEAU -Commlercial --Pro-development Are~ ~ ~~~~~a C 15_a Area : 20 ac- % imp 85 S. Sonj ---1:1 2 S CN AMZII 94 _ 0.6 CN cAMCO 7 0 4.3 CN AMCIII 98.0 0.2_- CN ANCIII 85 1.8 1 2 3 4 -16 1 7 1 8 1 9 2 0 2 .1 2 2 2 23 Snowmelt Concentr Rainfall Snownme Day of Snowmeil 'TSS Conc Rainfall Snowmeii Runoff ation Runoff it Rlunoff Melt Date Precio for imo=30 1Mo/II Runoff in Runoff TSS lWS CIS MoI i n Runoff CIS 1/5/8 7 0 1 3 0 0.00 . 0 -0.00 5 52 n/a .0.00 117/87 0.23 0.09 6 8 0.06 21 8 n/a 0.00 1/8/8 7 0.9 -- 0.32 _ 209 0.20 1 93 0.01 0.01 1/9/87 0.52 0.34 .- 219 0.21_ 19 0.01 0.01 1/15/87 0 15. 0.00 1 0.00- 353 nWe 0.00 1/1 6/87 0.98. 0.77 _ __ 463 0.-49 I7 0i--.16- 0.14- I 78-7 0.4 0.8 8-01 195 0.01 0.01 22 I1/1 8/87. 0.03 96.44 n/a 0.08 __ 0 0.02 0 n/a 0.00 0.00 2 3, 1/l9987. 0.62 -0.03 90.98 0. 43 0.08 _ 297. 0.32 1 72 0.04 0.00 0.03 2/28 a.15 0.00 I -0.00 3153 0.0 2/4/8 7 _ . 013_ 0.03 23_ 0.02 24 7 n/a 0.00 V_25/87 0.22 0.-08 6 30.0 22 O-~ n/ 0.00 __ 2/8/87 ~~~~~0.16 0.04 35 0.03 235 W e _ 0.00 2~/8/7 0.5 0.3 225. 0.22 __191 _ 0._02 _ __0.01 2/9/87 0.4 0.23, 155S 0.14. 199. 0.00 0.0 .!1~~~~~/87 0.16 . ~~~~~~~~0.00 2 0.00 32 8 nWe. 0.00 2/119/87 0.15 0.01 5 0.00 289 n/a 0.00 2!2 0/7 0.50.1I9D 128__ 0 12_ 203n/ __ We 0.00 2/21/87 0.17 0.540 0.3 22/a.0 2/125/8 7 0.29 .0.03 27 0.02 242 n/a 0.00 3/16/8 7 0.1I5 0.001 0.0 33na.0 3/2/8 _ .6 -0.00 2 -0.00. 32 n/a 0c.00 -3/28/187- 0.99-- 0.50 311- 0.3-1 __184 - 0.00 0.00 3/29/87 0.27 0.12 a s 0.08 21 2 n/a 0.00 3/30/87 0.1. 0 .01. . - 12 0.01_ 265 na_ 0.00 _-- _0 - 0. 8 -- _ _ _ _ _ _a _ _ _0 4/3/8 7 ~ ~ ~ ~ ~ ~ ~ ~ ~ / 0/8 n/a 0.00 - _91 _ .1 0.02 15 0.01 298 W e a 0.00 4____511/87 0.1 -__ ____ 0.00 27 0.02 3282 - -n/a 0.0 4/12/87 0.35 0.103' 297 0.02 2423 r/a 0.00 45 /3/87 0.23 ____ 0.09 68 00 7 1 / _____00 54/2087 0.15 0.0 31 0.02 238 1 n/a _ _ 0.00 I 5/8/87~~~- - 0 13 0.02 235 0.021 -247 .n/a00 5/23/87 0.12 0l 14 - W e 0.09 20 n/a 0.00 5/26/87 0.28 -O__ .0 24 .0 245 8n/a 00 5/sile7----7 0.146 0.03! 27 0.02 242 n/a 0.00 5/2/8 7 0.14 -0.05 42 70.03 2421 n/a 0.00 65/817 0.23 0.1; 98. 0.0 210 n 0.0 6/11/87 0.16 0.00 2 0.00 328.~~~~~~~~~~~~~~96 n/a -~0.00 6/12/87 0.5 ___ __ 0.04 ___ 31 0.02 238 n/a 0.00 51618_ 0.4 _ 0.315; 014 198 DO__________ 0.31 ~~~~~~~~~ ~~~~~ ~~~~ ~~~0.27 1082 0.10 209n6,.0 ___~~~~~~~~1 023 D.11 0.0 24 / 0.00 53/7 0.75 0.85 342 0.35 12 0.0 0.06 - - 4002.145 _____a______ 6/25/87 0.54. ___ _0.35 _ 3~ 02 1100'0.02 57/1187 0.361.64 0.0.04 227 n/a 0.00 7/12/I8 7 0.1 f_ _ _ __ 0.03 23 0.03 24 3 1 /a 0.00 7/15/87 0.26. 0.141 83' 0.07. 214' n/a 0.0 7/14/87 ~~~~~~ ~~~~~~ ~~~~0.61' 2670 0.276 1 87 0.03 0.03 7/26/87 0.14 0.00 2 0.00. 328: n/a 00 7/2/87' 0.26 0.1 83 0.0 214 f/ -0.00 -7/ 287 0. -_ _ 0.075 53 I0.04; 224 n/Ia 0.00- 7/29/187 _ 0.52- 4 ___ ___ 0.234 219 0.2--, 1 192a 0.001, 00 8/16/87- 0.15' - -________0.03 2 2 0.00' 253' n/a . 0.00 86/14/187 0.95 0.00 1 5 0.47 35 n/a 0.00 -8/15/870.14 I n a 2 0.I02 4' n/a 00 8/1 6/8 7. 0 0. 2 6 5 0.0.3! 22 / 0.00 8/17/8 7 0.29 0.04. 98' 0.02 210' n/a 00 8/21/87 0.14_ 0.02 15~ 0.01i 259 n/a 0.00 8/258/87. 0 .54 0.60' 367 0.22 191 0.012_ 0. 02 --_ ----7 .4 0.04 161 0.15' 227 0.00 0.00 7912/87 0.21'a 0.08' 2 3 : 0.05' 242 7 /a 0.00 7/1 /3/87 0.26' 0.08 88' 0.078 2122 n/a 0.00 9/4/1187 0.61 0.54 2 337 0.264 183; 0.03 0.03 97/2787 0.526 0.14 983 0.09 210, n/a 0.00 7129//8 7- 0.34 - -0.18 1243 0.011 2047 n/a 0.00 9/10/87 1.522 31.0 1 588 0.63' 1792 0.29' 0.24 8/116/87: 0.4402617 0.17 198 0.03022 n 0.00 B-1 1.71 7--- 0 2 9 0114 9a, 0.09210 n~a0.0 I~ ~~~~~~~118 . 4 0 . 2 15 00, 2 9 na -00 JUNEAU3 Area C~~~~~ommerchil Pmif-development Area-:. ~ I 5ac Area: - 20.ac %im~p 85 S %imp 2 S: CN AMACII 9 4 0. 6 _ CINIAMiI _ 7 0 4.3 1 2 3 4 1 6 I 17 18 _ 19 20 2 _ 21 2 .2 23 Snowmeil Concentr Rainfall Snowme Day of Snowmlell TSS conc Rainfall Snowmelf Runoff afion Runoff It Runoff Melt Dte preto forimn=30Imall) Runoff in Runoff TSS lbs ci s mo/I in Runoff efs 91 1218 7 0.17 0,05 4 0 0.03 232 F i l e .0 9/114/87 0.12 -0.02 1-9 0.01 252 n/a -- 0.00 9/15/8 7 0.14 .0.03 27 0.02 242 nla 0.00 9/1 6187 0.186 0.04 3-5 -0-.03- 2-35 n/a 0.00 9/-17/87 0.43 0.26 _71 06 197 " 0.00 0.00 9/118/8 7 0 13 . 0.03__ 23 . 0.02 24 7 n/'a - 0.00 9/19/87 0.4 0.27 ' AZ 0.17. 16 0.01 0.00 9/211/87. 0.11 0.02 1 - . 50.01 258 W e a 0.00 9/2 7/87- 0.3. . _ 0.04 _ 30 0.02 239 n/a _ 0.00 912 8187 0.89 0 ~ - ~.49.. - 31 0 0.31 184 0.05 0.05 9/29/87 0.65 0.4 _ 28-9 0.29_ 186 _ 0.04 _ _ _0.04 9M/30/7 0.6 3 0.4 4 278 0.20 18 7 0.04 0.03 1 0/1/97 0.8 0.69 ___421 0.44- 178 0.13 0.11 1-0/2/8-7 -0.46 0.28 187 0.18 195 0.01- 0.01 1 0/3/87 0.35 0 19 1 29 0.12 203 nia 0.00 1 0/5/87 0.53 O. 35 2 _ 25 0.22 1 91 0.02011 0106/87 0.1I9. 0.08 49 0.04 227 n/a 0.00 10/10/87 1.06 ____ ~~~~~~~~~~~0.5 -4 _ 33 0.35 18 .10.01 Oil/11/87 0.8C7-_ 0.48 299 0.30 185 0.05 0.04 10/l 1287 0.1I8 _ 0.06 44 0.04 22 9 fl/f 0.00 I10/ 1387. 03 5 0. 19 __ 129 0.12 203 n/a 0.00 10/1-4/8 7 0.31- 01 1 0TO80.10 -20 / 0.00 10/115/87 0.2 0.753 0.4 224 n/a 0.00 I10/17/87 0.14 -0.03 -27_ 0.02_ 242 Wit 0.00 10/18/87- 0.2j9 091 .80 09 210 O - /a - 0.00 10/ 1987 0.56 0.37 _ ___241 0.23 190o 0.02 0.02 1 0/22-/877 0.2-6 -0.02 I 19 0.01 251 nia 0.00 10/2 i3/87 E.5 03-8 -_246 __.24 1 89 0.02 0.02 1 0/24/8 7 0)73 0.53.. - 331 0.34 18 3 0X.0 _ 0.08 01028/07 -0.56 0.37 241' 0.23 ISO 0.0 0.02 _ 0102718 7 0.37 __ 0.20 1 39 0.13 202 0.00 __ 0.00 _10/28/87 0.31- 0.15 I 108 0.10 207 "la - 0.00 1 0/30/8 7 0.1, 0.01 1 2 0.01: 285. n/a 0.00 -10/31/187. 0.19 0.06 4 9' 0.04 227 W e a 0.00 11/11/87 0.48 6_ 0.25 187 0.18 1 95 0.01 0.0 11/2/87 0.57 _____ 0.38 246 0.24 1 89 0.02 0.02 -11/3/87 0.17 0.05.- 40! 0.03 232 n/a -0.00 111/5/87 0.53. .3 225 0.22 Is81 0.02 -co-,1 11/6/8 7 0.23 0.09 688 0.06 218: n/a 0.00 11/8/87 0.38 _____ __ 0.21 145, 0.13' 201, 0.00 0.00 11/9/87 0.15, 0.32 __209 0.20~ 1 93' 0.01 0.01 I1I/l0/87. -0.24 -____01 73 0.06 217 W e a 0.00 _ _ _ _ _118 _ _1 0_ _ _ 0 2 _ I~ .1 2 8Nt0 11/112/87 _ 0.11. 0.02 15. 0.01 258. Wea 0.00 11/14/87 0.25 0.11 ~ ~~~ ~~~~~ ~~~ 78 0.07 21 5 fl/a __ 0.00 I !~~~~~~~~~15!87 . .. . - ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~n/a n/a n/a fl/a n/a 00 l11/1787- n/8 n/a n/a n/a W e a 0.00 30-11/10887 0.03 52.6 n/a- ____ 0.08 a 0.02 0 n/a 0.00 0.00 3 1 11/1 9/8 7 0.11 0.03 47.12 0.02 0.08 28 0.06 85 nl/a 0.00 0.00 32 11/20/67 0.03 41.64 fl/a 0.08 0 0.02 0 Wea 0.00 0.00 11/24/8 7 0.47 0.12- a 6 0.08 213 n/a 0.00 11/- 25/87 0.Li I__ 0.04 _____35' 0.03 238, n/a , 0.00 11/26/6 7 0. 15 _____0.04 31 0.02 238' fl/a 0.00 11/l27/87 0.52- 0.34 219i 0.21' 18921 0.01 0.01 11/28/87 0.62, 0.43 273' 0.27 1871 0.04 0.03 11/29/8 7 0.5' 0.32' 209 0.20 193i 0.01 0.01 ____ 12/2/87' ~~0.241 0.02' 1 5 0.01' 2591 / 0.00 1L2/3/87 0.12 _______0.02, 19 0.01 252' n/a 0.00 -12/4187 _0.19 0.06' 4 9' 0.04, 227! Wea 0.00 1/087 0.38' 0.06 48! 0.04 227' We/ 0.00 --1-2/12/87 .-0.83 ______ 0.44. 278 0.28 1 871 0.04. 0.03 12138 02_6' 0.11 83' 0.07 21 4' -na - - 0.600 12/18/87 ___________ ~~~~~~Noa n/a n/a n/a , n/a 0.00 26_12/117/87 0.44' 0.03' 74.52 0.26 0.08' 1 97 0.22. 168: 0.00' 0.001 0.01 2 7 12/119/87 0.24_ 0.03 ..69.04 0.1I0 0.8 91 0.11 151' n/a 0.00 Co0 28a 12/2 0187 0.27~ 0.03' 63.56 _ 0.12 0.08' I105 0.13 153' n1/11 0.00' 0.00 12/23/87 ~~0.2~ 0.01 7 0.00 281' n/a 0.00 _12/24/87 1.1 3 ____0.92 541. 0.58 .173 0.24 -0.20 12/25/8 7 0.45 0.27 182 0.17 1 96 0.01 0.00 Totla 52.74 27.02 0.84; 1782 2.05. 0.04' - --efcafw lay_. 027' - 0.1I0 0.08. 70.12 0.08 214' 0.23' 0.02. 0.00 0.00 Ran(Fe -C 38.5 4 19 .1 . 1 12544 0.05 222: 1 41. 0.00 Maximum __ ~1.82 ______ 1.23' 0.08: 704! 0.77 552.07 0.29' 0.00 0.24 Minimum 0.1 0.00 0.08 0 0.00 0.01 0.00 0.00 0.00 44U BETHEL Residential Area: 5 ac Area: 1 % im 25 S:. % imp:_ . 40 TR 55 Factors: CN-AMC 11 8 5 1.8 CN-AMC 11 87 AsmdCN-AMvC III 94 --0.6 CN-AMC II I 95 Assumed Snowmelt Snowmelt TSS Day of for TSS conc Rainfall Snowmelt Runoff Concentrati Rainfall Snowmelt melt Date Rain (in) irnp=30 (mg/I) Runoff in Runoff in__TSS lbs ciofs_ on mngLl Runoff in Runoff in 8 19-Jan _ 0.03 8 7 0.03 3 0.005 8 87 0.04 9 20-Jan __ 0.03 84 __ 0.03 _ 2 0.005 __ 84 0.04 10 21 -Jan 0.03 81 0.03 2. 0.0.05 a 81 0.04 I11 22-Jan 0.03 78 0.03 2 0 .005 7 8 0.04 12 23-Jan 0.03- 76 0.03 2 0.005 _ 76 0.04 -13 24-Jan _ 0.03- 73.- -0.03. 2 0.005 73, -0.04 1 4. 25-Jan _ _ 0.03 70 0.03-- 2,_ 0.005. _70 ~ 0.04 1 5 26-Jan 0.03 67 0.03 2 0.005 _67 0.04 42-Fb .0 9 00 3 3 .___9_ 00 4 27-Feb 0.03 95 0.03 3 0.005 95 0.04 1 6-Mar 0.03. 1 06 0.03 3 0.005 __ 106- __ 0.04 1-21-Mar- 0-.047- 1-06 0.04 5- 0.008. 1-06 0.106 2 23-Mar 0.023 1 03 - - 0.02 2 0.004 1 03 0.03 3 24-Mar __ _ 0.047 100 0.04 5 0.008. 1 00 0.06 -4 25-Mar 0.030 -98 0.03 3. 0.005 9_8 0.04 I ~ ~~~5 26-Mar _ 0.020 95 0.02 2 0.004 95 __0.03 6 27-Mar 0.020 9 92 _ 0.02 __ 2 0.004 9 92 0.03 7 28-Mar 0.020' 89 0.02 _ 2 0.004 89 __ _ 0.03 29-Mar _ 0.020 87 _ 0.02 2 0.004 __ 87 __ 0.03 9 30-Mar________ 0.035 84 ___ 0.03 3 0.006 _ 8_4 0.05 1 0 31 -Mar 0.040 81 0.03 3 0.007 81 0.05 IL1-1 P 0.041 78 0.03. 3 0.007 78 ___0.05 12 _?_Apr .3 76 0.03' 3 0 .007 76 _0.05 -_ 14 4-Apr 0.038 70 ~ 0.03 3 0.007 _ 70 0.05. 13 3_-Apr_ 0.037 673 0.03: 2. 0.007 673 0.04 1 ' 6-Apr 0.026 65 0 .02______ 2 0.005 51 0 .0 167 0.038 62 0.03 2 0.007 62,_ 0.05 1 8 S-A r 0.031 __ 59 0.03 2 0.006 _ 59. 0.04 1 9.__ 9-Apr _ 0.030 ----56 0.03- 2 -0.005- 56- -0.0.4 20 10-Apr -~~ 0.026 54 0.02 1 0.005 _ 54 0.03 _21 _1-A__ 0.040 5 1 0.03 2 0.007 _ 51 0.05 __2_ 12-AP~_ 0.046 48 0.04 2 0.008 _ 48- 0.06 __ 23 13-Apr 0.046 45 0.04 2 0.008 45 0.06 24' 14-Apr' ~~0.063 43. 0.05, 3 0.011 43! 0.08 25, 15-Apr 0.050 40: 0.04. 2. 0.009 40 ~ 0.06 26' 16-Apr 0.035 37, 0.031 1' 0.006 37' 0.05 __ 27' 17-Apr 0.047- 351 0.04! 2 0.00-8 35i 0.06 28; 18-Apr 0.052' 32 0.04 2' 0.009 32' 0.07 29: 19-Apr ~~0.052 29 0.04' 1 0.009 29 0.07 2_7-MVay_ 0. 17' 0.00, 0 0.000 n/a 0.00' 28Ma 0.29 0.03 4 0.007 100 0.05 6-Jun 0.22 _ __ 0.00. 0 0.0-00! n/a 0.00- 15--Jun _0.22 ____0.00 0 0.000'n/a- 0.00- 16-Jun 0.27 0.03 _____3 0.005 103 0.04 23-Jun 0.12 ___ 0.00 ___ 0 0.000in/a 00 26-Jun 0.12 0.00 0 0.OO I n/a 0.00. 15-Jul 0.31 ___ 0.00i 0 0.000'n/a - 0.' ___17-Jul 0.23 __ 0.01 2 0.003 109__ 0.02~ 24-Jul- 0.31 0.00. 0' 0.OO0in/a 0.00 28-Jul 0.12 0.00 0' 0.000!n/a 0.00 I-Auq 0.2 0.00 0 0.OOO1n/a 0.00 PagelIof 4 BETHEL Residential Area: 5'ac Area: 1 % imp: 25 S: % imp: 40 TR 55 Factors: CN-AMC If 85 1.8 ON-AMCII1 8 7 ON-AMC III 94 0.6 CN-AMC III 9 5 AssumedI Assumed Snowmelt Snowmelt TSS Day of for TSS conc Rainfall Snowmelt Runoff Concentrati Rainfall Snowmelt melt Date Rain (in)_imp=30 (mg/I) Runoff in Runoff in TSS lbs cfs -on mg/I Runoff in Runoff inI *8-Aug 0.11 0.00 -0 0.000 n/a 0.00 _ 13--Aug. 0. 15S 0.00 0 0.000 n/a 0.00 15-Aug_ 0.52 0.15 1 5 0.031 8 6 0.18 17- Au 0_2 0I2200 4 700 24-Aug 0.24 0.02 2 0.004 107 0.03 - 7-S~~p-.1 1- 0.00 0--- 0.000-n/a 0. 00. 8-ep 0,15 0.00. O0 -0.0001 -146 0.00 17-Sep 0.26 0.00 -0 0.000 n/a 0.00I 18-Sep. 0.33 0.05 5 0.010 9 6 0.07 -19-Sep. 0.14 0.00 0 0 .000 164 0.00 *25-Sep 0.17 0.00 0 0.000 n/a _ 0.00 30-Sep_- 0.13 0.00 0 0 .000_n/a 0.00 1 -Oct O, 016 0.00 0 0.000 136 0.01 2-Oct 0.31 0.04 5 0.009. 9 8 0.06 -3-Oct 0.19 0.01 1 0.001 120 0.01 28-Oct 0.18 -- 0.00 -0 0.000._n/a __ 0.0-0 2-9-IOct* 0.1 0.00 0 0. 000 n/a __ 0.00 31 -Oct 0.3 0.04 ___ 4 0.008 99 0.05 24 27-Nov ___ 0.03 43 0.03 1 0.005 _43 0.04 25 28-Nov 0.03 _ 40 0.03 1 0.005 40 ___ 0.04 22 13-Dec __ 0.03 48 0.03 1 0.005 48 _ 0.04 23 14-Dec ____ 0.03 45 0.03 1 0.005 _ 45 0.04 -Total 6.67' 140 - - Median Day 0.20 __0.00 0.03. 2 0.005 81 0.00 0.04 Rain ___ 6.67 0.39 42 0.000 -107 0. 5 Snowmelt ___ ____ 1.3: 97'_ 0.0 1 _ 72 _2 .0 Maximum Summer Day _ 0.15 0.05 14.61 --0.031 164 0.18 00 -Minimum Overall 0.00 0.02 0.00 0.00 2 9 0.00 0.03 TSS - Winter % of Total 70%3 Page 2 of 4 BETHEL Commercial Pre-Development ac Area: 5 ac S:. _~~% imp: 2 S:- TR 55 Factors: 1.5 CN-AMC 11 73 3.7 0.5 CN-AMC III 8 7 1.5 I ~~~~~~~~~~Assumed Assumed Snowmelt Snowmelt TSS Day of for TSS conc Concentrati Rainfall Snowmelt Runoff melt Date Rain (in) imp=30 (mg/I) TSS lbs Runoff cfs on mg/I Runoff in Runoff in cfs I ~ ~~~~8 19-Jan 0.03 87 0.002 -0 -0.00 0.001 9 20-Jan 0.03 84 1 0.002 84 0.00- 0.001 10 21 -Jan 0.03 8 1 1 0.002 81 I 0.00 0.001 U~~~~~~ _ 22Ja __37 81 . 0 00 . 12 22-Jan 0.03 78 6 I 0.002 78- 0.00, 0.001 1 3 24-Jan 0.03 7 3 1 0. 002 7 3 0.00 0.001 1 4 25-Jan 0.03 _ 70 _ _1. -0.002. 70. 0.00- 0.001 1 5 26-Jan 0.03 6 7 _1 __0.002 67 0.00 0.001 4 27-Feb 0.03 98 1 0.002 9 8 0.00 0.001 5 28-Feb 0.03 95 1 _0.002 95 0.00 0.001 1 6-Mar 0.03 106 1 0.002 1 06 0.00, 0.001- 121-Mar* 0.047 1 06 I _ 0.003 1 06 0.01. 0.002 223-Mar 0.023 103 1 0.001 103 0.00 0.001 3 24-Mar 0.047 1 00 1 0.003 1 00 0.01. 0.002 425-Mar 0.030 Ss 1 0.002 98 0.00 0.001 526-Ma r ~ ~ ~ 0 _2 9_1001 9500 .0 626-Mar 0.020 92 1 0.001 92 0.00 0.001 6727-Mar 0.020 692 .0 200 .0 7 8Mr. 0.2 8 0.001. 89- 0.00. 0.001 8 29-Mar 0.020 87 1 0.001 __ 87 0..00- 0.001- 9__30-Mar _003 84. 1 0.002 __ 84 _'_0.01 0. 0-01 10 31-Mar 0.040 81 __ 1 0,002 81 0.01 0 .001 -1 ~ ~ _ 2_ 7008 1 002-7-0.01, 0.001 -1 55-___ 0 .037 _ 67 1 0.002 67 -0.01 0.001 I ~~~0.026 65 0~ 0.001 _65. 0.00. 0 .00 1 17_7- ~ 0.038 _62- 1 -0.002- 62- 0..0 1 0.001 1 8 8_ Apr .__ 0.031 5-9 1_ 0.002 59- 0.00 0.001 19 9_Ir 0.030 56 0 0.002 - 56 -0.00 0.001 20 10O-Apr _ 0.026 5 4 0 0.001 54' 0.00 0.001 21 _ i ~~ 0.040 _ 5I 1 0.002 51 -0.01- 0.001 22. 12-Apr. 0.046- _ 48 -1 0.003 48 0.01 _0.002 __ 23' 13-Apr __ 0.046 -4 5 __ 0.003 45 0.0 1- 0.002 24_ 14-Apr 0.063 43 1 0.003 43 ___ 0.01 0.002 25_ 15-Ap 0.050 40, 1 0.003. 40i 0.011 0.002 26- 16-APF __0.035__ 37- 0 0.002 ~ 37, 0.01 0.001 27'__17-Ap~__ 0.047 35. 0 0.003 as ! 0.01 0.002 2 8 1 8-Aqr 0.052 _32 _ 0 0.003 .32 0.01 0.002 2919--pr- 0.052 29 0 0.003 29 0.01 0.002 Z7-May_.. 0. 17 -0 0.000in/a __ _0.00 0.000 2_ -Mav 0.29 --1 0.002 .133 0.00 0.000 6-Junm 0,C.-2-2 ___ 0 0.000 n/a _ __ 0.00 0.000 15-Jun 0.22 0 0.000:n/a 0.00 0.000 16-Jun 0.27 1 0.002 13 0.00 0.00 0 23-Jun 0.12 0 0.000,n/a 0.00 0.000 26-Jun 0.12 __ 0 0.000 in/a -- 0.00 - --0 1-A5-Jul. 0.31 0 0.000 251 0.00 0.000 17-Jul_ 0.23 1 0.001 142' 0.00 0.000 24-Jul 0.31 ___ 0 0.000 251 0.00 0,.000 28-Jul___ 0.12 0 0.000! n/a 0.00 0.000 I1-Aun 0.2 0 0.000,n/a 0.00 0.000 U ~~~~~~~~~~~~~~~~Page 3of 4 BEITIEL Commercial Pre-Development ac Area: 5 ac S: ~~~~%np 2 S: TR 55 Factors: 1.5 CN-AMCII1 73 3.7 0.5 G N-AMC II I 87 1.5 Assumed Assumed Snowmelt Snowmelt TSS Day of for TSS conc Concentrati Rainfall Snowmelt Runoff melt Date Rain _(in) imp=30 (mg/I) TSS lbs Runoff cfs on mg/i Runoff in Runoff in cfs 8-u 052 0 0.008 116 0.00 0.000 13-Aug 0.15 0 0.000 n/a - 0.00 -0.000 I15-Aug 0.5 _ .0 1 0.03 ~0.006 17-A~ 0.24 0.001 14 .00.000 24-Aug 0,54. I 0.001- - 138 0.00. 0.000 *7-Sej 0.11 0 0.000~n/a __0.00 0.000 8-Sep 0.15 0 0.000---.. 172 0.0-0- 0.000 17Se .26 0 0.000 n/a 0.00 0.000 I1a-Sep 0.33 2- 0.003- 1-28 0.00- 0.000 I19-~p_ _0._1_4_ 0 0.000 lei 0.00 0.000 25-Sep 0.17 0 0.000~n/a _ 0.00 - 0.000 30-Sep. 0.13' 0, 0.000 n/a _ 0.00 0.000 1 -Oct 0.16 0 _0.000 166 0.00 -0.000 2-Oct 0.31 2 0.002 130 0.00 - 0.000 3-Oct 0.1I9 0 0.000 _153 0.00 0.000 28-Oct _. __ __00 I/ .0 . 28-Oct 0.18 0 0.000 n/a 0.00 -~0.000 31 -Oct 0.3 2 0 .002 131 0.00 0.000 24- 2-7-Nov. __ 0.03- 43 0 0.002 43 0.0 0.001 25-28-Nov 0.-03 40 0 0.002 40 0.00 0.001 22 13-Dec 0.03 48 0 0.002 48 0.00 0.001 23 14-Dec-- 0.03 45 0 0.002 45 0.00 0.001 Total_ 6.67 ___ 45 ____ 0 M~edian Day 0.20 1 0.002 ----8 1 0.00. 0.00 0 Rain - 6.67 _ 16 0.000 140 _0.03. 0 Snowm-elt 29 0.00 70 -0.2 .Maximum S ummer D~ay --- 4.82 0.008 251. 0.03 0.01 0.01 Minimum Overall 0.00 0.00 0 0.00 0 .00 0.00 TSS - Winter % of Total 64% 89%3 Page 4 of 4~~~~ I I I I Rainfall Runoff TR 55 CN Values for Juneau and Bethel I I I I I I I I I I I I I I I *MONTGOMERY WATSON BY DATE __ __ CLIENT SHEET -__ OF CHKD. BY DESCRIPTION____ JOB NO. ,.1~~~~~~~~~~~~~~~~~4 ~~~~ ---~~~~~~~~4 cow, TaL, .7 * -~ _e -eL~M 'jt~pe . �oKd;F~Jj c~~~~~~~~~ EN~L IS0/3 In, /~ JPP~-W*i UMiC ~~ -~~~-~~- w-- ~ ~ ~ C ~e Ac.F~jQ Y P,, U~ ~ cSS'&~2 ~A3~i'C C D c1 3 ru unn'r~o tAMC W~j c~~~ ~~~7 ~ EN iS I 1OI78) Department of Aru Urban Hydrology Servicenean for Small Watershed= Engineering Division Technical Release 55 June 1986 I Table 2-2a.-Runoff curve numbers for urban areasl Curve numbers for Cover description hydrologic soil group- Average percent Cover type and hydrologic condition impervious area2 A B C D Fully developed urban areas (vegetation established) Open space (lawns, parks, golf courses, cemeteries, etc.)3: Poor condition (grass cover < 50%) .............. 68 79 86 89 Fair condition (grass cover 50% to 75%) ........... 49 69 79 84 Good condition (grass cover > 75%) .............. 39 61 74 80 Impervious areas: Paved parking lots, roofs, driveways, etc. (excluding right-of-way) .......................... 98 98 98 98 Streets and roads: Paved: curbs and storm sewers (excluding right-of-way) .................................. 98 98 98 98 Paved; open ditches (including right-of-way) ....... 83 89 92 93 Gravel (including right-of-way) ................... 76 85 89 91 Dirt (including right-of-way) ..................... 72 82 87 89 Western desert urban areas: Natural desert landscaping (pervious areas only)4... 63 77 85 88 Artificial desert landscaping (impervious weed barrier, desert shrub with 1- to 2-inch sand or gravel mulch and basin borders) .............. 96 96 96 96 Urban districts: Commercial and business .......................... 85 89 92 94 95 Industrial ........................................ 72 81 88 91 93 Residential districts by average lot size: 1/8 acre or less (town houses) .......................65 77 85 90 92 1/4 acre ......................................... 38 61 75 83 87 1/3 acre ......................................... 30 57 72 81 86 1/2 acre ......................................... 25 54 70 80 85 1 acre ........................................... 20 51 68 79 84 2acres-12 46 65 77 82 2 acres .......................................... 12 46 65 77 82 Developing urban areas Newly graded areas (pervious areas only, no vegetation)5.................................. 77 86 91 94 Idle lands (CN's are determined using cover types similar to those in table 2-2c). 'Average runoff condition. and I1, = 0.2S. 2The average percent impervious area shown was used to develop the composite CN's. Other assumptions are as follows: impervious areas are directly connected to the drainage system. impervious areas have a CN of 98, and pervious areas are considered equivalent to open space in good hydrologic condition. CN's for other combinations of conditions may be computed using figure 2-3 or 24. 3CN's shown are equivalent to those of pasture. Composite CN's may be computed for other combinations of open space cover type. 4Composite CN's for natural desert landscaping should be computed using figures 2-3 or 24 based on the impervious area percentage (CN = 98) and the pervious area CN. The pervious area CN's are assumed equivalent to desert shrub in poor hydrologic condition. Composite CN's to use for the design of temporary measures during grading and construction should be computed using figure 2-3 or 24. based on the degree of development (impervious area percentage) and the CN's for the newly graded pervious areas. (210-VI-TR-55, Second Ed., June 1986) 2-5 Table 2-2c.-Runoff curve numbers for other agricultural lands' Curve numbers for Cover description hydrologic soil group- Hydrologic Cover type condition A B C D Pasture, grassland, or range-continuous Poor 68 79 86 89 forage for grazing.2 Fair 49 69 79 84 Good 39 61 74 80 Meadow-continuous grass. protected from 30 58 71 78 grazing and generally mowed for hay. Brush-brush-weed-grass mixture with brush Poor 48 67 77 83 the major element.3 Fair 35 56 70 77 Good 430 48 65 73 Woods-grass combination (orchard Poor 57 73 82 86 or tree farm).s Fair 43 65 76 82 Good 32 58 72 79 Woods.6 Poor 45 66 77 83 Fair 36 60 73 79 Good 430 55 70 77 Farmsteads-buildings, lanes, driveways, 59 74 82 86 and surrounding lots. 'Average runoff condition. and I:, = o.2S. 2'oo0: <W50 ground cover or heavily grazed'with no mulch. Fair: 50 to 735, ground covet and not heavily graze(l. Good: > 75% ground cover and lightly or only occasionally grazed. 3ploor: <50% ground cover. Fair:i 50 to 75% ground cover. Good: > 75%c ground cover. 4Actual curve number is less than 30: use CN = 30 for runoff computations. sCN's shoun were computed for areas with 50�/ woods and 50'7 grass (pasture) cover. Other combinations of conditions may be computed from the CN's for %wood(s and pasture. 61'oor: Forest litter,. small trees. and brush are destroye(l by heavy grazing or regulal burning. /a ir: Woods are grazed but not burned. and some tlrest litter covers the soil. (;o1: Woods are protected firom grazing, and litter andi brush adequately cover the soil. I (210-VI-TR-55, Second Ed., June 1986) 2-7 United Statesional Department of Nat i onal Agriculture Engineering Conservation I l Se�rvi:ce� Handbook I I Section 4 Hydrology I I l I I I I I I I I I I I I I 10.7 Table 10.1. Curve numbers (CN) and constants for the case I = 0.2 S 1 2 3 4 5 1 2 3 4 5 Cfor CN S Curve* CN for CN for S Curve* condi- starts condi- starts condi- conditions values* starts condi- conditions values* starts tion where tion where (inches) (inches) (inches) (inches) 100 100 100 0 0 60 40 78 6.67 1.33 99 97 100oo .11 .02 59 39 77 6.95 1.39 98 94 99 .204 .04 58 38 76 7.24 1.45 97 91 99 .309 .06 57 37 75 7.54 1.51 96 89 99 .417 .08 56 36 75 7.86 1.57 95 87 98 .526 .11 55 35 74 8.18 1.64 94 85 98 .638 .13 54 34 73 8.52 1.70 93 83 98 .753 .15 53 33 72 8.87 1.77 92 81 97 .870 .17 52 32 71 9.23 1.85 91 80 97 .989 .20 51 31 70 9.61 1.92 90 78 96 1.11 .22 50 31 70 10.0 2.00 89 76 96 1.24 .25 49 30 69 10.4 2.08 88 75 95 1.36 .27 48 29 68 10.8 2.16 87 73 95 1.49 .30 47 28 67 11.3 2.26 86 72 94 1.63 .33 46 27 66 11.7 2.34 85 70 94 1.76 .35 45 26 65 12.2 2.44 84 68 93 1.90 .38 44 25 64 12.7 2.54 83 67 93 2.05 .41 43 25 63 13.2 2.64 82 66 92 2.20 .44 42 24 62 13.8 2.76 81 64 92 2.34 .47 41 23 61 14.4 2.88 80 63 91 2.50 .50 40 22 60 15.0 3.00 79 62 91 2.66 .53 39 21 59 15.6 3.12 78 60 90 2.82 .56 38 21 58 16.3 3.26 77 59 89 2.99 .60 37 20 57 17.0 3.40 76 58 89 3.16 .63 36 19 56 17.8 3.56 75 57 88 3.33 .67 35 18 55 18.6 3.72 74 55 88 3.51 .70 34 18 54 19.4 3.88 73 54 87 3.70 .74 33 17 53 20.3 4.06 72 53 86 3.89 .78 32 16 52 21.2 4.24 71 52 86 4.08 .82 31 16 51 22.2 4.44 70 51 85 4.28 .86 30 15 50 23.3 4.66 69 50 84 4.49 .90 68 48 84 4.70 .94 25 12 43 30.0 6.oo 67 47 83 4.92 .98 20 9 37 40.0 8.00 66 46 82 5.15 1.03 15 6 30 56.7 11.34 65 45 82 5.38 1.08 10 4 22 90.0 18.00 64 44 81 5.62 1.12 5 2 13 190.0 38.00 63 43 80 5.87 1.17 0 0 0 infinity infinity 62 42 79 6.13 1.23 61 41 78 6.39 1.28 *For CN in column 1. I I Derivation of Snowmelt Runoff and TSS Loading from North Arctic/Orbit Data I I I I I I I I I I I I I I I I ~~MONTGOMERY WATSON BY ~~DATE ___ CLIENT-_________ SHEET-- -O CHKD. BY DESCRIPTION-- - O O k V~~Zmu-rffw )ArIL.~ VOL-L)MR 0 - P~A L; I Co~~~~~~~~~4fU~~~~~~~h ~~VOL.IW1 H~ ~ ~ ~ c ________ Itfi.- EN IS (10/781 MONTGOMERY WATSONI By DATE ..--___ CLIENT ____________ SHEETo CHKD. BY DESCRIPTION JO B NO. c (cf ) 1 i TA N~ cL PL vw AT 'nAAMw W ~4 6N) 7SS S~M. P L VOL I~ OF~J]~Z~ ~ ~r4O~ro72- P~AY "40tIl 114t T'sr. vc Tsx ~ lg~~~~~~~~~~~~'N~ ~~~~~g dasjm~Ao c1m eDrL1 T~s (W/oie/fr.7) ~ s4 4 ivt fu-tt~w QOL> kr DATA I'76 Fv T6,~ LoA/rG)6 &rr' TDXreMN&. S3Mil. ReACTsJ5H? oVE1ZThA- EN IS 110178) I O MONTGOMERY WATSON BY DATE ____ CLIENT SHEET --.- OF CHKD. BY DESCRIPTION J NO. ::~~~~~~~~~~: I~~~~~~~~~~~~~ 1~~~~~~~~~~~~~~~~i -' - -- ,-.- 7-~~~~~ -. -~ dI~ I~~~~~~~~~~~~~~~I j~~~~~~ ~~~~~ -/ -j~~ EN ~~~~~~~~ISd (10178 ~~~~~~~~~~~~~ ., ,. I' I~- ---.' - ' 73C , G I~~~~V .l? - yo~r n ~~~~~i~~- -G- p.'-Jl';l ~2~ ec: - EN iS (10/753 MONTGOMERY WATSON By DATE _____ CLIENT SHEET iL.... OF CHKD. BY DESCRIPTION��- JOB NO. Arr Ikc LUac a .74 c 5~~~3~~~~3 ~ ~ i V, ,6 / - �C I 3-5) , 3-f .-?3 IC\i)3'r,1Y: h~~~~~~~~~~~~~~~~~ f~jE~YoMP 7 _2 5t 40xU ,D "LDrro" 40 0,10 r CI~~~~~~~~~~~~~ s~s c- 3r.q aS- I -. ~ 70 ENc~rzb 55 [U (, 1178 EN ~~~ ~ ~ 3 is (10178 MONTGOMERY WATSON BY DATE ____ CLIENT SHEET - _ OF CHKD. BY DESCRIPTION JOB NO _____~~� ___ _O ______ ______ _'~ /9tv D = 2 g;/ys- "j I~~~~~ ..- :-:d/I j7:,t/ 'A" a , r f EN 15 (~~I WS)~ ENI1S (10/78) I~~~~~~~~~'" I~~~Nl 1/8 I �~~~~~~~~~~~~~ MARCH, 1988 BASIN SNOW COVER/SNOW PACK SOONh 0 05-I RESULTANT WIND cm. . L .. . lo~~~~~~~~~~~~~~~ph~~~~~~~I 4wr .as t TEMPERATURE if .20 f oIScHARGE crs ,o, *, cu It/ see .10I �--~~~~~ ~~~~~~ I ` iA~ .oa p~~~~~~~~~~~~~~~~~~~~~~~~~~~~~E PRECIPITATION I-O L In >.ot A i - r~~~~~~~~~~~~~~~~~ * WATER OUAUIY SAMPLE TAKEN I..... *I S 0 IS I 12 1 2 14 I II I? II Is 20 BREAKUP HYDROGRAPH FlGURE FOR 0 ',ORBIT BASIN low. ~~~~~~~~~MARCH, 1988 BASIN SNOW COVER/SNOW PACK M ron�oeI U RESULTANT WIND c"L L J U mom h0 J I I I lam,* I A' I dq? �mLOAT Ur 4-/ TEMPERATURE jur .20 -./v - "-.-I -4 -.1L- I OISCNAR E CFS 2? .15 .11u ~~.10 C.A - 1~~L~ ItrY I2'-7. I01.ii N~f r WATER QUAUTA SAMPLE TAKEN 'A A J. I 10 1 I P I r i~ j ,~, m 20 2' ~~22 22 24 25 26 27 It 2 20 MI BREAKUP HYDROGRAPH r ORBIT BASIN I ][~~~~~~~~~~~~~ 0oo0 IAPRIL, 1988 BASIN SNOW COVER/SNOW PACK RESULTANT WINO CL - - -- I~mp I #01111H � iI I. B tur .25 , r\ I\- i I TEMPERATURE Ilrl .2 0AL /1l OISCHARGE CFS aOr s I I I 2 I e1. I I\ 1\ A I I 0.0I* - V15\ sOIt r PRECIPITATION I 0.0' I I I * WATER OUAUTY SAMPLE TAKEN 1m. I.ml I I I 11 3 2 & * r el ? I I ]I [ BREAKUP HYDROGRAPH FIGURE ORBIT BASIN APRIL. 1988 EASIN SHOW COVnttSNOW PACK- _- 1- - .J I, RESULTANT WIND CAM= _ _ _ I I A" .25 A0 7 '- TEMPERATURE 'J .I -- DISCHARGE CFS 20' .II cuit / mile t - I o.0tI C/ J PRECIPITATION S . j In e~T I I I / - a M4 aL L . E W RT ,I ! * WATER OUAUTY SAMPLE TAKEN ,I.. BREAKUP HYDROGRAPH R FOR ORBIT BASIN MARCH, 1988 Ji 001 i~ BASIN SNOW COVER/SNOW PACK SOUTH a IOm.A RESULTANT WIND CAT" I W-HO --- momT 0 .60 401 .30 0 TEMPERATURE 007 3 0 -. - - p4 DISCHARGE CFS 2f .30 M? cu it / seeOMKA ""'"' .. Vh k-'--a .9 PRECIPITATION . In 0.04 ~ ' ~.~kII ~t\ ,,,_ LhlT~~~~~ ~~pr L. -6 P~~~~~Il _ LLnt- * WATER OUAULT SAMPLE TAKEN I .-. ..- I 10 1I 12 i1 to IS I is Is 20 I BREAKUP HYDROGRAPH ' URE FOR NORTH ARCTIC BASIN MARCH, 1988 BASIN SNOW COVER/SNOW PACK RESULTANT WIND c 7- TEMPERATURE I .- -A- -- A DISCHARGE CIF ofl cu / sees Cul/n. I A ___ _ 0oar.II- /I f' A nI fmarae4\~ A PRECIPITATION .10 ' in .0 rv _______ no 'I---+ J" * WATER DUALITY SAMPLE T AKEN C m,. I i I 20 2: ~~22 23 la 23 so 2 31 2 0 BREAKUP HYDROGRAPH ) 3LRE FOR NORTH ARCTIC BASIN 7 11 1 8~~~ U ~~~~~BASIN SNOW COVER/SNRILAC RESULTANT WIND C A M - --- - ~~~~~~~~~~~ljonmpI Wo- .,US~ Af\/G. .20 ~~~~~~~~~~~~~~I -__ Ir _I\_ BREAKUP HYDROGRAPH FGR II ~~~~~~FOR II NORTH ARCTIC BASIN J BASIN SNOW COVER/SNOW PACK APIL 198 I ~~~~~RESULTANT WIND Qmssilkv' NOUNS , 19 I- ~ ~ ~ ~ ~ ~ ~ I TEMPERATURE 3 4 DISCHARGE CFS I\ So\ II FOR II~~~~~~~~~~~~~~~~~~~I~cm- P~~~NRECIPIARTICOASN JL0 ~~~~~~~~~~~~~~w/RML IIT I I I Derivation of Annual Predevelopment TSS Based on Universal Soil Loss I Equation I I I I I I I I I I I I I I l l l l l l l l l l I O MONTGOMERY WATSON By DA TE - - ----- SHEET ___ OF CHKD. BY DESCRIPTION - Joe NO. 00 --7 L'ot:9 MC, 6YZ~IAX IN I ~ ~ c -O-q - Tr y Lol, J S,~cc~s,7&s~hjs ~~p~~~Cr - ;~~~~ O rW ~ ~ ~~C=L-Y~l~J ,2�L -.S - r5- '3k' = -~~~~~~~~A~;~ V ~ v- SIT-6M ir;-'Si- --j~e L*-Lx- EN IkiS 110178) I~~~~~~ ~ AFJhE - LCl? I ~ ~ : r~r - ?~ Y/ . /C I~ ~ ~ - 1/JJ7 (34 -5 ~ ~ I ~5O� I: I~~~~~~~~~~~~~~~'-i�n lit ..-- ~> R-~~~~~~ ~ F~ ~J i-S�3Q3~ I~I A AlEN iscc (107 O MONTGOMERY WATSON By DATE ____ CLIENT SHEET OF .. CHKO. BY DESCRIPTION am0 NO. ____ JNi~ Aoi i5 OF --A. * IC1,~~~~~~ -~a 2"0C5F . :7iA3kD; I 20 132o \~f3 ' 40 4- 2.0~~~~~U I g A' 20 k,9".~ o .~�T.-$�j~~ -v &v ~ ~ ~ ~ ~ ~ ~ ~~~~~ u EN IS (10178) I O MONTGOMERY WATSON SY DATE___ CLIENT SHEE_ OF CHKD. By DESCRIPTION -__ JOB NO. r ~,r -- 1. C- EN 15 (10/731 MONTGOMERY WATSON By .Lt/ DATE C CLIENT -- - - - - SHEET _O__ F CHKD. By DESCRIPTION Joe NO. cl, !3~~u !'TV, 2 5= 3_7% s.s� C-~SE~ ~D. Tue 's G, A c" ~~er N2DA ~t~si~L LS 1,3 ~~~jC-~~~~~L~~~y. ~~~~~r-77 z/a -,A r 5ac, EN IS 5~ AC &:. (dc0 L$= 13E cS kFur'AE 20/0 $LojS IC AC' e= 91 L$ 3 )~~~~J7AP ~ ~ ~ ~ ~ ~ rt *"--'- p b,23 p s,2 A- A -~5 ?. ~. LC 0 C� g, c, r ' 1.6 U/se ~s5 L= 93o' L ,~ EN is (101751 I O MONTGOMERY WATSON By -- ---- - - CLDAENTESHEET 0 F CHKD. By DESCRIPTION JO NO. P Ac 70 7 ~~7 7 ?qt) v I~ EN IS (10178) MONTGOMERY WATSONI By DATE~. CLIENT SHEET OF_-- O CHKD. BY ____DESCRIPTION - - NO.- _-_-__-_JeNo �-~~~~~~~~~~~~~~~~- - -.-. - -e ZLID w."D Y:r LJA : -r S a m .%/~ .6 D.)'z p( lbt0rL' '- a . 075 blCCY A 5 . 7_4~~ 8~~~~~~Ti $ PTAy EN~~ IS-018 I MONTGOMERY WATSON By DATE CLIENT L SHEET OF CHKD. By DESCRIPTION JOB NO. 9______ : "- 7�~~~~~~~~~ola A '0-r I 56&.V~~~~~~~~~h~~ L~~~~'w~~~~9~~~Th- S Q-~~~~~~~~~fl~, I A /. .6,1 -(I TI ' T~i oi --rs Lok-o i P, ~�~l ~ i - r I Ki 4 Ai, r-:-lr 'P 3 ~ Ls .2 13 AJ4D~J& fl -c 7~~~~~~~2 ~~~ ~ ~ , -, == Cs" I~~~~~~~c 1- W1se LA K! T~~2Sh{ :?. ~ i~~ij -3bs 2.A/ L4;f 3 L' CJ 1-3 4 IN I7 '7L ~ (Ci-w US h~-I\ losrc -( (~ I~~~~~~~~~~~~~~~~~~~li EN iS (101781 'A~~~~~~~~1 ALA~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~SK I I- VA I A. I I I I ---- Im Al, Of *IAT~~~~~~~~fl Kiln Al SOT Ar N ~ ~ ~ ~ ~ O.A 'S1. PAUP~~~~~~~~~~~~~~~~~~~~~~~~~~. (.Pe S.R ~~~~~2-YEAR S-HOUR RAINFALL. (INCHES) SO 2~~~~~~~~~~~~~~04 20~~~~~~~~~~~i 20 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I Fig. 5.2 R values for areas east of1040. Because of irregular topographyi~l,, United S aecalculate R values in this region by using local rainfall data. R is In. of 100 ft -tons/scre per in/hr. To convert R? to units of 10' J/ha per mm/hr, muiltiol 1.70. (20) Scale is in miles. .a *---, uIsit: pnecIse!50 tIr Kt is needed, other references (10, 20, 21) that explain how to calculate Type I individual storms and years from local data should be consulted. Type IA - "isoerodlent" map, prepared by Wischmeier for the USDA (20) and shown 40 - Type II - 5.2, is used to find the R value for sites east of the Rocky Mountains = ,ximately 1040 west longitude). R can he interpolated for points between X I ,es. Contact local soil conservation service offices for more detailed infor- I on R values in areas covered by this map. West of the 104th west merid- 30 - regular topography makes use of a generalized map impractical. For the a A In states, IH is calculated by using rainfall data. Results of investigations at I o 20- 10 - 012 3 6 9 12 1 18 21 24 Hours Fig. 5.4 Time distribution of rainfall within storm types. Adapted from unpublished data provided by Wendell Styner, U.S. Department of Agriculture, Soil Conservation Service, West Technical Service Center, Portland, Oregon, October 28, 1981. the Runoff and Soil Loss Data Center at Purdue University showed that R values in the western states could be approximated with reasonable accuracy by using 2-year, 6-hr rainfall data. (20) Regression equations for three different storm 3 Type IA types (I, IA, and II) are used to calculate R values. Figure 5.3 shows the distrib- 1ri Type I ution of type I, IA, and II storms throughout the western states. 0 Type 11 A storm type is distinguished by the rainfall distribution within the storm. Figure 5.4 illustrates the time distributions of rainfall within the three types of storms. A type 11 storm is characterized by gradually increasing rainfall followed by a strong peak in rainfall intensity that tapers off to low-intensity rain. Type II storms occur in the following areas: * The eastern parts of Washington, Oregon, and California (east of the Sierra Nevada) � All of Idaho, Montana, Nevada, Utah, Wyoming, Arizona, and New Mexico Type I and IA storms occur in a maritime climate. Type I is typical of storms that occur in southern and central California. These storms have a milder but Fig. 5.3 l)istribution of storm types in the definite peak similar to that of the type 11 storms. Type IA storms, which are western United States. (4) Type 11 storms characteristic of storms in coastal areas of northern California, Oregon, Wash- occur in Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Iltah, slid Wyoming ington, and the western slopes of the Sierra Nevada, have a low broad peak in also. the rainfall distribution. also. Find: The average annual R value for Sacramento, California. __ _ . g 4 f Given: The 2-year, 6-hr rainfall is 1.2 in (30.5 mm). Solution: Sacramento is in the type I storm area. Thus R = lf1.55p22 (0.0134 X (p, in mm)221 where p - 1.2 in (30.5 mm) R = 24.72, or 25 bThe rainfall erosion index does not account for erosion caused by snowmelt runoff. In any area where snow accumulates and the soil freezes, snowmelt runoff , increases erosion losses. Until researchers develop a predictive method for this __� - > r eP f,. _type of erosion, an addition component of the R value, termed R,, should he added to the rainfall erosion index to determine a total R factor R,. R. is esti- . / mated by multiplying the average total winter precipitation (December through . _,- /_ _ _ . March) in inches (mm/25.4) of water by 1.5 i(mm/25.4) X 1.5 = 0.059 X mmi. 5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 EXAMPLE 5.2 Consider a site that has an R factor of 25 and receives 16 in (406 mm) p = 2-year, 6-hr rain, in of precipitation during the four winter months: ---4- ~ 507----` 5 -- tR, =I 1.5(16 in) = 24 (0.059(406 mm) = 241 25 50 75 100 o = 2-year. 6-hr rain, mm = + R = 25 + 24 .6 Relations between average annual erosion index and 2-year, 6-hr rainfall in = 49 nia. (141 The R value is used to estimate the average annual soil loss. If erosion pro- tection is required for less than one year, the soil loss for a portion of a year can e differences in peak intensity are reflected in the coefficients of the equa- be estimated by using a derivative of the R value. Since R is proportional to or the rainfall factor. Figure 5.5 is a graphical representation of the equa- rainfall, the R value for a short time period can be calcula ted by mul tiplying the 'I'he equations, also shown on the curves for each individual storm type, average rainfall during the shorter time period by the annual R value and divid- ing the product by the average annual rainfall. For example, suppose you wish to estimate soil loss in January. January rainfall averages 2 in (51 mm), and R = 27p22 type II annual rainfall averages 20 in (510 mm). Then R = 16.55p22 type I R = 10.2p2'2 type IA 2 in 51mm Rj,, = 2]) in X R......lI 510 mm X R ..... p is the 2-year, 6-hr rainfall in inches. (If p is in millimeters, the equations ,.: R = 0.0219p22, type II; R = 0.0134p22, type I; JR = 0.00828p22, type EXAMPLE 5.3 R ? valrue is rounded to the nearest whole number. When the rainfall time Given: A site in California on the western slope of the Sierra Nevada where 2-year, 6- iR value is rou 5 anded to the nearesp t whole number. When the rairall time hr rainfall is 1.6 in (41 mm), December-March precipitation is 27.6 in (701 mm), and the ulion curves (Fig. 5.4) and the corresponding R value equations are com- storm type is IA. it is evident that the stronger the peak intensity of the typical storm, the the rainfall erosion index. Find: , R., and R,. Find: R, R,, and R,.~~~~~~~~~~~~~~~~~~~~~~1 It = 1).2p?' = 28.7 It., = 1.5(27.6 in) = 41.4 10.059(701 mm) = 41.41 o -- R, = R, - R = 28.7 + 41.4= 70.1 /\ /\ o,1 o, .(Example 5-4) Soil Erodibility Factor K " ,il erodibility factor K is a measure of the susceptibility of soil particles to / Iment and transport by rainfall and runoff. Texture is the principal factor 60/ ,ng K, but structure, organic matter, and permeability also contribute. K o , \ / / O range from 0.02 to 0.69. \A .'eral methods can be used to estimate a K value for a site, but a nomograph 50 ,d using analyses of site soils is the most reliable. If a recent soil survey for / ea has been published and minimal soil disturbance is anticipated, the K lo * - a listed in the survey of the soil series found on the site can be used. / \ / / Agraph Method . \. / A V referred method for determining K values is the nomograph method. Use ' ,', : ,'. nomograph requires a particle size analysis to determine the percentages. ,j,,,,,, , ,,- . I,I very fine sand, silt, and clay. The size range for each class is listed in ,, ,, , - r:I.. ASTM D-422 (1) is a standard hydrometer analysis for particle size ',' ,/,,;, ,, / \ / lution. (Specific particle sizes can be designated in the request for analysis.. ,......, .... i. ~/'' \v' l ypically, values are reported for specified size intervals, such as every 5 or % ' . The fee for a particle size analysis is normally only a small fraction of the Pcent srnd ee for a geotechnical report.) Fig. 5.6 Triangular nomograph for estimating K value. (6) See Table 5.3 for adjust- e determination of the K value should be based on the soil exposed during ments to K value under certain conditions. itical rainfall months. Subsoils exposed during grading will have K values tit from the topsoil K value. On large sites, several samples should be and analyzed separately to ensure that differences in soil texture are EXAMPLE 5.4 ed. If fill is imported, this material also should be characterized. Given: A soil with the following particle size distribution. X, more carefully the site soils are characterized, the more accurate the K will be. If analysis indicates significant variation in soil erodibility, it be advisable to use different K values for different parts of the site and to Component Size, mm Fraction, % vative approach is to use the highest value obtained by analysis for all Very fne sand 0.1-0.05 10 ,f the sitet since it may not be possible to know exactly what soils will he Silt 0.05-0.002 20 'd or how varied the soils are. Clay Less than 0.002 40 omograph developed by Erickson of the SCS-Utah office (6), based on the Il nomograph provided by Wischmeier (21), is reproduced in Fig. 5.6. To I nomograph, enter the triangle with any two of the particle size percents: Find: Texture and K value. ;ind and silt; silt and clay; or clay and total sand. Use whole numbers. Solution: Entering Fig. 5.1 with 40 percent total sand and 20 percent silt, the texture the dashed straight lines to their point of intersection. From that point, is found to be on the border between clay and clay loam. Entering Fig. 5.6 with the same parallel to the dotted curves to the right side of the triangle, where the K percents (see bold lines), the K value is found to be 0.19. are listed. Table 5.3 describes adjustments to the K factor. Adjustment I is a correction for very -II -II -I -I -C -9 9 -9 - ----- ~ mm.~ ~ ~;' "' ...... IS vnlue9 for following slope lengths I, ft (m) ~ .,pe gradiet !0 20 30 40 50 60 70 80 90 100 150 200 250 300 350 400 450 500 600 700 800 dio 900 1000 s,,';, 13.01 16.11 (9.11 112.2) 115.21 (18.31 121.31 (24.41 (27,41 13o.5) (46) 1611 (76) 1911 11071 1122)11371 11521 11831 12131 (244) (2741 1305) 0.5 0.06 0,07 0.07 0.08 008 0.09 0.09 0,09 0.09 0.10 0.10 0,11 0.11 0.12 0.12 0.13 0.13 0,13 0.14 0.14 ~:1 0,14 0,15 0.15 I 0.08 0.09 0,10 0.10 0.11 0,11 0.12 0.12 0.12 0.12 0.14 0.14 0,15 0.16 0.16 0.16 0.17 0.17 0.18 0.18 0.19 11.10 0,19 0.20 0.12 0.14 0,15 0.16 0.17 0,18 0,19 0.19 0.20 0.23 0.25 0,26 0.28 0.29 0.30 0.32 0.33 0.34 0.36 0.37 0.14 0,39 0.40 0,18 0.20 0.22 0,23 0.25 0.26 0.27 0.28 0.29 0.32 0.35 0,38 0.40 0.42 0.43 0.45 0.46 0.49 0.51 0.54 0.16 0.55 0.57 0,21 0.25 0,28 0.30 0.33 0.35 0.37 0.38 0.40 0.47 0.53 0,58 0.62 0.66 0.70 0.73 0.76 0.82 0.87 0.92 0,96 1.00 ~:! 5 0.17 0.24 0,29 034 0.38 0.41 0.45 0.48 0.51 0,53 0.66 0.76 0.85 0.93 1.00 1.07 !.13 1,20 1.31 1.42 !.51 tl,21 1.60 !.69 0.30 0.37 0.43 0.48 0.52 0.56 0.60 0.64 0.67 0.82 0.95 1.06 1.16 !.26 1.34 1.43 1,50 1.65 1,78 1,90 2,02 11.26 2.13 0.:17 045 0.52 0,58 0.64 0.69 0.74 0,78 0.82 1.01 !.17 i.30 1.43 i.54 !.65 1.75 1,84 2.02 2.18 2.33 2.47 :1 2,61 8 0.31 0,44 (I.54 0.63 0.70 0.77 0.83 0.89 0.94 0,99 1.21 1.40 !,57 1.72 1.85 1.98 2.10 2.22 2.43 2.62 2.80 0.37 2.97 3.13 0,52 0.64 0.74 0.83 0.91 0.98 !.05 I.!1 1.17 1.44 1.66 1.85 2.03 2.19 2.35 2.49 2.62 2.87 3.10 3.32 3.52 3.71 ,:1 !0 0.43 0.61 0.75 0.87 0.97 1.06 1.15 1.22 1.30 1.37 1.68 1.94 2,16 2.37 2.56 2.74 2.90. 3.06 3,35 3.62 3.87 4.11 4.33 II 0,50 0.71 0.86 1.00 !.12 1.22 i,32 !.41 !.50 1.58 1.93 2.23 2,50 2.74 2.95 3.16 3.35 3.53 3.87 4.18 4.47 4.74 ::1 4.99 12.5 0.61 0.86 1.05 1,22 1.36 1,49 !,61 1.72 1.82 1.92 2.35 2.72 3.04 3.33 3.59 3.84 4.08 4.30 4.71 5.08 5.43 5.76 15 6,08 0.81 !.14 !.40 !.62 1.81 !.98 2.14 2.29 2.43 2.56 3.13 3.62 4,05 4.43 4.79 5.12 5,43 5.72 6.27 6.77 7.24 7.68 :1 8.09 16.7 0.96 1.36 1.67 1,92 2,15 2.36 2.54 2.72 2.88 3.04 3.72 4.30 4.81 5.27 5.69 6.08 6.45 6.80 7.45 8.04 8.60 9.12 9.62 :1 20 i.29 i.82 2.23 2.58 2.88 3.16 3.41 3.65 3.87 4.08 5.00 5.77 6,45 7.06 7.63 8.16 8.65 9.12 9.99 10.79 11.54 12.24 12.90 :1 22 1.51 2.13 2.61 3.02 3.37 3.69 3.99 4.27 4.53 4.77 5.84 6,75 7.54 8.26 8.92 9.54 10.12 10.67 11.68 12.62 13.49 14.31 15.08 ~1 25 1.86 2.63 3.23 3.73 4.16 4.56 4.93 5.27 5,59 5.89 7.21 8.33 9.31 10.20 il.02 !1,78 12.49 13.17 14.43 15.58 16.66 17.67 18.63 30 2.51 3.56 4.36 5.03 5.62 6,16 6.65 7,11 7.54 7.95 9.74 11.25 12.57 13.77 14.88 15.91 16,87 17.78 19,48 21.04 22.49 23.86 25.15 I 3:1.3 2.98 4.22 5.17 5.96 6.67 7.30 7.89 8.43 8,95 9.43 11.55 13.34 14.91 16.33 17,64 18.86 20.00 21.09 23.10 24,95 26.67 28.29 29.82 35 3.23 4.57 5.60 6.46 7,23 7.92 8.55 9.14 9.70 10.22 12.52 14.46 16.16 17.70 19.12 20.44 21.68 22.86 25.04 27.04 28.91 30.67 32.32 I 40 4.0{! 5.66 6,9:1 800 8.95 9,80 10.59 !1.32 12.00 12.65 15.50 17,89 20.01 21.91 23.67 25.30 26.84 28.29 30.99 33.48 35.79 37.96 40.01 45 4.81 6.80 8.33 9,61 10.75 11.77 12.72 13.60 14.42 15.20 18,62 21,50 24.03 26.33 28.44 30.40 32.24 33.99 37.23 40.22 42.99 45.60 48.07 I 50 5,64 7,97 9.76 !1.27 12.60 13.81 14.91 15.94 16.91 17.82 21.83 25,21 28.18 30.87 33.34 35.65 37,81 39.85 43.66 47.16 50.41 53.47 56.36 55 6,48 9,16 il.22 12.96 14.48 15.87 17.14 18.32 19.43 20.48 25.09 28.97 32.39 35.48 38.32 40.97 43,45 45.80 50.18 54,20 57.94 61.45 64.78 ! 57 6.82 9.64 il.80 13.63 15.24 ]6,69 18.03 19.28 20.45 21.55 26.40 30.48 34.08 37.33 40.32 43.10 45,72 48. I9 52.79 57.02 60.96 64.66 68.15 60 7.32 10.35 12.68 14.64 16,37 17.93 19.37 20.71 21.96 23.15 28.35 32.74 36.60 40,10 43.31 46.30 49.11 51.77 56.71 61.25 65.48 69.45 73.21 I 66,7 8.44 !!.93 14.61 16.88 18,87 20,67 22.32 23.87 25.31 26.68 32,68 37.74 42.19 46.22 49.92 53.37 56.60 59.66 65.36 70.60 75.47 80.05 84.38 70 8.98 12,70 15,55 17.96 20.08 21,99 23.75 25.39 26.93 28.39 34.77 40.15 44.89 49.17 53.11 56.78 60,23 63.48 69.54 75.12 80.30 85.17 89.78 75 9.78 13.83 16,94 19,56 21.87 23,95 25.87 27.66 29.34 30.92 37.87 43,73 48.89 53.56 57.85 61.85 65,60 69.15 75.75 81,82 87,46 92.77 97.79 ! 80 10.55 14.93 18.28 21.11 23.60 25.85 27.93 29.85 31.66 33,38 40.88 47.20 52.77 57.81 62.44 66.75 70,80 74.63 81.76 88.31 94.41 100.13 105.55 85 11.30 15.98 19.58 22.61 25,27 27.69 29.90 31.97 33.91 35.74 43.78 50.55 56,51 61.91 66.87 71,48 75,82 79.92 87,55 94.57 101.09 107.23 113.03 90 12.02 17.00 20,82 24.04 26.88 29,44 31.80 34.00 3606 38.01 46.55 53,76 60.10 65,84 71.11 76.02 80,63 84.99 93,11 100.57 107.51 !14.03 120.20 95 12.71 17.97 22.01 25.41 28.41 31.12 33.62 35.94 38.12 40.18 49.21 56.82 63.53 69.59 75.17 80.36 85.23 89.84 98,42 106.30 !!3.64 120.54 127.06 I 100 13.36 18.89 23.14 26.72 29,87 32.72 35.34 37,78 40.08 42.24 51.74 59,74 66.79 73.17 79.03 84.49 89.61 94.46 103,48 111.77 119.48 126.73 133.59 ,,Inted from 4.56Xs (65.4{ Xs' )( I )' l~ { !(I.(lO0 t ~'l~ t 0.065 1,5- topographic factor 7'~ { ,, slope length, ft Im X 0.3048! s ,, slope steepness, m = e~l~ment dependent upon slope ateepnm (0.2 for slopes < ! %. 0.3 fo! slopes ! 1o3~. 0.4 for slopes 3.5 to 4.S%. nnd 0.5 for slopes > 5~'I. ) 1 1.9 2.8 __JType lf cover C factor reduction, % None 1.0 effect of length is not as great as the effect of slope angle: LS increases 30 Native vegetation (undisturbedl 001 99 percent for each doubling of length. For example, on a 2:1 slope, IS doubles Temporary seedings: * L is quadrupled: go90% cover, annual grasses, no mulch 0.1 90 Wood fiber mulch, g ton/acre (1.7 t/ha), with seedt 0.5 50 Slope 2:1 2:1 2:1 Excelsior mat, jutet 0.3 70 L.ength 30 ft (9.1 ml 60 ft (18.3 m) 120 ft (36.6 ml Straw mulcht I.S 9.76 13.81 19.42 1.5 tons/acre (3.4 t/ha), tacked down 0.2 80 Factor increase 1 1.4 2 4 tons/acre (9.0 t/ha), tacked down 0.05 95 'Adspted from Refs. 11, 15, and 20 ;, very long slopes and especially, long, steep slopes, should not be con- tForslpes ip to ted. 'Those that already exisl should not be disturbed. ope length can be shortened by installing midslope diversions. Local build- odes often require terraces or drainage ditches at specified intervals. Chap- if a complete cover of newly seeded annual grasses is well established before the I of the Uniform Building Code specifies a 30-ft (9.1-m) interval. (9) Several onset of rains. ,n control manuals recommend 15-ft (4.6-m) intervals between terraces. (2, In many areas, seed and wood fiber mulch are applied hydraulically shortly 'lecause these intervals are defined as vertical rise, the slope length would before the rainy season. The early rains cause the seeds to germinate, but a com- mewhat longer. plete grass cover is not established until at least 4 weeks later. During the ger- -creasing steepness will require use of more land and so must be incorpo- mination and early growth period, the wood fiber mulch provides only marginal early in the project design. To ensure slope stability, a maximum gradient protection. A C value of 0.5 is an appropriate average representing little protec- Iuently recommended by the soils engineer. tion initially and more thorough protection when the grass is well established. On bare soils mulch can provide immediate reduction in soil loss, and it per- forms better than temporary seedings in some cases. Straw mulch is more effec- tive than wood fiber mulch; it reduces loss about 80 percent (C value, 0.2) when Cover Factor C it is applied at the rate of 3000 lb/acre (3.4 t/ha) and tacked down. Additional over factor C is defined as the ratio of soil loss from land under specified reduction is obtained with 8000 Ih/acre (90 t/ha) of straw, but this rate may not or mulch conditions to the corresponding loss from tilled, bare soil. The C be cost-effective. the same as the runoff coefficient C used in the rational method. Wood fiber mulch alone (without seed) provides very little soil loss reduction; I he [SIE, thie C factor reduces the soil loss estimate according to the effec- it primarily helps seeds to become established so that the new grass can provide ss of vegetation and mulch at preventing detachment and transport of soil e erosion control. Other products, such as jute, excelsior, and paper matting, les. On construction sites, recommended control practices include the seed- provide an intermediate level of protection; the C value equals approximately grasses and the use of mulches. These measures are often considered "tem- 0.3. Test results of various mulch treatments are presented in Chap. 6. ,"-they are designed to control erosion primarily during the construction Permanent landscaping may be added later, or temporary erosion control may be left as a permanent cover. Any product that reduces the amount 5.2f Erosion Control Practice Factor P exposed to raindrop impact will reduce erosion. Table 5.6 lists C factors ious ground covers. The C values for vegetation were obtained from USDA The erosion control practice factor P is defined as the ratio of soil loss with a Itions (14, 20); those for mulch were obtained from Burgess Kay at the given surface condition to soil loss with up-and-down-hill plowing. Practices that sity of California, Davis, who tested materials on experimental plots reduce the velocity of runoff and the tendency of runoff to flow directly down- a rainfall simulator. (l 1) slope reduce the P factor. In agricultural uses of the USLE, P is used to describe en the soil surface is bare, C is 1.0. At the other end of the scale, undis- plowing and tillage practices. In construction site applications, P reflects the native vegetation is assigned a value of 0.01; hence the advantage of roughening of the soil surface by tractor treads or by rough grading, raking, or ig as much existing vegetation as possible is clear. A C value of 0.1 is used disking. - -9 ms -eil -sl - IP -P m _ - _ _ I I I I I I I I I I I Appendix B I I I I I Appendix B Algorithm for Determining Minimum Surface Area for Sedimentation Basin Assumptions Made in Determining Inflow for Sedimentation Basin Sizing Sedimentation Basin - Design and Quantities Sedimentation Basin - Costs Land Development Costs Stormwater Controls in Coastal Alaska .3 page B-I June, 1995 I I I Algorithm for Determining Minimum Surface Area for Sedimentation BasinI I I I I I I I .I I I I I I I I I . MONTGOMERY WATSON B my _ DATE C6 CLIENT D6SH _____ __ HEET _OF CHICO. BY DESCRIPTION N____O. ~ To or374Jr3 SO)~2 F;AC~. Ad3;~ o- 4P5r , )H~s) 1', '' i~uC~~vc~ 0 p(Aw-Jrj~ p~lr~>'J~-l d~iayi~t&- cL i~ -p ~ ~4{c, -t e~ly I POJ/L' dA=V ta17 Yr Fo o'-yi. J -r1A / ~ r~ub�ivuv ~ h~4ilbv &P r~t& . e~i~ S~1'vC- i-" 'a cv rr ;-eo (b/ rt'i/Wca-- 1,~~~~~~~~~~~~~~~~~2 AJf~~~~~~1pr( ~~~~~~pI~~~ --~t C~~~~c, (.�3~~~~~~~~~~~~T �a, r EN, IS (10/78' (OI) MONTGOMERY WATSON DATE -__- CLIENT SHEET ---L OF CHKO. BY DESCRIPTION JOB NO. n-tm~~~~~~~~~~~~~~~~~~~~~- 4 -5 V) ro L~r~L o per VS 483, 9'-l w SIC-_ 5o 7 I EN Sn~wu ~ , zj.-!: r~L~ab6 e~~IS 7~~~~~~~~~~~~i!L-~ - ~~ C~~ f~L, -iBh i~P~~r~~~P~C eiiL )'iD .-f$ . a- C. . Jc 'Jr-''O~ ~ y c04iv '4u.~ -5 jp~ k*-C~Lrcr& ru;-l', >,~C' ~c; ~ _ C- I ~n~~L.r53 ' L .J ~~~� � ,� �~~~~~~~~~~~~~. EN iS (10/781 I MONTGOMERY WATSON By Wk -- DATE ___ CLIENT SHEETO F CHKD. BY DESCRIPTION JO NO. H ~ ~~~~~~~ t o re Ao , S yl.&- V.e-zr~ Ii~W-Z-ILI~A. r r ( ")-,J rL 'i' I~~~~~~~~~~~~~~~~~~~~~ CL L,~~~~~~ rr'c 1170~~~ U1-~~~ CC '" ilc~~~~~lr UV I N I S ( 10S178 EN iSfO/4 (�) MONTGOMERY WATSON By_______DATE -__- CLIENT SHETO CHKD. BY DESCRIPTION JOB NO. I~~~~~~~MI ~~JUJk rE .i -s-P' Ij}- - - L 1~~~~~~~~~~~~~~~~~~~~~~~~~L EN~e IS (10178)z,,�n a 9z~~~ lZ;; _ 4i ~~~rA '- Uf 2S~~~~~~s a~ ~ ~ ~ ~ I~~~~~ P1~ �i~ rlo~~~~~~~~~� UATh 46�IP~~:~~ U~L � ~~6~~c LIhyd&fj /I ~Hs~ ,-4vJb ~~ ~ r~%r' EN 1 (11k8 I .~~MONTGOMERY WATSON By______ DATE ___- CLIENT SHEET O CHKD. By DESCRIPTION______ Joe NO. 't'oM GU- " \) D~VJ4, I bjk*AJ-FO -, %rtD~ g1 ri~~~. IQ ( C e ~~~~~az ~ ~ ~ ~ ~ / )~o may. It rn'ro I~~~~0bS YVOk-' C45/ a /'P- p /' '- .(& Fb~' 69' FAL-MVL- I~ VS - 1~~~~~~7-? P7PT D r4 VOi-L LM4 DZTP-/7e7gT orJ 7A1, m I EN IS 178 (� MONTGOMERY WATSON eBY ~_ DATE ___ CLIENT ____ SHEET CHKD. BY DESCRIPTION JOB NO. 72-d4, VO Sc IL~~~~~~~ 6IJ in Dansh,, EN 1i f10/781 ,yule .-i rartcle size Ulstribution Analyses for Suspended Sediment in Storm WaterStorm Water 100% 75% Percent /-./-E- 1(00th Ave. Grab, Q=0.3 cfs -- i/100th Ave. Grab, Q=0.6 cfs Passing 50% G . By Weight /-/-.- 100th Ave. Grab, Q=0.3 cfs ----- Basin Inlet Composite #1 --E- Basin Inlet Composite #2 25% Source: (JMM, 1992) 0% 0 10 20 30 40 50 60 Particle Diameter, microns I I I Sedimentation Basin -Design and QuantitiesI I I I I I I -I I I I I I I I I units Anchorage Juneau Residential industrial Commercial Residential Industrial Sedimentation Basin - Quantities Surface area required IN sf 90 400 1600 450 2600 Iw = Dimensions of base pool i max(3'd2,w=sqrt(A14)) ft 1 8 I 18 20 1 8 25 I~~~~~~ ~ ~~~~~ 1--4w It 72 72 80 72 102 minimum pond surface iApond ft 1296 1296 1600 1296 2600 Idepth (d) range: 3to 6ft i t a 3 3 3 3 pond volume VOLpond=.5'(Ab+A)'d cf 1944 1944 2586 1944 4844 depth from ground to top ofI Ipond (do) 5 ft5 5 5 5 5 pond bottom area Ab=(l-2d(3:1) x~ (w-2d(3:1)) SI 0 0 124 0 629 Ag=(i+2dg(4:1 ))'(w+2dg(4:, a round surface area Ii)) sf 6496 646 7200 6496 9299 - IVOLex=(.5'(A+Ag)+Apond) I excavation !/27 Cy 216 216 259 216 400 Overall site length I Ltot = (40+2'(5,4: 1)+I+5) ft167 167 175 16719 I Mot I Overall site width ~ (5+215,4:114w+20+5 ft 88 88 90 88 95 _ area of site I Atot = Ltot ' Wtot sf 14,696 14,696 15,750 14,696 18,811 1 VOLlet=.5'(3+6)'(40+5(4:1 I inlet/outlet )+5)11.5 CY 16 16 16 16 16 Road Surface Arced = 20'(Ltot-5) sf 3,240 3,240' 3,400i 3,240 3,84 I SA = (Ltot'Wtot)-Aroad- landscaping iApond sf 10,160 10,160 10,750 10,160 12,371 Concrete on-grade broad crested weir I width of weir top 'T = .67 ft 0.67 0.67 0.67 0.67 0.67 Height of weir-fdn to top H=7 ft 7 7 __7 77 width of weir at fdn I b = T +2H(2:1) I t 28.67 28.67 28.67 28.67 28.67 Length of weir structure Lweir=w+2'(l(4:1)+3) ft 32 32 34 32 39.4950976 Aweir=.51((b+T)H-(H- End area of weir ..5)+(b+t-1)) sf 10.6 10.6 10.6 10.6 10.6 forms~liweir If 192 192 204 192 236.970585 wwrn I Lweir '(2 '(sqrt(2'HA2)) + - sf 655 655 696 655 808 concrete ~Aweir'L weirl27 CY 13 13 13 13 115 Cutlet Noe 1 5 if 15 15 15 1 5 1 5 MONTGOMERY WATSON BY ')DAE___CLIENT__ r~j --DAT N SHEETOF' CHKO. BY JoeSRNTONS?/vY2&. c �-~ ~~~~~~~~~~~~ NO%5 I (2I V~~~~~ U -~~~~~~~~~~~~~~~~4 - - __ Fp-otv,~~~ ~~~ - vv I'-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~,. LAW~~34 vi~~~~~~~~~~~ EN IS (10178)~~~~~~~~~~~~~~~~~~~~~~~~ . IMONTGOMERY WATSON B y DATE CLIENT SHEET OF CHKD. BY DESCRIPTION -452A)j2� JOB NO. CIJc&$ -TNJ ~~~~~~~ -tZC'r: 1 -9- E - I S 1 0C7z81 I~~~~ I~~~~LE-~~ ~ - -'LC 6' ~~ J4' -x - I izis I ~lo - -j I~ ~ ~ ~ ,,, EN iS75 r O MONTGOMERY WATSON BY _--- DATE .3-L~CLIENT SHEET OF CHKD. BY DESCRIPTION Vv' 23_ Joe NO. �- ~~~~~~~~~ le ~ ~f� Li% VJ . T: �I~ -_ uY -C U 3'~~~~~ I`~E �~ I -~~~~-. ~-2 -.c3 r3i " CeT- !N: z~~~~~E I Alw L ~ ::L\1 ~ ~ wP2 BE -1S ' V1J/781 EN~~~~~~~~~~~ \\ T' N 8< I I I I Sedimentation Basin -Costs I I I I I I I I I I I I I I I units Anichorage Jna Ssdhem s sson BasIn - Costs from Mearis 1995 Heavy Construction Cost Dt Anchioracoe City Cost Index all others 1.37 forms 1.24 wwrn 1.44 concrete 1.56I Juneau - use 105% of Anchrorage costs Construsctiont Costs Land mats 1 36 res. S5 Ind.Sl 2comn $S/ 6 5 12 6 5 Land $ 19.W4 16.20 40.800) 19,440 18.238 Excavation and Gradmo UitCssfoMeng mobidemtob 370.00 S/ea S IM 505 505 530 530I front Mid loader 1.48 S/cy 4 737523 459 84 outlet Pipe 25.50 I SNl 522 52 522 58 5so fonnsain lace 2.11 SM 501 501 532 526 649 taint www 35.00 VWca 331 331 351 347 429 slab an grade _ 100.00 S/cv 1.951 1.951 2.073 2.048 2,528 InWetOultet Channel 19.05 $/cV 423 423 423 444 444 Access (road) Pavement bass 5.25 $/sv 2.580 2,580 . 2.707 2.709 3.210 Prepare and roll 1.26 Vsav 819, 619 650 650 770 Fancino fencino 12.35 SMt 8.260 8.260 8,597 8,673 10.000 Posts 89.00. S/ea 486 486 4836 510 510 date 925.00 S/orina 1.263 1.263 1,263 1.326 1.326 LandAcapnal rougoh grade 18.55 SIMat 257 257 272 270 329 aeed-alooe mrs ~~~~19.20 Whist 266 266 282 2834I Subtotal $ 27.308 24,068 49.0836 27.701 28.195 25% Contingency 5 6,827 6.017 12.271 6.925 _ 7.049 1 5% Engineering , 4.096 3.610. 7.363 4.155 __4.229 TCC -Tota Capital Coat 5 38.231 33.695 68,720 38.782 39.4~72 Post Per unit volume of Pond 5 20 17 27 20 a Annusiftod - IM%25yrs 5 4,212 3,712 7.571 4.273 __4.349 0.10 i rateI Sits Maintenance Frequent Site Mainteancpes _ mowina-11Oxiy 1.68 S/inst 233 233 247 245 298 watonno -water 11 - x/vr 11.80 SAWtBig a le 81 66 859 I'M4 waterntla-no" set-upD-5WY 2.78 S/AW 193 -9 0 202 246 lartilizet2xfvr 2.76 1 S/mat 77 77 el so__8 98 wowdcontrol 2xiyr 0.28 S/msf 8 a 10 Subtotal 51.328 1.328 1.405 -1.395 1.698 Occasional basin cleanoultifevery 8 wa________ mnoi-demob 370 S/ea 505 505 505 530 -505 .5 Pond11 volumne 38 38 48 36 go excavate @ .5 PMn o 1.48 S/cv 73 73 97 7 9 diaosoa- hault8hrs 2.818 S/cv 142 142 188 149 370 reseed- .25 of landscaped aite 19.20 S/mat 7 7 8 89 Subtotal 5 763 763 846 799 1..165 ________________ ~present value for 811h yr 54,736 4,736 5.252 4.962 7.233 ____________ ~~~present value for 1611, yr S 7,708 7,708 8.547 8.075 11,771 ___present value fr2t r $9.572 9.572 _10,615 10.028 14.619 annualize sum of 3 cleanou 5 2.425 2.425 2,690 2.541 3,704 Total O&M _____ 3.754 -3.754 4.095 3.936 5.402 TAC -Total Annual Cost 5 7.966 7.466 11.666 8.208 9.751 TACcar developed acre S 1.593 747 1.167 1.642 488a I I I I Land Development Costs I I I I I I I I I I I I I I I units _ _ Anchorage _ _ Juneau Residential Industrial Commercial Residential Industrial Land Use Development Costs Commercial and Industrial . Development Area acres Development % Impervious % 5 _ 10 10 5 20 38 _ 50 85 40 50 buildingand site dev costs from Means _$/sf _ 50 64 50 Anchorage Cost Index: 126.7 % land cost $ 1,306,800 2,178,000 5,227,200 1,306,800 4,138,200 bldg size _ ___ sf - _ 108,900 _ 123,420 _ 217,800 bldg and site dev cost $ 6,912,613 9,992,244 14,516,487 Total Site Development Cost $ 9,090,613 15,219,444 18,654,687 Annualization _ $ _._._1,001,495 1,676,697 _ 2,055,151 0.1 rate _ 25 period ___ TCC as a Share of ProjectCost _% 0.371 0.452 .. 0.212 TAC as Share of Annualized ProJect Cost % 0.745 0.696 ___ 0.474 Residential number of houses 18 18 median house price $ 109,700 113,500 median annual mortgage _ . . . $ 9,111 ___ 9,427 15% down, 30 yrs, .08 rate + 10/0%insurance, taxes median household income $ 43,946 __47924 TCC per house/average house price % 1.936 _ _ 1.898 TCC/land price . % 2.926 2.968 TAC per house/average house price .. . . 4.857 . 4.838 TAC per house/median household income I % 1.007 0.952 m, m -- -9 mC m m -P -I m --