WRC Research Report No. 214

APPLICATIONS OF GIs DATABASES AND WATER QUALITY MODELING FOR AGRICULTURAL NONPOINT SOURCE

POLLUTION CONTROL

Ming T. Lee David C. White

Illinois State Water Survey and

Department of Agricultural Economics University of Illinois at Urbana-Champaign

Project No. S- 119-ILL ISSN 0073-5442

Water Resources Center University of Illinois at Urbana-Champaign

205 North Mathews Avenue Urbana, Illinois 6 180 1

December 1992

The University of Illinois is an equal opportunity/affirmative action institution.

CONTENTS Page

1 . INTRODUCTION .............................................................................................................. 1

............................................. Promises and Problems of GIs for Water Quality Modeling 2 ........................................................................................................... Acknowledgments 3

2 . RELATED STUDIES ......................................................................................................... 5

3 . OBJECTIVES ................................................................................................................... 7

...................................................................................... 4 . SELECTION OF STUDY AREAS 9

Highland Silver Lake Watershed .................................................................................... 9

................................................................................... 5 . PREPARATION OF DATABASES 13

..................................................................................... Data Collection and Preparation 13 GIs Database ............................................................................................................... 13

..................................................................................................... Remote Sensing Data 16 Digital Elevation Model Data ......................................................................................... 20

................................................................................... Preparation of AGNPS Input File 21 ............................................................................................................. Data Processors 22

...................................................................................... 6 . WATER QUALITY MODELING 23

AGNPS Model .............................................................................................................. 23 ............................................................................... Runoff Volume and Peak Discharge 23

Upland Erosion .............................................................................................................. 24 Sediment Routing ........................................................................................................... 24

......................................................................................................... Nutrient Transport 25 ................................................................................. Chemical Oxygen Demand (COD) 25

.................................................................................................................. Methodology 25

7 . ECONOMIC ANALYSIS ................................................................................................. 33

........................................................................................... Rural Clean Water Program 33 Preparation of Databases for Economic Analysis ............................................................ 33

........................................................................................................ Crop Rotation Data 34 ....................................................................................................................... Soil Data 34

Crop Yields and Tillage Systems .................................................................................... 35 .............................................................................................................. Economic Data 35

.................................................................................................................. Comparisons 37

iii

8 . DISCUSSION ................................................................................................................. 4 1

GIs and Water Quality Modeling ................................................................................... 41 Concept of Pollution Potential Ratings .......................................................................... 42 Locating Pollution Control Measures to Minimize Costs ................................................. 42

.................................................................................. 9 . SUMMARY AND CONCLUSIONS 43

10 . REFERENCES .................................................................................................................. 45

APPENDICES ....................................................................................................................... 49

Appendix A . Costs of Individual Crops. Rotations. and Net Returns .............................. 49 Appendix B . Structural Costs ..................................................................................... 53 Appendix C . Highland Silver Lake Field Monitoring Data ............................................. 55

TABLES

Page Table 1 . Adjustment of AGNPS Model for Best Management Practices ................................ 19

........................................ Table 2 . Sediment On-Site and Off-Site Pollution Potential Rating 26

..................................... Table 3 . Net Return Categories for Highland Silver Lake Watershed 37

Table 4 . Sediment Pollution Potential Rating for Highland Silver Lake Watershed ................. 38

Table 5 . Combined Net Return and Sediment Pollution Potential Rating Classes ................... 38

FIGURES Page

Figure 1 . General location map showing the stream sections and streambed material ................................................................................................... collection sites 10

Figure 2 . Working scheme for integration of databases and modeling ................................. 14

Figure 3 . Maps of Highland Silver Lake watershed showing (a) land use. (b) slope. .................................................................................... (c) streams. and (d) soils 15

Figure 4 . Maps of Highland Silver Lake watershed showing (a) structural and (b) nonstructural best management practices (1 984- 1990) ........................... 17

Figure 5 . Illustration of (a) Vector Average Method and (b) spatial distribution ..................................................... of aspects with same vector average direction 18

........................................... Figure 6 . Soil erosion rate of Highland Silver Lake watershed 27

................................. Figure 7 . Sedunent delivery ratio of Highland Silver Lake watershed 28

.............................................. Figure 8 . Sedunent yield of Highland Silver Lake watershed 29

Figure 9 . Sedimentation potential rating of Highland Silver Lake watershed (1 984) ............ 31

Figure 10 . Changes of sedimentation potential of Highland Silver Lake watershed ....................................................................................................... (1984-1990) 32

Figure 1 1 . Cost effectiveness of best management practices in Highland Silver Lake ............................................................................................... watershed (1990) 40

ABSTRACT

Physical and economic effects of nonpoint source pollution were analyzed by using the Geographic Information System (GIs) and an Agricultural Nonpoint Source Pollution (AGNPS) water quality model. The Highland Silver Lake watershed, a 50-square-mile dramage basin, was used as the test site. The GIs database consists of map layers of soil type, land use, farm boundaries, best management practices (BMPs), stream, scanned aerial photography, and digital elevation models OEMs). Water quality modeling was conducted before and after BMP implementation Soil erosion rate and sediment transport were used as an example of a pollution source and its transport. The pollution potential rating for each cell (10 acres) was assessed by considering both soil erosion rate and sediment delivery ratios to the receiving waters. Then a combination of economic costs and pollution potential were rated based on the model user's own criteria. The method established could be used as a tool for incorporating the on-site water quality sources, their off-site transport capacities, economic costs, and the model user's criteria. The potential of GIs technology for nonpoint source pollution management are discussed. Additional research is also suggested.

APPLICATIONS OF GIs DATABASES AND WATER QUALITY MODELING FOR AGRICULTURAL NONPOINT SOURCE POLLUTION CONTROL

by Ming T. Lee and David C. White

CHAPTER 1 INTRODUCTION

Nonpoint source pollution is a by-product of a variety of land uses, including farming, timber harvesting, mining, and construction. It also results when rain washes pollutants from urban areas into sewer systems and storm drains. According to a 1990 General Accounting Office (GAO) report, agriculture accounts for the largest share of the nation's nonpoint source pollution, affecting about 50 to 70 percent of waters through soil erosion from croplands and overgrazed grassland, and pesticide and fertilizer runoff. The report says that the greatest remaining barrier to controlling nonpoint pollution is that many U.S. Department of Agriculture (USDA) fkm programs reinforce the use of farming practices that contribute to soil erosion and water pollution.

The report further states that other problems confronting state and local efforts to control nonpoint source pollution are: 1) insufficient monitoring data on both the scope and the impacts of the problems and on the effectiveness of potential solutions; 2) political sensitivities involved in local control of the land uses that indirectly cause this type of water pollution; and 3) available funds remain overwhelmingly oriented toward controlling point sources rather than nonpoint sources. However, U.S. Environmental Protection Agency (USEPA) analysis of comparative risk posed by various pollution problems suggests that nonpoint source water pollution poses a level of health risk comparable to that presented by point sources, and also poses substantially more serious ecological risks. The ongoing bias in funding reflects USEPA statutory mandates, which place greater emphasis on programs to control point source pollution.

Point source water pollution controls alone are insufficient to meet the objectives of the National Clean Water Act (Public Law 92-500). In the 1986 National Water Quality Inventory Report to Congress, 37 states indicated that industrial wastewater was the cause of surface water failing to support its designated use for 9 percent of streams and rivers, 1 percent of lakes, and 8 percent of estuaries. On the other hand, nonpoint sources were cited as the cause of failure to support designated use for 65 percent of streams and rivers, 76 percent of lakes, and 45 percent of estuaries. The assessment concluded that nonpoint sources appear to have become the dominant pollutant contributors as industrial and municipal wastewater are increasingly being brought under control.

Assessing nonpoint source pollution and developing a control plan is difficult for many reasons. The first factor is the high cost of collecting field data when there are widespread and poorly defined discharges into receiving waters. The second factor is the diffuse nature of the processes that generate the pollution and the spatially interactive nature of the processes that

transport the material into the receiving waters. These spatial characteristics make it necessary to use maps (or geographic databases) to describe the transport and accumulation of the pollutants. The third fhctor is the large temporal variation of pollutant loads, for example, between wet and dry weather conditions. Because of this variability over time, long-term records are needed to define the water quality loadings. To overcome these spatial and temporal problems, computer- aided technologies such as the Geographic Information System (GIs), image processing, and physical-based models are potentially very useful, either separately or in combination.

Federal, state, and local regulatory and planning agencies are increasingly adopting G I S / i i e processing technology to store and manipulate spatial data. Clearly, one way to increase the usefulness of the GIs technology is to integrate it with physical-based computer simulation techniques. Developing and testing such an integrated system is urgently needed.

Geographic databases, satellite and scanned images, and digital elevation model (DEM) data are being used to refine the databases for water quality modeling. This report shows an initial effort toward developing methodology that uses these technologies to categorize and map land areas according to their potential for pollution hazards. Regional planners would then be able to use the maps and the related attribute data to formulate a decision table to set priorities in their effort to control nonpoint source pollution.

Promises and Problems of GIS for Water Quality Modeling

Future success of water quality modeling depends on the modeler's ability to display timely and economical customized summaries of a variety of climate, land use, and soil information, which is integrated to reflect watershed behavior. Customized summaries are designed to meet a user's specific data assessment or data display needs, and the ability to provide varied products requires a high degree of flexibility in data management. One way to achieve flexibility is to use a highly automated and intelligent information system such as GIs. The GIs technology has promise for enhancing information services for planners and engineers since it can lead to greater insights, more precise results, more comprehensive studies, more easily shared data, more objective .

analysis, less cost for graphic outputs, and other benefits. There are problems, however, in database creation, in training of users, and in interfacing with existing and future analytical models or procedures. Numerous research and development tasks are required for GIs to completely fulfill its promise.

Efforts to integrate GIs technology with physical-based simulation models, using existing systems, expose a number of deficiencies. The deficiencies relate to spatial lumping, difficulty of parameter estimation, model calibration in a spatial context, selection of a proper scale, error migration from various sources of input data, lack of existing databases, and lack of established Idcages among hydrologic, geochemical, and soil chemical processes (Novotny and Chesters, 1989; Rose et al., 1990). The research being reported is an initial effort to address such deficiencies and develop a useful planning tool.

Acknowledgments

This report was prepared under a research grant with the Illinois Water Resources Center, University of Illinois at Urbana-Champaign. Dr. Glenn Stout, Director of the Water Research Center is the research grant administrator.

The project was conducted under the guidance of Richard Sernonin (Chief), John Shafer (Head of Hydrology Division), and Michael L. Terstriep (Director, Office of Spatial Data Analysis & Information). Shery Zimmerrnan conducted most of the GIs data processing. Dr. J.J. Kao conducted the digital elevation analysis. Ying Ke conducted the image processing. Becky Howard typed and formatted the final draft of the report, and Eva Kingston edited the report. John Brother and Linda Riggin prepared the maps and figures.

CHAPTER 2 RELATED STUDIES

Remote sensing and GIs are two areas of computer-aided technologies that have potential to expand the usefulness of physical-based water quality models. The application of remote sensing in hydrology is reported in numerous publications and papers such as the Manual of Remote Sensing (Colwell, 1983) and REFLEX (Department of Energy, 1988; Hooghart, 1990). Published research demonstrates the interfacing of remotely sensed data with hydrologic model output (Beasley, 1980; Mace, 1983; Schaal, 1986; Rango and OWeill, 1982; Schultz, 1988; Department of Energy, 1988; Salomonson et al., 1983). Urban runoff is estimated by using LANDSAT data (Ragan and Jackson, 1975). Research on data acquisition includes obtaining land surfhce temperatures using thermal i&ared sensors (Sequin and Itier, 1983), and mapping soil moisture using microwave technology (Day and Petersen, 1983; Eagleson and Lin, 1976).

In the area of using GIs in hydrology, Lee and Camacho (1987) used the technology to simulate agricultural nonpoint source pollution fiom a 50-square mile watershed. Gilliland and Baxter-Potter (1987) used the GIs to produce the pollution potential maps of suspended solids and bacteria densities. Terstriep and Lee (1989) demonstrated that urban runoff quality can be cost- e M v e l y modeled by using the GIs. And future GIs applications in hydrology are outlined by Wallis (1988).

There are important restrictions in the use of remote sensing data in a physical-based water quality model. First, present LANDSAT images are limited by the spatial resolution capabilities of the on-board sensor. For example, the thematic mapper (TM) has a 30-meter spatial resolution, and the SPOT (French satellite) has a 20-meter resolution for multi-spectral data and a 10-meter resolution for panchromatic data, whereas at least 2-meter resolutions are required for delineating stream and infrastructure features in watersheds. To overcome this problem, a digital scanner can be used to digitize color or inf?ared transparencies for a resolution up to 2 meters. The cost of this process for a small study area is comparable to that of alternative manual methods.

A second limitation is that generally available remote sensing information lacks slope and elevation data. To overcome this problem, the digital elevation data used in this project were obtained fiom terrain analysis. This same terrain analysis for water quality modeling is used by numerous researchers (Panuska and Moore, 199 1; Maidrnent 1989, Morse et al., 199 1).

Few existing hydrologic models incorporate the kind of high-resolution spatial data available from the technologies just described. A model with this capability is needed for circumstances where site-specific information is essential and scenario comparisons are an important source of information for decision-makers.

CHAPTER 3 OBJECTIVES

The fist objective of this study was to test a spatially distributed model that incorporates high-resolution digital images, the GIs database, and digital elevation data for analysis of detailed hydrologic and water quality effects resulting fiom man-made or natural disturbances in agricultural watersheds.

The second objective of this study was to develop a method to account for potential nonpoint source water pollutants fiom an entire hydrologic unit on the basis of the sources of the pollutants and their transport. This methodology links the mandated water pollution goals for entire hydrologic units that are predominantly agricultural and the land management practices included in conservation plans developed hrm-by-hrm.

CHAPTER 4 SELECTION OF STUDY AREAS

The study area was selected for this project based on availability of field data and geographic information databases. The Highland Silver Lake (HSL) watershed (Makowski and Lee, 1983; 1986; Lee and Carnacho, 1987) is located in Madison County, Illinois. The HSL watershed has a dramage area of 3 1,560 acres (49.32 square miles).

The types of GIs and tabulated data needed for water quality modeling are dependent upon whether the modeling is being verified or being applied. Model verification requires both watershed characteristics and data on water quality and quantity. Model application, however, requires only the watershed characteristics data.

Highland Silver Lake Watershed

The HSL watershed is located in southwestern Illinois approximately 30 miles east- northeast of St. Louis, Missouri, as shown in figure 1. Most of the watershed is in the eastern portion of Madison County, with a small part in Bond County.

Highland Silver Lake is an impoundment of East Fork Silver Creek, a tributary of the Kaskaskia River. The lake lies entirely in Madison County. The dam is about one mile northwest of the city of Highland. At normal pool, the lake has a storage capacity of 6,336 acre-feet, a mean depth of 10.6 feet, and a maximum depth of approximately 28 feet.

Long-term weather records show the maximum and minimum temperatures at the nearest climate station to be 1 10°F and -15"F, respectively. The mean temperature was 55.3"F. Average annual precipitation and snowfall are 36.75 inches and 14.3 inches, respectively. The other climate variables are as follows:

Coldest month January Warmest month July Length of growing season 192 days Wettest month June Driest month January

The HSL watershed has an elevation range of 630 feet in the northwestern portion to a lake elevation of 500 feet. Greatest relief is to the east of the lake where elevations of 600 feet may be found within 1500 feet of the lake. This steep relief provides a contrast to the relatively flat upper portion of the watershed with shallow valleys and low-gradient streams. East Fork Silver Creek, the lake's major tributary, is joined by Little Silver Creek and two unnamed tributaries above the lake as shown in figure 1.

Two major soils associations are found in the watershed. The Virden-Piasa-Darmstadt association, which occupies the nearly level upper portion of the watershed, contains silt and clay soils, many of which have a high concentration of sodium in the subsoil. The main management concerns with these soils include d r a i i e , controlling water erosion and improving fertility. The

Hickory-Elco-Rosetta association, which occupies the steeper areas near streams and around the lake, also contains silts and clays, which have high sodium subsoils. The main management concerns with these soils are also controlling water erosion and improving fertility.

Agriculture is the dominant land use in the watershed, with about 82 percent of the land devoted to rowcrop production. Permanent grassland and woodland comprise about 5 and 4 percent of the watershed, respectively. The remaining 9 percent of the land serves a variety of uses includq urban land, water bodies, feedlots, wildlife habitat, transportation, and gravel pits. The village of Grantfork is the only urban area in the watershed.

The 600-acre HSL is the public water supply for the city of Highland and nearby communities, serving a population of about 8800. It also serves as noncontact recreation, mainly fishing. There is one public boat launch. The city of Highland maintains a public park on the southeast corner of the lake. Uses of the lake are impaired by high suspended sediment and turbidity.

Suspended sediment in the HSL reduces light penetration and impairs vital biological functions such as respiration, feeding, growth, and reproduction of game fish. Sediment deposition is a related problem, which also contributes to the deterioration of fish habitat and spawning areas. Largemouth bass are not reproducing. Overall angler success is poor, and species composition in the catch is below the optimum conditions established for lakes in Illinois.

The turbid water also affects water treatment costs. It is estimated that about 14 percent of annual water treatment costs can be attributed to high turbidity levels.

Although phosphorus and nitrogen concentrations in the lake are high, they do not impair current lake uses.

Soil erosion from agricultural land has been identified as the primary source of pollution delivered to the lake. Soil erosion rates were estimated to be 3 tons per acre per year with a sediment delivery rate of 47 percent. Soils with highest erosion potential and fine particle composition were designated as critical; this includes natric (hlgh-sodium) soils with 2 percent or greater slope, or nonnatric soils with 5 percent or greater slope.

An economic analysis of the HSL Rural Clean Water project was conducted by Setia et al. (1988).

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Figure 2. Working scheme for integration of databases and modeling

GIs Display Display Device

lmage or GIs Processor

1 8

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Spatial Data

Digital lmage DEM Digitized

A

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s Sampled Data - - - - - # . - - - -

Inventory Data

Data Processor Mathematical Model

Processor Data Processor (AGNPS)

Database Management

(ARC/INFOl

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slope areas were delineated and digitized as polygons. The associated attribute data consist of the slope classification and area in acres. Figure 3b shows the slope delineation for HSL watershed.

Streams and monitoring stations The stream network on the watershed was mapped from USGS 7.5-minute topographic maps. The stream network was digitized as line and point data as shown in figure 3c. The associated attribute data consist of stream junctions, stream order, stream slope, and stream monitoring stations.

Farm boundaries. Because of the importance of farms as management entities, selected farm and field boundaries were stored as a separate layer. This ensures that important tracts of land can be easily identified.

Best management practices A best management practice (BMP) is "a practice or combination of practices that are determined by a state or designated area-wide planning agency to be the most effective and practicable (including technical, economical, and institutional considerations) means to control point and nonpoint pollutants at levels compatible with environmental quality goals" (Wisconsin SWCS, 1989). For the HSL watershed, 12 BMPs were developed by the USDA (Lee and Camacho, 1987). Records of the adoption or installation of BMPs are maintained by the Madison County Soil Conservation Service field office.

For the purposes of water quality modeling and economic analysis, the 1984 and 1990 records of BMP adoption were used. The BMPs developed for the HSL watershed consist of structural and nonstructural categories of practices. The structural BMPs include animal waste management systems, terrace systems, diversions, waterways, and sediment detention basins. The nonstructural3MPs include cropland protective cover, conservation tillage systems, stream border protection, permanent vegetative cover for critical areas, tree-planting, and fertilizer management.

The BMP data needed for model input were stored as characteristics associated with individual ten-acre grid cells as shown figure 5a. Nonstructural BMPs were recorded as adjustments to the cropping factor (C value) of the Universal Soil Loss Equation (USLE). The adjustments were obtained from previous erosion modeling research (Young et al., 1987).

The structural BMPs installed on the HSL watershed were designed based on site characteristics and design standards. Recording information for each installed structure turned out to be a time-consuming job. Thus, structural BMPs were reflected in the databases and modeling by making appropriate adjustments (table 1) to data being used to represent the behavior of the affected cells as shown in figure 4b.

Remote Sensing Data

One application of remote sensing is to inventory land uses. Every substance found on the earth's surface has its own specific spectral reflectance characteristics and spatial arrangement (texture). These characteristics were captured by remote sensors. Once correlated to ground truth, the remotely sensed characteristics can be used to identify specific groups of materials on the land surface and produce a land cover map.

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Table 1. Adjustment of Agricultural Nonpoint Source Pollution (AGNPS) Model Input Parameter for Best Management Practices (BMPs)

1) Grass Waterway - No change in channel slope - Change side slope to 10 percent - Change Manning roughness to 0.0027

2) Terrace System - Reduce SCS curve number by 10 - Change slope length to 120 feet - Change impoundment factor to 2 with representative drainage area of 3 acres

and outlet pipe diameter of 8 inches

3) Sediment Detention Basin - Change impoundment factor to 2 with representative drainage area

of 4 acres and outlet pipe size of 8 inches

4) Diversion Structure - Change impoundment factor to 1 with representative

drainage area of 3 acres and outlet pipe size of 8 inches - Change slope length to 120 feet

Spatial resolution refers to the smallest object on the ground that can be distinguished by a particular remote sensing technique. Spectral resolution refers to the width of bands in the electromagnetic spectrum that can be distinguished and the number of the bands used. Radiometric resolution refers to the relative sensitivity to differences in signal strength. In general, increasing resolution of spatial, spectral, and radiometric capabilities also increases the amount of data and data management complexity. Consequently, the trade-off between data resolution and data- processing costs depends on the practical needs of the end-users.

High resolution aerial color photography film is suitable for engineering applications, but it is mainly used for qualitative human interpretation because of the limitation of the scanning technology, computer storage, and availability of computer software. Recently, with drastic improvements in computer hardware and software, and increases in use of GIs databases, there is a growing need to incorporate the scanned color film data into the hydrologic and engineering, applications (Scarpace, 1978; Scarpace and Quirk, 1982).

Color film records information in the form of varying amounts of color dyes in the processed imagery. To measure the presence of these dyes at a specific spatial location, the film was placed in the light of an optical system, and its density was measured by the transmittable factor due to the film's light-absorbing and scattering elements, as well as the spectral characteristics of the incident light and detector. Geometric corrections were also necessary before performing the classification.

In this project, the color photographic information was obtained from National High Altitude Aerial photographs (in the following format: 22.5 centimeters (cm) x 22.5 cm, or 9 inches (in.) by 9 in.). The photographs were scanned for three spectral bands. The scale of the

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For a flat watershed such as the HSL, several methods were explored before a consistent and rational dramage pattern was developed from DEM aspect data.

Vector average method. Computing the vector average of aspects of all DEM pixels within an AGNPS cell is a simple method. Figure 5a illustrates how it might work. One drawback of the vector average method is the lack of consideration of spatial distribution of aspects in a cell. For example, in figure 5b, two sets of nine pixels are averaged to the same direction, although the cells actually have two different directions due to the spatial distribution of aspects. The second drawback is that no consideration is given to steepness while the dramage direction is being determined within the cell.

Weighted average method. To take into account the influence of relative steepness, a weight based on pixel slope is introduced. A higher weight is given to pixels with steeper slopes. This method turns out to be difficult because meaningful relative weights are hard to define.

Elevation average method. Instead of using the slope and aspect of the pixels within the cell, the elevation information stored in the DEM data can be used to determine the approximate drajnage direction of the model cell. The arithmetic average elevation of the pixels within the cell is computed. For each cell, the dramage direction is then assigned to the adjacent cell with the lowest elevation. The drawback of this method is similar to that of the vector average method in that internal dramage directions may be contradictory and closed loop drainage patterns can be generated. In rough terrain, this method allows large elevation changes to overwhelm realistic, but subtle dramage patterns.

Stream network method. This method forces the dramage pattern to match the existing main stream network by using both the DEM data and stream hydrography data from topographic maps. This method is capable of matching all existing stream flow directions. In this study, the chances of having conflicting dramage directions or loops are less than with any of the other methods.

Preparation of AGNPS Input File

The procedure for preparing input to the AGNPS model from DEM, GIs, and digital image data is summarized as follows:

1) Divide the studied watershed into small ten-acre grid cells. 2) Collect DEM, GIs, and image data. 3) Process DEM data to produce slopes and drainage direction data. 4) Process GIs data to produce soil type data. 5) Process image data to produce land use classifications. 6) Generate an AGNPS input file containing data produced by previous steps.

To demonstrate the integration of DEM, GIs, and digital image data using the above procedure to support water quality modeling, the HSL watershed in southern Illinois was analyzed. The procedure is briefly described in the next section.

Data Processors

Several computerid programs were developed using FORTRAN and ERDAS Toolkit (ERDAS, 1989) routines to process the DEM, GIs, and digital image data, and generate an input file for AGNPS. The DEM data were processed for the slopes and aspects of pixels. Because the dramage direction of an AGNPS cell is estimated by averaging the aspects within the cell, sometimes it may be irrational, especially in steep terrain. To ensure connectivity of the drainage pattern, these irrational directions were altered by using the stream network method. This alteration is reasonable because the stream network data have been vedied against ground truth.

By processing the GIs, DEM, and digital image data, the following parameters required by AGNPS were determined: SCS curve number, land slope, channel slope, drainage pattern, slope shape, field slope length, Manning's roughness coefficients, soil erodibility factor, cover and management factor, sufice condition constant, soil texture, fertilizer availability factor, and chemical oxygen demand (COD) factor. With idonnation on these parameters and other manually prepared ones, an AGNPS input file was automatically generated by a computer processor.

CHAPTER 6 WATER QUALITY MODELING

To simulate the changes in sediment and water quality parameters in the watershed due to the implemented and proposed BMPs, a spatially distributed model was needed. The AGNPS model (Young et al., 1987) was selected because of its ability to reflect BMP changes.

AGNPS Model

The AGNPS model is gridcell-oriented for a single-storm event. The cells are uniform square areas. The model predicts runoe eroded and transported sediment, and the nitrogen, phosphorus, and COD concentrations camed by runoff into downstream cells, which are iIIt~rco~C!Ct€!.d to form a complete watershed drainage pattern.

The model calculates runoff volume and peak discharge, total upland erosion, total channel erosion, and divides the eroded material for each cell into five classes (clay, silt, small aggregates, large aggregates, and sand). The sediment transport for each cell is also calculated in five of the particle-size classes and the total. The pollutant transport portion is subdivided into soluble pollutants and sediment-attached pollutants.

Runoff Volume and Peak Discharge

The storm water volume RF was calculated as:

RF = (RL - 0.3 s ) ~

RL +0.8 S

where RF = runoff (in.) RL = storm precipitation (in.) S = retention factor = 1000 / CN - 10 CN = SCS curve number

The peak channelized flow through each cell was approximated as:

where Qp = peak discharge (tt3/sec) A = up slope contributing area (sq mi) CS = channel slope (ft/mi) RO = runoff volume (in.) LW= length to width ratio for the watershed

Upland Erosion

Upland erosion was approximated using a modified version of the USLE (Wischmeier, 1978), which contains a factor to describe the slope shape:

where A = soil loss EI30 = thirty minute rainfill energy- intensity K = soil erodibility factor LS = slope length factor C = cover management factor P = conservation practice factor Ssf = slope adjustment factor

Calculations to correct for a convex slope were based on a 75-foot slope length in which the upper, middle, and lower thirds of the slope have gradients of 2,7, and 12 percent, respectively. The concave slopes were based on respective top to bottom slopes of 12, 7, and 2 percent, respectively.

Sediment Routing

The sediment routing was modeled for five particle-size classes: clay, silt, small aggregate, large aggregate, and sand. The sediment was routed through the watershed using the steady-state continuity equation:

where QS(x) = sediment discharge at the downstream end of the channel reach (pounds/sec) Qs(0) = sediment discharge at upstream end of the channel reach (pounds/sec) Qsl = lateral sediment inflow rate (poundslsec) X = downstream distance (ft) W = channel reach width (ft) L =channel length D(x) = sediment deposition rate at point x (pounds/sec/ft2)

The sediment deposition rate D(x) is determined as:

where Vss = particle fall veloci (Wsec) 7 q(x) = runoff rate at x (ft /set)

qs(x) = sediment discharge per unit width (pounds/sec/ft) gs(x) = effective sediment transport capacity represented by a modified version

of Bagnold's stream power equation (pounds/sec/ft)

Nutrient Transport

The nutrients (nitrogen or N and phosphorous or P) were routed through the watershed in solution or were absorbed by the sediments. Sediment-absorbed nutrient yield was estimated by using:

NUT,,d = ( N K d ) ( f l ) ( E R ) and

ER = 7.4 Qs (0.2Tf)

where NUTsed = nutrient ( N or P) transported by sediment NUTsl = soil nutrient content in the field Qs = sediment yield by sediment routing SY = sediment yield ER = enrichment ratio Tf = soil texture correction factor

The soluble nutrient yields were determined by the rahfkll, soil fertility, and leaching. The soluble N and P in the discharge are functions of

1) available soluble N or P content in the soil 2) available N or P in the rainfall 3) rate constant for downward movement of N or P into the soil 4) total storm event infiltration 5) rate constant for N or P movement into runoff water 6) total storm runoff volume 7) soil porosity fictor 8) N and P contribution due to rainfall

Chemical Oxygen Demand (COD)

The total soluble COD was computed from the average runoff and the average COD concentration measured in runoff volume.

Methodology

The methodology developed in this project has been designed to automate and support the use of the AGNPS model.

Run AGNPS model. To generate input data for AGNPS automatically, requires five GIS files (watershed boundary, slope, aspect, land use, and soil), DEM data, and image data, as described in chapter 5.

For verification purposes, the HSL watershed was used. Both 1984 and 1990 conditions were modeled. The 1984 data were selected because BMP implementation was just beginning on the watershed then. In 1990, rougldy 80 percent of the 6525 acres of the critical areas had been treated with BMPs. Simulations were run using representative storm events ranging from 0.7 to 4.5 inches. The AGNPS version 3.5 was used and the simulations were run on a PC. Because the output was transferred to an attribute file of a GIS storage file using ACRANFO software, all the output can be displayed using this plotting software.

Figure 6 shows the soil erosion rate of a 4.5-inch storm event of the 1984 conditions. Notice that the areas of simulated high erosion match the steep slope areas of the watershed.

Compute cell-twutlet sediment delivety ratio. In order to show the amount of soil erosion contributed to the receiving water body (the HSL for this example), a short computer program was developed to accumulate sediment delivery rates. Figure 7 shows the cell-to-outlet sediment delivery ratio. The result indicates that the cells having high delivery ratios are located near the lake and the main tributaries: the farther from the lake, the smaller the sediment delivery ratio.

Compute Sediment Yield Given the cell-to-outlet sediment delivery ratio, the sediment yield of each individual cell to the lake is a product of multiplying the sediment delivery ratio by the total soil erosion of the cell. Figure 8 shows the sediment yield of the cell of the HSL of the 4.5-in. storm event. The results show the proportion of sediment produced by each cell that is delivered to the lake. As expected, it requires both a high erosion rate and a high sediment delivery ratio to produce a high cell-to-outlet sediment yield.

Defne pollution potential rating. To evaluate the nonpoint source pollution, both on-site and off-site effects need to be considered. Soil erosion is a good indicator of the on-site damage

'

and sediment yield from the cell to outlet is a good indicator of the off-site damage. One way to combine both considerations is to develop a categorical decision table as shown in table 2. This kind of decision table provides a relative and site-specific rating. The ratings vary with the watershed being studied.

Table 2. Sediment On-Site and Off-Site Pollution Potential Rating

Sediment delivew ratios Soil erosion rate 0.0-0.35 0.35-1.00 1.00-100

0.00-1.25 L LM M 1.25-2.00 LM M HM 2.00-10.0 M HM H

Note: L = low, LM = low median, M = median, HM = high median, and H = high.

I 7 Sediment Delivery Ratio rx Less than 1% m 1%-2%

2% -10% Greater than 10%

Figure 7. Sediment delivery ratio of Highland Silver Lake watershed

Ratings developed through a process like this can be applied to each cell of the watershed and mapped. Figure 9 shows the five sediment pollution potential ratings in the HSL watershed based on table 2.

Run 1990 data with BMP scenario. To model the benefits provided by installation of BMPs in the HSL watershed, data on both the nonstructural and structural BMPs were obtained from the Soil Conservation Service in Madison County. The data were verXed and mapped (figure 4). The nonstructural BMPs involve maintenance of land cover. These are represented by adjusting the USLE cover factor in the model input. The structural BMPs generally are small and located within one cell. They are reflected by introducing new SCS curve numbers, channel slopes, Manning roughness fkctors, channel side slopes, impoundment b r s , dramage areas, and outlet pipe diameters.

Display changes behveen 1984 and 1990. After adjusting the AGNPS input data to reflect the BMP implementation, the AGNPS model was re-run. The results represent the water quality after BMP implementation. In order to compare the "before" and "after" BMP scenarios, figure 10 shows changes of the sediment pollution potential ratings. Moderate improvement of pollution potential rating was defined as one-step improvement, and significant improvement as a two-step improvement. As can be observed, the map of improvements displays information not only about the reduction of erosion rate and the cell-to-outlet sediment delivery ratios, but also about the inherent characteristics of the soils and topography.

Other scenario analyses: During evaluation and comparison of results, several important issues can be raised. After evaluating the overall water quality impact, a decision maker may want to examine the effect on water quality of further changes in land uses within the watershed. For example, some areas may change from agricultural to residential. Such scenario analysis can be handled by making appropriate changes in model inputs for specific locations (cells) within the watershed. Then the simulation model can be run to show the results of the proposed changes.

CHAPTER 7 ECONOMIC ANALYSIS

Even with accurate physical-based modeling available, land managers still face a problem of incorporating economic trade-offs into their decision making. Linking the modeling to GIs can provide planners a mechanism for assigning and displaying those trade-of&. For example, the BMP costs can be separated into the actual costs of installation and the loss of income suffered by land users. Since the HSL watershed represents the trade-off decisions of the Rural Clean Water Program (RCWP), it seems appropriate to describe the objectives of that program.

Rural Clean Water Program

The RCWP was a federally sponsored experimental program designed to control agricultural nonpoint source pollution in rural watersheds to improve water quality. Initiated in 1980, the RCWP was established as a ten-year experiment offering cost-sharing and technical assistance incentives for voluntary implementation of BMPs. The RCWP objectives were to: 1) achieve improved water quality in the approved project area in the most cost-effective manner possible, in keeping with provision of adequate supplies of food, fiber, and a quality environment; 2) assist agricultural landowners and operators to reduce water pollutants and to improve water quality in rural areas to meet water quality standards or goals; and 3) develop and test programs, policies, and procedures for the control of agricultural pollution.

With a total appropriation of $64 million, the RCWP funded 21 projects across the country. These projects represented a wide range of pollution problems and impaired water uses. The projects were selected from state lists of priority watersheds developed during the Section 208 planning process in the 1972 Clean Water Act. Projects were located in Alabama, Delaware, Florida, Idaho, Illinois, Iowa, Kansas, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Nebraska, Oregon, Pennsylvania, South Dakota, Temessee/Kentucky, Utah, and Vermont. While water quality monitoring has been performed in all 21 projects, five of the RCWP projects (Idaho, Illinois, Pennsylvania, South Dakota, and Vermont) were selected to receive additional funding for comprehensive monitoring and evaluation.

In Illinois, the specific goals for the HSL watershed were to reduce the amount of sediment and sediment-related pollutants entering the lake by applying BMPs to 10,500 critical acres in 85 operators' contracts.

Preparation of Databases for Economic Analysis

Two years of data from the HSL watershed were used in this study. These years represent the watershed before (1984) and after (1990) the installation of BMPs to control erosion and water quality loadings. The data were used to demonstrate the potential of linking a GIs database with a watershed simulation (sediment delivery) model to evaluate alternative strategies of controlhg sediment - by controlling on-site erosion. By adding economic data to the GIs, evaluations can be enriched by including economic impacts of strategies to control nonpoint source pollution.

Separate databases were created in ARC/INFO for the two different years. A common grid system was used in both years to divide the 30,520-acre watershed into ten-acre cells. The common cells permit direct comparisons between the pre- and post-treatment situations, and provide the spatial organization necessary to run the AGNPS model - to estimate sediment delivery.

Because an inter-agency effort was involved in collecting the pre-treatment data, and six years intervened between pre- and post-treatment data collection, differences existed in what information was available and how it was recorded. These differences had to be resolved to make the data sets comparable. Although a cellular database (based on the grid system) existed at the time of the 1984 effort, information, such as crop rotations, was stored in ARCmJFO in relation to polygons representing actual farm fields. In contrast, the 1990 data were all origmally stored in the cellular format and thus were immediately available. The 1990 data included grid number and its associated crop, crop rotation, crop management factor, and any structural BMPs. The assumptions and generalizations needed to achieve consistency between 1984 and 1990 data are discussed below.

Crop Rotation Data

For consistency, it was later decided to use the 1990 crops and rotations data for both the 1984 and 1990 economic determinations. This allows a more realistic representation of the effects of different C values and BMP structures on the site.

Soil Data

The physical modeling and economic analysis in this study were intended to evaluate the sediment reduction and profitability of alternative long-term land-use choices. A land-use choice was made for each ten-acre cell and included selection of a multi-year crop rotation and, in some cases, one or more structural improvements. The profitability associated with any particular '

choice was estimated based on the crops in the selected rotation, the inherent productivity of the soil, and the cost of any structural improvement.

The profitability estimates require the expected yield for each crop in each rotation for the soil found in each cell. This information was obtained from the Madison County Soil Survey (SCS, 1990).

Like the 1984 crop data, the soil type information was stored in ARCANFO in the polygon format. Because the soil map units do not coincide with the ten-acre cells of the grid system, it was necessary to reconcile the information. An intersection was created in ARC/INFO between the soil coverage and the grid coverage to create approximately 11,000 polygons. A sort was done to provide a listing of all soil types (and their respective acres) in each cell. Each cell was then assigned the soil type of the predominant soil. A comparison between soils information stored in ARC/INFO in 1984 and the current Madison County soil survey, completed in March 1986, showed some discrepancies, which were corrected in favor of the current survey.

Crop Yields and Tillage Systems

An existing crop yield database contained yields for corn, soybeans, wheat, and hay for each soil type (SCS, 1990). Two new databases were created by intersecting the land-use databases for 1984 and 1990, containing crop rotation, C values, BMPs, and soil type, with the crop yield database. New data fields for crop prices were added. One of four different tillage classes (conventional, conservation, and two classes of no-till) was assigned to each cell, based on the C value and crop rotation information. The two no-till classes were used to account for the different effects that no-till practices can have on yield, depending on the soil type. Cost-of- production estimates were recorded in the database for each tillage clks.

Economic Data

An economic dimension was added to the GIs databases by expanding the tabular attribute data associated with specific land uses. A system was developed to compute cost of production and gross income for any combination of crop rotation, tillage, and yield that the GIs attribute tables could contain.

In addition, the cost of installing any structural BMPs was also computed and included as attribute data in each cell containing such a BMP.

Cost of produdion. Crop production cost estimates are composed of two elements. The per-acre component consists of the expenses that are essentially independent of yield. The second component is the much smaller, but not insigndicant, per-bushel cost that does vary with the amount of commodity produced. These per-bushel costs are associated with drymg, transportation, and replacement fertilizer.

Cost estimate equations were stored in the database for the land use associated with each cell. The weighted average cost for all the crops in the selected rotation was adjusted as needed for the expected yield for the soil in that cell. The net cost per acre (on an annual basis) of a rotation would be generalized as:

Production Cost = ni (ICi + ki yi) / T {,Il I where

K = costs that are independent of yield, k = costs that are proportional to yield, y = yield of crop i, n = occurrences of crop i in rotation,

N T = years in rotation (could differ fiom , because of double-cropping), and

i=l 1 = crop

See appendix A for a full list of equations.

BMP strudural costs. Over the course of the RCWP project, a variety of structures were installed to help control erosion. The total cost of each of these structures was obtained fiom the Agricultural Stabilization and Conservation Service (ASCS). An equivalent annual cost for each structure was estimated by assuming a 20-year structure life and an 8 percent interest rate. The annual equivalent cost was then recorded in the database for each ten-acre cell.

Two different types of cost exist, depending on the structure. Some simply have an overall construction cost: for example, sediment settling basins and grade stabilization structures. Other structures vary tremendously in both size and cost: for example, terraces, waterways, and diversions. Field borders lower erosion but have no associated cost. See appendix B for a list of structural BMPs and their associated costs and effects.

Net return. Like costs, gross income for a crop rotation is a weighted average of the crops being grown, taking into amunt the productivity of the soil type found in a particular ten-acre cell. In general:

where I =income pi = price of crop i, Yi = yield of crop i, ni = occurrence of crop i in rotation,

T = years in rotation (could differ from " because of doublecropping), and C i= l

1 = crop.

Net return is simply the gross income minus all costs, including any structural costs.

where Rn = net return I =income

% = production costs Cs = structure cost

Since the actual time between 1984 and 1990 was irrelevant to the objectives of this study, 1990 prices were used for both 1984 and 1990 data. This allows comparisons of the two land-use patterns without the complications of price changes.

Comparisons

Once costs were estrmated, economic effects of land-use choices were studied. Costs and sedimentation resulting fiom land-use choices are linked through net returns and sediment hazard ranlung. For example, each cell can be classified by average net return per acre, as shown in table 3. It must be emphasized that the classifications that are presented, both in net returns and pollution potential, were created just for illustration. In an actual regional planning application, it would be the responsibility of the decision-maker(s) to make judgments about both the number of categories and where the separations would be.

Table 3. Net Return Categories for Highland Silver Lake Watershed

Class 1 2 3 4

Net return $0 or less $1-$80 $81-$150 $15 1 or more

Since the GIs database contains the information needed to determine the net returns associated with each cell, assigning a net return class to each cell can be automated. The automation program uses Boolean logic to sort the cells on the basis of their net return attributes.

Classifymg the cells on the basis of their sediment pollution potential is more complicated but is automated in a similar manner. As can be seen in table 5, this classification requires simultaneous consideration of two characteristics. Boolean logic easily accommodates this classification based on intersections or, in this case, unions of characteristics.

And sediment pollution potential for each cell can be assigned as described in chapter 6, and summarized in table 4.

Thus fkr, the classification exercise has only lumped the watershed cells into two different, as yet unassociated dimensions. Now, similar logic can be used to firther classifL the cells by simultaneous consideration of both sediment pollution potential and economics.

The trade-off between pollution potential and economic cost is important to land management decisions. It is related to what the public might have to pay for pollution reduction. Table 5 illustrates a decision table that summarizes such trade-offs. It should be reemphasized that the number of categories, the divisions between the categories, and the numeric rating assigned to each category reflect judgments made by the analysts - rather than any natural or economic thresholds or values. Nevertheless, the categories can have as much meaning as the analysts give them, and the ability to provide a map showing the spatial distribution of the trade-offs may be of considerable value to decision-makers.

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CHAPTER 8 DISCUSSION

GIs and Water Quality Modeling

The analysis has illustrated that AGNPS pollution due to land-use choices can be simulated by using GIs databases linked with water quality models. GIs databases contain both spatial and tabulated data. The spatial data consist of vector data, scanned images, and digital elevation models. Water quality models simulate rainfall-runoff, erosion-sedimentation, and water quality loadings in individual cells, and route pollutants through cells linked to represent a surface dramage pattern. The model has to be verified with data from field observations. The spatially distributed model outputs were processed and stored as a part of the tabulated GIs attribute data. The outputs of the model were treated as layers within the GIs databases.

Historic records of implemented BMPs were treated as a layer of spatial data that related the BMP type and location. To depict the effects of BMPs, model inputs were adjusted according to the physical and chemical changes of BMPs. The outputs of the revised model were compared with the origmal outputs. The differences of the "before" and "after" results represent the physical impacts of the BMPs.

This project developed automated procedures for introducing appropriate economic attributes into a GIs database of an agricultural watershed. The GIs database was then linked to a physical-based watershed simulation model to support analysis of land-use scenarios. While this has been an initial effort to develop the necessary data management technology, several benefits can be expected from refinement of this technology.

Separation of databases and models. Collecting, storing, and maintaining data is an overwhelming task. The systems designed to summarize and display the data are of considerable value in their own right. By establishing automated lmkage systems between existing databases and analysis techniques (such as simulation models), data storage and summarizing systems can remain relatively undisturbed by innumerable, unique applications that might require data. At the same time, development of lmkages may well lead to improvements in the database systems to the extent that recommended changes can be shown to increase general usefulness of the data. Automated lmkage systems could also be programmed using the data to evolve with less concern about access to appropriate data.

Sharing of databases. Existing databases each have specific predesigned procedures, contents, and accuracies. As a group, they form a resource for many different research disciplines and levels of government decision-making. Making these databases more accessible to a variety of users, will help control the costs of data collection and maintenance.

Automation of modeling procedure. Like many other analytical procedures, water quality modeling involves many repetitive tasks. Even after a model has been verified and meets the required accuracy of its intended use, it will typically be exercised many times to simulate a range of background conditions and decision scenarios. Each run requires a different set of input data. Automated data lmkage and retrieval procedures can greatly reduce time-consuming and often error-prone manual data preparation tasks.

Concept of Pollution Potential Ratings

Measuring and managing water quality is a complex challenge. Water quality includes hydrologic parameters as well as physical and chemical constituents. For many management purposes, concerns as complex as water quality are often summarized with indices, representative of many underlying measures but, more easily tracked andlor linked to decisions. The Water Quality Index (WQI) used by the Illinois Environmental Protection Agency (IEPA, 1982), is an example of such an index. This project is not proposing a particular index. Rather, we have tried to demonstrate how a number of physical, economic, and choice variables can be systematically combined, on a more-or-less ad hoc basis, to provide relevant tabular and spatial information to a decision-making body.

The rating that we generated allows us to present alternative watershed management choices in terms of the trade-offs they represent between erosion and sedimentation potentials, on the one hand, and their economic benefits, on the other. In general, however, a rating can reflect a very simple decision rule or a very complicated multi-pollutant rule, embodying collective judgments and inputs from both the technical and management perspectives. Once a rating system is defined, however, a present status measurement can be made and used as a baseline for considering alternatives.

Once established, the rating system can be used to document change over time - perhaps, as part of a monitoring program. In the retrospective demonstration, using the HSL watershed, we produced maps and tables displaying the benefits obtained over a number of years from implementation of BMPs.

An example of automated data manipulation might involve incorporating Boolean logic into data editing. Perhaps a routine could be developed to find all data cells possessing a particular set of characteristics and install a given land use or BMP.

Locating Pollution Control Measures to Minimize Costs

The kind of system demonstrated in this project would probably be of most value as a tool for modeling watershed management alternatives to achieve maximum pollution control for minimum cost. The retrospective modeling of the HSL watershed has demonstrated the ability to model alternative scenarios. Tabular summaries and maps of alternative scenarios and of changes between scenarios were generated. By imagining the 1990 information as a proposed scenario instead of an historic record, it is easy to see how information of this type would be quite valuable to decision-makers trying to design a balanced, affordable land-use plan for the watershed.

Our efforts have been on developing the data management procedures to incorporate new (economic) information and link GIs databases to a simulation model. Further work is needed to make the entire system more accessible. Currently, modeling a new land management scenario will require creating a new land-use map for the watershed. This cumbersome task involves cell-by-cell editing of the database. Such a process needs to be made much easier, if the system is to fulfill its promise as a "what-if' planning tool.

CHAPTER 9 SUMMARY AND CONCLUSIONS

Summary

Control of onpoint source pollution is the major task remaining after point source water pollution is gradually brought under control. Assessing nonpoint source pollution and developing a control plan is difficult for many reasons, including diffused spatial sources and large temporal variations in wet and dry periods. To overcome these spatial and temporal problems, computer- aided technology, such as the Geographic Information System (GIs) and physical-based models used separately or together, is potentially very useful.

The first objective of the study was to test a spatially distributed model that incorporates the GIs database for analysis of hydrologic and water quality effects. The second objective was to develop a method to account for potential physical and economic impacts for the entire hydrologic unit on the basis of the nonpoint sources and their transport. The 50-square-mile HSL watershed was used for the test site. Data from the GIs, scanned photography and digital elevation were then compiled. The Agricultural Nonpoint Source Pollution (AGNPS) model was used to determine the hydrologic response and water quality loadings. Soil erosion rate and sediment transport were used as water quality indicators of pollution sources and their transport. Pollution potential ratings for each ten-acre cell were assessed by considering both soil erosion rate and sediment delivery ratios to the receiving waters. Then a combination of economic costs and pollution potential were rated based on the model user's own criteria such as ranges of economic costs and pollution levels. The method could be used as a tool for incorporating on-site water quality sources and their off-site transport capacities, economic costs, and trade-off analysis.

Conclusions

Based on the results of the study, the following conclusions can be drawn:

1) The research project illustrated that a physical-based model such as AGNPS can be successfully interfaced with GIs databases to conduct a realistic analysis of on-site water pollution source control, off-site transport responses, economic costs, and trade-off analysis.

2) The study showed that GIs technology has the advantages of separate databases and models, thereby enhancing the sharing of databases and the automation of modeling procedures.

3) The study further demonstrated how physical, economic, and user-chosen variables can be systematically combined to provide relevant tabular and spatial information to a decision- making body.

4) Further work is needed, however, to make the physical model and GIs interfkce more accessible and user-friendly.

CHAPTER 10 REFERENCES

Beasley, D.B. 1980. Interfacing Basinwide Modeling Methodology with Remotely Sensed Cropping and Management Data. Chicago, IL: USEPA Great Lakes National Program office.

Colwell, R.N. editor. 1983. Manual of Remote Sensing, 2nd edition. Falls Church, VA: American Society of Photogrammetry.

Department of Energy. 1988. Program Plan and Summary of Remote FIuvial Experimental (REELEX). Washington, DC: Department of Energy, Office of Energy Research, Office of Health and Environmental Research, Ecological Research Division.

Eagleson, J., and W. Lin. 1976. Remote Sensing of Soil Moisture by a 21-cm Passive Radiometer. Journal of Geophysical Research 8 1 :366.

Environmental System Research Institute (ESRI). 1989. A R C / ' User's Manual Version 5.0. Redlands, CA.

ERDAS. 1989. ERDAS User's Manual. Atlanta, GA.

Fegeas, R.G., R.W. Claire, S.C. Guptill, K.E. Anderson, and C.A. Hallam. 1983. Land Use and Land Cover Digital Data. U.S. Geological Survey Circular 895-E.

General Accounting Office. 1990. Nonpoint Source Pollution Control. Washington DC: GAO.

Gilliland, M.W., and W. Baxter-Plotter. 1987. A Geographic Information System to Predict Non- point Source Pollution Potential. Water Resources Bulletin 23(2):281-292.

Hooghart, J.C. 1990. Water Management and Remote Sensing. In Proceedings and Information No. 42, Wageningen, The Netherlands: Netherlands Remote Sensing Board.

IEPA. 1982. Illinois Water Quality Inventory Report 1980-1 981. Illinois Environmental Protection Agency, Springfield, IL.

Lee, M.T., and R. Camacho. 1987. Geographic Data Base and Watershed Modeling for Evaluation of the Rural Clean Water Program in the Highland Silver Lake Watershed. Illinois State Water Survey Contract Report 42 1.

Mace, T.H. 1983. Landsat UTS Tillage Survey Demonstration: Fairgrove, Michigan. TS- AND-83064. Environmental Monitoring System, Las Vegas, NV: USEPA.

Maidment, D.R. 1989. Design Automation for Site Development. Sixth Conference on Computing in Civil Engineering, T.O. Barnwell, Jr. (ed.). New York, NY: ASCE, pp. 699-706.

Morse, G.A., A. Eatherall, A. Jenkins, and J. Finch. 1991. Environmental Analysis Using Modelling and Geographic Information Systems, A Case Study - Agricultural Non- point Source Pollution Modelling Using Geographic Information Systems. Wallingford, United Kingdom: Institute of Hydrology.

Novotny, V., and G. Chesters. 1989. Delivery of Sediment and Pollutants from Nonpoint Sources: A Water Quality Perspective. Journal of Soil and Water Conservation 44(6):568-576.

Panuska J. C., and I. D. Moore. 1991. Water Quality Modeling: Terrain Analysis and the Agricultural Non-Point Source Pollution (AGNPS) Model. University of Minnesota Water Resources Research Center Technical Report No. 132.

Ragan, R.M., and T.J. Jackson. 1975. Use of Satellite Data in Urban Hydrology Models. Journal of the Hydraulics Division, American Society of Civil Engineers 101 (HY12): 1469-1475.

Rango, A., and P. ONeil. 1982. Effective Watershed Management Using Remote Sensing Technology. In Remote Sensing for Resource Management, C.J. Johannsen and J.L. Sanders (eds.). Akeny, IA: Soil Conservation Society of America.

Rose, C.W., W.T. Dickinson, H. Ghadiri, and S.E. Jorgensen. 1990. Agricultural Nonpoint Source Runoff and Sediment Yield Water Quality (NPSWQ) Models - User's Perspective. In Proceedings of International Symposium on Water Modeling of Agricultural Nonpoint Sources. USDA, Agricultural Research Service ARS-81, pp. 145- 169.

Salomonson, V.V., T.J. Jackson, J.R. Lucas, G.K. Moore, A. Rango, T. Schmugge, and D. Scholz. 1983. Water Resources Assessment. In Manual of Remote Sensing, Vol. II, pp. 1497-1570, J.E. Estates (4.). Falls Church, VA: American Society of Photogrammetry.

Scarpace, F.L. 1978. Densitometry on Multi-emulsion Imagery. Photogrammetric Engineering and Remote Sensing 44(10):1279-1292.

Scarpace, F.L., and B.K. Ouirk. 1982. Applications of Analytical Densitometry to Remote Sensing. Journal of Applied Photographic Engineering 8(3): 147-152.

Schaal, G.M. 1986. Analysis of Thematic Mapper ClassiJication of Tillage Practice in Seneca County, Ohio. Chicago, IL: USEPA, Great Lakes National Program Office .

Schultz, G.A. 1988. Remote Sensing in Hydrology. Journal of Hydrology 100:239-265.

Sequin, B., and B. Itier. 1983. Using Midday Surface Temperature to Estimate Daily Evaporation from Satellite Thermal IR Data. International Journal of Remote Sensing 4(2):37 1-383.

Setia, P.P., R.S. Magleby, and D.G. Carvey. 1988. Illinois Rural Clean Water Project - An Economic Analysis. Washington, DC: U.S. Department of Agriculture, Economic Research Service.

SIMRPC. 1985. Highland Silver Luke Rural Clean Water Project: Summary Report, Fiscal Year 1985. A report prepared for the Illinois Rural Clean Water Coordinate Committee, Southwestern Illinois Planning Commission, Collinsville, IL.

Simon, D.B., R.M. Li, and B.E. Sprank. 1978. Storm Water and Sediment RunoflSimulation for a System of Multiple Watersheds. Fort Collins, CO: Civil Engineering Department, Colorado State University.

Soil and Water Conservation Society, Wisconsin Chapter. 1989. State Regulation of Soil Erosion. Journal of Soil and Water Conservation 44(3): 209-2 11.

Soil Conservation Service. 1990. Personal communication with District Conservationist, Madison County, Illinois.

Terstriep, M.L., and M.T. Lee. 1989. Regional Storm Water Modeling Q-ILLUDAS and ARCANFO. In Proceedings of the 1989 Conference of Computing for Civil Engineers, New York, NY: ASCE, pp. 338-345.

U.S. Department of Agriculture. 1986. Soil Survey of Madison County, Illinois. Illinois Agricultural Experiment Station Soil Report No. 120. Urbana, Illinois.

U.S. Geological Survey. 1978. US. GeoData. Digital Elevation Models Data Users Guide 5. Reston, VA: USGS.

Wallis, J.R. 1988. The GIS/Hydrology Interface: The Present and the Future. In Proceedings of National Symposium of Geographic Information System for Geoscience. Denver, CO.

Wischmeier, W.H., and D.D. Smith. 1978. Predicting Rainfall Erosion Losses - A Guide to Conservation Planning. USDA Agricultural Handbook No. 537, Washington, DC.

Young, R.A., C.A. Oustad, D. Bosch, and W.P. Anderson. 1987. AGNPS, Agricultural Nonpoint Source Pollution Model, A Watershed Analysis Tool. U.S. Department of Agriculture, Agricultural Research Service, Conservation Report 35, Washington, DC.

APPENDIX A

PART 1. COSTS OF INDIVIDUAL CROPS

Conventional Tillage

Corn (Continuous) Corn after Soybeans Soybeans Wheat Wheat Double-Crop Soybeans

Conservation Tillage

Corn (Continuous) Corn after Soybeans Soybeans Wheat Wheat Double-Crop Soybeans

No-till w*

Corn (Continuous) C = 105 + .16(.9Yc) Corn after Soybeans C = 93 + .16(.9Yc) Soybeans C = 56 + .02(.95Y,) Wheat C = 60 + .02Yw Wheat Double-Crop Soybeans C = 107 + .02(.6(.95Y,) + Y,)

Tillage Classes 2 & 3' Corn (Continuous) C = 105 + .16Yc Corn after Soybeans C = 93 + .16Yc Soybeans C = 56 + .02Y, Wheat C = 60 + .02Yw Wheat Double-Crop Soybeans C = 107 + .02(.6Y, + Y,)

Conventional Conservation No-till Permanent Hay (Alfalfa)

Establish $200 $140 $140 Maintain $100 $100 $100

Single Year Hay (Clover) $120 $1 10 $1 10

Notes: yc = corn yield (bushels) y, = soybean yield (bushels) y, = wheat yield (bushels) C = cost

Tillage class 1 refers to poorly drained soils, tillage class 2 refers to somewhat poorly drained soils, and tillage class 3 refers to welldrained soils.

PART 2. COSTS OF ROTA'ITONS

Conventional

CS C = 80.00 + .08Y, + .01Y, CSWDCSB C = 93.00 + .05Y, + .01Y, + .OIYw CSWM C = 86.50 + .04Y, + .01Y, + .OIYw CCSWAAAA C = 104.00 + .MY,

Conservation

CS C = 75.00 + .08Y, + .OlY, CS WDCSB C = 86.00 + .05Yc + .OIYs + .OIYw CSWM C = 80.00 + .MY, + .OIYs + .OIYw CCSWAAAA C = 96.00 + .MY,

No-till Tillage Classes 2 & 3

CS C = 75.00 + .08Y, + .OlY, CSWDCSB C = 85.00 + .05Y, + .OIYs + .OIYw CSWM C = 80.00 + .MYc + .OIYs + .OIYw CCSWAAAA C = 92.00 + .MY,

SW C = 58.00 + .OIYs + .OIYw CSWAAAAA C = 106.00 + .02Yc

Tillage Class 1 CS C = 75.00 + .08(.9)Y, + .01(.95)Ys CSWDCSB C = 85.00 + .05(.9)Yc + .01(.95)Ys + .OIYw CSWM C = 80.00 + .04(.9)Y, + .01(.95)Y, + .OIYw CCSWAAAA C = 92.00 + .04(.9)Y,

SW C = 58.00 + .01(.95)Y, + .OIYw CSWAAAAA C = 106.00 + .02(.9)Y,

Notes: Y, = corn yield (bushels) Y, = soybean yield (bushels) Yw = wheat yield (bushels)

On soils in tillage class 1, no-till corn yield is .9 the normal yield. On soils in tillage class 1, no-till sovbean yield .95 the normal yield.

CS denotes corn and soybean rotation. CSWDCSB denotes corn, soybean, wheat, and double-crop soybean rotation. CSWM denotes corn, soybean, wheat, and meadow rotation. CCSWAAAA denotes corn, soybean, wheat, and 4-year alfalfa rotation. SW denotes soybean and wheat rotation.

PART 3. NEI' RETURNS OF ROTATIONS

Conventional

CS N.R. = @,YC + p,YJ/2 - (80.00 + .08Yc + .01YJ CSWDCSB N.R. = @,YC + 1.6p,Y, + pwYw)/3 - (93.00 + .05Yc + .01Y, + .OIYw) CSWM N.R. = @,YC + p,Y, + pwYw + phYJ/4 - (86.50 + .MYc + .OlY, + .OIYw) CCSWAAAA N.R. = (2pcYc + p,Y, + pwYw + 4phYJ/8 - (104.00 + .04Y,) Conservation

CS N.R. = @,YC + p,YJ/2 - (75.00 + .08Yc + .01YJ CS WDCSB N.R. = @,YC + 1.6p,Y, + pwYw)/3 - (86.00 + .05Yc + .01Y, + .OIYw) CSWM N.R. = @,YC + p,Y, + pwYw + phYJ/4 - (80.00 + .MYc + .01Y, + .OIYw) CCSWAAAA N.R. = (2pcYc + p,Y, + pwYw + 4phYb)/8 - (96.00 + .MYc)

NO-till Tillage Classes 2 & 3

CS N.R. = @,YC + p,YJ/2 - (75.00 + .08Yc + .01YJ CSWDCSB N.R. = @,YC + 1.6psYs + pwYw)/3 - (85.00 + .05Yc + .01Y, + .OIYw) CSWM N.R. = @,YC + p,Y, + pwYw + phYJ/4 - (80.00 + .MYc + .01Y, + .OIYw) CCSWAAAA N.R. = (2pcYc + p,Ys + pwYw + 4phYJ/8 - (92.00 + .MYc)

SW N.R. = @,Y, + pwYw)/2 - (58.00 + .01Y, + .OIYw) CSWAAAAA N.R. = @,YC + p,Y, + pwYw + 5phYJ/8 - (106.00 + .02Yc)

Tillage Class 1 CS N.R. = @,(.9)YC + p8(.95)YJ/2 - (75.00 + .08(.9)Yc + .01(.95)YJ CSWDCSB N.R. = @,(.9)YC + 1.6p8(.95)Y, + pwYw)/3 - (85.00 + .05(.9)Yc + .01(.95)Y, + .OIYw) CSWM N.R. = @,(.9)YC + p,(.95)Y, + pwYw + phYJ/4 - (80.00 + .04(.9)Yc + .01(.95)Y, + .OIYw) CCSWAAAA N.R. = (2pc(.9)Yc + p,(.95)Y, + pwYw + 4phYJ/8 - (92.00 + .04(.9)Yc)

SW N.R. = @,(.9)Y, + pw(.95)Yw)/2 - (58.00 + .01(.95)Y, + .OIYw) CSWAAAAA N.R. = @,(.9)YC + p,(.95)Y, + pwYw + 5phYJ/8 - (106.00 + .02(.9)Yc)

Notes: NR C DCSB S W M A PC P, Pw Ph

= net return = corn = double crop soybean = soybean = wheat = meadow = alfalfa = corn price Yc = corn yield (bushels) = soybean price Y, = soybean yield (bushels) = wheat price Yw = wheat yield (bushels) = hay price Y, = hay yield (bushels)

Tillage class 1 refers to poorly drained soils, tillage class 2 refers to somewhat poorly drained soils, and tillage Class 3 refers to well drained soils. On soils in tillage class 1, no-till corn yield is .9 of the normal yield. On soils in tillage class 1, no-till soybean yield .95 of the normal yield.

APPENDIX B

STRUCTURAL COSTS

Terraces Cost

per cell

ASC share $4,722 Farmer share $1,782

Waterwavs Cost

per cell

ASC share $666 Farmer share $277

Diversions Cost

per cell

ASC share $7,869 Farmer share $2,937

Sediment Basins Cost

per cell

ASC share $2,092 Farmer share $897

Grade Stabilization Structures Cost

per cell

ASC share $2,800 Farmer share $1,200

Unit

ft

Unit

ft

Unit

struct

Unit

struct

Unit

struct

Cost per unit

Cost per unit

Cost per unit

Cost per unit

Cost per unit

APPENDIX C HIGHLAND SILVER LAKlZ FIELD MONITORING DATA

Tabulated Data

For model verification purposes, tabular data such as precipitation, runoff, and water quality loadings are needed.

Three years of storm events occurring in the HSL watershed have been recorded (1982- 1984). Three gaging stations and seven field sites were used in conjunction with raingages located at three different sites. See figure 1 for location of gages. In order to hcilitate data utilization, an index has been created that displays pertinent information regarding each storm event.

Data Collection

Precipitation. Precipitation was monitored at three locations within the HSL watershed, as shown in figure 1. The sites were located so as to obtain both the temporal and spatial variations of precipitation. The criteria for site selection included a location away from objects that might interfere with precipitation collection, landowner cooperation, and ready accessibility of the site.

Belfort Universal Recording Raingages (weighmg type) provided a continuous time distribution graph of precipitation. The total amount and rate of precipitation could be obtained from graphs of precipitation (rain andlor snow). Comparison of hydrographs from all the stations allowed the calculation of spatial and temporal distribution of the storm events.

The charts were collected on a weekly basis and sent to the Illinois State Water Survey (ISWS) for digitizing. The records were then used to calculate daily and monthly precipitation, and storm frequency.

Runofi Runoff was monitored on both a field and sub-basin level. At the field level, stations at eight sites were eventually installed. On the larger sub-basin level, three stations were installed on the major tributaries to the lake. In addition, the total watershed was monitored at the spillway of the lake.

Field Sites

To monitor the runoff at the field sites, 2-foot and 4.5-foot H-flumes were used. These flumes act as open-channel flow nozzles calibrated so that flow is correlated with depth. They are designed to provide a fixed stage-discharge rating relationship needed to measure flow. Detailed information on the H-flume specifications is available in Brakensiek et al. (1979). This flume design was selected because it can pass debris and heavy sediment loads while remaining accurate over a wide range of flow conditions.

Final site selection for the flumes was determined by a comprehensive monitoring and evaluation team, and installation was performed by ISWS personnel. Site selection criteria were:

1) Representative slopes, soils, and land use 2) Outlet into a defined watercourse 3) Landowner cooperation 4) Rural Clean Water Program (RCWP) k d i n g to ensure BMP implementation (if not a

control site) 5) Accessibility 6) Expected peak flow of less than 80 cfs, with approximately 100 acres of area where

possible 7) Suitability for equipment installation and operation

Six 4.5-foot H-flumes were installed at sites FS1, FS3, FS4, FS5, FS6, and FS7 (see figure 1). One 2-foot H-flume was installed at a feedlot operation, FS8. Some additional flow information was obtained by installing a depth-measuring device in association with a culvert that was part of a sructural BMP (site FS2).

At all seven H-flume sites and at the one culvert site, stages were monitored during runoff events. There was no flow between events at the field sites. A stilling well was attached to each flume or culvert to calm the water, and depth was measured with a Leupold and Stevens Type F recorder, Model 68. The recorder starts automatically when a predetermined stage is reached.

Since field sites were located on watersheds of widely varying area, the recorders were adjusted to record from 12 to 48 hours to capture the entire flow event characteristic of each field or sub-basin. To record the actual timing of a flow event, an instrument was developed to record the time when the recorder tripped. This was inexpensively handled by using a mercury switch and a modified digital quartz clock. When the recorder tripped, the mercury switch was closed, which stopped the clock.

Streamgaging stations. Measuring streamflow on perennial streams was not done with engineered flumes. Rather depth-recording equipment was installed at three sites where flow through the stream's existing cross section could be calibrated to the measured depth. Two of these

'

sites are on East Fork Silver Creek and one is on Little Silver Creek. (For locations, refer to figure 1; for drainage areas, refer to table Cl.)

Each streamgaging station had a continuous water-level recorder (Leupold and Stevens, Type A, Model 71) installed on top of a stilling well constructed of 24-inch cormgated metal pipe. The pipe, with its longitudinal axis oriented vertically, was either bolted to a bridge or buried in the streambank. The bottom 2 to 3 feet of pipe were placed below the normal water surface so that low flow stages could also be obtained. When the pipe was buried in the streambank, the water level within the stilling well was connected to the stream by two 2-inch horizontal pipes. The instruments were placed above the anticipated high water level and were readily accessible.

The stagedischarge relationship was obtained for the gaging station sites by discharge measurement. Data were extrapolated by using the stream cross-sectional data to calculate a backwater profile using WSP2, a computer program developed by the Soil Conservation Service (1976). Both sets of data were plotted, and the curve with the best fit was used for the stage- discharge relationship. Streamflow was then calculated by using the recorded stage levels and the stagedischarge relationship.

Table C 1. Drainage Areas, Soil Types, and Best Management Practices above the Monitoring Sites

Drainage Monitoring area

site (acres) Soils

FS 1 47l Natric Conventional tillage (no BMPs)

FS2 43l Natric Grassed waterway and sediment retention basin

FS3 29 Mollic/Alfic Grassed waterway, diversion, and no till

FS4 115 Natric Minimum tillage (partial acreage) and small terrace with diversion

FS5 332l Natric Conventional tillage (no BMPs)

FS6 58l Natric Reduced tillage

FS7 1801 Natric/Alfic/Mollic Conventional tillage (no BMPs)

FS8 3l Natric Vegetative filter &P

GS 1 19,916 Natric/Alfic/Mollic (see footnote 2)

GS2 13,080 (see footnote 2) II

GS3 3,037 (see footnote 2) II

SW 31,564 (see footnote 2) II

Notes: l ~ a s e d on field measurement. ~BMPs were applied in a portion of the subwatershed. Details may be found in SIMRPC (1985). FS = field site GS = gaging station SW = spillway site

Water level records from the streamgaging stations and flume sites were digitized and entered into the University of Illinois CYBER computer by ISWS personnel.

Spillway. In addition to the monitoring stations already mentioned, the amount of flow leaving the watershed was monitored at the spillway of the lake. The height of water in the lake was monitored daily by water treatment plant personnel. Daily stage readings were obtained from a porcelain enamel staff gage located near the water treatment plant. From the relative elevations of the spillway and staff gage, the lake level with respect to the spillway was obtained.

The stagedischarge relationship was obtained by performing hydraulic calculations from the spillway geometry. The daily stage readings could then be transformed into daily discharge.

Water Quality

Water quality sampling was performed at each of the stations where runoff quantity was monitored (eight field sites, three gaging stations, and the lake spillway).

Methods of sampling. Water quality samples were obtained by three methods:

1) Handdipped bottle - The sample bottle is used to obtain a water sample. This method is usually used for periodic sampling during low flows.

2) Weighted bottle - A metal bottle holder attached to a rope is used to immerse the sample bottle into the stream or lake. This method is used for routine samples during medium to high flows.

3) Automated sampler - The ISCO model 1680 was used in this project. The sampler was self-starting, activated by an electronic water level sensor. Samples were obtained by an internal pump that withdrew a specified amount of water at set time intervals. Samples were stored in separate bottles within the sampler. This method of sampling was used during storm runoff events to provide information on the variation of the water quality under changing flow conditions.

Sampling schedule. Four sampling frequencies were used for water quality monitoring: three per week (Monday, Wednesday and Friday), biweekly, monthly, and storm event. Table C2 lists the sampling schedule and the parameters analyzed for each type of site.

ASCII data. The water quality data were stored as ASCII files. Both the gaging site and the date of the storm event were included in the data file.

The files are made up of two components - raingage information and a hydrograph for the site. The raingage component contains data for the subevents that make up the storm, with entries for all three raingages. First, the date and the particular raingage where the information was recorded are listed. Then, a four-column format lists the time (hrlrnin), accumulated rain (in.), increment rainfall (in.) and rainfall intensity (in./hr). An entry at the end of these columns lists the total rainfall for the subevent.

Table C2. Sampling Frequency and Sampled Parameters at the Monitoring Sites

Three per week Biweekly Monthly Event basis Spillway 1 2 3 - Gagrng stations 1 2 3 4 Field sites - - - 4

Notes: 1 - Total suspended solids, total volatile suspended solids, turbidity, dissolved oxygen, pH,

temperature, and conductivity. 2 - Same parameters as in 1, plus total phosphorus, total Kjeldahl nitrogen, dissolved

phosphorous, ammonia, nitrate-nitrite, and chemical oxygen demand. 3 - Same parameters as in 1 and 2, plus calcium, magnesium, sodium, potassium, barium, boron,

beryllium, cadmium, chromium, copper, cobalt, iron, lead, manganese, nickel, strontium, zinc, and mercury.

4 - Total suspended solids, total volatile suspended solids, turbidity, total Kjeldahl nitrogen, total phosphorus, and chemical oxygen demand.

The hydrograph begins with heading listing where the information was recorded, the starting time and date of the entire storm event and its duration. An eight-column format gives the time (hr), flow (cfs) and water quality when available (total suspended solids, total volatile solids, total Kjeldahl nitrogen, total phosphorus, turbidity, and COD. The gage file concludes with data for volume of flow both in cubic feet and in inches, the peak flow, and the time at which it occurred. A field site file differs in that it only lists one volume in cubic feet.

Lotus Index Due to the number of events, separate indexes were created for the field sites and gaging stations. The indexes were created in Lotus 1-2-3, version 2.0, using the "data- query" series of commands. This format was chosen due to its widespread availability and ease of use. With it, one can select a single variable or sets matching multiple criteria.

The index contains a summary of the information that is available in each file. Information pertinent to the site is presented in columnar order: the disk i.d., field site number, date, and the raingage used to determine actual rainfall. Raingage selection was determined by proximity to the sampling site based on straight-line distance. The emphasis then shifts to the event: storm duration, r a i d , runoff, and number of individual samplings for total suspended solids, total volatile solids, phosphorous, and nitrogen during the event. Duration is that listed for the hydrograph in the file, while the rainfall is the total for the particular raingage selected.

Using the index Dataquery allows both flexibility and manageability in selecting a data set. The following section summarizes both our experience and information provided in the Lotus 1-2-3, version 2.0 Reference Manual. It is intended to show dataquery benefits.

To use dataquery, one must enter an input range, selection criteria, and an output range. Up to 32 fields are allowed. All three ranges (input, criteria, and output) must use the exact same field names and appear at the top of the ranges. Enter the different ranges by selecting "/Data Query Input,Output, or Criterion." The database should consist of both the field names and all the records to be searched. The criterion range can be located anywhere in the database where there is enough space. The criterion range need not have all the fields, only those that are used to select

records. However, if different fields will be used at different times, it is best to copy all the names in the beginning. Selection specifications are then entered in the second and subsequent rows, underneath the appropriate name. For a search that meets all criteria, multiple criteria should be entered in the same row. Enter criteria in multiple rows if any of the criteria can be satisfied. The output range can be created in two &&rent ways. Both require field names. For the first method, spec@ a multiple-row output range, with enough rows to hold the records that will be selected. The second method uses only the field names. Any records satisfjmg the criteria will be copied underneath the output field names, over any information that was previously recorded there. To best preserve information, we found a multiple range to be most useful. It is easiest to enter the database (aka input) field names and then copy them to both the criterion and output ranges. However, field names, as long as they appear as in the database, may be in any order. "/Data Query Extract" copies all or part of the matching records into the output range.

Determining Selection Criteria

1) Up to 32 criterion ranges are allowed. 2) For exact matches, enter the label as it appears in the database. 3) ? may be used to match any single character, while * may be used to match all characters to

the end of the label: ?22 = 122,222,422, 522, etc, but not 232. fa* = fall, fat, farther, but not feet.

4) Preceding a label with a - searches the database for1 1 labels except that one. 5) Logical operators may be used ( <, < =, =, >, > =, < > ). 6) When using a logical operator, specify the first cell of the field in the equation (e.g.,

+c2<=83.). This will return a value in the criterion range - 1 if true, 0 if Ealse. Use "/Range Format Text" if you want the formula to appear in the criteria cell.

7) Compound logical formulas can be used to create compound criteria matching more than one condition. These include "#and#," "#not#," and "#or#." For example, "+h2>=1000#and#+h2~=20000~1 will select all records from our database with a runoff between 1000 and 20,000 cfs.

8) Use relative cell addresses for values within the database, and absolute addresses for values outside the database.

Below is an example of criteria for selecting all field sites with a runoff between 1000 and 20,000 cfk occurring in the months of June and September for 1982 and 1983 and the records that were selected.

DISK site YEAR month date RAMGAGE rain runoQcfs) runoff duration tss tusd tkn tphs +C2<=83 +D2=6#OR#+D2=9 +H2> 100O#AND#+H2<20000

Disk Site Date Raingage Rain Runofl(cfS) Run08 Duration tss tvsd tkn tphs F2 F2 9/14/82 R2 2.58 3308.8 0.02 8 F3.1 F3 9/17/82 R1 1.01 1548 0.01 1 1 1 1 1 F6.1 F6 6/22/82 R1 0.72 7299 0.03 2 F6.1 F6 9/8/82 R2 1.4 5595.9 0.02 1.5 5 5 5 5 F6.2 F6 6/3/83 R2 4.65 2889.2 0.01 2.5 5 5 5 5 F7.1 F7 6/7/82 R1 0.51 6752.2 0.01 0.6

Notes: tss = total suspended solids tkn = total kjeldahl nitrogen tvsd = total. volatile suspended solids tphs = total phosphorus

Selected events. Events for the study had to meet certain requirements. Data should be available for all the sites. A highenergy intensity @I) was desired, as well as water quality information. A number of preliminary dates were selected based on these criteria and their rahfkll vs. runoff and water quality concentrations graphed.