Fort Cobb Basin - Modeling and Land Cover Classification DRAFT. Submitted to. Oklahoma Department of Environmental Quality.

Transcription

1 Fort Cobb Basin - Modeling and Land Cover Classification DRAFT Submitted to Oklahoma Department of Environmental Quality Submitted by Dr. Daniel E. Storm Mr. Phillip R. Busteed Mr. Michael J. White Biosystems and Agricultural Engineering Department Division of Agricultural Sciences and Natural Resources Oklahoma State University January 19, 2006 I

2 Acknowledgment We would like to thank the Oklahoma Department of Environmental Quality, Oklahoma Conservation Commission, and the Oklahoma State County Extension Offices in Caddo, Washita, Custer County for all of their help. We would also like to thank David Nowlin, Mark S. Gregory, Dirk Webb, and Dr. Hailin Zhang for providing crop management and county soil information which was critical to developing a good model. We would also like to thank Applied Analysis Incorporated (AAI) for their hard work and dedication to this project. Their land cover image was a crucial portion of this project. II

3 Table of Contents 1 - Introduction SWAT 2000 Input Data... 2 Topography... 2 Soils... 2 Applied Analysis Incorporated Land Cover... 2 Subbasin Delineation... 3 HRU Distribution... 3 Weather Data... 3 Soil Test Phosphorus and Management Operations... 4 Cattle Stocking Rate Verification... 4 Reservoirs and Pond Input Data... 5 Concentrated Animal Feeding Operations Model Calibration and Validation Flow Calibration Flow Validation Nutrient Parameter Modifications Total Phosphorus and Sediment Loads Model Limitations and Conclusions Model Limitations Conclusions Model Update with 2005 Land Cover References Appendix A - AAI Land Cover Classification Report... A1 Appendix B - Crop Management... B1 Appendix C -Reservoir and Ponds Information...C1 Appendix D - Soil Test Data...D1 Appendix E - Stream Flow Data... E1 Appendix F - Water Quality Data... F1 Appendix G - Subbasin Properties...G1 III

4 List of Figures Figure 2.1 Ten meter USGS Digital Elevation Model with county boundaries for the Fort Cobb Basin Figure 2.2 Thirty meter Applied Analysis Incorporated Landsat derived land cover with county boundaries for the Fort Cobb Basin Figure 2.3 Center pivot irrigation systems in the Fort Cobb Basin identified from 2003 one m digital aerial photography. Figure 2.4 Land cover coverage incorporating Applied Analysis Incorporated land cover data, center pivot locations, and National Agricultural Statistics Service data Figure 3.1 U.S. Geological Survey (USGS) stream gage and Cooperative Observation Network (COOP) weather station locations in the Fort Cobb Basin Figure 3.2 General SWAT model calibration procedure Figure 3.3 Average annual flow calibration procedure used for the SWAT model Figure 3.4 Monthly flow calibration procedure used for the SWAT model Figure 3.5 Observed and calibrated SWAT simulated flow at Cobb Creek near Eakley, OK ( ) Figure 3.6 Scatter plot of daily average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley Figure 3.7 Scatter plot of monthly average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley Figure 3.8 Scatter plot of annual average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley Figure 3.9 Validation time series for observed and SWAT predicted flow at the Cobb Creek near Eakley, OK ( ) Figure 3.10 U.S. Geological Survey, Oklahoma Conservation Commission, and U.S. Fish and Wildlife Service water quality gage locations in the Fort Cobb basin Figure 3.11 Observed total phosphorus concentrations vs SWAT model (hydrologic calibrated model with modified nutrients parameters) predictions for the Fort Cobb basin Figure 3.12 Observed total phosphorus concentrations vs SWAT model (hydrologic calibration only) predictions for the Fort Cobb basin Figure 4.1 Subbasins draining to the Fort Cobb Reservoir and to Cobb Creek below the reservoir dam only. Cobb Creek drains 853 km 2 of which 799 km 2 drains to the Fort Cobb Reservoir, and 6.3% of the Cobb Creek Basin is downstream of the Fort Cobb Reservoir dam.. 31 IV

5 List of Tables Table 2.1 Applied Analysis Incorporated land cover by percentage within the Fort Cobb Basin based on 2001 TM Imagery Table 2.2 Crop and management breakdown based on the 2005 Fort Cobb Basin Agricultural Field Management Survey for the period Table 2.3 Minimum C factor by crop and tillage for agricultural HRUs used in the SWAT Model Table 2.4 Comparison of the number of cattle simulated in the SWAT model and estimates derived from the Oklahoma Agricultural Statistics Service Table 2.5 Concentrated Animal Feeding Operations in the Cobb Creek Basin. Derived from the Oklahoma Department of Agriculture, Food and Forestry database Table 3.1 Drainage area and flow per unit area from to for available USGS stations in the Fort Cobb Basin Table 3.2 Parameter values use to calibrate flow and modify total phosphorus load for the Fort Cobb SWAT model Table 3.3 Parameter values use to calibrate the Fort Cobb SWAT model for flow only Table 3.4 Summary statistics for SWAT model hydrologic calibration for flow at Cobb Creek near Eakley gage for the period 1/ / Table 3.5 Summary statistics for SWAT model hydrologic validation for flow at Cobb Creek near Eakley gage for the period 1/ / Table 3.6 Observed and predicted total phosphorus concentrations using three different averages for both the hydrologic calibration SWAT model and the hydrologic calibration with modified nutrient parameters SWAT model shown Table 3.7 All water quality sites in the Fort Cobb Basin and the number of samples collected during baseflow, high flow, and recession flow Table 4.1 SWAT simulated loads by land cover for the Fort Cobb Basin for the period 1/ / Table 4.2 SWAT simulated loads by year for the Fort Cobb Basin for the periods of 1/1995 to 12/ Table 6.1 SWAT simulated winter wheat sediment loading based on tillage implementation and acreage in the Fort Cobb basin Table 6.2 SWAT simulated winter wheat total phosphorus loading based on tillage implementation and acreage in the Fort Cobb basin V

6 Table 6.3 SWAT simulated peanut sediment loading based on tillage implementation and acreage in the Fort Cobb basin Table 6.4 SWAT simulated peanut total phosphorus loading based on tillage implementation and acreage in the Fort Cobb basin Table 6.5 SWAT simulated loads per unit area for all peanut and winter wheat scenarios Table 6.6 SWAT simulated loadings for all land covers in the updated model runs (except peanut and winter wheat) in the Fort Cobb basin for Table 6.7 SWAT simulated loads per unit area for all land covers for the previous (hydrological calibration only) SWAT model for Table 6.8 Comparison of simulated sediment and total phosphorus loads for all SWAT model runs from VI

7 1 - Introduction The Oklahoma Department of Environmental Quality (ODEQ) is developing a Total Maximum Daily Load (TMDL) for the Fort Cobb Reservoir/Cobb Creek Basin. The Fort Cobb Basin is located in Southwestern Oklahoma in Caddo, Washita, and Custer Counties. The basin area is 314 square miles and the surface area of the Fort Cobb Reservoir is 4,100 acres. The Fort Cobb Reservoir and six stream segments in its basin are listed on the Oklahoma 303(d) list as being impaired by nutrients, pesticides, siltation, suspended solids, and unknown toxicity. The purpose of the project is to estimate total phosphorus loads to the reservoir using the Soil Water Assessment Tool (SWAT) 2000 model (Arnold et al., 1998; Arnold et al., USDA, Agricultural Research Service. Grassland, Soil, and Water Research Laboratory, 2002) for the time period 1996 to

8 2 - SWAT 2000 Input Data The SWAT 2000 model was used to estimate erosion and total phosphorus loads from the upland areas of the basin. SWAT is a distributed parameter basin scale model developed by the USDA Agricultural Research Service at the Grassland, Soil and Water Research Laboratory in Temple, Texas (Neitsch et al., 2001). SWAT is included in the U.S. Environmental Protection Agency s (EPA) release of Better Assessment Science Integrating Point and Nonpoint Sources (BASINS). Because SWAT is a distributed model, data requirements are vast and data manipulation is extensive. These requirements are met using an ArcView GIS interface, which generate model inputs using commonly available GIS data. These GIS data are summarized by the interface and converted to a form usable by the SWAT model. Topography Topography was defined by a digital elevation grid (Figure 2.1). Seamless elevation grids for the United States are available for downloading via the USGS Seamless Data Distribution System ( A 10 meter digital elevation model (DEM) was used in this study, which was a higher resolution than the common 30 meter DEM. The high resolution 10 m DEM was used to calculate subbasin parameters, such as slope, slope length, and to define the stream network. The resulting stream network was used to define the layout of the subbasins. Characteristics of the stream network, such as channel slope, length and width, were derived from the DEM. Soils Soil characteristics were defined by SWAT using soil GIS data. SWAT uses either STATSGO (State Soil Geographic Database), NRCS (Natural Resources Conservation Service) MIADS, or SSURGO (Soil Survey Geographic Database) data to define soil attributes for each soil. SSURGO data were not available for Caddo County. Therefore, NRCS MIADS (200 m) data were selected over the STATSGO data because of the higher level of detail. Applied Analysis Incorporated (AAI) Land Cover A 30-meter land cover data layer was supplied by Applied Analysis Inc. (AAI) (Appendix A). The data layer was created from Landsat TM imagery collected on June 10, Seven land covers were defined with the imagery. The land cover classes and percentages are presented below and given in Appendix A: # Barren (Bare Soil) - 0.2% # Forest - 6.7% # Pasture % # Planted/Cultivated 1-46% # Planted/Cultivated 2-5.0% # Urban - 0.5% # Water - 1.9% Planted/Cultivated categories 1 and 2 differ by the amount of vegetation present. Planted/Cultivated 1 was sparely vegetated, and Planted/Cultivated 2 was bare. The Fort Cobb Basin was primarily crop land with several different types of crops grown (i.e. corn, wheat, rye, peanuts, and others). To adequately model the basin, more detail was needed about agricultural land types. A detailed 2

9 survey was given in 2005 to Oklahoma State University (OSU) Cooperative Extension Service Agents and Specialists to gain an understanding of agricultural practices and land covers that occurred from 1996 to This survey went into great detail about the different types of crops in the basin along with different tillage practices, common double crops, fertilization rates, cattle stocking rates, and harvest dates. A copy of the survey is given in Appendix B. Results from the survey indicated that over thirty different agricultural land covers/practices occurred in the basin. With so many different agricultural land covers, SWAT would have a difficult time modeling the basin at this level of detail due to the complexity of the input files. As a result, land cover classifications were consolidated on the basis of similar agricultural practices, such as tillage type, harvest and plant dates, irrigation practices, and fertilizer rates. The final product produced twelve separate agricultural land covers, which are presented in Tables 2.1 and 2.2 along with their area percentages within the basin. The two cultivated land covers provided by AAI needed to be modified to represent all twelve crops grown in the basin. The first step was dividing crop land into irrigated and non-irrigated land, and locating center pivot irrigation locations. Center pivot irrigation locations were tagged from 2003 aerial photography to aid in accurately identifying irrigated fields (Figure 2.3) (ftp://okmaps.onenet.net, Digital Orthographic Photography, dates vary). Since the exact location of these different crops were unknown, the twelve cultivated land covers were randomly assigned throughout the basin based on a uniform distribution (Figure 2.4). It should be noted that there were other types of irrigation that could not reliably be identified from aerial photography. However, the land area from these other irrigation types was relatively minor compared to the center pivot systems. Subbasin Delineation The subbasin layout was defined by SWAT using the DEM, a stream burn-in theme, and a table of additional outlets. The stream burn-in theme consisted of digitized streams; its purpose was to help SWAT define stream locations correctly in flat topography. A modified REACH3 file from the US Environmental Protections Agency's BASINS model was used. A stream threshold value of 750 ha was used to delineate subbasins. Threshold area was the minimum contributing upland area required to define a single stream. The result was 90 subbasins. Fewer subbasins would simplify the modeling process, but this level of detail was needed to adequately represent the basin. HRU Distribution Each of the 90 subbasins were split into HRUs (Hydraulic Response Units) by SWAT. The land use [%] over subbasin area threshold was changed from the default 20% to 0%. This threshold determined the minimum percentage of any land cover in a subbasin that will become an HRU. The soil class [%] over subbasin area was also reduced from its default value of 20% to 0%. By reducing these thresholds to 0%, all land cover and soil combinations were represented. The total number of HRUs was 2,599. Weather Data Observed daily precipitation, and minimum and maximum temperatures were used in the SWAT model. Tabular weather data from the NOAA Cooperative Observation Network or COOP data (Surface Data, Daily, NOAA National Climatic Data Center, 2003) were used in all modeling. A total of six weather stations were used in the model. COOP data were seldom continuous for long periods of time. Missing days and even months were common. The period of record at the stations were typically inconsistent, so the number of active 3

10 stations may change with time. When SWAT detects missing data at a station, SWAT generates simulated weather. Therefore, gaps in a station s record are filled using interpolated data from surrounding stations. Shepherd s weighted interpolation was used, because it is computationally efficient. Shepherd s method uses weighting factors derived from the distance to nearby stations within a fixed radius: where Z o was the precipitation at the station of interest in mm, Z i was the precipitation at station i in mm, and W i was the weighting factor at station i. Weighting factors were calculated using the distance between stations: for And for where R was the radius of influence in meters, and d i was the distance from station of interest to station i in meters. Soil Test Phosphorus and Management Operations Land cover specific data, such as soil test phosphorus (STP) and fertilization practices, from were not widely available. STP for common agricultural land covers were derived from OSU Soil, Water and Forage Analytical Laboratory county level averages for the period STP data are given in Appendix D. Fertilization and management practices from were based on OSU recommendations and knowledge from local OSU Cooperative Extension Service and Conservation District personnel (Appendix B). Adjustments were made to several parameters to better represent the basin in SWAT. Minimum C Factors, part of the MUSLE equation for soil erosion, were defined based on the crop and type of tillage (Table 2.3). Also, the type of tillage implemented for each crop can have a direct effect on surface runoff and erosion. The NRCS Curve Number for each crop and tillage method was adjusted accordingly based on Rawls and Richardson (1983) and Soil Conservation Service (1972). Rawls and Richardson (1983) created a new set of Curve Numbers for conservation tillage based on residue cover and soil disturbance. For conventional tillage operations, the SCS (1972) Curve Numbers were used. Cattle Stocking Rate Verification To determine the average stocking rate used for pastures in the SWAT model, we estimated the total number of cattle in the basin and divided it by the area being grazed. County level National Agricultural Statistics Service (NASS) cattle estimates for the period were combined with land cover data to estimate the number of cattle within the basin. We assumed that cattle were evenly distributed across all agricultural land in each county. From these data we estimated the total number of cattle and calves in the basin to be 38,700 head. The SWAT model does not simulate individual cattle. Instead SWAT uses a daily biomass removal and manure deposition to represent the presence of grazing cattle. The amount of forage cattle will consume depends on the type and growth stage of the animal in question. Because there are many different types of cattle in the basin, we used the animal unit concept to define stocking rates. One animal unit can be expressed as a cow and calf pair or two-400 lb stockers; both would 4

11 consume a similar amount of forage. Because of the short duration grazing typical for small grains, we adjusted the estimate by including the duration of grazing. An animal unit*year was defined as one animal unit grazing for 365 days. The total number of animal units*years simulated in the model was 21,248. Since the NASS derived estimate is the number of cattle and calves, these estimates were not directly comparable without assuming a specific type of animal and how long the cattle were kept in the basin (Table 2.4). It is important to note that SWAT prevents grazing when the available biomass is less than the parameter BIOMIN. This parameter was set such that overgrazing was not allowed. Therefore, the actual amount of grazing in the model was less than 21,248 AU*yr. In the context of the SWAT model, it was better to overestimate stocking rates and control grazing using BIOMIN to ensure full forage utilization. This was the approach used in this study. Reservoir and Pond Input Data The size and locations of large reservoirs (> 50 acres) were taken from the U.S. Army Corps of Engineers National Inventory of Dams (NID). The total surface area and volume of these water bodies are listed by subbasin in Appendix C, and were included as reservoirs in the SWAT model. Small reservoirs and ponds (< 50 acres) were taken from USGS 7.5 minute quad maps. All known ponds and reservoirs were located and digitized into a GIS layer. The total surface area and approximate volume are listed by subbasin in Appendix C. These smaller water bodies (< 50 acres) were included as ponds in the SWAT model. Concentrated Animal Feeding Operations Only three Concentrated Animal Feeding Operations (CAFOs) were located in the Cobb Creek Basin. Approximate CAFO locations and animal numbers were taken from an Oklahoma Department of Agriculture, Food and Forestry coverage available at the Oklahoma Department of Environmental Quality website. These metadata were listed at the following web address: Details of each CAFO are given in Table 2.5. Due to the limited number and size of the CAFOs in the basin, we did not account for these in our analysis. 5

12 Figure 2.1 Ten-meter USGS Digital Elevation Model with county boundaries for the Fort Cobb Basin. 6

13 Figure 2.2 Thirty meter Applied Analysis Incorporated Landsat derived land cover with county boundaries for the Fort Cobb Basin. (Planted/Cultivated categories 1 and 2 differ by the amount of vegetation present. Planted/Cultivated 1 was sparely vegetated, Planted/Cultivated 2 was bare). 7

14 Figure 2.3 Center pivot irrigation systems in the Fort Cobb Basin identified from 2003 one m digital aerial photography. 8

15 Figure 2.4 Land cover coverage incorporating Applied Analysis Inc. land cover data, center pivot locations, and National Agricultural Statistics Service data. 9

16 Table 2.1 Applied Analysis Inc. land cover by percentage within the Fort Cobb Basin based on 2001 TM Imagery. Fraction of Basin Land Cover Type (%) Alfalfa 1.1 Bare Soil 0.2 Forest 6.2 Pasture 41.0 Peanut with Double Crop Winter Wheat (Conventional Tillage) 2.9 Peanut with Double Crop Winter Wheat (Conservation Tillage) 1.0 Peanut Winter Fallow 3.8 Rye (Conventional Tillage) 7.6 Rye (Conservation Tillage) 3.2 Grain Sorghum w/ Double Crop Winter Wheat 4.8 Grain Sorghum Winter Fallow 5.2 Urban 0.1 Water 2.4 Corn With Double Crop Winter Wheat 1.2 Winter Wheat for Grain (Conservation Tillage) 4.4 Winter Wheat for Grain (Conventional Tillage) 9.2 Winter Wheat for Pasture 5.6 Table 2.2 Crop and management breakdown based on the 2005 Fort Cobb Basin Agricultural Field Management Survey for the period Cropland Type Crop Type Farming Method % of Cropland Irrigated Cropland Peanuts Winter Fallow with a Conventional Tillage Practice 38% Peanuts Double Crop with Winter Wheat and Conventional Tilllage 29% Peanuts Double Crop with Winter Wheat and Conservation Tilllage 10% Corn Double Crop with Winter Wheat and Conventional Tilllage 12% Alfalfa Standard Farming 11% Non-Irrigated Cropland Grain Sorghum Winter Fallow with a Conventional Tillage Practice 12% Grain Sorghum Double Crop with Winter Wheat and Conventional Tilllage 13% Wheat for Grain Summer Fallow with a Conventional Tillage 23% Wheat for Grain Summer Fallow with a Conservation Tillage 11% Wheat for Pasture Summer Fallow with Conventional Tillage (Graze Out) 14% Rye Summer Fallow with a Conventional Tillage 19% Rye Summer Fallow with a Conservation Tillage 8% 10

17 Table 2.3 Minimum C factor by crop and tillage for agricultural HRUs used in the SWAT Model. Crop Tillage Min C Factor Source Wheat Conservation 0.01 NRCS, USDA and Wischmeier and Smith 1978 Wheat Conventional 0.03 NRCS, USDA and Wischmeier and Smith 1978 Peanuts Conservation 0.12 NRCS, USDA and Wischmeier and Smith 1978 Peanuts Conventional 0.19 Mutchler et al Sorghum Conventional 0.16 NRCS, USDA and Wischmeier and Smith 1978 Corn Conventional 0.20 NRCS, USDA and Wischmeier and Smith 1978 Alfalfa Conventional 0.00 NRCS, USDA and Wischmeier and Smith 1978 Rye Conservation 0.01 NRCS, USDA and Wischmeier and Smith 1978 Rye Conventional 0.02 NRCS, USDA and Wischmeier and Smith 1978 Table 2.4 Comparison of the number of cattle simulated in the SWAT model and estimates derived from the Oklahoma Agricultural Statistics Service. Used in SWAT (Animal Unit Years) Type of animal Animal Units Per Animal Duration of Grazing in the basin (Days) Equivalent Animals in SWAT NASS Estimate (Animals) Difference 21,248 Adult Cow ,500 38,700-37% 21, lb stocker ,000 38,700-10% 21,248 Cow calf pair ,500 38,700-37% 21, lb stocker ,250 38,700 58% 21,248 Adult Cow ,681 38,700 28% 21, lb stocker ,972 38,700 83% 21,248 Cow calf pair ,681 38,700 28% 21, lb stocker ,201 38, % Table 2.5 Concentrated Animal Feeding Operations in the Cobb Creek Basin. Derived from the Oklahoma Department of Agriculture, Food and Forestry database. Company Address Type Animal Units FARMERS F & F FARMS INC RT 2 BOX 37 Cattle 750 HARVEY FARMS RT 2 BOX 140 Cattle 2700 LIERLE, TERRY RT 2, BOX 143A Swine

18 3- Model Calibration and Validation Calibration is the process by which model parameters are adjusted to make its predictions agree with observed data. SWAT was designed for use on large ungaged basins and can be used without calibration. However, calibration generally improves the reliability and reduces the uncertainty of the model predictions. Validation is similar to calibration except the model is not modified. Validation tests the model with observed data that are not used in the calibration process. Flow Calibration Few stream gage data were available to calibrate the Fort Cobb Basin SWAT model for the period January 1995 to December The only suitable gage was Cobb Creek near Eakley (USGS , Appendix E). The hydrologic calibration was performed almost entirely with data from this gage. Another gage down stream of the Fort Cobb Reservoir was also utilized to corroborate the calibration (Figure 3.1). Calibration parameters from the Cobb Creek watershed were applied to all ungaged areas since older USGS stream gage data ( ) indicated that runoff volume per unit area was similar in other parts of the basin (Table 3.1). Note that Cobb Creek Near Fort Cobb is downstream of the reservoir and is subject to additional water losses (evaporation, seepage, etc.) that occur in reservoir, and therefore it is expected to have a much lower flow per unit area. Calibration was an iterative process that progresses from one model parameter to another and from course to fine parameter modifications until the difference between the model predictions and observed data met a pre-determined goodness of fit criterion. Block diagrams of these procedures are shown in Figures 3.2 to 3.4. These block diagrams are general at best, since it is not possible to represent all the specific details or decisions made by the modeler. If we could, models would be objectively calibrated via software. Many of the blocks actually represent many highly specific decisions based on many aspects of the model predications and observed data comparisons. The SWAT model was calibrated on an annual and monthly basis for flow for the period of January 1995 to December The results of the flow calibration are shown in Table 3.4 and Figures 3.5 to 3.8. As expected, R 2 decreased with shorter comparison intervals and all were considered an excellent fit to the observed data. R 2 for daily comparisons fell to 0.52, which is acceptable considering the model was not calibrated to daily flow. The Nash-Sutcliffe efficiency was used as an indicator of goodness of fit (Nash and Sutcliffe, 1970). For the calibration period, the Nash- Sutcliffe efficiencies were between 0.38 to 0.48, which was acceptable. Relative errors for daily, monthly and annual total flow were between 0 and -0.9 percent, which was also acceptable. Relative error for daily flow at the Cobb Creek near Fort Cobb gage, which was downstream of the reservoir, was 5.9%. Reservoir permeability was adjusted slightly to improve the calibration at this gage. Based on relative errors, coefficients of determination, Nash-Sutcliffe efficiencies, and the graphs the flow calibration was considered acceptable. Flow Validation The SWAT model was validated for daily, monthly and annual flow at the Cobb Creek near Eakley gage for the period January 1980 to December The hydrologic parameters used in the calibration period were used in the validation period. Flow validation indicated whether the model functions properly and predicted reasonable results under conditions outside the calibration period. The summary statistics are given in Table 3.5 and the time-series for the validation period is shown 12

19 in Figure 3.9. Even though the weather was much drier during the validation period, the model provided reasonable predictions for total, surface and base flows. Relative errors for daily, monthly and annual flow were all below 4.1 percent for total flow. Based on relative errors, coefficients of determination, Nash-Sutcliffe efficiencies, and the graphs the flow validation was considered acceptable. Nutrient Parameter Modifications Two separate models were developed, one was adjusted to more closely match observed nutrient concentration data and the second was not. Insufficient water quality and flow data were available to perform a traditional nutrient calibration. If sufficient observed water quality data were available, nutrient load estimation software, such as Loadest2 or Estimator, would have been implemented. These programs use both observed nutrient concentration and flow data to estimate nutrient load (kg/day or lbs/day). SWAT would then be calibrated by comparing these load estimates to SWAT s predicted loads. SWAT nutrient parameters were adjusted to better match total phosphorus concentration data collected throughout the basin. These nutrient parameters were modified by comparing individual instantaneous water quality observations to daily model predictions at the same location. There are several concerns when calibrating the SWAT model using daily concentration comparisons as opposed to more traditional load comparisons. These observed water quality data were grab samples, not flow weighted daily mean concentrations. SWAT predicts average daily concentrations. This difference may have a significant impact when calibrating nutrients during storm events or high flow periods immediately following a runoff event. In summary, observed instream total phosphorus concentration may vary dramatically in a single day especially when there is significant surface runoff, and SWAT predicts only the average daily concentration. There is uncertainty in calibrating a model on a daily basis for both stream flow and nutrients. The variation in observed vs predicted flow is evident in Figures 3.6 to 3.8, which increases from monthly to daily predictions. Accurate daily predictions of stream flow time requires detailed observed data at multiple locations throughout the basin, which was not available. In other words, prediction of annual or monthly stream flow will be more accurate than daily stream flow predictions. The same is true for nutrient concentrations, since calibrating the model on a daily basis for nutrient concentration is heavily dependent on stream flow. The locations of the water quality sampling sites is given in Figure 3.10 and Table 3.7. The vast majority of these observed samples were taken under base flow conditions. Base flow samples are typically not indicative of in-stream concentrations during high flow events and thus their utility are limited. A total of 231 total phosphorus samples (Table3.7) were used in this phase of the project over the period of However, only 39 samples from four days were collected during high flow events. Of these 39 samples, only nine samples obtained on 6/17/2000 may have been collected during the rising limb or near the peak of the hydrograph. The other 30 samples were collected one to four days after the storm event. These water quality data, obtained from various State and Federal agencies, are listed in Appendix F. In-stream nutrient processes may be very important during base flow conditions. SWAT does not have a fully tested in-stream model, and thus we disabled it for our study (Houser and Hauck 2002). Without in-stream processes, SWAT produces wide variations in daily nutrient concentrations governed by surface runoff events. Without the in-stream model, the streams are treated basically as pipes and all nutrients are conservative. While in-stream processes are not as important during runoff events, this results in base flow comparisons having limited utility. 13

20 It is important to note that stream flow data were not collected with most of these water samples. For example, we can only assume that the sample collected on June 17 th 2000 was during high flow and not before the storm event. This is crucial because samples may have been collected during base flow conditions or sometime before or after peak flow. The total phosphorus concentrations of these nine samples, discussed earlier, were very similar to concentrations collected during base flow conditions during the same time of the year. Also, adjusting a model with water quality data collected one to four days after a storm event can present another issue. Past studies have shown that during a runoff event total phosphorus concentrations peak before stream flows peak. After the initial flush of sediment and nutrients, the concentration of both sediment and total phosphorus quickly fall back to levels similar to base flow conditions (Baker et al., 2003; Correll et al., 1999; Lerch et al., 2001). Correll et al. (1999) and Baker et al. (2003) found that concentrations of total phosphorus fell sharply within a few hours after peak discharge. As a result of these findings, we elected to generate two models, one with a hydrologic calibration with modified nutrient parameters, and a second with a hydrologic calibration only. The nutrient parameters were modified within the SWAT manual recommended parameter ranges. Caution should be exercised when utilizing nutrient loads from either of these models. The model with the hydrologic calibration and modified nutrient parameters was not truly calibrated or validated for nutrients due to limited available data. Parameters for the hydrologic calibration with modified nutrient parameters are given in Table 3.2. Parameters for the hydrologic calibration model only are given in Table 3.3. Observed vs predicted daily total phosphorus concentrations are given in Tables 3.11 and 3.12 for the hydrologic calibrated SWAT model and the hydrologic calibrated SWAT model with modified nutrient parameters, respectively. 14

21 Figure 3.1 U.S. Geological Survey (USGS) stream gage and Cooperative Observation Network (COOP) weather station locations in the Fort Cobb Basin. 15

22 Figure 3.2 General SWAT model calibration procedure. 16

23 Figure 3.3 Average annual flow calibration procedure used for the SWAT model. 17

24 Figure 3.4 Monthly flow calibration procedure used for the SWAT model. 18

25 Observed Predicted 80 Flow (cms) /1/1995 5/1/1995 9/1/1995 1/1/1996 5/1/1996 9/1/1996 1/1/1997 5/1/1997 9/1/1997 1/1/1998 5/1/1998 9/1/1998 1/1/1999 5/1/1999 9/1/1999 1/1/2000 5/1/2000 9/1/2000 1/1/2001 5/1/2001 9/1/2001 Date Figure 3.5 Observed and calibrated SWAT simulated flow at Cobb Creek near Eakley, OK ( ). 19

26 Predicted Flow (cms) y = 1.14x R 2 = Observed Flow (cms) Figure 3.6 Scatter plot of daily average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley. 20

27 14 Predicted Monthly Flow (cms) y = 1.37x R 2 = Observed Monthly Flow (cms) Figure 3.7 Scatter plot of monthly average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley. 21

28 2.5 Predicted Average Annual Flow (cms)) y = 1.54x R 2 = Observed Annual Average Flow (cms) Figure 3.8 Scatter plot of annual average ( ) observed and SWAT simulated flows at Cobb Creek near Eakley. 22

29 Observed streamflow Predicted streamflow 80 Flow (cms) /1/1980 7/1/1980 1/1/1981 7/1/1981 1/1/1982 7/1/1982 1/1/1983 7/1/1983 1/1/1984 7/1/1984 1/1/1985 7/1/1985 1/1/1986 7/1/1986 1/1/1987 7/1/1987 1/1/1988 7/1/1988 1/1/1989 7/1/1989 Date Figure 3.9 Validation time series for observed and SWAT predicted flow at the Cobb Creek near Eakley, OK (1980 to1989). 23

30 Figure 3.10 U.S. Geological Survey, Oklahoma Conservation Commission, and U.S. Fish and Wildlife Service water quality gage locations in the Fort Cobb basin. 24

31 2.5 Predicted TP Concentration (mg/l)) y = 1.08x R 2 = Observed TP Concentration (mg/l) Figure 3.11 Observed total phosphorus concentrations vs SWAT model (hydrologic calibrated model with modified nutrients parameters) predictions for the Fort Cobb Basin. 25

32 4.0 Predicted TP Concentrations (mg/l) y = 1.86x R 2 = Observed TP Concentrations (mg/l) Figure 3.12 Observed total phosphorus concentrations vs SWAT model (hydrologic calibrated only model) predictions for the Fort Cobb Basin. 26

33 Table 3.1 Drainage area and flow per unit area from to for available USGS stations in the Fort Cobb Basin. Station Drainage Area (mile^2) Flow/Area (cfs/mile^2) Cobb Creek Near Eakley Lake Creek Near Eakley Willow Creek Near Albert Cobb Creek Nr Fort Cobb Table 3.2 Parameter values used to calibrate flow and modify total phosphorus load for the Fort Cobb SWAT model. Value Variable Description 0 GW_DELAY Groundwater delay [Days] 0.02 GW_REVAP Groundwater "revap" coefficient 0.01 ALPHA_BF Baseflow Alpha Factor [Days] 0 REVAPMN Threshold depth of water in the shallow aquifer for "revap" to occur [mm] 0.75 RCHG_DP Deep aquifer percolation fraction 0.4 GW_SPYLD Specific yield of a shallow aquifer 0.95 ESCO Soil evaporation compensation factor 0.04 AWC Soil maximum available water content -8 CN SCS Curve number adjustment for soil moisture condition II 0.7 PSP Phosphorus availability index 0.8 USLE_P Universal Soil Loss Eq. support practice factor 30 SLSUBBSN Average slope length [meters] 0.3 RES_K Reservoir Permeability (Fort Cobb Reservoir only) Table 3.3 Parameter values used to calibrate the Fort Cobb SWAT model for flow only. Value Variable Description 0 GW_DELAY Groundwater delay [Days] 0.02 GW_REVAP Groundwater "revap" coefficient 0.01 ALPHA_BF Baseflow Alpha Factor [Days] 0 REVAPMN Threshold depth of water in the shallow aquifer for "revap" to occur [mm] 0.77 RCHG_DP Deep aquifer percolation fraction 0.4 GW_SPYLD Specific yield of a shallow aquifer 0.95 ESCO Soil evaporation compensation factor 0.04 AWC Soil maximum available water content -12 CN SCS Curve number adjustment for soil moisture condition II 0.3 RES_K Reservoir Permeability (Fort Cobb Reservoir only) 27

34 Table 3.4 Summary statistics for SWAT model hydrologic calibration for flow at Cobb Creek near Eakley gage for the period 1/ /2001. Time Step Total Flow (cms) Surface Flow (cms) Base Flow (cms) Daily Observed Flow Predicted Flow Relative Error (%) Coefficient of Determination 0.55 Nash-Sutcliffe Efficiency 0.38 Monthly Observed Predicted Relative Error (%) Coefficient of Determination 0.83 Nash-Sutcliffe Efficiency 0.48 Annual Observed Predicted Relative Error (%) Coefficient of Determination 0.91 Nash-Sutcliffe Efficiency

35 Table 3.5 Summary statistics for SWAT model hydrologic validation for flow at Cobb Creek near Eakley gage for the period 1/ /1989. Time Step Total Flow (cms) Surface Flow (cms) Base Flow (cms) Daily Observed Flow Predicted Flow Relative Error (%) Coefficient of Determination 0.62 Nash-Sutcliffe Efficiency 0.61 Monthly Observed Predicted Relative Error (%) Coefficient of Determination 0.76 Nash-Sutcliffe Efficiency 0.75 Annual Observed Predicted Relative Error (%) Coefficient of Determination 0.65 Nash-Sutcliffe Efficiency

36 Table 3.6 Observed and predicted total phosphorus concentration using different averages for both the hydrologic calibration SWAT model and the hydrologic calibration with modified nutrient parameters SWAT model shown. Total P Total P Total P Hydrologic Calibration With Hydrologic Calibration Only Modified Nutrient Parameters Type of Average Observed Simulated Simulated (mg/l) (mg/l) (mg/l) Average Flow Weighted Flow Weighted With Observed Flow Only Table 3.7 All water quality sites in the Fort Cobb basin and the number of samples collected during baseflow, high flow, and recession flow. Name Latitude Longitude Baseflow Highflow Recession # of Samples # of Samples # of Samples Willow Creek upstream Willow Creek Tributary Willow Cr downstream Lake Cr downstream Crooked Creek Camp Creek Downstream Below Dam Small Tributary Small Wetland Lake Creek one Lake Creek two Upper Willow Crk Lower Willow Crk Upper Lake Creek Lower lake Creek Upper Cobb Creek Lower Cobb Creek Eakley Creek Crooked Creek Camp Creek No Name Creek TOTAL

37 4 - Total Phosphorus and Sediment Loads The SWAT model was used to estimate total phosphorus and sediment load to the reservoir and from the entire Cobb Creek Basin. Table 4.1 displays the load by land cover as predicted by SWAT. The predicted sediment load to the reservoir for the period 1/1995 to12/2001 was 128,000 metric ton annually based on the hydrologic calibrated model with modified nutrient parameters and 190,000 metric tons annually for the hydrologic calibration only model. The vast majority (94%) of the Cobb Creek basin drains to Fort Cobb Reservoir (Figure 4.1), and thus there is only a few percent difference in the loads. The SWAT predicted annual sediment load (Table 4.2) agreed reasonably well with an estimated sediment load estimate based on a U.S. Bureau of Reclamation study (Ferrari 1994). The U.S. Bureau of Reclamation estimated a reservoir volume loss due to sediment at 6,966 acre-feet over a 34 year period from 1959 to They concluded that approximately acre-feet of sediment entered the reservoir annually. To convert this volume into an approximate weight of sediment, two assumptions were made. First, it was assumed that the reservoir had a 100% trapping efficiency. According to Vanoni (1977) and Brune (1953), reservoirs over 10,000 acre-feet or more will trap nearly all sediment entering the water body. The Fort Cobb Reservoir has a volume of 72,500 acre-feet. The second assumption was the specific density of the sediment. Lane and Koelzer (1953) and Vanoni (1977) estimated a specific density of 54 lb/ft 3 for an Oklahoma reservoir. With these two assumptions, it was estimated that approximately 218,000 Mt per year of sediment entered the Fort Cobb Reservoir from 1959 to The observed numbers are slightly different then the model predictions, but this could easily be explained by differences in the period of record. The SWAT model with the hydrologic calibration with modified nutrient parameters predicted an annual total phosphorus loading of 84,000 kg/yr. The hydrologic calibration only model predicted an annual total phosphorus loading of 133,000 kg/yr. Both models may have over predicted total phosphorus concentrations during high flow periods, but may have underestimated concentrations during base flow periods (Figures 3.10 and 3.11). However, there is a high degree of uncertainty due to the lack of observed storm flow samples. Most of the observed data collected between November 1997 to December of 2001 contained primarily base flow samples. In addition, the high flow samples were collected during the recession portion of the hydrograph or one to fours days after a storm event. These samples likely underestimated high flow total phosphorus concentrations (Correll et al., 1999; Baker et al., 2003). 31

38 Figure 4.1 Subbasins draining to the Fort Cobb Reservoir and to Cobb Creek below the reservoir dam only. Cobb Creek drains 853 km 2 of which 799 km 2 drains to the Fort Cobb Reservoir, and 6.3% of the Cobb Creek Basin is downstream of the Fort Cobb Reservoir dam. 32

39 Table 4.1 SWAT simulated loads by land cover for the Fort Cobb Basin for the period 1/ /2001. Fraction of Basin (%) Hydrologic Calibration With Modified Nutrient Parameters Sediment (Mg/ha) Total P (kg/ha) Hydrologic Calibration Only Sediment (Mg/ha) Total P (kg/ha) Crop/Landcover type Tillage Alfalfa Bare soil Forest Pasture Peanut with Double Crop Wheat Conventional Peanut with Double Crop Wheat Conservation Peanut Winter Fallow Conventional Rye Conventional Rye Conservation Grain Sorghum w/ Double Crop Wheat Conventional Grain Sorghum Winter Fallow Conventional Urban Water Corn Double Crop Wheat Conventional Winter Wheat for Grain Conservation Winter Wheat for Grain Conventional Winter Wheat for Pasture Conventional Average annual Total P loading for entire basin kg/yr kg/yr Average annual Total Sediment loading for entire basin Mg/yr Mg/yr Table 4.2 SWAT simulated loads by year for the Fort Cobb basin for the period of 1/ /2001. Hydrologic Calibration Only Hydrologic Calibration With Modified Nutrient Parameters Relative Difference Total P Sediment Yield Total P Sediment Yield Total P Sediment Yield Year (kg/yr) (Mg/yr) (kg/yr) (Mg/yr) % 31% % 35% % 34% % 33% % 34% % 32% % 33% Annual Avg % 33% 33

40 5 - Model Limitations and Conclusions Model Limitations There are several limitations to this study that should be noted. Limitations may be the result of data used in the model, inadequacies in the model, or using the model to simulate situations for which it was not designed. Hydrologic models will always have limitations, because the science behind the model is not perfect nor complete, and a model by definition is a simplification of the real world. Understanding the limitations helps assure that accurate inferences are drawn from model predictions. The uncertainty associated with water quality models is difficult to fully quantify. According to MacIntosh et al. (1984), there are two major types of uncertainty, knowledge uncertainty and stochastic uncertainty. Stochastic uncertainty is due to the random nature of natural systems, like rainfall. Weather is the driving force for any hydrologic model and thus uncertainty in the rainfall or the rainfall distribution across the watershed is important. Rainfall can be quite variable especially in the spring and summer when convective thunderstorms produce precipitation with a high degree of spatial variability. It may rain heavily at one location, but be dry a short distance away. Because rainfall is important, it represents a major source of uncertainty. Knowledge uncertainly stems from measurement errors and the inability of the model to accurately simulate the physical, chemical, and biological processes (MacIntosh et al., 1984). No point sources were included in this analysis, although there are several minor sources in the basin. These other sources, such as CAFOs, septic tanks and small communities, were considered negligible. There is uncertainty associated with specifying uniform management for a land cover category. It is not practical to specify management for every field in the basin, and thus a typical management was selected and applied basin-wide for each land cover type. Management operations include grazing, fertilization, tillage, planting, and harvesting. Conclusions A large portion of the Cobb Creek Basin is involved in agriculture. Cropland is the primary source of sediment and nutrients. The Cobb Creek Basin sediment load from upland areas as predicted by SWAT was 128,000 and 190,000 metric tons annually for the hydrologic calibrated model with modified nutrient parameters and the hydrologic calibrated only model, respectively. SWAT predicted sediment load agreed reasonably well with the estimated sediment load given by the U.S. Department of Reclamation (Ferrari 1994). They estimated an annual sediment loading of approximately acre-feet per year (~218,000 Mtons/year). There are a couple of important factors that need to be considered when comparing these two sediment loadings. First, the sediment loading estimated by the Bureau of Reclamation is calculated from volume loss in the reservoir that spans 34 years (1959 to 1993). The first 30 years are outside the scope of years examined in this study. Second, the climate during this 30 year time period would likely impact the average sediment loading. The SWAT model predicted annual total phosphorus loads of 84,000 and 133,000 kg/yr for the hydrologic calibrated model with modified nutrient parameters and the hydrologic calibration only model, respectively. A comprehensive calibration of the model for total phosphorus was not possible due to insufficient observed data. The SWAT model was modified slightly to more closely match observed individual water quality observations at the same location and time in the model. 34

41 The lack of observations during storm flow events increases the uncertainty in the predicted total phosphorus loads. Most, if not all observations, were collected during base flow conditions or several days after a storm event. Past research on agricultural watersheds noted that concentrations of total phosphorus, total nitrogen, and sediment rapidly decreased to base flow concentrations immediately following a storm event (Correll et al. 1999; Baker et al., 2003). Concentrations peak during the rising limb of the hydrograph before peak discharge and fall back to base flow concentrations along the declining limb of the hydrograph. 35

42 6 - Model Update with 2005 Land Cover The Fort Cobb SWAT model presented in the previous chapters was updated in this section to represent 2005 farming practices and land cover in the basin. A new SWAT model was set up identically to the previous model detailed in this report. Only land cover and management practices were changed in the updated model. During the Summer of 2005 (May - September), two residents of Caddo County, Oklahoma were hired to conduct a field survey of all cultivated fields in the basin. They collected several pieces of pertinent information that enabled us to accurately develop a new land cover map. The information they collected included: - Current crop - Previous double crop (if possible) - Tillage practice (if possible) - Presence of irrigation - Cattle grazing - Vegetation height. Each cultivated field was mapped using National Agricultural Imagery Program (NAIP) aerial photos and ArcMap software. These individuals drove the entire basin with a laptop connected to a Global Positioning System (GPS) unit with real time tracking. When they encountered a cultivated field, they delineated field boundaries and other information using NAIP photos displayed within ArcMap. To improve accuracy, the GPS unit would plot an icon or marker to represent their location on the aerial photos. The survey was complied to create a highly detailed crop data layer. The advantage to this approach compared to the previous model was the ability to distinguish crop types. The AAI Inc. land cover could only distinguish whether or not a field was cultivated. Section 6 of this report describes in detail the steps performed to address this issue with AAI s cultivated fields. Even though the AAI Inc. classified image lacked specific crop type, it did contain valuable information about the location of forested regions, pastureland, urban areas, and water bodies. The AAI Inc. and the field survey land cover were merged together to form a new land cover. This new land cover replaced the previous AAI Inc. land cover used in the previous model runs, and was used for all updated model runs. Since it was not possible to determine all farming practices (i.e. tillage, double crop, fallow, etc.) from the field survey, six models were developed to simulate three different peanut farming procedures with two different wheat farming practices (3x2 factorial design). This allowed us to bracket the management for peanuts and wheat. Updated winter wheat and peanut sediment and total phosphorus loads are given in separate tables with various loads depending on the percentage of area. If, for example, 25% of all peanuts are winter fallow, 50% are double cropped with winter wheat (conventional tillage), and 25% are double cropped with winter wheat (conservation tillage), then the loads can be estimated based on these fractions. The same procedure can be applied for winter wheat (conventional or conservation tillage). These tables allow for a more accurate bracket of loads based on the potential amount of double cropping and conservation tillage of peanut and wheat in the basin (Tables 6.1 to 6.5). Results According to the SWAT model, winter wheat under conventional tillage predicted sediment and total phosphorus loads of 34,000 Mg/yr and 22,000 kg/yr, respectively. Winter wheat under conservation tillage predicted loads of 12,500 Mg/yr of sediment and 16,000 kg/yr of total phosphorus. Sediment 36

43 loads for peanuts ranged from 24,000 Mg/yr (winter fallow) to 3,500 Mg/yr (peanut double cropped with wheat and conservation tillage). Total Phosphorus loads for peanuts ranged from 11,000 kg/yr (winter fallow) to 3,000 kg/yr (peanut double cropped with wheat and conventional tillage). Conservation tillage had a slightly higher total phosphorus load which was dominated by soluble phosphorus. Past research in agricultural watersheds have found similar results with total phosphorus loads with conventional and conservation tillage (Alberts and Spomer 1985; Johnson et al., 1979; Soileau et al., 1994). Johnson et al. (1979) noted that increased residue cover and decreased fertilizer incorporation as a possible explanation for slight increase in phosphorus with conservation tillage. The other land covers in the basin yielded a collective sediment yield of 34,600 Mg/yr. Total phosphorus load for the other land covers was 60,600 kg/yr (Table 6.6). Table 6.8 shows the range of sediment and total phosphorus loads for all six model runs compared to the previous model (hydrologic calibration only model) given in the report. Conclusions SWAT predicted sediment yields ranged from 190,000 Mg/yr ( hydrologic calibration only model) to 50,000 Mg/yr (2005 land cover model). Total phosphorus loads ranged from 133,000 kg/yr ( model) to 79,600 kg/yr. All six updated SWAT models with the 2005 land cover resulted in lower sediment and total phosphorus loads compared to the previous 2001 SWAT model. The average sediment yield ( ) for all six scenarios (~68,800 Mg/yr) was 54% lower than the sediment yield for the previous Fort Cobb model. A similar reduction was estimated for total phosphorus loads. The average total phosphorus load for all six scenarios (~85,800 kg/yr) resulted in a 20% reduction from the previous model. There could be a variety of reasons for the change in loads from 2001 to One dominant factor could be the dramatic change in land use. Many farmers are shifting from row crops to wheat and pasture in the basin. Both pasture and winter wheat increased in acreage in the basin. All of the row crops in the basin had a collective decrease of 75% in the number of acreage. This shift is thought to be, in part, the result of changes to peanut subsidies in the current Farm Bill. After 2001, peanut acreage dropped by 61% (9,500 acre reduction). Another factor could be the 2005 land cover. This data layer is more detailed than the 2001 land cover image supplied by AAI. The previous land cover contained no specific locations for each crop grown in the basin. Percentage of each row crop and small grain land cover was estimated from surveys given to extension specialist. These percentages were used to estimate approximately how much land was occupied by each crop. More detail about this situation is covered in section two (SWAT 2000 Input Data - Applied Analysis Incorporated Land Cover). This method of estimation can raise some uncertainty because the exact number of acreage for each crop and location is unknown. 37

44 Table 6.1 SWAT simulated winter wheat sediment loads based on tillage implementation and acreage in the Fort Cobb basin. Area Cover % Winter wheat Conventional Winter Wheat Conservation (acre) Sediment (Mg/yr) Sediment (Mg/yr)

45 Table 6.2 SWAT simulated winter wheat total phosphorus loads based on tillage implementation and acreage in the Fort Cobb basin. Area Cover % Winter wheat Conventional Winter Wheat Conservation (acre) Total Phos. (kg/yr) Total Phos. (kg/yr)

46 Table 6.3 SWAT simulated peanut sediment loads based on tillage implementation and acreage in the Fort Cobb basin. Area Cover % Peanut winter fallow (conventional) Peanut Double crop Winter Wheat Peanut Double crop Winter Wheat (acre) Conventional Tillage Conventional Tillage Conservation Tillage Sediment (Mg/yr) Sediment (Mg/yr) Sediment (Mg/yr) Table 6.4 SWAT simulated peanut total phosphorus loads based on tillage implementation and acreage in the Fort Cobb basin. Area Cover % Peanut winter fallow (conventional) Peanut Double crop Winter Wheat Peanut Double crop Winter Wheat (acre) Conventional Tillage Conventional Tillage Conservation Tillage Total Phos. (kg/yr) Total Phos. (kg/yr) Total Phos. (kg/yr)

47 Table 6.5 SWAT simulated loads per unit area for all peanut and winter wheat scenarios. Crop Tillage Area (ha) Sediment (Mg/ha) Total P (kg/ha) Peanut Fallow Conventional Peanut Double Cropped With Wheat Conventional Peanut Double Cropped With Wheat Conservation Winter Wheat Conventional Winter Wheat Conservation Table 6.6 SWAT simulated loads for all land covers in the updated SWAT model runs (except wheat and peanut) in the Fort Cobb basin for Crop/Landcover type Fraction of Basin (%) Area (ha) Sediment (Mg/ha) Total P (kg/ha) Alfalfa Bare soil Forest Pasture Peanut * * Grain Sorghum Winter Fallow Urban Water Cotton Corn Double Crop Wheat Winter Wheat for Grain * * Winter Wheat for Pasture Average Annual Total P Loadings without Wheat or Peanut Average annual Total Sediment loadings without Wheat or Peanut *Refer to the Winter Wheat and Peanut Tables to Estimate Sediment and Total Phosphorus loads 41

48 Table 6.7 SWAT simulated loads per unit area for all land covers for the previous (hydrologic calibrated only) SWAT model for Fraction of Basin (%) Sediment (Mg/ha) Total P (Kg/ha) Crop/Landcover type Tillage Alfalfa Bare soil Forest Pasture Peanut with double crop wheat Conventional Peanut with double crop wheat Conservation Peanut winter fallow Conventional Rye Conventional Rye Conservation Grain Sorghum w/ double crop wheat Conventional Grain Sorghum winter fallow Conventional Urban Water Corn double crop wheat Conventional Winter Wheat for grain Conservation Winter Wheat for grain Conventional Winter Wheat for Pasture Conventional

49 Table 6.8 Comparison of simulated sediment and total phosphorus loads for all SWAT models from 1975 to Model Runs Sediment Yield (Mg/yr) Total Phos. (Kg/yr) Previous Model (Hydrologic Calibration Only) Peanut Winter Fallow (Conventional Till) with Winter Wheat (conventional Till) Peanut Doubled Cropped with Winter Wheat (Conventional Till), Winter Wheat (Conventional Till) Peanut Doubled Cropped with Winter Wheat (Conservation Till), Winter Wheat (Conventional Till) Peanut Winter Fallow (Conventional Till) with Winter Wheat (conservation Till) Peanut Doubled Cropped with Winter Wheat (Conventional Till), Winter Wheat (Conservation Till) Peanut Doubled Cropped with Winter Wheat (Conservation Till), Winter Wheat (Conservation Till)

50 7 - References Alberts, E.E., R.G. Spomer Dissolved Nitrogen and Phosphorus in Runoff from Watershed in Conservation and Conventional Tillage. Journal of Soil and Water Conservation 40(1): Arnold, J.G., R. Srinivasan, R. S. Mittiah, and J. R. Williams Large Area Hydraulic Modeling and Assessment: Part I- Model Development. Journal of the American Water Resources Association 34(1): Baker, J. L., M. M. Agua, P. A. Lawlor, and S. W. Melvin Nutrient Transport in an Agricultural Watershed as Affected by Hydrology ASAE Annual International Meeting, Las Vegas, NV, July. Paper No Brune, G.M Trap Efficiency of Reservoirs. Transactions of the American Geophysical Union. 34(3): Cooperative Observation Network. Surface Data Daily. NOAA. National Climatic Data Center Correll, David, L., T. E. Jordan, and D. E. Weller Transport of Nitrogen and Phosphorus from Rhode River Watersheds During Storm Events. Water Resources Research. 35(8) Ferrari, Ronald L Fort Cobb Reservoir 1993 Sedimentation Survey. U.S. Bureau of Reclamation. Denver, Colorado. Haan, C.T., B.J. Barfield, J.C. Hayes Design Hydrology and Sedimentation for Small Catchments. Academic Press INC. Houser, J.B., L. M. Hauck Analysis of InSTream Water Quality Component of SWAT (Soil Water Assessment Tool). Proceedings of the TMDL Environmental Regulations Conference. ASAE, St. Joesph, MI, Pages Johnson, H.P., J.L. Baker, W.D. Shrader, and J.M. Laflen Tillage System Effects on Sediment and Nutrients in Runoff from Small Watersheds. Transactions of the American Society of Agricultural Engineers 22(5): Lane, E.W., and Koelzer, V.A Density of Sediment Deposited in Reservoirs. Report No. 9 of Study of Methods Used in Measurement and Analysis of Sediment loads in Streams. St. Paul United States Engineering District, St. Paul, MN. Lerch, Robert N., J. M. Erickson, and C. M. Wicks Intensive Water Quality Monitoring in Two Karst Watersheds of Boone County, Missouri. Proceedings from 2001 National Cave and Karst Management Symposium. Pages Mutchler, C. K., L. L. McDowell, and J. R. Johnson Erosion from Reduced-Till Cotton USDA-ARS and North Mississippi Branch Experiment Station Nash, J. E. And J. V. Sutcliffe River Flow Forecasting Through Conceptual Models 1. A Discussion of Principles. Journal of Hydrology 10: Natural Resources Conservation Service. Plant Nutrient Content Database. USDA,

51 Neitsch, S.L., J.G. Arnold, J.R. Williams Soil and Water Assessment Tool User s Manual Version Blackland Research Center. Oklahoma Agricultural Statistics Service. USDA Oklahoma Digital Orthographic Photos. ftp://okmaps.onenet.net. Rawls, Walter J. And H. H. Richardson Runoff Curve Numbers for Conservation Tillage. Journal of Soil and Water Conservation 38(6): Soil Conservation Service, U.S. Department of Agriculture National Engineering Handbook. Section 4, Hydrology. Washington D.C. Soileau, J.M., J.T. Touchton, B.F. Hajek, and K.H. Yoo Sediment, Nitrogen, and Phosphorus Runoff with Conventional and Conservation Tillage Cotton in a Small Watershed. Journal of Soil and Water Conservation 49(1): Vanoni, Vito A Sedimentation Engineering. American Society of Civil Engineers Manuals and Reports on Engineering Practices No. 54. American Society of Civil Engineers. New York, NY. 745 pg. Wischmeier, W. H., D.D. Smith Predicting Rainfall Erosion Losses - A guide to Conservation Planning, Agricultural Handbook 537, U.S. Department of Agriculture. SWAT Arnold, Jeff. et. al. USDA. Agricultural Research Service. Grassland, Soil, and Water Research Laboratory C Factors used for erosion prediction in Michigan, NRCS-USDA 45

52 Appendix A AAI Land Cover Classification Report 46

53 Development of Current Digital Landcover Data Using 30 m TM (Landsat 7 ETM+) Imagery for the Fort Cobb Basin Prepared for Dr. Daniel Storm Dept. of Biosystems and Ag Engineering Oklahoma State University by Applied Analysis Inc. 630 Boston Road, Suite 201 Billerica, Massachusetts September 12, 2002 Fort Cobb Basin Project Introduction The purpose of this project was to develop a digital landcover data layer using recent (June 10, 2001) 30 m resolution Landsat TM imagery for the Fort Cobb Basin. Satellite imagery has been used since the 1970 s as an accurate and cost effective tool for deriving vegetation and landcover information. Digital processing techniques involving the statistical analysis of image data representing various portions of the electromagnetic spectrum allows for definition of areas that reflect solar radiation in a similar manner. These areas may then be related to landcover or vegetation types through the use of ground truth information. For this project, a traditional classification method was used where pixels are selected that represent patterns or landcover features that can be recognized or identified with help from other sources, such as ground data, aerial sources (photography, orthophoto quads) or maps. Knowledge of the types of information desired in the end product is required prior to the onset of classification. By identifying patterns, the software is trained to identify pixels with similar characteristics. Applied Analysis Inc. (AAI) relied on local sources to assist in collection of georeferenced ground truth data to ensure the accuracy of the final product. This type of landcover data can be used to conduct watershed assessments, resource inventories, and to detect change in ecosystems. Page A2

54 Ground Truth Ground truth data and information was provided to Applied Analysis, Inc by Monty Ramming, Oklahoma Conservation Commission (OCC) and Dr. Daniel Storm, Oklahoma State University (OSU). The ground truth data included 1 meter resolution Digital Orthophoto Quarter Quads (DOQQ) from 1995 for the entire Fort Cobb Basin. This data is low altitude panchromatic photography and was registered to the June 10, 2001 Landsat 7 ETM+ scene. Additional ground truth information included a detailed ground survey of two 16 square mile quads located within the watershed. These quads were selected because they contained a representative sample of all the cover types of interest in the watershed and exhibited a high level of spectral variability in the Landsat image. AAI provided OCC copies of the DOQQ s for these quad areas. OCC conducted an extensive ground survey to locate and map large contiguous areas of each cover type. Additionally, OCC provided photographs of select fields of each cover type. These photos along with the field survey were the basis for labeling the spectral classes into the appropriate land cover categories. Methods This project mapped landcover types across the Fort Cobb Basin and used a whole pixel classification technique. In this study, we used an unsupervised iterative self-organizing data analysis (ISODATA) clustering algorithm. ISODATA is a widely used clustering algorithm that makes a large number of passes through an image using a minimum spectral distance routine to form clusters. It begins with an arbitrary cluster mean and each time the clustering repeats, the means of these clusters are shifted. The new cluster means are used for the next iteration. This iteration process continues until statistically distinct features emerge. The methods used to generate the final cover type map across Fort Cobb Basin included a multi-step ISODATA analysis technique. Because of the complex nature of the landcover types across the watershed and the spectral similarity between these landcover categories, four iterations of ISODATA clustering were required to accurately map landcover types. Each iteration of classification generated 100 spectral classes. Spectral convergence threshold was set to 95 percent. The initial classification produced 100 classes which were displayed on top of the Landsat image and DOQQ s as a thematic layer. By visual interpretation of the Landsat imagery and DOQQ s, a set of spectral classes was identified as containing the majority of the forest cover types. The thematic layer was then recoded such that all identified forest classes were recoded to 0 and all other classes were recoded to 1. This layer was saved as a separate file and used as a mask. The mask was applied to the original Landsat image and all pixels that fell within the forest classes were removed. The output masked image was the original Landsat image with all forest pixels removed. This image was then used as the input for the second ISODATA clustering. The second classification iteration generated 100 spectral classes using the same number of iterations and convergence threshold. This classification was used to extract water Page A3

55 from the Landsat imagery. The classification results were again displayed on the Landsat and DOQQ imagery. A set of spectral classes was identified for the category. The set of spectral classes were recoded and saved as a separate file. This file was used as a mask to remove water features from the original image. The output image was the original Landsat image with all forest and water pixels removed. This image (containing mainly pasture and cropland fields) was used as the input for the third classification. The third classification iteration produced 100 spectral classes. This classification was used to identify and map pasture and planted/cultivated types across the Fort Cobb Basin. The cover type categories included pasture, planted/cultivated 1, planted/cultivated 2 and barren areas. There is tremendous temporal change within and between these cover types. For example, a typical field in the Fort Cobb Basin can be rotated amongst a wide variety of cultivated crops and pasture types. Because of this temporal change and lack of temporal coincidence between the imagery acquisition and ground truth data collection, the ground truth data could not be relied upon solely to guide the selection of spectral classes for the pasture and cultivated categories. A set of decision criteria was established to guide the labeling of spectral classes into landcover categories. The decision criteria are as follows: 1. Pasture a. Fields with a high to moderate vegetative biomass state; b. These fields were relatively homogeneous in their spectral response and in their apparent color in the Landsat imagery; c. These fields included cultivated pasture, native pasture and rangeland. 2. Planted / Cultivated 1 a. Fields with a low vegetative biomass state; b. These fields were relatively heterogeneous in their apparent color in Landsat imagery; c. These fields contained some vegetative spectral response with a significant soil component; d. These fields included wheat, peanuts, cotton and other row crops. 3. Planted / Cultivated 2 a. Fields with no vegetative spectral response; b. These fields were relatively homogeneous in their apparent color in Landsat imagery; c. Fields which have been recently tilled or have such a low vegetative biomass state as to not be spectrally of visually apparent; d. Contiguous fields > 1 acre. 4. Barren a. Fields with no vegetative biomass; b. Contiguous fields sized < 1 acre. Page A4

56 These decision criteria were used as a guide for labeling spectral classes into landcover types. The primary means for labeling these spectral classes was the apparent color of the pixels in the Landsat imagery. Each spectral class was analyzed to see what cover types it was detecting. The decision criteria were then used to label that class to an appropriate landcover type. The third classification was also used to identify any additional forest or water pixels that may have been missed in the two previous classification iterations. Once all the spectral classes were labeled to the appropriate landcover category, the image was recoded such that each landcover category was given a unique identifier. The June 10, 2001 Landsat imagery showed a significant amount of bare soil fields across the Fort Cobb Basin. The reason for this, according to the Oklahoma Conservation Commission, was that the wheat harvest was underway at that time. Recently harvested wheat fields exhibit an overwhelming soil spectral response in Landsat imagery. Additionally, standing dry wheat fields, due to their lack of chlorophyll, exhibit a similar spectral response as bare soil. Because of the large temporal difference between imagery and ground truth, we were unable to identify which of these spectrally bright fields were standing wheat fields or bare soil. It should be noted that this spectral similarity does not preclude detection of dry wheat fields in Landsat imagery. If temporally coincident ground truth and imagery are acquired, there are several spectral techniques which could be used to detect this crop condition. Because of the previously noted spectral response, many fields in the third classification fell into one or two spectral classes. As a means to further separate landcover categories in these recently tilled or dry fields, a fourth classification iteration was run. The soil classes were subsetted from the Landsat image. The fourth classification iteration on these high soil areas produced 100 spectral classes. The decision criteria described above were used to separate these spectrally bright fields into the planted/cultivated and barren categories. The set of spectral classes for each category were recoded and saved as separate files. An additional analysis of Clump and Sieve was used to separate these bare soil fields between the landcover types of planted/cultivation 2 and barren. Clump and Sieve are spatial analysis tools to analyze raster data based on class identity and spatial relationship. The fields classified as barren in the fourth classification were run through a clump and sieve routine. All contiguous bare soil fields larger than one acre were reclassified as planted/cultivation 2. All contiguous bare soil fields one acre or less were left in the barren category. The urban category in the Fort Cobb Basin is underrepresented in this classification because the roads are too narrow to be detected in 30 meter Landsat data. The small town of Fort Cobb was classified as urban by using the roads vector layer to identify the town limits. Page A5

57 The final landcover map for the June 10, 2001 Landsat 7 ETM+ image was produced using standard image overlay techniques. The forest pixels from the first classification, the water pixels from the second classification, the pasture and cultivation types from the third and forth classification, and the clump and sieve and urban layers were added together and recoded to unique identifying numbers. Finally, the classes were color coded and output to a final thematic map. A riparian habitat assessment was also performed in the Fort Cobb Basin. Hydrologic data layers for the basin were acquired from the USGS via the Oklahoma Digital Atlas. A 100 meter buffer was extended from these hydrologic features to create and assess the spatial distribution of landcover types in the riparian zone. The riparian assessment was unsmoothed, to retain a finer minimum mapping unit and thus increase the spatial utility of each landcover type for best management practice implementation targeting purposes. Results With image processing complete, the final results were grouped into 7 landcover classes. The final percentages for landcover in the Fort Cobb basin were calculated and are presented below. Landcover (by percentage) within the Fort Cobb Basin Urban 0.5% Pasture 39.72% Planted / Cultivated % Planted / Cultivated % Forest 6.68% Barren 0.20% Water 1.89% Total 100% The basin was dominated by planted/cultivated 1 (46.44%) followed by pasture (39.72%). The other classes exhibited smaller percentages. This was due to the coarse spatial resolution of the Landsat imagery, which allowed some of the narrower roads/urban features and water bodies (streams and creeks) to go undetected or classified with another neighboring landcover type. In addition to classifying the entire Fort Cobb basin, a detailed riparian zone land cover classification was produced for 100 m buffer around hydrologic features in the watershed. The final results for this riparian zone were quantified and are presented below: Landcover percentages within the riparian zone of Fort Cobb Basin Urban 0.00% Page A6

58 Pasture 48.47% Planted / Cultivated % Planted / Cultivated % Forest 32.42% Barren 0.07% Total 100% Discussion The landcover classification for the watershed and riparian zone maps the spatial distribution of landcover throughout the Fort Cobb Basin. The classification categories planted/cultivated 1, planted/cultivated 2 and barren map the spatial distribution of high soil component fields across the watershed and within the riparian buffer. These classification categories are ranked in order of increasing bare soil reflectance. As the bare soil component comprised such a large percentage of the individual pixels classified in these three landcover types throughout the watershed and there was not temporally coincident ground truth data, the whole pixel ISODATA procedure provides the most reliable, accurate results for landcover analysis. Subpixel analysis would have been an appropriate technical approach if temporally coincident ground truth data were available and if the image were selected in a more appropriate season. Subpixel analysis is able to detect materials that comprise as little as 20 percent of the pixel. Thus, utilizing the Subpixel Classifier process in areas with very low vegetative cover, less than 20 percent of a pixel, would have created many errors of commission. The riparian zone classification offers a qualitative targeting method to spatially locate high risk landcover types within the riparian corridor. These highest risk landcover types would include bare soil/barren, planted/cultivated 1, and planted cultivated 2. When combined with estimates of nonpoint source loadings attributed to subwatersheds through SWAT modeling, it is anticipated that the combination will provide the watershed project coordinator with a mechanism to proactively identify and recruit landowners that are likely contributing to the overall degradation of water quality within the Fort Cobb Basin. Page A7

59 APPENDIX B CROP MANAGEMENT B-1

60 Crop Date Description Comments % of tota l cropland Peanut 14-Mar Fertilize N 30 lbs/acre (46-0-0) Average fertilizer rate for entire watershed 17% WINTER FALLOW 14-Mar Fertilize 12lbs/a P2O5 Average fertilizer rate for entire watershed Irrigated 15-Mar Disk plow with two passes (PNUT) 16-Mar Springtooth Harrow conventional tillage 20-May Plant peanuts 21-May Auto irrigate Based on water stress 10-Oct Harvest/kill Peanut Peanuts 15-May Harvest/kill Winter Wheat 12.75% DOUBLE CROP 17-May Fertilize N 30 lbs/acre (46-0-0) Average fertilizer rate for entire watershed WITH WHEAT 17-May Fertilize 12lbs/a P2O5 Average fertilizer rate for entire watershed Irrigated 18-May Disk plow with two passes (PNU1) 19-May Springtooth Harrow conventional tillage 20-May Plant peanuts 21-May Auto Irrigation Based on water stress 10-Oct Harvest/kill Peanuts 12-Oct Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed 13-Oct Disk plow with two passes 14-Oct Springtooth Harrow 15-Oct Plant winter wheat Peanuts 15-May Harvest /Kill Wheat 4.25% DOUBLE CROP 17-May Fertilize N 30 lbs/acre (46-0-0) Average fertilizer rate for entire watershed WITH WHEAT 17-May Fertilize 12lbs/a P2O5 Average fertilizer rate for entire watershed Conservation tillage 18-May Conservation Tillage Irrigated 20-May Plant Peanuts (PNU2) 21-May Auto irrigation Based on water stress 10-Oct Harvest/kill Peanuts 12-Oct Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed 13-Oct Conservation Tillage 15-Oct Plant Wheat Grain Sorghum 14-May Fertilize N 60 lbs/acre (46-0-0) Average fertilizer rate for entire watershed 7% WINTER FALLOW 14-May Fertilize 12lbs/a P2O5 Average fertilizer rate for entire watershed Dryland 15-May Disk plow with two passes (SORG) 16-May Springtooth Harrow conventional tillage 1-Jun Plant Sorghum 20-Sep Harvest/kill Sorghum Grain Sorghum 5-Jun Harvest/kill Winter Wheat 7% DOUBLE CROP 7-Jun Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed WITH WHEAT 7-Jun Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed Dryland 8-Jun Disk plow with two passes (SOR1) 9-Jun Springtooth Harrow conventional tillage 10-Jun Plant Sorghum 15-Sep Harvest/kill sorghum 28-Sep Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed 29-Sep Disk plow with two passes 30-Sep Springtooth Harrow 1-Oct Plant winter wheat Winter Wheat 10-Jun Harvestkill Wheat 13% FOR GRAIN 30-Jun Disk plow with two passes conventional tillage 1-Jul Springtooth Harrow Dryland 10-Aug Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed (WHTG) 10-Aug Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed 20-Sep Plant wheat 1-Dec Grazing operation 1/3 au/acre for 90 days B-2

61 Crop Date Description Comments % of total cropland Winter Wheat 10-Jun Harvest/kill Wheat 6.20% FOR GRAIN 30-Jun conservation tillage conservation tillage 10-Aug Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed Dryland 10-Aug Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed (WHG1) 20-Sep Plant wheat 1-Dec Grazing operation 1/3 au/acre for 90 days Winter Wheat 7.80% GRAZE OUT 10-Jun Harvest/kill Wheat conventional tillage 1-Aug Disk plow with two passes Dryland 2-Aug Springtooth Harrow (WHTP) 10-Aug Fertilize N 60lbs/acre (46-0-0) Average fertilizer rate for entire watershed 10-Aug Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed 15-Sep Plant wheat 15-Nov Grazing operation 1/3 au/acre for 150 days 15-Feb Fertilize N 40lbs/acre (46-0-0) Corn 15-Mar Harvest/kill Wheat 5.68% DOUBLE CROP WHE 16-Mar Fertilize N 120 lbs/acre (46-0-0) Average fertilizer rate for entire watershed conventional tillage 16-Mar Fertilize P2O5 5lbs/acre Average fertilizer rate for entire watershed irrigated 25-Mar Disk 2 passes (WHCO) 26-Mar Springtooth Harrow 27-Mar Plant Corn 28-Mar Auto irrigation Based on water stress 16-Sep Harvest/kill Corn 25-Sep Fertilize N 60lbs (46-0-0) Average fertilizer rate for entire watershed 26-Sep Disk 2 passes 27-Sep Springtooth Harrow 1-Oct Plant Wheat Oct 1 Alfalfa 1-Apr Harvest Alfalfa 5% Irrigated 15-May Harvest Alfalfa (ALFA) 1-Jul Harvest Alfalfa 29-Aug Fertilize P2O5 17 lbs/acre Average fertilizer rate for entire watershed 1-Sep Harvest Alfalfa 15-Oct Harvest Alfalfa Rye 10-Jun Harvest Rye 10.50% conventional tillage 30-Jun Disk 2 passes (RYE) 1-Jul Springtooth 10-Aug Fertilize N 60lbs (46-0-0) Average fertilizer rate for entire watershed 10-Aug Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed 20-Sep Plant Rye 21-Sep Auto irrigation Based on Plant stress 15-Nov Grazing operation 1/3 au/acre for 180 days Rye 10-Jun Harvest Rye 4.50% conservation tillage 30-Jun conservation tillage (RYE1) 10-Aug Fertilize N 60lbs (46-0-0) Average fertilizer rate for entire watershed 10-Aug Fertilize P2O5 12Ibs/acre Average fertilizer rate for entire watershed 20-Sep Plant Rye 21-Sep Auto irrigation Based on Plant stress 15-Nov Grazing operation 1/3 au/acre for 180 days B-3

62 Fort Cobb Basin Agricultural Field Management Survey for Practices During This survey will be used to gain a general understanding of the agricultural practices in the drainage area of the Fort Cobb Reservoir for the period Your information will be combined with other surveys from several individuals to help create a computer simulation of this area. This simulation will be used to gain insight on the movement of nutrients to support the TMDL process. This series of questions is difficult, and we do not expect that you will have exact answers, just give your best estimate based on you experience. If you cannot answer a question leave it blank. For questions that are not possible, not done in your area, or not applicable write N/A. If at any time you require assistance do not hesitate to contact Mike White, Oklahoma State University, (405) [email protected] for assistance. (1a) Name: (1b) Position: (1c) Company / Agency: (1d) Time at Current or Similar Position: (1e) County of residence or employment: (1f) Phone number: (1g) address: Personal Information and Experience (1h) On the following map, circle the area you are familiar with. If you are using a digital copy, use the space below to indicate the area. This is the area, which will be associated with the information you give in this survey. Please do not give information about other areas. (1i) How would you rate your familiarity with the agricultural practices in the area indicated in question # 1g on a scale of 1 to 10 with 10 being intimately familiar: General Agriculture Questions (2a) What percentage of fields is tested for soil phosphorus before the application of phosphorus fertilizers? B-4

63 (2b) What percentage of fields is tested for soil nitrogen before the application of nitrogen fertilizers? (2c) Of the samples collected what percentage is sent to the OSU soil testing lab or the county extension office for analysis? (2d) What percentage farmers base their fertilizer applications on OSU guidelines? (2e) Are acid soils a concern in your area? (2f) Is banding of phosphorus fertilizers with the seed a common practice; if so what percentage of cultivated fields would use it? (2g) Want percentage of cultivated land receives swine or other manures; how is it applied? (2h) Is irrigation water applied based on plant stress, regular scheduling, or some other method? (2i) What are the most common types of nitrogen and phosphorus fertilizer used? B-5

64 Questions About Pasture Systems The questions in this section pertain strictly to pasture systems. If you would like to elaborate on any question, space is provided at the end of section. Please site the question number with your elaboration. (3a) What percentage of pastures are warm season? (3b) What percentage of pastures are native grasses? (3c) What percentage of pastures are cool season? (3d) What percentage of pastures are mixes of warm and cool season? (3e) What percentage of pastures are grazed? (3f) What percentage of pastures are cut for hay and grazed? (3g) What percentage of pastures are cut for hay only and are not grazed? (3h) How many times is hay cut? (3i) When are these cutting(s)? (3j) What stocking rate is typical during the growing season for fertilized pastures? (Animal Units/acre preferred) (3k) What stocking rate is typical during the growing season for unfertilized pastures? (Animal Units/acre preferred) (3l) What percentage of fields is overgrazed (less than 2" standing forage) during the growing season? (3m) What percentage of fields is overgrazed (less than 2" standing forage) during the winter? (3n) What types and how much supplemental feed are used? Where does it come from? (3o) What percentage of pastures commonly receives commercial nitrogen fertilizer? What is a typical application rate (lbs N/acre)? (3p) What percentage of pastures commonly receives commercial phosphorus fertilizer? What is a typical application rate (lb p205/acre)? (3q) What percentage of pastures commonly receives animal waste? What waste is applied, and at what rate? Elaborate on any question if needed here: B-6

65 General Crop Questions about Cultivated fields (4) Table 1 contains a table with 5 general questions about 20 cropping systems. Please fill in these data as completely as possible for the area circled in question # 1h, if you do not know the answer, leave the square blank. For questions that are not possible, not done in your area, or not applicable write N/A. If you know additional cropping systems that have any significant acceptance in you area, add them to the table. B-7

66 Cropping Systems Wheat (Summer fallow) Wheat/Soybean Wheat/Cotton Wheat/Grain Sorghum Wheat/Peanut Wheat/Corn Wheat/Summer Forage or Silage Cotton (Winter fallow) Cotton (With nonwheat cover) Peanut (Winter Fallow) Peanut (With nonwheat cover) Corn (Winter fallow) Corn (With nonwheat cover) Soybeans (Winter fallow) Soybeans (With non-wheat cover) Grain Sorghum (Winter Fallow) Grain Sorghum (With non-wheat cover) Other Small Grains (Oats, Barley, ect.) Alfalfa Silage Annual Forage (Except pasture) Vegetables (4a) Which of the following cropping systems are used in your area. (Mark with an X ) General Crop System Questions - Table 1 (4b) Estimate the percentage of all cultivated land that best fits this cropping system. (4c) What percentage of these fields would be under irrigation? (4d) What percentage of these fields are plowed or clean tilled at least once a year? (4e) What percentage of these fields utilize notill or reduced tillage at least once a year? (4f) What is the most common Cover Crop or Double Crop? (If applicable) Canola Other Specify Other Specify B-8

67 Specific Questions about Particular Cropping Systems (5) Attached are a number of sheets with questions about specific cropping systems. These questions are used to gather specific information about the management practice for each crop. Please fill out the sheets for cropping systems marked in question 4a. These questions are similar from crop to crop. If you are unfamiliar or a particular crop system is not used in your area do not answer the questions. Again we realize these questions are difficult and not always applicable, please give your best estimate. If one or more of your crops marked in question # 4a is not listed in this section, use the space below to tell us about it. Use the questions asked about other cropping systems as a guide to the kind of information to provide. B-9

68 (5a) Wheat - Summer Fallow Percent of fields by tillage system When is the majority of tillage performed? Is tillage used to control summer weeds? Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What percentage of farmers use split applications of fertilizer for this crop? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied in first application. How much Phosphorus (lb p2o5/acre) is typically applied in first application. How much nitrogen (lb/acre) is typically applied in second application. How much Phosphorus (lb p2o5/acre) is typically applied in second application. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-10

69 (5b) Wheat - Soybeans Double Crop Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Speaking strictly of tillage for Wheat what percentage of fields would fall into the following categories. % % % % Speaking strictly of tillage for Soybeans what percentage of fields would fall into the following categories. % % % % General Crop Management Questions Wheat Soybeans What is the average harvesting date? What is the average planting date? When is fertilizer applied? For Split application use (Aug 1/Feb 15) Nitrogen (lb/acre) typically applied. Phosphorus (lb p2o5/acre) typically applied What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-11

70 (5c) Wheat - Cotton Double Crop Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Speaking strictly of tillage for Wheat what percentage of fields would fall into the following categories. % % % % Speaking strictly of tillage for green beans what percentage of fields would fall into the following categories. % % % % General Crop Management Questions Wheat Cotton What is the average harvesting date? What is the average planting date? When is fertilizer applied? For Split application use (Aug 1/Feb 15) Nitrogen (lb/acre) typically applied. Phosphorus (lb p2o5/acre) typically applied What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments B-12

71 (5d) Wheat - Grain Sorghum Double Crop Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Speaking strictly of tillage for Wheat what percentage of fields would fall into the following categories. Speaking strictly of tillage for Sorghum what percentage of fields would fall into the following categories. General Crop Management Questions Wheat Sorghum What is the average harvesting date? What is the average planting date? When is fertilizer applied? For Split application use (Aug 1/Feb 15) Nitrogen (lb/acre) typically applied. Phosphorus (lb p2o5/acre) typically applied What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-13

72 (5e) Wheat - Peanut Double Crop Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Speaking strictly of tillage for Wheat what percentage of fields would fall into the following categories. Speaking strictly of tillage for Soybeans what percentage of fields would fall into the following categories. General Crop Management Questions Wheat Peanut What is the average harvesting date? What is the average planting date? When is fertilizer applied? For Split application use (Aug 1/Feb 15) Nitrogen (lb/acre) typically applied. Phosphorus (lb p2o5/acre) typically applied What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-14

73 (5f) Wheat-Corn Double Crop Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbance s such as planting and fertilizer application Speaking strictly of tillage for Wheat what percentage of fields would fall into the following categories. % % % % Speaking strictly of tillage for Corn what percentage of fields would fall into the following categories. % % % % General Crop Management Questions Wheat Corn What is the average harvesting date? What is the average planting date? When is fertilizer applied? For Split application use (Aug 1/Feb 15) Nitrogen (lb/acre) typically applied. Phosphorus (lb p2o5/acre) typically applied What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-15

74 (5g) Cotton - Winter Fallow or Non Wheat Cover Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Percent of fields by tillage system % % % % When is the most tillage performed? What percentage of field will have winter cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop other than wheat? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-16

75 (5h) Peanut - Winter Fallow or Non Wheat Cover Percent of fields by tillage system Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbances such as planting and fertilizer application Planting Date Harvest Date When is the most tillage performed? What percentage of field will have winter cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop other than wheat? NA When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-17

76 N/A (5i) Corn - Winter Fallow or Non Wheat Cover Management by Tillage System Questions Notill No Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface tillage operations, only soil disturbances such as planting and fertilizer application Percent of fields by tillage system % % % % When is the most tillage performed? What percentage of field will have winter cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop other than wheat? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-18

77 (5j) Soybeans - Winter Fallow or Non Wheat Cover N/A Management by Tillage System Questions Notill No Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduce d till Most soil residue is left on soil surface tillage operations, only soil disturbances such as planting and fertilizer application Percent of fields by tillage system % % % % Planting Date Harvest Date When is the most tillage performed? What percentage of field will have winter cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop other than wheat? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-19

78 (5k) Grain Sorghum - Winter Fallow or Non Wheat Cover Percent of fields by tillage system Management by Tillage System Questions Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface Notill No tillage operations, only soil disturbance s such as planting and fertilizer application Planting Date Harvest Date When is the most tillage performed? What percentage of field will have winter cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop other than wheat? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? NOTES: B-20

79 (5l) Canola Management by Tillage System Questions Notill No Moldboard plow nearly all residue is incorporated. Conventional tillage Less aggressive implements than plows, most crop residue is incorporated Reduced till Most soil residue is left on soil surface tillage operations, only soil disturbances such as planting and fertilizer application Percent of fields by tillage system % % % % Planting Date Harvest Date When is the most tillage performed? What percentage of field will have a cover crop? What implement(s) would most commonly be used? General Crop Management Questions What is the average harvesting date? What is the average planting date? What is the most common cover crop/double crop? When is fertilizer applied? For Split application use (Aug 1/Feb 15) How much nitrogen (lb/acre) is typically applied. How much Phosphorus (lb p2o5/acre) is typically applied. What percentage of fields are grazed? Which months are grazed? What is the approximate stocking rate (au/acre)? Is animal waste commonly applied, if so what rates (ton/acre) and type? Notes or comments: B-21

80 Appendix C Reservoir and Ponds Information C-1

81 Reservoirs in the SWAT Model Name Subbasin Surface area (ha) Volume (acre-feet) FORT COBB SCS-COBB CREEK SITE SCS-COBB CREEK SITE SCS-COBB CREEK SITE SCS-COBB CREEK SITE Garland Stevens OKNONAME Ponds in the SWAT Model SUBBASIN Area (ha) Volume (acre-feet) C-2

82 SUBBASIN Area (ha) Volume (acre-feet) C-3

83 Appendix D Soil Test Data D-1

84 The average and weighted average Soil Test Phosphorus (STP) data for Washita, Caddo, and Custer County ( ). Crop Washita Custer Caddo Weighted average Wheat Alfalfa Grain Sorghum Peanut Soybean Corn rye Average annual STP values for peanut YEAR COUNTY AVERAGE ANNUAL STP 1994 CADDO CADDO CUSTER CADDO CUSTER CADDO CADDO CUSTER CADDO CADDO CADDO D-2

85 Average annual STP values for alfalfa YEAR COUNTY AVERAGE ANNUAL STP 1994 WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA Average annual STP values for grain sorghum YEAR COUNTY AVERAGE ANNUAL STP 1995 CADDO CUSTER CADDO CUSTER WASHITA CADDO WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CUSTER WASHITA CADDO WASHITA D-3

86 Average annual STP values for corn YEAR COUNTY AVERAGE ANNUAL STP 1994 CADDO CADDO CADDO CUSTER CADDO CUSTER CADDO CADDO CADDO Average annual STP values for rye YEAR COUNTY AVERAGE ANNUAL STP 1995 CADDO CADDO CADDO D-4

87 Average annual STP values for Winter Wheat YEAR COUNTY AVERAGE ANNUAL STP 1994 CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA CADDO CUSTER WASHITA D-5

88 Appendix E Stream Flow Data E-1

89 Average annual stream flow for Cobb Creek at Eakley, OK ( ) U.S. Geological Survey gage number Year AVERAGE CFS AVERAGE CMS E-2

90 Average annual stream flow for Cobb Creek at Fort Cobb, OK ( ) U.S. Geological Survey gage number YEAR AVERAGE CFS AVERAGE CMS E-3

91 Appendix F Water Quality Data F-1

92 AGENCY DATE SWAT SUBBASIN SITE NAME FLOW CMS (Observed) TP (Obs.) SWAT FLOW (S) FWS 11/3/ EC FWS 11/3/ UWC FWS 11/3/ CrC FWS 11/3/ LCC FWS 11/3/ CaC FWS 11/3/ UCC FWS 11/3/ ULC FWS 11/3/ LWC FWS 11/3/ LLC FWS 11/3/ NNC FWS 2/10/ EC FWS 2/10/ UWC FWS 2/10/ CrC FWS 2/10/ LCC FWS 2/10/ CaC FWS 2/10/ UCC FWS 2/10/ ULC FWS 2/10/ LWC FWS 2/10/ LLC FWS 2/10/ NNC FWS 5/2/ EC FWS 5/2/ UWC FWS 5/2/ CrC FWS 5/2/ LCC FWS 5/2/ CaC FWS 5/2/ UCC FWS 5/2/ ULC FWS 5/2/ LWC FWS 5/2/ LLC FWS 5/2/ NNC FWS 8/3/ UWC FWS 8/3/ CrC FWS 8/3/ LCC FWS 8/3/ CaC FWS 8/3/ UCC FWS 8/3/ ULC FWS 8/3/ LWC FWS 8/3/ LLC OCC 13-Aug Lake Creek site # OCC 13-Aug Lake Creek site # OCC 15-Sep Lake Creek site # OCC 15-Sep Lake Creek site # OCC 13-Oct Lake Creek site # OCC 13-Oct Lake Creek site # FWS 29-Oct EC < FWS 29-Oct UWC FWS 29-Oct CrC FWS 29-Oct LCC FWS 29-Oct CaC FWS 29-Oct UCC FWS 29-Oct ULC FWS 29-Oct LWC FWS 29-Oct LLC OCC 15-Dec Lake Creek site # OCC 15-Dec Lake Creek site # FWS 04-Jan EC FWS 04-Jan UWC FWS 04-Jan CrC FWS 04-Jan LCC FWS 04-Jan CaC FWS 04-Jan UCC FWS 04-Jan ULC FWS 04-Jan LWC FWS 04-Jan LLC FWS 04-Jan NNC F-2

93 AGENCY DATE SWAT SUBBASIN SITE NAME FLOW CMS (Observed) TP (Obs.) SWAT FLOW (S) OCC 11-Jan Lake Creek site # OCC 11-Jan Lake Creek site # OCC 09-Feb Lake Creek site # OCC 09-Feb Lake Creek site # OCC 17-Mar Lake Creek site # OCC 17-Mar Lake Creek site # FWS 20-Apr EC FWS 20-Apr UWC FWS 20-Apr CrC FWS 20-Apr LCC FWS 20-Apr CaC OCC 20-Apr Lake Creek site # OCC 20-Apr Lake Creek site # FWS 20-Apr UCC FWS 20-Apr ULC FWS 20-Apr LWC FWS 20-Apr LLC FWS 20-Apr NNC OCC 25-Apr Lake Creek site # OCC 20-May Lake Creek site # OCC 20-May Lake Creek site # OCC 21-Jun Lake Creek site # FWS 12-Jul EC FWS 12-Jul UWC FWS 12-Jul CrC FWS 12-Jul LCC FWS 12-Jul CaC FWS 12-Jul UCC FWS 12-Jul ULC FWS 12-Jul LWC FWS 12-Jul LLC FWS 12-Jul NNC OCC 20-Jul Lake Creek site # OCC 20-Jul Lake Creek site # OCC 17-Aug Lake Creek site # OCC 17-Aug Lake Creek site # FWS 11-Sep EC FWS 11-Sep UWC FWS 11-Sep CrC FWS 11-Sep LCC FWS 11-Sep CaC FWS 11-Sep UCC FWS 11-Sep ULC FWS 11-Sep LWC FWS 11-Sep LLC OCC 20-Sep Lake Creek site # OCC 20-Sep Lake Creek site # OCC 19-Oct Lake Creek site # OCC 19-Oct Lake Creek site # OCC 09-Nov Lake Creek site # OCC 09-Nov Lake Creek site # FWS 13-Dec EC FWS 13-Dec UWC FWS 13-Dec CrC FWS 13-Dec LCC FWS 13-Dec CaC FWS 13-Dec UCC FWS 13-Dec ULC FWS 13-Dec LWC FWS 13-Dec LLC FWS 13-Dec NNC FWS 15-Mar EC F-3

94 AGENCY DATE SWAT SUBBASIN SITE NAME FLOW CMS (Observed) TP (Obs.) SWAT FLOW (S) FWS 15-Mar CaC FWS 15-Mar UCC FWS 15-Mar ULC FWS 15-Mar LWC FWS 15-Mar LLC USGS 6/17/ USGS 6/17/ USGS 6/17/ USGS 6/17/ USGS 6/17/ USGS 6/17/ USGS 6/17/ Add USGS 6/17/ Add USGS 6/17/ USGS 6/17/ FWS 6/22/ EC FWS 6/22/ UWC FWS 6/22/ CrC FWS 6/22/ LCC FWS 6/22/ CaC FWS 6/22/ UCC FWS 6/22/ ULC FWS 6/22/ LWC FWS 6/22/ LLC FWS 6/22/ NNC USGS 7/13/ USGS 7/13/ USGS 7/13/ USGS 7/13/ USGS 7/13/ USGS 7/13/ USGS 7/13/ Add USGS 7/13/ Add USGS 7/13/ USGS 7/13/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 9/18/ USGS 11/29/ USGS 11/29/ USGS 11/29/ USGS 11/29/ USGS 11/29/ USGS 11/29/ USGS 11/29/ Add USGS 11/29/ Add USGS 11/29/ USGS 11/29/ USGS 2/14/ USGS 2/14/ USGS 2/14/ USGS 2/14/ USGS 2/14/ USGS 2/14/ USGS 2/14/ Add USGS 2/14/ Add USGS 2/14/ USGS 2/14/ USGS 4/23/ USGS 4/23/ F-4

95 AGENCY DATE SWAT SUBBASIN SITE NAME FLOW CMS (Observed) TP (Obs.) SWAT FLOW (S) USGS 4/23/ USGS 4/23/ USGS 4/23/ USGS 4/23/ USGS 4/23/ Add USGS 4/23/ Add USGS 4/23/ USGS 4/23/ USGS 6/21/ USGS 6/21/ USGS 6/21/ USGS 6/21/ USGS 6/21/ USGS 6/21/ USGS 6/21/ Add USGS 6/21/ Add USGS 6/21/ USGS 6/21/ USGS 9/16/ USGS 9/16/ USGS 9/16/ USGS 9/16/ USGS 9/16/ USGS 9/16/ USGS 9/16/ Add USGS 9/16/ Add USGS 9/16/ USGS 9/16/ USGS 12/19/ USGS 12/19/ USGS 12/19/ USGS 12/19/ USGS 12/19/ USGS 12/19/ USGS 12/19/ USGS 12/19/ F-5

96 APPENDIX G Subbasin Properties G-1

97 Figure F1 Subbasin layout used in the Cobb Creek SWAT model. G-2

98 Table F1 Slope in percent by land cover and subbasin used in the SWAT model. NOTE: Identical crop types were consolidated to save space. Subbasin Forest Pasture Peanut Sorghum Urban Water Wheat Wheat Rye Bare Corn Alfalfa Grain Pasture soil G-3

99 Table F1 Slope in percent by land cover and subbasin used in the SWAT model (cont). Subbasin Forest Pasture Peanut Sorghum Urban Water Wheat Wheat Rye Bare Corn Alfalfa Grain Pasture soil G-4

100 Table F2 Percent of total basin area in each subbasin and land cover combination used in the SWAT model. Peanut Double Crop Wheat Conventional Peanut Double Crop Wheat Conservation Subbasin Alfalfa Bare Soil Forest Pasture Peanut Conventional RYE Conventional RYE Conservation G-5

101 Table F2 Percent of total basin area in each subbasin and land cover combination used in the SWAT model (Cont.). Grain Sorghum Double Crop Grain Corn Double Wheat Grain Wheat Grain Wheat Subbasin Wheat Sorghum Urban Water Crop Wheat Conservation conventional Pasture Total G-6

102 Table F2 Percent of total basin area in each subbasin and land cover combination used in the SWAT model (Cont.). Peanut Double Crop Wheat Conventional Subbasin Alfalfa Bare Soil Forest Pasture Peanut Conventional RYE Conventional RYE Conservation G-7 Peanut Double Crop Wheat Conservation

103 Table F2 Percent of total basin area in each subbasin and land cover combination used in the SWAT model (Cont.). Grain Sorghum Double Crop Grain Corn Double Wheat Grain Wheat Grain Wheat Subbasin Wheat Sorghum Urban Water Crop Wheat Conservation conventional Pasture Total Total G-8