The Soil and Water Assessment Tool (SWAT) is a physically model to estimate impact of land cover on water, sediment, and agricultural chemical yields in large, complex yields in large, complex watersheds with fluctuating soils, land use, and management conditions for long periods of time. ln order to simulate the movement of sediment and nutrients, the Davis Creek Watershed is subdivided into 31 homogeneous sub basins, having unique soil and land use properties. The data for each subbasin is grouped into categories of land cover, soil, management within sub basin, draining the sub basin. The objectives of this study are to classify the most polluted subbasins in the watershed with the aim of determining the most appropriate land uses (e.g., agriculture, industrial, commercial, residential) in this surrounding areas through a 14 years period (1999-2013), examining impact of land use change on runoff sediment load and nutrient yield for 2001 and 2011 and providing recommendations on the best management practices for controlling and reducing source pollution. Through examination of the simulated results, most pollutant subbasins are identified, land cover impacts are examined. This information, while valuable and useful, needs to be further verified in the field for supporting water quality decision making in the Davis Creek Watershed.

The deterioration of water quality due to human-driven alternations has an adverse effect on the environment. More than 50% of surveyed surface water bodies in the United States (US) are classified as impaired waters as per the Clean Water Act. The pollutants affecting the water quality in the US are classified as point and non-point sources. Pollutant mitigation strategies such as the selective implementation of best management practices (BMPs) based on the severity of the pollution could improve water quality by reducing the amounts of pollutants. Quantifying the efficiency of a specific management practice can be difficult for large watersheds. Complex hydrologic models are used to assess water quality and quantity at watershed scales. This study used a Soil and Water Assessment Tool (SWAT) that can simulate a longer time series for hydrologic and water quality assessments in the Yazoo River Watershed (YRW). This research aims to estimate streamflow, sediment, and nutrient load reductions by implementing various BMPs in the watershed. BMPs such as vegetative filter strips (VFS), riparian buffers, and cover crops were applied in this study. Results from these scenarios indicated that the combination of VFS and riparian buffers at the watershed scale had the highest reduction in sediment and nutrient loads. Correspondingly, a comparative analysis of BMP implementation at the field and watershed scale showed the variability in the reduction of streamflow, sediment, and nutrient loads. The results indicated that combining VFS and CC at the field scale watershed had a greater nutrient reduction than at the watershed scale. Likewise, this study investigated the soil-specific sediment load assessments for predominant soils in the YRW, which resulted in soil types of Alligator, Sharkey, and Memphis soils being highly erodible from the agricultural-dominant region. This study also included the effect of historical land use and land-cover (LULC) change on water quality. The analysis revealed that there was a significant decrease in pastureland and a simultaneous increase in forest and wetlands, which showed a decreasing trend in hydrologic and water quality outputs. Results from this study could be beneficial in decision-making for prescribing appropriate conservation practices

Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient dynamics. Meanwhile, densification associated with urban growth will create more impervious surfaces which will alter sediment and nutrient loadings. Land and water managers often rely on models to develop possible future scenarios and devise management responses to these projected changes. We use the Soil and Water Assessment Tool (SWAT) to assess the sensitivities of stream flow, sediment, and nutrient loads in two urbanizing watersheds in Northwest Oregon, USA to various climate and urbanization scenarios. We evaluate the spatial patterns climate change and urban growth will have on water, sediment and nutrient yields. We also identify critical source areas (CSAs) and investigate how implementation of vegetative filter strips (VFS) could ameliorate the effects of these changes. Our findings suggest that: 1) Water yield is tightly coupled to precipitation. 2) Large increases in winter and spring precipitation provide enough sub-surface storage to increase summertime water yields despite a moderate decrease in summer precipitation. 3) Expansion of urban areas increases surface runoff and has mixed effects on sediment and nutrients. 4) Implementation of VFS reduces pollutant loads helping overall watershed health. This research demonstrates the usefulness of SWAT in facilitating informed land and water management decisions.

The Soil Conservation Service (SCS) Curve Number (CN) method is a widely used model developed by the Natural Resources Conservation Service (NRCS) that estimates the surface runoff generated from a land area using factors such as land cover, soil type, and precipitation depth. However, this empirical model was developed over 60 years ago with limited data; thus, NRCS proposed revising the method with support of an ASCE-ASABE task group. The proposed revisions include updating the initial abstraction estimation in the CN equation and modifying the curve number for all land use and soil hydrologic group combinations. The proposed revisions are expected to improve the hydrologic response estimation, especially for smaller precipitation events. Since surface runoff is the driving force that transports sediment, nutrients, and other chemicals in natural systems, this study aims to understand how predicted ecohydrological indicators vary between the original and the proposed revised versions of the SCS Curve Number method using the Soil and Water Assessment Tool (SWAT). Additionally, a small case study site was selected to understand how the revisions alter design storm runoff analysis. The Spring Creek watershed, located in central Pennsylvania, was selected as the study watershed for the SWAT simulation since it contains a mixture of forested, agricultural, and urban land covers. Results from the simulation study indicate a 5-13% increase in annual runoff predictions with the revised equation compared to the original equation. Though most average monthly runoff depths increased by 8-25% using the revised equation, winter months generally simulated runoff depths that were 1-5% lower than the original equation. Average daily nutrient loadings in the streamflow at the watershed outlet increased by 9-16% and 3-10% for nitrate and mineral phosphorus loadings, respectively. When using the revised equation, the average daily erosion and sediment loading in the streamflow was consistently within 1% of the original equation. The SWAT simulation results suggest that current agricultural and urban best management practices (BMPs) will experience higher runoff volumes on a yearly time scale, while the small-scale case study results suggest that urban BMPs can be designed to be smaller in size if rural lands are urbanized. More detailed evaluations of the proposed SCS curve number method are required on water quality simulations, stormwater management, and BMP designs.

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, used the Soil and Water Assessment Tool to simulate streamflow and nitrate loads within the Cedar River Basin, Iowa. The goal was to assess the ability of the Soil and Water Assessment Tool to estimate streamflow and nitrate loads in gaged and ungaged basins in Iowa. The Cedar River Basin model uses measured streamflow data from 12 U.S. Geological Survey streamflowgaging stations for hydrology calibration. The U.S. Geological Survey software program, Load Estimator, was used to estimate annual and monthly nitrate loads based on measured nitrate concentrations and streamflow data from three Iowa Department of Natural Resources Storage and Retrieval/Water Quality Exchange stations, located throughout the basin, for nitrate load calibration.

Rivers deliver significant macronutrients and sediments to lakes that can vary substantially throughout the year. These nutrient and sediment loadings, exacerbated by winter and spring runoff, impact aquatic ecosystem productivity and drive the formation of harmful algae blooms. The source, extent and magnitude of nutrient and sediment loading can vary drastically due to extreme weather events and hydrologic processes, such as snowmelt or high flow storm events, that dominate during a particular time period, making the temporal component (i.e., time over which the loading is estimated) critical for accurate forecasts. In this work, we developed a data-driven framework that leverages the temporal variability embedded in these complex hydrologic regimes to improve loading estimates. Identifying the "correct" time scale is an important first step for providing accurate estimates of seasonal nutrient and sediment loadings. We use water quality concentration and associated 15-minute discharge data from nine watersheds in Vermont’s Lake Champlain Basin to test our proposed framework. Optimal time periods were selected using a hierarchical cluster analysis that uses the slope and intercept coefficients from individual load-discharge regressions to derive improved linear models. These optimized linear models were used to improve estimates of annual and "spring" loadings for total phosphorus, dissolved phosphorus, total nitrogen, and total suspended loads for each of the nine study watersheds. The optimized annual regression model performed ~20% better on average than traditional annual regression models in terms of Nash-Sutcliffe efficiency, and resulted in ~50% higher cumulative load estimates with the largest difference occurring in the "spring". In addition, the largest nutrient and sediment loadings occurred during the "spring" unit of time and were typically more than 40% of the total annual estimated load in a given year. The framework developed here is robust and may be used to analyze other units of time associated with hydrologic regimes of interest provided adequate water quality data exist. This, in turn, may be used to create more targeted and cost-effective management strategies for improved aquatic health in rivers and lakes.