Iowa finds itself positioned at the epicenter of agricultural pollution due to the intensity of crop and livestock production, fertilizer inputs, altered hydrological landscapes, and other factors. To address such issues, the overarching objective of this research work was to understand the implications of an expansion in bioenergy crops as mandated by the Environmental Protection Agency's Renewable Fuel Standard 2 (through 2022) on hydrology and water quality in an agricultural watershed. In this research, the Soil Water Assessment Tool (SWAT) model was calibrated and validated using field data obtained through water quality sensors and grab samples, and then model parameters were estimated for sensitivity and uncertainty analysis. Scenarios were generated based on Renewable Fuel Standards and evaluated for understanding the impacts of expanding bioenergy production on hydrology and water quality. Also output from an agent-based model was incorporated into SWAT for simulating watershed responses to different crop market scenarios.

Applications of the SWAT model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the tool. In a standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover (LULC), soil, and slope, but located in different places within a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as LULC, soil, crop rotation and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for the Raccoon River watershed in Iowa for 2002 to 2010 and the Big Creek River watershed in Illinois for 2000 to 2003. SWAT was able to replicate annual, monthly and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner. The calibrated and validated SWAT model was then used to assess agricultural scenario and climate change impacts on watershed water quantity, quality, and crop yields. Modeling impacts of agricultural scenarios and climate change on surface water quantity and quality provides useful information for planning effective water, environmental, and land use policies. Despite the significant impacts of agriculture on water quantity and quality, limited literature exists modeling the combined impacts of agricultural scenarios and climate change on crop yields and watershed hydrology. Here, SWAT, was used to model the combined impacts of five agricultural scenarios and three climate scenarios downscaled using eight climate models. These scenarios were implemented in a well calibrated SWAT model for the Raccoon River watershed (RRW), IA. We run the scenarios for the historical baseline, early-century, mid-century, and late-century periods. Results indicate that historical and more corn intensive agricultural scenarios with higher CO2 emissions consistently result in more water in the streams and greater water quality problems, especially late in the 21st century. Planting more switchgrass, on the other hand, results in less water in the streams and water quality improvements relative to the baseline. For all given agricultural landscapes simulated, all flow, sediment and nutrient outputs increase from early-to-late century periods for the RCP4.5 and RCP8.5 climate scenarios. We also find that corn and switchgrass yields are negatively impacted under RCP4.5 and RCP8.5 scenarios in the mid and late 21st century. Finally, various agricultural best management practice (BMP) scenarios were evaluated for their efficiency in alleviating watershed water quality problems. The vast majority of the literature on efficiency assessment of BMPs in alleviating water quality problems base their scenarios analysis on identifying subbasin level simulation results. In the this study, we used spatially explicit HRUs, defined using ArcGIS-based pre-processing methodology, to identify Nitrate (NO3) and Total Suspended Solids (TSS) hotspots at the HRU/field level, and evaluate the efficiency of selected BMPs in a large watershed, RRW, using the SWAT model. Accordingly, analysis of fourteen management scenarios were performed based on systematic combinations of five agricultural BMPs (fertilizer/manure management, changing cropland to perennial grass, vegetative filter strips, cover crops and shallower tile drainage systems) aimed to reduce NO3 and TSS yields from targeted hotspot areas in the watershed at field level. Moreover, implications of climate change on management practices, and impacts of management practices on water availability and crop yield and total production were assessed. Results indicated that either implementation of multiple BMPs or conversion of an extensive area into perennial grass may be required to sufficiently reduce nitrate loads to meet the drinking water standard. Moreover, climate change may undermine the effectiveness of management practices, especially late in the 21 st century. The targeted approach used in this study resulted in slight decreases in watershed average crop yields, hence the reduction in total crop production is mainly due to conversion of croplands to perennial grass.

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

"Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method"--Abstract, page iv.