Modeling water quantity and quality in an agricultural watershed in the Midwestern US using SWAT: Assessing implications due to an expansion in 'biofuel' production and climate change

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. Finally SWAT model output under eighteen scenarios, was generated for six different climate models and analyzed to see changes in various water quantity outputs e.g. surface flow, base flow, and ET.;The SWAT Model was calibrated and validated within statistically acceptable limits e.g. R2 > 0.85 of observed monthly hydrologic mass and R2 > 0.7 for nutrients loads. Sediment load was reduced by 15% due to conversion of corn acreage into switch grass on high elevation land with a slope of >5% (roughly 12% of the watershed). Model simulations also showed that linear climatic inputs (i.e. linear temporal trends increase in precipitation and max/min air temperature) can generate non-linear responses amongst different components of the water cycle (i.e. surface flow, base flow, ET, and deep percolation rates) in the watershed model. This research effort will help to produce a prototype Intelligent Digital Watershed (IDW) to understand the interactions between water and human systems, with the goal of a sustainable agricultural economy. The IDW should enable discovery of scenarios that result in water quality that meets water quality standards.