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.

A Soil and Water Assessment Tool (SWAT) model was developed for the Iowa-Cedar River Basin (ICRB), a 32,660 km2 watershed dominated by agricultural land cover (~70%) to simulate hydrology and water quality for the prediction of stream discharge, nitrate loads, and nitrate concentration under climate and land use change scenarios. Iowa exports as much as 20% of the nitrogen entering the Gulf of Mexico at the mouth of the Mississippi, contributing to Gulf hypoxia as well as local threats to water quality in the ICRB. The model utilized a combined autocalibration and sensitivity procedure incorporating Sequential Uncertainty Fitting (SUFI) and generalized additive models. This procedure resulted in Nash-Sutcliffe Efficiency (NSE) goodness-of-fit statistics that met literature guidelines for monthly mean stream discharge (NSE>̲0.60) and daily nitrate load (NSE>̲0.50). Artificial neural networks coupled with SWAT stream discharges aided in the simulation of daily mean nitrate concentrations that met the literature guideline (NSE>̲0.50). The North American Regional Climate Change Assessment Program (NARCCAP) provided an ensemble of 11 climate change scenarios. NARCCAP is a multi-institutional effort to simulate climate change at the mesoscale by downscaling global circulation models (GCM) with regional climate models (RCM). The resulting GCM-RCM produced synthetic precipitation and temperature time-series that drove the SWAT simulations and scenarios. The land use scenarios were a collaboration with the U.S. Army Corps of Engineers, using a rule-based GIS method to generate scenarios that (1) maximized agricultural productivity, (2) improved water quality and reduced flooding, and (3) enhanced local biodiversity. The SWAT simulations and ensemble climate change scenarios resulted in a warmer and wetter climate with greater and more extreme discharge in all seasons except summer where the models indicate a somewhat higher probability of extreme low flows (p-value

This dissertation describes the modeling efforts on the Upper Mississippi River Basin (UMRB) using the Soil and Water Assessment Tool (SWAT) model. The main goal of this study is to apply the SWAT model to the UMRB to evaluate the model as a tool for agricultural policy analysis and climate change impact analysis. A sensitivity analysis using influence coefficient method was conducted for eight selected hydrologic input parameters to identify the most to the least sensitive parameters. Calibration and validation of SWAT were performed for the Maquoketa River Watershed for streamflow on annual and monthly basis. The model was then validated for the entire UMRB streamflow and evaluated for a climate change impact analysis. The results indicate that the UMRB hydrology is very sensitve to potential future climate changes. The impact of future climate change was then explored for the streamflow by using two 10-year scenario periods (1990 and 2040s) generated by introducing a regional climate model (RegCM2) to dynamically downscale global model (HadCM2) results. The combined GCM-RCM-SWAT model system produced an increase in future scenario climate precipitation of 21% with a resulting 50% increase in total water yield in the UMRB. Furthermore, evaluation of model-introduced uncertainties due to use of SWAT, GCM, and RCM models yielded the highest percentage bias (18%) for the GCM downscaling error. Building upon the above SWAT validation, a SWAT modeling framework was constructed for the entire UMRB, which incorporates more detailed input data and is designed to assess the effects of land use, climate, and soil conditions on streamflow and water quality. An application of SWAT is presented for the Iowa and Des Moines River watersheds within the modeling framework constructed for the UMRB. A scenario run where conservation tillage adoption increased to 100% found a small sediment reduction of 5.8% for Iowa River Watershed and 5.7% for Des Moines River Watershed. On per-acre basis, sediment reduction for Iowa and Des Moines River Watersheds was found to be 1.86 and 1.18 metric tons respectively. Furthermore an attempt to validate the model for the entire UMRB yielded strong annual results.