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<0.05). The land use scenarios for SWAT showed that nitrate load and discharge positively and linearly scale with percent of agricultural land area (p-value<0.05).

In the southwestern United States, the North American monsoon (NAM) is the main driver of severe weather and accounts for nearly half the annual precipitation. How the monsoon has behaved in the past and how it will change in the future is a question of major importance for natural resource management and infrastructural planning. In this dissertation, I present the results of three studies that have investigated North American monsoon variability and change from the perspective of paleoclimate records, future climate change projections, and simulation of the low-frequency variability with the longest retrospective atmospheric reanalysis. In the first study, a monsoon-sensitive network of tree-ring chronologies is evaluated within its ability to reproduce NAM variability during the past four centuries. Matrix methods are used to detect the low-frequency spatiotemporal variability. The treering chronologies can reasonable characterizes the dominant modes of NAM climate variability. The monsoon tree-ring network is able to reproduce the interannual variability of cool and warm season precipitation, in a manner similar to the period of the instrumental record. Earlywood and latewood adjusted chronologies reveal low frequency climate variability at decadal and longer timescales that is beyond the ability of the instrumental record to temporally well resolve. This low-frequency climate variability seems to be part of a much larger cycle that coincides with the occurrence of multiyear persistent droughts. In the second study, we consider the modes of natural climate variability identified in the previous study to objectively assess the degree of physical uncertainty in climate change projections for NAM from Regional Climate Models (RCMs) used in the North American Regional Climate Change Assessment Program (NARCCAP). Climate change projection models are evaluated mainly on their ability to represent warm season driven by quasi-stationary Rossby wave trains and El Niño Southern Oscillation – Pacific Decadal Variability (ENSO-PDV). It is concluded that use of the NARCCAP model ensemble mean for NAM climate projections is probably not suitable. NARCCAP RCMs are largely a slave to their driving global models and their error in the specification of large-scale atmospheric circulation. Only one out of eight NARCCAP RCMs has a reasonable representation of the seasonal cycle of monsoon precipitation and ENSOdriven interannual variability in both the 20th and 21st centuries. No decadal variability was observed in any of the NARCCAP RCMs. In the third study, the low-frequency drought signal found with tree-ring chronologies is further explored within the framework of a regional climate modeling. Version 2 of the Twentieth-Century Reanalysis (DD-20CR) is dynamically downscaled over a contiguous U.S.-Mexico domain. Statistic analysis of the DD-20CR suggests that the low-frequency drought signal in the Southwest is driven by atmospheric circulation changes on global to continental scales that affect precipitation in Central American as well. DD-20CR reproduces the spatial patterns of precipitation associated with climate variability at decadal and longer timescales in a manner that compares well with observational records and tree-ring chronologies. Low-frequency climate variability is therefore likely responsible for the multiyear persistent droughts in the last four centuries, as independently evaluated from the tree-ring monsoon-sensitive network.

One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena.