Climate change impacts hydrology, nutrient cycling, agricultural conservation practices, and greenhouse gas (GHG) emissions. The Chesapeake Bay and its watershed are subject to the largest and most expensive Total Maximum Daily Load (TMDL) ever developed. It is unclear if the TMDL can be met given climate change and variability (e.g., extreme weather events). The objective of this dissertation is to quantify the impact of climate change and climate on water resources, nutrient cycling and export in agroecosystems, and agricultural conservation practices in the Chesapeake Bay watershed. This is accomplished by developing and employing a suite of modelling tools.

GHG emissions from agroecosystems, particularly nitrous oxide (N2O), are an increasing concern. To quantify N2O emissions a routine was developed for the Soil and Water Assessment Tool (SWAT) model. The new routine predicts N2O and di-nitrogen (N2) emissions by coupling the C and N cycles with soil moisture, temperature, and pH in SWAT. The model uses reduction functions to predict total denitrification (N2 + N2O production) and partitions N2 from N2O using a ratio method. The SWAT nitrification routine was modified to predict N2O emissions using reduction functions. The new model was tested using GRACEnet data at University Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot measurements of N2O flux and the model predictions for both test sites and suggest that N2O emissions are particularly sensitive to soil pH and soil N, and moderately sensitive to soil temperature/moisture and total soil C levels.

The new GHG model was then used to analyze the impact of climate change and extreme weather conditions on the denitrification rate, N2O emissions, and nutrient cycling/export in the 7.4 km2 WE38 watershed in Pennsylvania. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. To quantify the impact of climate change we forced the new GHG model with downscaled and bias-corrected regional climate model output and derived climate anomalies to assess their impact on hydrology, nitrate (NO3-), phosphorus (P), and sediment export, and on emissions of N2O and N2. Model-average (± standard deviation) results indicate that climate change, through an increase in precipitation, will result in moderate increases in winter/spring flow (2.7±10.6 %) and NO3- export (3.0±7.3 %), substantial increases in dissolved P (DP, 8.8±19.8 %), total P (TP, 4.5±11.7 %), and sediment (17.9±14.2 %) export, and greater N2O (63.3±50.8 %) and N2 (17.6±20.7 %) emissions. Conversely, decreases in summer flow (-12.4±26.7 %) and the export of P (-11.4±27.4 %), TP (-7.9±24.5 %), sediment (-4.1±21.4 %), and NO3- (-12.2±31.4 %) are driven by greater evapotranspiration from increasing summer temperatures. Increases in N2O (20.1±29.3 %) and decreases in N2 (-13.0±14.6 %) are also predicted in the summer and driven by increases in soil moisture and temperature.

In an effort to assess the impact of climate change at a regional level, the model was then scaled-up to the entire Susquehanna River basin and was used to evaluate if agricultural best management practices (BMPs) can offset the impact of climate change. Agricultural BMPs are increasingly and widely employed to reduce diffuse nutrient pollution. Climate change can complicate the development, implementation, and efficiency of BMPs by altering hydrology, nutrient cycling, and erosion. We select and evaluate four common BMPs (buffer strips, strip crop, no-till, and tile drainage) to test their response to climate change. We force the calibrated model with six downscaled global climate models (GCMs) for a historic period (1990-2014) and two future scenario periods (2041-2065) and (2075-2099) and quantify the impact of climate change on hydrology, NO3-, total N (TN), DP, TP, and sediment export with and without BMPs. We also tested prioritizing BMP installation on the 30% of agricultural lands that generate the most runoff (e.g., critical source areas-CSAs). Compared against the historical baseline and excluding the impact of BMPs, the ensemble model mean (± standard deviation?) predictions indicate that climate change results in annual increases in flow (4.5±7.3%), surface runoff (3.5±6.1%), sediment export (28.5±18.2%) and TN (9.5±5.1%), but decreases in NO3- (12±12.8%), DP (14±11.5%), and TP (2.5±7.4%) export. When agricultural BMPs are simulated most do not appreciably change the overall water balance; however, tile drainage and strip crop decrease surface runoff generation and the export of sediment, DP, and TP, while buffer strips reduced N export substantially. Installing BMPs on critical source areas (CSAs) results in nearly the same level of performance for most practices and most pollutants. These results suggest that climate change will influence the performance of BMPs and that targeting BMPs to CSAs can provide nearly the same level of water quality impact as more widespread adoption.

Finally, recognizing that all of these model applications have considerable uncertainty associated with their predictions, we develop and employ a Bayesian multi-model ensemble to evaluate structural model prediction uncertainty. The reliability of watershed models in a management context depends largely on associated uncertainties. Our Objective is to quantify structural uncertainty for predictions of flow, sediment, TN, and TP predictions using three models: the SWAT-Variable Source Area model (SWAT-VSA), the standard SWAT model (SWAT-ST), and the Chesapeake Bay watershed model (CBP-model). We initialize each of the models using weather, soil, and land use data and analyze outputs of flow, sediment, TN, and TP for the Susquehanna River basin at the Conowingo Dam in Conowingo, Maryland. Using these three models we fit Bayesian Generalized Non - Linear Multilevel Models (BGMM) for flow, sediment, TN, and TP and obtain estimated outputs with 95% confidence intervals. We compare the BGMM results against the individual model results and straight model averaging (SMA) results using a split time period analysis (training period and testing period) to assess the BGMM in a predictive fashion. The BGMM provided better predictions of flow, sediment, TN, and TP compared to individual models and the SMA during the training period. However, during the testing period the BGMM was not always the best predictor; in fact, there was no clear best model during the testing period. Perhaps more importantly, the BGMM provides estimates of prediction uncertainty, which can enhance decision making and improve watershed management by providing a risk-based assessment of outcomes.