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. / Ph. D. / 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. 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 different modeling tools. First, GHG emissions model was developed to quantify nitrous oxide (N₂O) emissions from agroecosystems, which are an increasing concern. The new model was then tested using observed N₂O emissions 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. Second, the new GHG model was then used to analyze the impact of climate change and extreme weather conditions on the N₂O emissions, and nutrient cycling/export in small and regional watershed scale. To quantify the impact of climate change we forced the new GHG model with downscaled and bias-corrected regional climate model date to assess their impact on hydrology, nitrate (NO₃-), phosphorus (P), and sediment export, and on emissions of N₂O and N₂. Finally, recognizing that all of these model applications have considerable uncertainty associated with their predictions, we developed and employed a Bayesian multi-model ensemble to evaluate structural model prediction uncertainty.

Water quality management for agricultural production is a complicated and interesting problem. Hydrological and economic factors must be considered when designing strategies to reduce nutrient runoff from agricultural activities. This dissertation is composed of three chapters that investigate cost-effective ways to mitigate water pollution from agricultural nonpoint pollution sources and explore farmers' incentives when participating in water quality trading programs. Chapter 1 investigates landscape targeting of best management practices (BMPs) based on topographic index (TI) to determine how targeting would affect costs of meeting nitrogen (N) loading goals for Mahantango watershed, Pennsylvania. We use the results from two climate models and the mean of the ensemble of seven climate models to estimate expected climate changes and the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model to predict crop yields and N export. Costs of targeting and uniform placement of BMPs across the entire study area (4.23 km2) are compared under historical and future climate scenarios. We find that with a goal of reducing N loadings by 25%, spatial targeting methods could reduce costs by an average of 30% compared with uniform BMP placement under three historical climate scenarios. Cost savings from targeting are 38% under three future climate scenarios. Chapter 2 scales up the study area to the Susquehanna watershed (71,000 km2). We examine the effects of targeting the required reductions in N runoff within counties, across counties, and both within and across counties for the Susquehanna watershed. We set the required N reduction to 35%. Using the uniform strategy to meet the required N reduction as the baseline, results show that costs of achieving a regional 35% N reduction goal can be reduced by 13%, 31% and 36% with cross-county targeting, within-county targeting and within and across county targeting, respectively. Results from Chapters 1 and 2 suggest that cost effectiveness of government subsidy programs for water quality improvement in agriculture can be increased by targeting them to areas with lower N abatement costs. In addition, targeting benefits are likely to be even larger under climate change. Chapter 3 investigates the landowner's nutrient credit trading behavior when facing the price uncertainty given the credits are allowed to be banked for future use. A two-step decision model is used in this study. For the first step, we determine the landowner's application level of a BMP on working land in the initial time period. The nutrient credits awarded to the landowner depend on the nutrient reduction level at the edge of field generated by the BMP application. For the second step, we use an intertemporal model to examine the landowner's credit trading behavior with stochastic price fluctuations over time and with transaction costs. The theoretical framework is applied with a numerical simulation incorporated with a hydro-economic model and dynamic programming. Nutrient Management (NM) is selected as the BMP on working land to generate N credits. We find that gains to the landowner from credit banking increase with higher price volatility and with higher price drift, but that gains are larger with price volatility. However, for a landowner holding a small amount of nutrient credits, the gains from credit banking are small due to transaction costs. / Doctor of Philosophy / Two considerations are critical for efforts to mitigate nutrient runoff from nonpoint sources: cost effectiveness of strategies to reduce nutrient runoff and landowners' incentives to participate in these programs. This dissertation is composed of three manuscripts, aiming to evaluate the cost effectiveness of government subsidy programs for water quality management in agriculture and investigate the landowner's incentives to participate in water quality trading programs for the Chesapeake Bay watershed. Chapter 1 investigates gains from targeting Best Management Practices (BMPs) under current and future climate conditions based on the soil characteristics relative to uniform BMP application for a small experimental watershed (4.23km2). Chapter 2 scales up the study area to a 71,000 km2 watershed and treats each county within the watershed as a representative farm to explore economic gains from targeting within county and across county based on counties' physical conditions and agricultural patterns. Both Chapters show that cost-effectiveness of government subsidy programs can be improved by spatial targeting BMPs to areas with lower abatement costs. Gains from targeting increase under climate change. In Chapter 3 we shows how a landowner's revenues from nutrient credit selling will be affected if the credits are allowed to be banked for future use when she faces price uncertainty. We find that gains to the landowner from credit banking increase more with higher price volatility than with higher price drift. Gains from banking are largely reduced by transaction costs associated with trading.