Water quality models in the Colorado River Basin have been developed for the basin, river, and individual reservoirs. They are used to support water quality programs within the basin. The models are periodically reviewed and updated to improve the accuracy of simulations. Improving the usefulness of the Lake Powell model, one of the key reservoirs in the basin, is the subject of this study. Lake Powell is simulated using a hydrodynamic and water quality model, CE-QUAL-W2. Previously the model has been used at Lake Powell to simulate hydrodynamics, temperature, and total dissolved solids with a reasonable degree of accuracy. An additional parameter, dissolved oxygen, will be added to the simulations and then calibrated with observed data to verify accuracy. Dissolved oxygen distributions in Lake Powell vary seasonally and change under different hydrologic cycles. They are a function of physical, biological, and chemical processes. Few measurements of these processes in Lake Powell exist. To compensate for the lack of data an empirical method of loading oxygen demand to the model is developed and tested. Observed limnological processes in the reservoir guide the development of the empirical methods. The methods are then tested in 16 year model simulations and compared with dissolved oxygen measurements from the 16 year period. By accurately reproducing the dissolved oxygen distributions the Lake Powell model will have improved accuracy and also broaden its usefulness.

Over the past decades, climate change and urbanization have altered the regional hydro-environments, causing a series of stormwater management problems including urban flood and non-point pollution. Low impact development (LID) has been identified as a sustainable strategy for stormwater management. However, given the complex impacts of climate change and urbanization on hydro-environments, the performance of LID strategy under future changes remains largely unexplored. Accordingly, this research characterized the LID performance under changes in climate and urbanization. To provide an additional reference to sustainable stormwater management, the following specific topics were addressed: (1) Through hydraulic and water quality modeling, the LID performances of flood mitigation and pollution removal were systematically evaluated at the city scale. (2) Through uncertainty analysis, the impact of model parameter uncertainty on the LID performance was taken into account. (3) Through sensitivity analysis, the impact of LID technical parameters (e.g., surface features, soil textures) on the LID performance was quantified. (4) Through scenario analysis, the LID performances under different hydrological patterns were compared. (5) Through spatial analysis, the distribution of LID performance on different land-cover types was determined. (6) Through adopting general circulation model (GCM) projections, the LID performance under future climate scenarios with different representative concentration pathways (RCPs) was investigated. (7) Through adopting integrated assessment model (IAM) projections, the LID performance under future urbanization scenarios with different shared socioeconomic pathways (SSPs) was explored. (8) By coupling climate and urbanization projections with land-cover distribution, the spatiotemporal trends of LID performance in the future were characterized.:Table of Contents List of Abbreviations VII List of Peer-Reviewed Publications on the Ph.D. Topic IX List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic X 1 General Introduction 1 1.1 Background 1 1.2 Objectives 3 1.3 Innovation and Contribution to the Knowledge 3 1.4 Outline of the Dissertation 4 1.5 References 5 2 Literature Review 9 2.1 Hydraulic and Water Quality Modeling 9 2.1.1 Hydraulic Model 9 2.1.2 Water Quality Model 10 2.2 Low Impact Development (LID) 10 2.2.1 LID Practice 10 2.2.2 LID Performance 11 2.3 Performance Evaluation 13 2.3.1 Scenario Analysis 13 2.3.2 Spatial Analysis 13 2.3.3 Uncertainty Analysis 14 2.3.4 Sensitivity Analysis 14 2.4 Future Changes in Climate and Urbanization 15 2.4.1 Climate Change 15 2.4.2 Future Urbanization 16 2.5 References 17 3 Impact of Technical Factors on LID Performance 27 3.1 Introduction 28 3.2 Methods 30 3.2.1 Study Area 30 3.2.2 Model Description 31 3.2.2.1 Model Theory 31 3.2.2.2 Model Construction 31 3.2.2.3 Model Calibration and Validation 32 3.2.2.4 Model Uncertainty Analysis by GLUE Method 34 3.2.3 Hydrological Pattern Design 35 3.2.4 LID Strategy Design 35 3.2.4.1 Implementation of LID Practices 35 3.2.4.2 Sensitivity Analysis by Sobol’s Method 36 3.2.5 Correlation Analysis Using a Self-Organizing Map 37 3.2.6 Description of the RDS Load Components 37 3.3 Results 38 3.3.1 RDS Migration and Distribution in Baseline Strategy 38 3.3.1.1 RDS Migration under Hydrological Scenarios 38 3.3.1.2 RDS Distribution on Land-Cover Types 39 3.3.2 RDS Removal in LID Strategies 40 3.3.2.1 RDS Removal by LID Strategies 40 3.3.2.2 Spatial Distribution of the RDS Removal 42 3.3.2.3 LID Parameter Sensitivity Analysis Result 43 3.4 Discussion 45 3.4.1 Factors Influencing RDS Migration in the Baseline Strategy 45 3.4.2 RDS Removal Performance by LID Strategy 46 3.5 Conclusions 47 3.6 References 47 4 Impact of Hydro-Environmental Factors on LID Performance 53 4.1 Introduction 54 4.2 Methods 56 4.2.1 Study Area 56 4.2.2 Modeling Approach 56 4.2.2.1 Model Theory 56 4.2.2.2 Model Construction 56 4.2.2.3 Model Calibration and Validation 57 4.2.2.4 Model Uncertainty Analysis 57 4.2.3 LID Performance Analysis 58 4.2.3.1 LID Practice Implementation 58 4.2.3.2 LID Performance Evaluation 58 4.2.4 Hydrological Pattern Analysis 59 4.2.4.1 Scenario of ADP Length 59 4.2.4.2 Scenario of Rainfall Magnitude 59 4.2.4.3 Scenario of Long-Term pre-Simulation 60 4.2.5 Sensitivity Analysis of Hydrological Scenarios 60 4.3 Results 61 4.3.1 LID Performance under Different ADP Lengths 61 4.3.2 LID Performance under Different Rainfall Magnitudes 62 4.3.3 Spatial Distribution of LID Performance 63 4.3.4 Sensitivities of LID Performance to ADP Length and Rainfall Magnitude 66 4.4 Discussion 68 4.4.1 Impact of ADP Length and Rainfall Magnitude on LID Performance 68 4.4.2 Spatial Heterogeneity of LID Performance 68 4.4.3 Research Implications 69 4.5 Conclusions 70 4.6 References 71 5 Impact of Future Climate Patterns on LID Performance 77 5.1 Introduction 78 5.2 Methods 80 5.2.1 Study Area 80 5.2.2 Hydraulic and Water Quality Model 80 5.2.2.1 Model Development 80 5.2.2.2 Model Calibration and Validation 81 5.2.3 Climate Change Scenario Analysis 81 5.2.3.1 GCM Evaluation 81 5.2.3.2 Greenhouse Gas (GHG) Concentration Scenario 82 5.2.3.3 GCM Downscaling 83 5.2.4 LID Performance Analysis 83 5.2.4.1 Implementation of LID Practices 83 5.2.4.2 Evaluation of LID Performance 84 5.2.4.3 Sensitivity Analysis on LID Performance 86 5.3 Results 86 5.3.1 Hydrological Characteristics under Future Climate Scenarios 86 5.3.2 LID Performance under Future Climate Scenarios 87 5.3.2.1 LID Short-Term Performance 87 5.3.2.2 LID Long-Term Performance 90 5.3.3 Impact of ADP Length and Rainfall Magnitude on LID Performance 92 5.3.3.1 LID Performance Uncertainty 92 5.3.3.2 Spatial Distribution of LID Performance 93 5.3.3.3 Sensitivity of LID Performance to Climate Change 95 5.4 Discussion 97 5.4.1 LID Performance in Short-Term Extremes and Long-Term Events 97 5.4.2 Impact of Climate Change on LID Performance 97 5.4.3 Research Implications 99 5.5 Conclusions 100 5.6 References 100 6 Impact of Climate and Urbanization Changes on LID Perfor-mance 109 6.1 Introduction 110 6.2 Methods 112 6.2.1 Study Area 112 6.2.2 Modeling Approach 112 6.2.2.1 Model Development 112 6.2.2.2 Model Calibration and Validation 113 6.2.3 Future Scenario Derivation 113 6.2.3.1 Climate Change Scenario 113 6.2.3.2 Urbanization Scenario 115 6.2.4 Flood Exposure Assessment 115 6.2.5 Implementation and Evaluation of LID Strategy 117 6.2.5.1 Implementation Scheme of LID Strategy 117 6.2.5.2 Performance Evaluation of LID Strategy 117 6.3 Results 118 6.3.1 Flood Exposure in Baseline and Future Scenarios 118 6.3.1.1 Hydrological Change in Future Climate Scenarios 118 6.3.1.2 Catchment Change in Future Urbanization Scenarios 118 6.3.1.3 Population and GDP Exposures to Flood in Future 121 6.3.2 Flood Exposure with Consideration of LID Strategy 123 6.3.2.1 Runoff Mitigation Performance of LID Strategy 123 6.3.2.2 Peak Mitigation Performance of LID Strategy 124 6.3.2.3 Population and GDP Exposures to Flood under LID Strategy 125 6.4 Discussion 126 6.4.1 Climate Change and Urbanization Exacerbated Flood Exposure Risk 126 6.4.2 LID Strategy Mitigated Flood Exposure Risk 126 6.5 Conclusions 127 6.6 References 127 7 Discussion and General Conclusions 133 7.1 Stormwater Management Performance of LID Strategy 133 7.2 Impact of Influencing Factors on LID Performance 134 7.3 LID Performance under Future Changes 135 7.4 Research Implications 136 7.5 References 137 8 Outlook of Future Research 139 8.1 Optimization of LID Performance 139 8.2 Cross-regional Study on Future Changes 139 8.3 Macro-scale Flood Risk Management 140 8.4 References 141 9 Appendices 143 9.1 Appendix for Chapter 3 143 9.1.1 The Determination of the GLUE Criteria 143 9.1.2 Model Uncertainty Analysis 143 9.1.3 The LID Installation Location 144 9.1.4 Figures 145 9.1.5 Tables 147 9.2 Appendix for Chapter 4 153 9.2.1 Scenario of Long-term pre-Simulation 153 9.2.2 Figures 153 9.2.3 Tables 158 9.3 Appendix for Chapter 5 164 9.3.1 Tables 164 9.4 Appendix for Chapter 6 169 9.4.1 Figures 169 9.4.2 Tables 170 9.5 Data Source 177 9.6 References 178

In the development of the watershed, hydrodynamic, and water quality models for Back Bay of Biloxi in Mississippi, the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS 2.0) - Nonpoint Source Model (NPSM) was selected as the watershed model. The hydrodynamic and water quality models DNYHYD5 and EUTRO5 were selected as the tidally influenced bay models. The watershed model simulated nonpoint source flow and pollutant loadings for all sub-watersheds, routed flow and water quality, and accounted for all major point source discharges in the Back Bay of Biloxi watershed. Time varying output from the watershed model was applied directly to the Back Bay of Biloxi model. The Bay models, in turn simulated hydrodynamics and water quality, including water depth, velocities, and fecal coliform concentrations. Both watershed and Bay models were calibrated and verified against observed data. The calibrated/verified model was used as a planning tool to assess the water quality in the Watershed and the Bay as well as for calculating Total Maximum Daily Load (TMDL) and Waste Load Allocation (WLA).

The savannas of southern Africa are a highly variable and globally-important biome supporting rapidly-expanding human populations, along with one of the greatest concentrations of wildlife on the continent. Savannas occupy a fifth of the earth's land surface, yet despite their ecological and economic significance, understanding of the complex couplings and feedbacks that drive spatiotemporal patterns of change are lacking. In Chapter 1 of my dissertation, I discuss some of the different theoretical frameworks used to understand complex and dynamic changes in savanna structure and composition. In Chapter 2, I evaluate spatial drivers of water quality declines in the Chobe River using spatiotemporal and geostatistical modeling of time series data collected along a transect spanning a mosaic of protected, urban, and developing urban land use. Chapter 3 explores the complex couplings and feedbacks that drive spatiotemporal patterns of land cover (LC) change across the Chobe District, with a particular focus on climate, fire, herbivory, and anthropogenic disturbance. In Chapter 4, I evaluated the utility of Distance sampling methods to: 1) derive seasonal fecal loading estimates in national park and unprotected land; 2) provide a simple, standardized method to estimate riparian fecal loading for use in distributed hydrological water quality models; 3) answer questions about complex drivers and patterns of water quality variability in a semi-arid southern African river system. Together, these findings have important implications to land use planning and water conservation in southern Africa's dryland savanna ecosystems. / Ph. D.

<p>Current watershed-scale, nonpoint source (NPS) pollution models do not represent the processes and impacts of agricultural best management practices (BMP) on water quality with sufficient detail. To begin addressing this gap, a novel process-based, watershed-scale, water quality model (WEPP-WQ) was developed based on the Water Erosion Prediction Project (WEPP) and the Soil and Water Assessment Tool (SWAT) models. The proposed model was validated at both hillslope and watershed scales for runoff, sediment, and both soluble and particulate forms of nitrogen and phosphorus. WEPP-WQ is now one of only two models which simulates BMP impacts on water quality in ‘high’ detail, and it is the only one not based on USLE sediment predictions. Model validations indicated that particulate nutrient predictions were better than soluble nutrient predictions for both nitrogen and phosphorus. Predictions of uniform conditions outperformed nonuniform conditions, and calibrated model simulations performed better than uncalibrated model simulations. Applications of these kinds of models in real-world, historical simulations are often limited by a lack of field-scale agricultural management inputs. Therefore, a prototype tool was developed to derive management inputs for hydrologic models from remotely sensed imagery at field-scale resolution. At present, only predictions of crop, cover crop, and tillage practice inference are supported and were validated at annual and average annual time intervals based on data availability for the various management endpoints. Extraction model training and validation were substantially limited by relatively small field areas in the observed management dataset. Both of these efforts contribute to computational modeling research and applications pertaining to agricultural systems and their impacts on the environment.</p>