The deleterious effects of multiple stressors on global water resources have become more significant over the past few decades. Anthropogenic activities such as industrialization, urbanization, deforestation, and increased application of agricultural nutrients have led to a decline in overall quality of our aquatic environment. Additionally, these activities have increased greenhouse gas concentrations globally, warming the earth’s atmosphere and eventually having a detrimental effect on global water and energy balances. The global water cycle has been altered, leading to its overall intensification and an increase in frequency of extreme floods and droughts. Addressing increasing water demands coupled with declining water quality and a depletion of water resources requires new approaches in water management. In determining optimum management actions, it is critical to understand the spatial and temporal variability and trends in water quantity and quality. This research aims to improve our knowledge of anthropogenic and natural impacts on water resources by evaluating and refining the science of predicting pollutant (nutrient and sediment) loadings from medium- to large-scale watersheds. To enable these goals, this research is centered on large watersheds in the Midwestern United States, which have been some of the primary sources of nutrient and sediment loadings to downstream water bodies such as the Gulf of Mexico and Lake Erie. In total, 14 watersheds in Illinois, Indiana, Ohio, and Michigan, with extensive water quality datasets, are analyzed in different stages of this research. Most of these watersheds are predominantly agricultural with intensive row-cropped farmlands and have a network of sub-surface tile drainage systems. Pollutant loadings and associated hydrological processes have been simulated using four major modeling approaches: statistical modeling, empirical modeling, physically based modeling, and data mining methods. This report includes eight chapters. The first three chapters describe the problem and research objectives, study area, and data preparation and processing. Next, the impacts of available water quality data on concentration and load predictions and trend calculations are assessed based on traditional statistical methods and several new, improved, and modified approaches (Chapter 4). This segment emphasizes the difficulties in predicting nutrient load and concentration trends under changing climatic conditions, highlighting the importance of continuous nutrient monitoring. Next, two data mining techniques (the nearest-neighbor method and decision trees), scarcely used in hydrology, were applied to predict the missing Nitrate Nitrogen (NO3-N) concentrations for two extensively monitored watersheds in the Lake Erie basin. These predictions (Chapter 5) are important in load estimations and demonstrate the potential of data mining to produce results comparable with statistical and empirical methods presented in the previous chapter. In Chapter 6, statistical regression techniques are used to assess the role of large load events in predicting Total Suspended Solids (SS), Total Phosphorus (TP), and NO3-N annual loads. A novel constituent-specific baseflow separation technique based on mechanistic differences in nutrient and sediment loadings is proposed and applied. As a result, regression relationships between the largest annual loads and total annual loads were developed for all three constituents. An Analysis of Covariance (ANCOVA) indicated that these relationships are often statistically indistinguishable from each other when applied to watersheds with a similar land use. Then, in Chapter 7, the temporal patterns of pollutant loadings from large Midwestern watersheds are analyzed using circular statistics. Critical periods of high loadings, precipitation, and river flow were identified. While river flows and pollutant loadings are highest in late winter and early spring (e.g., March and April), rainfall totals are highest during late spring and early summer (e.g., May through August). Finally, Chapter 8 shows the results based on the physically based SWAT model. The model is calibrated for river discharge and water quality in the largest watershed in the Lake Erie basin, the Maumee River watershed. The calibrated model is used to gauge the impacts of future projected climate change from the mid-century and late-century time periods on the hydrology and water quality in the watershed. The results indicate that climate change could have a significant impact on sediment and nutrient loads, and that more detailed studies are needed to more accurately assess this impact and its confidence limits.
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