DESIGNING WATERSHED-SCALE STRUCTURAL BEST MANAGEMENT PRACTICES USING EVOLUTIONARY ALGORITHMS TO ACHIEVE WATER QUALITY GOALS

Author

Prakash D. Kaini, Southern Illinois University CarbondaleFollow

Date of Award

12-1-2010

Degree Name

Doctor of Philosophy

Department

Engineering Science

First Advisor

Nicklow, John

Abstract

Water quality has been a major concern in the United States and elsewhere because of its impact on people's daily lives and on the environment. There are two main sources of water pollution: point sources and non-point sources, which are differentiated based on their mode of generation. Pollution generated from point sources has been effectively controlled by the implementation of the National Pollution Discharge Elimination System (NPDES) program, under the auspices of the 1972 Clean Water Act (CWA). However, a large portion of the nation's water remains polluted, mainly due to non-point sources of pollution. The Total Maximum Daily Load (TMDL) program within the CWA regulates water pollution by controlling both point and non-point sources. Structural and non-structural Best Management Practices (BMPs) have been recognized as effective measures for controlling non-point sources of pollution. These practices are designed on an on site basis in most cases. The objective of this research is to develop methodologies that can be used to design structural BMPs as measurements for controlling non-point sources of pollution (i.e. sediment and nutrients) on a larger spatial scale, that of a watershed. The Soil and Water Assessment Tool (SWAT), a semi-distributed model that simulates hydrological processes, has been selected for this study. The most sensitive model parameters with respect to discharge and sediment yield are identified by a parameter sensitivity analysis. Latin Hypercube Sampling One-at-a-time (LH-OAT), a global sensitivity analysis method, has been adopted for this purpose. SWAT has been calibrated by using these parameters to accurately simulate runoff and sediment yields from the watershed. An automatic calibration model using a genetic algorithm that optimizes the parameter values has been used. In addition, an uncertainty analysis of these selected parameters has been conducted to analyze the robustness of the model's predictions. Both single- objective and a multi-objective Optimal Control Models (OCM) have been developed by coupling SWAT with evolutionary algorithms, optimizing types, sizes, and locations of structural BMPs to achieve the desired level of treatment goals (the reduction of sediment and nutrient yields) at the watershed outlet. The single-objective OCM optimizes BMPs to a user-defined level of the treatment goals while the multi-objective OCM simultaneously optimizes BMPs for various degrees of treatment goals. The state-of-the-art multi-objective evolutionary algorithm that has been used in the study is the Non-Dominated Sorting Genetic Algorithm (NSGA-II). In addition, the single-objective OCM is applied to control increased sediment yield due to projected future climate scenarios. In conclusion, this research has developed methodologies that can cost-effectively improve water quality goals in agricultural watersheds by integrating a contemporary hydrological model with evolutionary algorithms.