Multi criteria decision support system for watershed management under uncertain conditions, A

2012 Summer.Includes bibliographical references.Nonpoint source (NPS) pollution is the primary cause of impaired water bodies in the United States and around the world. Elevated nutrient, sediment, and pesticide loads to waterways may negatively impact human health and aquatic ecosystems, increasing costs of pollutant mitigation and water treatment. Control of nonpoint source pollution is achievable through implementation of conservation practices, also known as Best Management Practices (BMPs). Watershed-scale NPS pollution control plans aim at minimizing the potential for water pollution and environmental degradation at minimum cost. Simulation models of the environment play a central role in successful implementation of watershed management programs by providing the means to assess the relative contribution of different sources to the impairment and water quality impact of conservation practices. While significant shifts in climatic patterns are evident worldwide, many natural processes, including precipitation and temperature, are affected. With projected changes in climatic conditions, significant changes in diffusive transport of nonpoint source pollutants, assimilative capacity of water bodies, and landscape positions of critical areas that should be targeted for implementation of conservation practices are also expected. The amount of investment on NPS pollution control programs makes it all but vital to assure the conservation benefits of practices will be sustained under the shifting climatic paradigms and challenges for adoption of the plans. Coupling of watershed models with regional climate projections can potentially provide answers to a variety of questions on the dynamic linkage between climate and ecologic health of water resources. The overarching goal of this dissertation is to develop a new analysis framework for the development of optimal NPS pollution control strategy at the regional scale under projected future climate conditions. Proposed frameworks were applied to a 24,800 ha watershed in the Eagle Creek Watershed in central Indiana. First, a computational framework was developed for incorporation of disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. This study highlighted the use of multiobjective approaches for proper calibration of watershed models that are used for pollutant source identification and watershed management. Second, an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices was presented. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was used to identify optimal types and locations of conservation practices for nutrient and pesticide control. This study showed that mixed discrete-continuous optimization method identifies better solutions than commonly used binary optimization methods. Third, the conclusion from application of NSGA-II optimization followed by development of a multi criteria decision analysis framework to identify near-optimal NPS pollution control plan using a priori knowledge about the system. The results suggested that the multi criteria decision analysis framework can be an effective and efficient substitute for optimization frameworks. Fourth, the hydrologic and water quality simulations driven by an extensive ensemble of climate projections were analyzed for their respective changes in basin average temperature and precipitation. The results revealed that the water yield and pollutants transport are likely to change substantially under different climatic paradigms. And finally, impact of projected climate change on performance of conservation practice and shifts in their optimal types and locations were analyzed. The results showed that performance of NPS control plans under different climatic projections will alter substantially; however, the optimal types and locations of conservation practices remained relatively unchanged

Decision Support for Watershed Management Using Evolutionary Algorithms

An integrative computational methodology is developed for the management of nonpoint source pollution from watersheds. The associated decision support system is based on an interface between evolutionary algorithms (EAs) and a comprehensive watershed simulation model, and is capable of identifying optimal or near-optimal land use patterns to satisfy objectives. Specifically, a genetic algorithm (GA) is linked with the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) for single objective evaluations, and a Strength Pareto Evolutionary Algorithm has been integrated with SWAT for multiobjective optimization. The model can be operated at a small spatial scale, such as a farm field, or on a larger watershed scale. A secondary model that also uses a GA is developed for calibration of the simulation model. Sensitivity analysis and parameterization are carried out in a preliminary step to identify model parameters that need to be calibrated. Application to a demonstration watershed located in Southern Illinois reveals the capability of the model in achieving its intended goals. However, the model is found to be computationally demanding as a direct consequence of repeated SWAT simulations during the search for favorable solutions. An artificial neural network (ANN) has been developed to mimic SWAT outputs and ultimately replace it during the search process. Replacement of SWAT by the ANN results in an 84% reduction in computational time required to identify final land use patterns. The ANN model is trained using a hybrid of evolutionary programming (EP) and the back propagation (BP) algorithms. The hybrid algorithm was found to be more effective and efficient than either EP or BP alone. Overall, this study demonstrates the powerful and multifaceted role that EAs and artificial intelligence techniques could play in solving the complex and realistic problems of environmental and water resources systems

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

Nonpoint Source Pollution Control Using a Multi-Objective Optimization Tool for Best Management Practices Selection and Spatial Placement in the Lower Bear River Watershed, Utah

This dissertation presents a set of approaches to help address water quality problems related to total phosphorus loads in water bodies. Water quality degradation is caused by many nonpoint sources such as agricultural runoff, fertilizers applications, and bank erosion. Three studies present methodologies for water quality protection from degradation in watersheds. The first study demonstrates the application of a watershed simulation tool that can quantify flows in the watershed, the amount of released pollutants and identify the areas contributing to the pollutants’ release in the watershed. The second study presents a simple combination tool that can pair potential management practices with the identified nonpoint sources areas to generate cost-effective combinations of management practices for reducing excess phosphorus loading to water bodies. The last study develops an optimization framework that recommends the area optimum sizes that are available for implementing management practices. These studies were applied to real-case problems to reduce excess nutrients within the Lower Bear River Watershed in northern Utah and expected to improve the management of nutrient control plans under the allocated funds

Four essays on environmental policy under uncertainty with applications to water quality and carbon sequestration

In this thesis, I present four essays that deal with several diverse issues in environmental economics, ranging from soil carbon sequestration, to a design of a pollution permit trading program, to proposing watershed-scale solutions to water quality problems, both on state and regional scale.;The first essay is titled Environmental policy under benefit and cost uncertainty: application to soil carbon offsets . I characterize an optimal spatial allocation of land parcels to specific environmental practices explicitly dealing with uncertainty in both the benefits and program costs. The results provide a magnitude of uncertainty discount for soil carbon offsets and the margin of safety necessary in the budget to ensure at the planning stage that the program\u27s costs will not exceed the planned expenditures.;The second essay is titled Optimal design of permit markets with an ex ante pollution target . In this essay, the design of permit trading programs when the objective is to minimize the cost of achieving an ex ante pollution target; that is, one that is defined in expectation rather than an ex post deterministic value, is examined. I demonstrate that to minimize expected abatement costs regulators must use information on the joint distribution of firms\u27 abatement costs, as well as the pollution delivery coefficients. As a result, the optimal trading ratio is a function of the delivery coefficient, as well as the moments of abatement costs, and the total permit allocation deviates from the pollution goal. These findings differ from a typical permit market design, where no cost information is needed to achieve cost-efficiency, the trading ratio is set to the ratio of pollution delivery coefficients, and the permit allocation exactly equals the pollution goal.;The third and the fourth chapters of the thesis build a simulation-optimization modeling framework for the analysis of efficient nonpoint source pollution reduction strategies. These essays integrate modern multi-objective optimization tools with a realistic water quality model to provide decision-makers with sets of cost-efficient pollution reduction solutions.;In the third essay, titled Efficient reductions in local and state-level nonpoint source nutrient pollution: an application to the state of Iowa, I incorporate a water quality model, SWAT, in conjunction with detailed information on conservation practices, into an evolutionary search algorithm to find allocations of conservation practices that minimize the costs of achieving given water quality targets for all the major watersheds in the state of Iowa.;In the final essay, titled Searching for efficiency: least cost nonpoint source pollution control with multiple pollutants, practices, and targets , I examine the policy implications of efficient control of nonpoint source pollution using a spatially explicit model of a large and critically important agricultural region: the Upper Mississippi River Basin in the central U.S. I derive the conservation production possibility frontier that explicitly incorporates the tradeoffs between pollution control costs and water quality benefits, between different pollutants, or between different control targets. The regional scale of the modeling framework facilitates the investigation of relevant policy analyses related to the growing dead zone in the Gulf of Mexico

A decision support tool (R-SWAT-DS) for integrated watershed management

Best management practices (BMPs) can be used effectively to reduce nutrient and sediment loads generated from point sources or non-point sources to receiving water bodies. Methodologies for optimal, cost effective BMP selection and placement are needed to assist watershed management planners and stakeholders. We developed a modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. The framework integrates the Soil and Water Assessment Tool (SWAT) watershed model, spatial representation of BMPs, an economic component, and multi-objective optimization routines in the R environment. The framework can be used to launch individual or iterative BMPs simulations, or search for optimal strategies. Advanced plotting, mapping and statistical analysis functionalities that facilitate the interpretation and assessment of the results are included