Curses, Tradeoffs, and Scalable Management:Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case’s relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP

Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future

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