Many Objective Water Resources Planning and Management Given Deep Uncertainties, Population Pressures, and Environmental Change
Open Access
- Graduate Program:
- Civil Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 27, 2013
- Committee Members:
- Patrick M Reed, Dissertation Advisor
- Peggy Ann Johnson, Committee Member
- Seth Adam Blumsack, Committee Member
- Michael Gooseff, Committee Member
- Gregory Characklis, Special Member
- Keywords:
- water supply
- environmental management
- uncertainty analysis
- visual analytics
- optimization
- robustness
- decision making
- Abstract:
- Climate change and population growth require adaptation strategies that can ensure a sufficient amount of water supply over long planning horizons. In the past, water resources planning has been done using single-objective benefit-cost analysis, where a single estimate of a project's costs and benefits is calculated to select funded projects. The calculation of monetary benefit functions, however, is heavily dependent on several critical assumptions. For example, the analysis must assume that the preferences of diverse stakeholder groups will not change in the future system states. Moreover, operational design and implementation of engineered water resources systems must consider a broad suite of risk-based performance objectives. This dissertation research advances water resources planning and decision support techniques that can confront the limiting challenges associated with classical approaches. We specifically advance a many objective approach using multiobjective evolutionary algorithms (MOEAs) that allows planners to generate and evaluate planning alternatives that can balance diverse planning goals and objectives. This dissertation contributes two new many objective planning frameworks, collections of techniques that use many objective analysis to further our understanding of how to improve planning under uncertainty. The first framework, termed de Novo Planning, incorporates the concept of learning into many objective planning formulations. De Novo Planning addresses the fact that planning formulations themselves change as decision makers solve problems and analyze results. Global sensitivity analysis using Sobol' variance decomposition is used to determine an appropriate level of complexity for decision variables in the system. Multiple problem formulations are then constructed and solved using a MOEA to test the insights learned through the sensitivity analysis. The second planning innovation is termed Many Objective Robust Decision Making (MORDM). MORDM addresses deep uncertainty, a situation in which stakeholders do not know or cannot agree on the full suite of risks that are posed to their system. Deep uncertainty can severely impact the expected performance of planning alternatives in ways that are difficult to predict. This issue is especially relevant since most system planning under uncertainty is evaluated using a single best estimate of the distributions of data. Estimates from historical system information and their associated likelihoods, though, could be incorrect. For example, climate change can alter the magnitude and timing of streamflow availability, which makes the historical data an unreliable indicator of future events. Robust Decision Making (RDM) has been advocated as a way to address this issue, by evaluating a wide array of plausible futures to show future system vulnerabilities. The MORDM framework introduced in this thesis bridges many objective analysis with RDM, by evaluating solutions in the many objective tradeoff with an ensemble of alternative futures that investigate key assumptions and uncertainties, quantifying the solutions' robustness, and facilitating choice of robust solutions for a final negotiated decision. The dissertation's planning innovations are demonstrated using two test cases with differing hydrologic characteristics and regulatory structures. The first test case explores how to improve the supply reliability of a single city in the Lower Rio Grande Valley (LRGV) of Texas. The LRGV case study uses risk-based planning triggers to control a city's use of a water market, with transfers between agricultural use and municipal supply. The goal is to highlight how non-structural adaptation such as water marketing can aid water management in the arid western U.S. Problems of water availability are also becoming more apparent in the eastern U.S., where water planning was traditionally focused on flood management and droughts were not often considered a serious issue. The second test case explores multi-sector long-term supply planning for the Lower Susquehanna portion of the Susquehanna River Basin in Pennsylvania and Maryland. Two many objective problem formulations for the Lower Susquehanna expose biases and challenges of classical planning formulations. Subsequent exploration of deep uncertainty suggests critical modeling assumptions for the test case. Insights from the Susquehanna test case have the goal of assisting reservoir planning for infrastructure systems in the eastern U.S.