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Abstract: Limited streamflow information makes water resource decisions more challenging. Moreover, modeling choices for addressing streamflow uncertainty may benefit some stakeholders more than others. This dissertation offers three relatively parsimonious approaches for screening multi-stakeholder water resources decisions, all of which account for streamflow uncertainty. While these approaches ... read moreare applicable to many water resources problems, I present applications that (i) characterize changes in flood variability, (ii) identify long-term floodplain management plans robust to uncertain climate change, and (iii) evaluate tradeoffs between reservoir storage benefits and riverine ecosystem conservation. First, I present a parsimonious two-stage ordinary least squares (OLS) regression model, which detects trends in both the central tendency and variability of annual floods. Neglecting increasing (decreasing) trends in the coefficient of variation at stations with concurrent increases in the mean annual flood can lead to the substantial under-design (over-design) of flood protection strategies. This OLS regression modeling approach also provides trend uncertainty estimates (type I and II errors) useful for decision-making. Second, in some cases, there may be insufficient information for modeling changes in floods probabilistically, especially when characterizing impacts of future climate change using different General Circulation Models (GCMs). Under these circumstances, minimax regret optimization models can still identify adaptation plans that minimize both the over- and under-design consequences of climate change uncertainty. I introduce a mixed integer programming (MIP) optimization model identifies sequences for implementing river reach- and property-scale adaptation measures that are robust to uncertain changes in floods during the 21st century. Third, attenuating hydrologic extremes beyond critical thresholds can also harm ecosystems and communities adapted to them. In settings with short pre- and post-dam records, it is often difficult to rule out that these changes are, in fact, due to dam operations and not random variability. Hypothesis tests offer information about (i) the likelihood of losing hydropower production by unnecessarily changing dam operations and (ii) the likelihood of adverse ecological impacts by not changing them when necessary. Moreover, this probabilistic information can be incorporated into a Bayesian decision-making framework that evaluates the potential hydropower and ecosystem consequences of different operating rules.
Thesis (Ph.D.)--Tufts University, 2017.
Submitted to the Dept. of Civil Engineering.
Advisor: Richard Vogel.
Committee: Paul Kirshen, James Limbrunner, and Charles Kroll.
Keywords: Water resources management, Hydrologic sciences, and Civil engineering.read less
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