Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

Abstract

The widespread uncertainty regarding future changes in climate, socioeconomic conditions, and demographics have increased interest in vulnerability‐based frameworks for long‐term planning of water resources. These frameworks shift the focus from projections of future conditions to the weaknesses of the baseline plans and then to options for reductions in those weaknesses across a wide range of futures. A consistent challenge for vulnerability‐based planning is how to assess the relative likelihood of the occurrence of the multidimensional and codependent uncertainties to which the system or plan is vulnerable. This work proposes a methodological solution to the problem, demonstrated in this case as an extension to Decision Scaling framework. The proposed approach first generates a wide range of futures using stochastic simulators, and then stress tests the system across those futures to identify vulnerabilities relative to stakeholder‐defined performance thresholds. The relative likelihood of the vulnerabilities is then explored using a Bayesian belief network of the knowledge domain of the water resources system. The Bayesian network provides a formal representation of the joint probabilistic behavior of the system conditioned on the uncertain but potentially useful sources of information about the future, including historical trends, expert judgments, and model‐based projections. The proposed approach is applied to compare four design options for a dam project in the Coastal Province of Kenya with respect to the reliability and net present value metrics. Results show that incorporation of belief information helps better distinguishing of the available options, principally by magnifying the differences between the computed net present values.

Discovering Dependencies, Trade‐Offs, and Robustness in Joint Dam Design and Operation: An Ex‐Post Assessment of the Kariba Dam

“…This would require extending our robust design framework to include the selected deep uncertainties within a second optimization phase aimed at identifying more robust system configurations. In addition, more sophisticated, state‐of‐the‐art techniques for stochastic weather generator coupled with a hydrological model and assessing system robustness (e.g., Ray et al, ; Taner et al, , ) can be readily integrated within the broader context of alternative designs, operations, and their trade‐offs afforded by our framework. In the end, it will be interesting to test it on complex transboundary, multireservoir systems, in order to understand potential interactions among several dams planned for the near future and that must be operated to satisfy other management objectives (e.g., environmental flow requirements).…”

“…Stationary, intrinsic hydroclimatic variability might thus not be fully characterized within the historical records due to the restricted number of observed data available compared to the longer return periods of hydrologic extremes, leading to strong biases in the water infrastructure design. As for nonstationary, deeply uncertain futures that might unfold, other works (e.g., Bonzanigo et al,; Jeuland & Whittington, ; Ray et al, ; Taner et al, , ; The Nature Conservancy, ) addressed the robustness of dam designs in an a posteriori analysis by reevaluating predefined system configurations over multiple future climatic and nonclimatic uncertainties (e.g., streamflows, irrigation demand, and electricity prices). Ray et al () and Cervigni et al () included a sample of different possible futures in the optimization process in order to design robust system configurations.…”

Trade-Offs between Human and Environment: Challenges for Regional Water Management under Changing Conditions

“…Moreover, these two frameworks can be combined to holistically identify sectorial and overall system vulnerabilities under different combinations of changing conditions (e.g., changes in water availability, climate, water demand and management policies) [58]. To facilitate decision-making under uncertainty, these possible future conditions can be weighted to provide a notion of their probability of occurrence in the future, e.g., using Bayesian approach, based on historical observations, paleorecords and stakeholders' viewpoints about future and management policies [99,100].…”

Can Exploratory Modeling of Water Scarcity Vulnerabilities and Robustness Be Scenario Neutral?

“…In this study, we explore how vulnerability assessments performed over competing hypotheses of how future hydrology might evolve dictate which uncertainties are found to most control water shortages for different users in an institutionally complex, multiactor system, and subsequently, which users are found to be most robust. Several studies have compared how robustness ranks of alternative management strategies or multiple water users (i.e., policies and objectives) differ under alternative definitions of robustness (Herman et al, 2015;Giuliani & Castelletti, 2016;Spence & Brown, 2018;McPhail et al, 2018;Hadjimichael, Quinn, Wilson, et al, 2020), or under alternative assumptions about the range and joint distribution of uncertain factors (i.e., the experimental design) (Moody & Brown, 2013;Taner et al, 2019;Reis & Shortridge, 2019). Yet none of these studies has explored if and how the importance of uncertain factors differs under alternative experimental designs.…”