Risk, Robustness and Water Resources Planning Under Uncertainty

Abstract

Risk‐based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost‐benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk‐based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision‐theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade‐off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state‐of‐the ‐art regional climate simulations to inform the estimation of risk and robustness.

A Probabilistic Risk Assessment of the National Economic Impacts of Regulatory Drought Management on Irrigated Agriculture

“…The baseline (1977–2004) data set provides 100 simulated climate ensemble members that could have occurred in the past, while the near future (2022–2049) and far future (2072–2099) represent climate time series that might occur in the future under the Representative Concentration Pathway 8.5 (RCP8.5) emissions scenario. This weather@home2 data set has been previously successfully applied for risk analysis in water resources planning in the Thames basin (Borgomeo et al, ).…”

Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

“…Info-Gap theory conceptualizes robustness as the greatest level of the radial distance around a starting point (e.g., a best-guess estimate of the future) with satisfactory performance across a prespecified uncertainty space (Ben-Haim, 2006;Sniedovich, 2010). This work is informed by a long-standing debate regarding the value and use of probabilistic information to support robustness-based planning (Borgomeo et al, 2018;Groves & Lempert, 2007;Parson et al, 2007;Shortridge et al, 2017). One side of the debate suggests that there is no value in using probabilities, as such information may imply a greater degree of certainty about the future than exists and that the inference may, in turn, be misleading for decision-makers (Dessai & Hulme, 2004;Gong et al, 2017;Groves & Lempert, 2007;Lempert, 2000).…”

Robust Adaptation to Multiscale Climate Variability

“…A typical goal is to create systems which are robust in the sense that they perform well over a wide range of plausible futures (Borgomeo et al, 2018;Lempert & Collins, 2007) and which fail along noncatastrophic modes (Brown, 2010). A typical goal is to create systems which are robust in the sense that they perform well over a wide range of plausible futures (Borgomeo et al, 2018;Lempert & Collins, 2007) and which fail along noncatastrophic modes (Brown, 2010).…”

“…This need has motivated a multitude of approaches for estimating the probability distribution of future climate risk and for choosing between different risk mitigation instruments based on these estimates (see, e.g., . A typical goal is to create systems which are robust in the sense that they perform well over a wide range of plausible futures (Borgomeo et al, 2018;Lempert & Collins, 2007) and which fail along noncatastrophic modes (Brown, 2010). Although climate risk has traditionally been managed with centrally planned structural instruments (e.g., a levee), the high price (Papakonstantinou et al, 2016), environmental costs (Dugan et al, 2010), and vulnerability to biased climate projections (Lempert & Collins, 2007) have recently dampened enthusiasm.…”