Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Abstract

Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision‐making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.

A Bayesian Decision Model for Optimum Investment and Design of Low-Impact Development in Urban Stormwater Infrastructure and Management

“…where C capital is the capital cost of LID, PV O&M is the present value of the maintenance costs, n is the number of year in service, and i is the discount rate reflecting the depreciation in value over time (Reis and Shortridge, 2020). A discount rate of 2% was adopted in this study (Dong, 2018).…”

Robust decision outcomes with induced correlations in climatic and economic parameters

“…Furthermore, some conclusions from scenario generation procedures appear consistent across the different case studies evaluated; for instance, the three studies cited above all find that uniformly distributed scenarios result in different robustness scores and vulnerability characterizations than centrally distributed scenarios. However, they differ in terms of the impact they observe on robustness rankings, with Quinn et al (2020) showing a high influence on robustness rankings that is not apparent in McPhail et al (2020) or Reis and Shortridge (2020). This suggests that the degree to which scenario generation methods influence DMDU outcomes is likely to vary on a case-by-case basis, and points toward the need for multiple comparisons that can highlight common patterns and themes.…”

“…Latin hypercube sampling is often used to generate random uniform samples across multiple input parameters, under the implicit assumption that parameters are independent from each other (Bryant & Lempert 2010;Hadka et al 2015;Kwakkel et al 2016a, b). While this process aims to completely sample the multivariate input space in an efficient manner, the assumption of independence between the input variables can result in simulation outcomes that converge around central values (Reis and Shortridge 2020) and may simulate unrealistic conditions. Other studies use ensembles of GCMs (Brown et al 2012;Bryant & Lempert 2010;Groves & Lempert 2007;Herman et al 2014;Kalra et al 2015;Lempert et al 2006) or paleoclimate data (Tingstad et al 2014;Quinn et al 2020) to generate scenarios for exploratory modeling simulations.…”

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.…”