Decision Science Can Help Address the Challenges of Long‐Term Planning in the Colorado River Basin

Abstract

Long-term planning in the Colorado River Basin is subject to deep uncertainty. Reclamation has invested in Decision Making under Deep Uncertainty (DMDU) methods that can help address the challenges. ABSTRACT: "Deep uncertainty" is a term that describes planning contexts in which it is impossible to determine the likelihood of any given set of future conditions, there are conflicting performance objectives and priorities, and decision outcomes are unpredictable. Evidence of the relevance of this term in the Colorado River Basin can be found in the unexpected severity and length of the ongoing drought, the existence of a wide range of future streamflow and demand projections and anticipated system requirements, as well as in the diverse range of stakeholders, viewpoints, and priorities that must be considered when developing and analyzing future operating policies. Decision Making under Deep Uncertainty (DMDU) is a branch of decision science devoted to developing planning approaches that can address these challenges. Reclamation began exploring DMDU techniques in 2012 in the Colorado River Basin Water Supply and Demand Study when it used the concepts of adaptation, vulnerability, and robustness in the design and evaluation of portfolios of options and strategies to address a potential supply-demand imbalance in coming decades. This article presents the basis for continuing to explore DMDU techniques and briefly describes how Reclamation's recent and ongoing DMDU studies could contribute to future planning efforts in the Colorado River Basin.

Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018

“…For example, in USA, snow is the largest source of water storage in the arid western states (Hall et al, 2008;Li et al, 2017), where the Colorado River, which originates from the Rocky Mountains, highly depends on the melt water from snow (Li et al., 2017). The river provides drinking water to 40 million people and irrigates over five million acres of agricultural land across seven states in the USA and two in Mexico (Smith et al, 2022). The warming climate will cause snowpack losses in the Rocky Mountains, which will, in turn, affect the availability of water in Colorado River basin states (Hall et al, 2008;Kundzewicz et al, 2008, Li et al, 2017.…”

Origin, importance, and predictive limits of internal climate variability

“…This suggests that a perspective focused on risk and robust decision making (Sutton 2019, Mankin et al 2020, Reed et al 2022 is more pertinent to users of climate model information than the focus on a 'best-estimate' . This is increasingly being recognized by application communities dependent on climate science, such as climate-related health impacts (Garcia-Menendez et al 2017, Fiore et al 2022 or water resource management (Harding et al 2012, Chegwidden et al 2019, Smith et al 2022. A current example is the scoping of new guidelines for the management of the Colorado River in the Western U.S.…”

“…Yet, hydrologic projections based on climate model simulations show a paralyzingly wide range of outcomes . The water management community thus uses methods of Robust Decision Making to develop guidelines that perform optimally across this wide range of futures rather than assuming a best estimate (Smith et al 2022). However, these approaches do not currently distinguish sources of climate projection uncertainty, continue to undersample internal variability (Mankin et al 2020), and do not incorporate new observational constraints on regional climate projections (e.g.…”

New Potential to Reduce Uncertainty in Regional Climate Projections by Combining Physical and Socio‐Economic Constraints

“…This need not lead to overconfidence, as perfect model tests (Brunner, Pendergrass, et al., 2020) and emergent constraint protocols (Hall et al., 2019) can help guard against that. We also encourage continued use of high impact‐low likelihood scenarios (such as SSP5‐8.5) in risk assessments that do seek to explore those (Lawrence et al., 2020) or that already apply algorithms of robust decision making to develop adaptation strategies for a wide range of plausible but perhaps unlikely futures (Lempert, 2019; Smith et al., 2022). In fact, physical climate scientists will continue to be interested in running climate models with high emissions scenarios, as they provide a large signal‐to‐noise ratio, which aids process understanding and provides training grounds for emulators.…”