Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins

Abstract

Water resources systems models enable valuable inferences on consequential system stressors by representing both the geophysical processes determining the movement of water and the human elements distributing it to its various competing uses. This study contributes a diagnostic evaluation framework that pairs exploratory modeling with global sensitivity analysis to enhance our ability to make inferences on water scarcity vulnerabilities in institutionally complex river basins. Diagnostic evaluation of models representing institutionally complex river basins with many stakeholders poses significant challenges. First, it needs to exploit a large and diverse suite of simulations to capture important human-natural system interactions as well as institutionally aware behavioral mechanisms. Second, it needs to have performance metrics that are consequential and draw on decision-relevant model outputs that adequately capture the multisector concerns that emerge from diverse basin stakeholders. We demonstrate the proposed model diagnostic framework by evaluating how potential interactions between changing hydrologic conditions and human demands influence the frequencies and durations of water shortages of varying magnitudes experienced by hundreds of users in a subbasin of the Colorado River. We show that the dominant factors shaping these effects vary both across users and, for an individual user, across percentiles of shortage magnitude. These differences hold even for users sharing diversion locations, demand levels or water right seniority. Our findings underline the importance of detailed institutional representation for such basins, as institutions strongly shape how dominant factors of stakeholder vulnerabilities propagate through the complex network of users.

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

“…For each parameter, the resulting Delta index measures the normalized expected shift in the distribution of the response variable induced by the parameter. We choose Delta‐MIM for this study because it does not require a specific sampling scheme and includes the effects of high‐order statistical moments in the response metrics of interest (Hadjimichael et al., 2020). The broad array of combinations in the LHS hydrologic parameter samples may cause CLM5 convergence failures where the model crashes, making it difficult to implement other SA methods that require a specific sampling scheme.…”

Multisector Dynamics: Advancing the Science of Complex Adaptive Human‐Earth Systems

“…Innovative approaches to experimental design can (a) improve the representation of the deep uncertainties affecting a system (for example due to internal variability as well as uncertainties surrounding model structures and inputs), (b) help to sample potential futures, and (c) shed light on the impacts of uncertainties on consequential MSD outcomes (e.g., Lehner et al, 2020;Tebaldi et al, 2021). Applying scenario discovery methods on the generated output space can identify critical combinations of uncertain factors, consequential human actions, or tipping points that drive poor outcomes (e.g., Dolan et al, 2021;Hadjimichael et al, 2020;Lamontagne et al, 2018Lamontagne et al, , 2019. Combined with many-objective optimization approaches, these methods create avenues to search through the space of potential actions and uncertainties to identify adaptive pathways of change across multisectoral objectives (e.g., Herman et al, 2020;Trindade et al, 2020).…”

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

“…After producing the ensemble simulations, we use the Delta moment-independent sensitivity analysis method (Delta-MIM) to calculate the sensitivity score of the 15 hydrological parameters 55,56 . We selected Delta-MIM for this study because it does not require a specific sampling scheme and includes effects of high-order statistical moments in the response metrics of interest 57 . Delta-MIM exploits an empiric density-based measure that identifies the parameters that most influence the entire distribution of the response variable (i.e., it captures higher order interactive effects beyond mean and variance responses).…”