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 multi-sector 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 sub-basin 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.

Planning under deep uncertainty, when probabilistic characterizations of the future are unknown, is a major challenge in water resources management. Many planning frameworks advocate for “scenario-neutral” analyses in which alternative policies are evaluated over plausible future scenarios with no assessment of their likelihoods. Instead, these frameworks use sensitivity analysis to discover which uncertain factors have the greatest influence on performance. This knowledge can be used to design monitoring programs and adaptive policies that respond to changes in the critical uncertainties. However, scenario-neutral analyses make implicit assumptions about the range and independence of the uncertain factors that may not be consistent with the coupled human-hydrologic processes influencing the system. These assumptions could influence which factors are found to be most important and which policies most robust. Consequently, the assumptions of uniformity and independence could have decision-relevant implications. This study illustrates these implications using a multi-stakeholder planning problem within the Colorado River Basin, where hundreds of rights-holders vie for the river’s limited water under the law of prior appropriations. Variance-based sensitivity analyses are performed to assess users’ vulnerabilities to changing hydrologic conditions using four experimental designs: 1) scenario-neutral samples of hydrologic factors, centered on recent historical conditions, 2) scenarios informed by climate projections, 3) scenarios informed by paleo-hydrologic reconstructions, and 4) scenario-neutral samples of hydrologic factors spanning all previous experimental designs. Differences in sensitivities and user robustness rankings across the experiments illustrate the challenges of inferring the most consequential drivers of vulnerabilities to design effective monitoring programs and robust management policies.

Urban water utilities, facing rising demands and limited supply expansion options, increasingly partner with neighboring utilities to develop and operate shared infrastructure. Inter-utility agreements can reduce costs via economies of scale and help limit environmental impacts, as substitutes for independent investments in large capital projects. However, unexpected shifts in demand growth or water availability, deviating from projections underpinning cooperative agreements, can introduce both supply and financial risk to utility partners. Risks may also be compounded by asymmetric growth in demand across partners or inflexibility of the agreement structure itself to adapt to changing conditions of supply and demand. This work explores the viability of both fixed and adjustable capacity inter-utility cooperative agreements to mitigate regional water supply and financial risk for utilities that vary in size, growth expectations, and independent infrastructure expansion options. Agreements formalized for a shared regional water treatment plant with fixed or adjustable treatment capacities, coupled with structured financing for partner utilities, are found to significantly improve regional supply reliability and financial outcomes. Regional improvements in performance, however, mask tradeoffs among individual agreement partners. Adjustable treatment capacity allocations add flexibility to inter-utility agreements but can compound the financial risk of each utility as a function of the decision-making of the other partners. Often the sensitivity to partners' decision-making under an adjustable agreement degrades financial performance, relative to agreements with fixed capacities allocated to each partner. Our results demonstrate the significant benefits cooperative agreements offer, providing a template to aid decision-makers in development of water supply partnerships.

The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce. Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth systems science to better address interconnected risks, increase resilience, and improve sustainability. This will require convergent research and the integration of ideas and methods from multiple disciplines. Understanding the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in the context of energy transitions and increased future shocks.

Regional cooperation among urban water utilities is a powerful mechanism for improving supply reliability and financial stability in urban water supply systems. Through coordinated drought mitigation and joint infrastructure investment, urban water utilities can efficiently exploit existing water supplies and reduce or delay the need for new supply infrastructure. However, cooperative water management brings new challenges for planning and implementation. Rather than accounting for the interests of a single actor, cooperative policies must balance potentially competing interests between cooperating partners. Structural imbalances within a regional system can lead to conflict between cooperating partners that destabilize otherwise robust planning alternatives. This work contributes a new exploratory modeling centered framework for assessing cooperative stability and mapping power relationships in cooperative infrastructure investment and water supply management policies. Our framework uses multi-objective optimization as an exploratory tool to discover how cooperating partners may be incentivized to defect from robust regional water supply partnership opportunities and identifies how the actions of each regional partner shape the vulnerability of its cooperating partners. Our methodology is demonstrated on the Sedento Valley, a highly challenging regional urban water supply benchmarking problem. Our results reveal complex regional power relationships between the region's cooperating partners and suggest ways to improve cooperative stability.

The role of individual and collective human action is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems towards either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services towards some human end. We trace the salient features of modeled representations of human systems by organizing the typology around three key questions: 1) Who are the actors in MSD systems? 2) What are their actions? 3) How and for what purpose are these actors and actions operationalized in a computational model? We use this typology to critically examine existing models and chart the frontier of human systems modeling for MSD research.

The value of streamflow forecasts to inform water infrastructure operations has been extensively studied. Yet, their value in informing infrastructure design is still unexplored. In this work, we investigate how dam design is shaped by information feedbacks. We demonstrate how flexible operating policies informed by streamflow forecasts enable the design of less costly reservoir relative to alternatives that do not rely on forecast information. Our approach initially establishes information bounds by selecting the most informative lead times of perfect streamflow forecasts to be included in the infrastructure design. We then analyze the design and operational sensitivities relative to realistic imperfect streamflow forecasts synthetically modeled to explicitly represent different biases. We demonstrate our approach through an ex-post analysis of the Kariba dam in the Zambezi river basin. Results show that informing dam design with perfect forecasts enable attaining the same hydropower production of the existing dam, while reducing infrastructure size and associated capital costs by 20%. The use of forecasts with lower skill reduces this gain to approximately 15%. Finally, the adoption of forecast information in the operation of the existing system facilitate an annual average increase of 60 GWh in hydropower production. This finding, extrapolated to the new planned dams in the basin, suggests that consideration of forecast informed policies could yield power production benefits equal to 75% of the current annual electricity consumption of the Zambian agricultural sector. Forecast information feedbacks have a strong potential to become a valuable asset for the ongoing hydropower expansion in the basin.