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To aid California's water sector to better understand and manage future climate extremes, we present a method for creating a regionally consistent ensemble of plausible daily future climate and streamflow scenarios that represent natural climate variability captured in a network of tree‐ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo‐reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin Valley of California. To assess the compound effects of climate change, we create temperature series that reflect projected scenarios of warming and precipitation series that have been scaled to reflect thermodynamically driven shifts in the distribution of daily precipitation. We then use these weather scenarios to force hydrologic models for each of the five subbasins. The paleo‐forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non‐stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision‐relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the San Joaquin.

Assessing impacts on coupled food-water systems that may emerge from water policies, changes in economic drivers and crop productivity requires an understanding of dominant uncertainties. This paper assesses how a candidate groundwater pumping restriction and crop prices, crop yields, surface water price, electricity price, and parametric uncertainties shape economic and groundwater performance metrics from a coupled hydro-economic model (HEM) through a diagnostic global sensitivity analysis (GSA). The HEM used in this study integrates a groundwater depth response, modeled by an Artificial Neural Network (ANN), into a calibrated Positive Mathematical Programming (PMP) agricultural production model. Results show that in addition to a groundwater pumping restriction, performance metrics are highly sensitive to prices and yields of perennial tree crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater. Furthermore, results indicate that performing a GSA for two different water baseline conditions used to calibrate the production model, dry and wet, result in different sensitivity indices magnitudes and factor prioritization. Diagnostic GSA results are used to understand key factors that affect the performance of a groundwater pumping restriction policy. This research is applied to the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California, region reliant on groundwater and vulnerable to surface water shortages.

The United Nations Framework Convention on Climate Change agreed to “strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty” (UNFCCC 2015). Designing a global mitigation strategy to support this goal poses formidable challenges. For one, there are trade-offs between the economic costs and the environmental benefits of averting climate impacts. Furthermore, the coupled human-Earth systems are subject to deep and dynamic uncertainties. Previous economic analyses typically addressed either the former, introducing multiple objectives, or the latter, making mitigation actions responsive to new information. This paper aims at bridging these two separate strands of literature. We demonstrate how information feedback from observed global temperature changes can jointly improve the economic and environmental performance of mitigation strategies. We focus on strategies that maximize discounted expected utility while also minimizing warming above 2 °C, damage costs, and mitigation costs. Expanding on the Dynamic Integrated Climate-Economy (DICE) model and previous multi-objective efforts, we implement closed-loop control strategies, map the emerging trade-offs and quantify the value of the temperature information feedback under both well-characterized and deep climate uncertainties. Adaptive strategies strongly reduce high regrets, guarding against mitigation overspending for less sensitive climate futures, and excessive warming for more sensitive ones.

Hydrologic variability can present severe financial challenges for organizations that rely on water for the provision of services, such as water utilities and hydropower producers. While recent decades have seen rapid growth in decision‐support innovations aimed at helping utilities manage hydrologic uncertainty for multiple objectives, support for managing the related financial risks remains limited. However, the mathematical similarities between multi‐objective reservoir control and financial risk management suggest that the two problems can be approached in a similar manner. This paper demonstrates the utility of Evolutionary Multi‐Objective Direct Policy Search for developing adaptive policies for managing the drought‐related financial risk faced by a hydropower producer. These policies dynamically balance a portfolio, consisting of snowpack‐based financial hedging contracts, cash reserves, and debt, based on evolving system conditions. Performance is quantified based on four conflicting objectives, representing the classic tradeoff between “risk” and “return” in addition to decision‐makers’ unique preferences toward different risk management instruments. The dynamic policies identified here significantly outperform static management formulations that are more typically employed for financial risk applications in the water resources literature. Additionally, this paper combines visual analytics and information theoretic sensitivity analysis to improve understanding about how different candidate policies achieve their comparative advantages through differences in how they adapt to real‐time information. The methodology presented in this paper should be applicable to any organization subject to financial risk stemming from hydrology or other environmental variables (e.g., wind speed, insolation), including electric utilities, water utilities, agricultural producers, and renewable energy developers.

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 are found to significantly improve regional supply reliability and financial outcomes, despite highly correlated weather and climate across neighboring supply systems (e.g., concurrent drought events). Regional improvements in performance, however, mask tradeoffs among individual agreement partners. Adjustable treatment capacity allocations add flexibility to inter‐utility agreements but can compound financial risk to 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 the development of water supply partnerships.

Water scarcity is a growing problem around the world, and regions such as California are working to develop diversified, interconnected, flexible, and resilient water supply portfolios. To meet these goals, water utilities, irrigation districts, and other organizations will need to cooperate across scales to finance, build, and operate shared water supply infrastructure. However, planning studies to date have generally focused on partnership‐level outcomes (i.e., highly aggregated cost‐benefit analyses), while ignoring the heterogeneity of benefits, costs, and risks across the individual partners. This study contributes an exploratory modeling analysis that tests thousands of alternative infrastructure partnerships in the Central Valley of California, using a daily scale simulation model (CALFEWS) to evaluate the effects of new infrastructure on individual water providers. The viability of conveyance and groundwater banking investments are as strongly shaped by partnership design choices (i.e., which water providers are participating, and how is the project's debt distributed?) as by extreme hydrologic conditions (i.e., floods and droughts). Importantly, most of the analyzed partnerships yield highly unequal distributions of water supply and financial risks across the partners, so that only 8% of the partnerships explored are capable of providing water to each partner for under $200/ML. Partnership viability is especially rare in the absence of groundwater banking facilities (1%), or under dry hydrologic conditions (1%), even under explicitly optimistic assumptions regarding climate change. Given these results, we outline several major policy implications for institutionally complex regions such as California, which are currently investing heavily in cooperative approaches to resilient water portfolio design.