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Abstract Synthetic ensemble forecasts are an important tool for testing the robustness of forecast‐informed reservoir operations (FIRO). These forecasts are statistically generated to mimic the skill of hindcasts derived from operational ensemble forecasting systems, but they can be created for time periods when hindcast data are unavailable, allowing for a more comprehensive evaluation of FIRO policies. Nevertheless, it remains unclear how to determine whether a candidate synthetic ensemble forecasting approach is sufficiently representative of its real‐world counterpart to support FIRO policy evaluation. This highlights a need for formalfit‐for‐purposevalidation frameworks to advance synthetic forecasting as a generalizable risk analysis strategy. We address this research gap by first introducing a novel operations‐based validation framework, where reservoir storage and release simulations under a FIRO policy are compared when forced with a single ensemble hindcast and many different synthetic ensembles. We evaluate the suitability of synthetic forecasts based on formal probabilistic verification of the operational outcomes. Second, we develop a new synthetic ensemble forecasting algorithm and compare it to a previous algorithm using this validation framework across a set of stylized, hydrologically diverse reservoir systems in California. Results reveal clear differences in operational suitability, with the new method consistently outperforming the previous one. These findings demonstrate the promise of the newer synthetic forecasting approach as a generalizable tool for FIRO policy evaluation and robustness testing. They also underscore the value of the proposed validation framework in benchmarking and guiding future improvements in synthetic forecast development. 

Abstract California faces cycles of drought and flooding that are projected to intensify, but these extremes may impact water users across the state differently due to the region's natural hydroclimate variability and complex institutional framework governing water deliveries. To assess these risks, this study introduces a novel exploratory modeling framework informed by paleo and climate‐change based scenarios to better understand how impacts propagate through the Central Valley's complex water system. A stochastic weather generator, conditioned on tree‐ring data, produces a large ensemble of daily weather sequences conditioned on drought and flood conditions under the Late Renaissance Megadrought period (1550–1580 CE). Regional climate changes are applied to this weather data and drive hydrologic projections for the Sacramento, San Joaquin, and Tulare Basins. The resulting streamflow ensembles are used in an exploratory stress test using the California Food‐Energy‐Water System model, a highly resolved, daily model of water storage and conveyance throughout California's Central Valley. Results show that megadrought conditions lead to unprecedented reductions in inflows and storage at major California reservoirs. Both junior and senior water rights holders experience multi‐year periods of curtailed water deliveries and complete drawdowns of groundwater assets. When megadrought dynamics are combined with climate change, risks for unprecedented depletion of reservoir storage and sustained curtailment of water deliveries across multiple years increase. Asymmetries in risk emerge depending on water source, rights, and access to groundwater banks. 

Abstract This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research. 

Abstract Forecast informed reservoir operations (FIRO) is an important advance in water management, but the design and testing of FIRO policies is limited by relatively short (10–35 year) hydro‐meteorological hindcasts. We present a novel, multisite model for synthetic forecast ensembles to overcome this limitation. This model utilizes parametric and non‐parametric procedures to capture complex forecast errors and maintain correlation between variables, lead times, locations, and ensemble members. After being fit to data from the hindcast period, this model can generate synthetic forecast ensembles in any period with observations. We demonstrate the approach in a case study of the FIRO‐based Ensemble Forecast Operations (EFO) control policy for the Lake Mendocino—Russian River basin, which conditions release decisions on ensemble forecasts from the Hydrologic Ensemble Forecast System (HEFS). We explore two generation strategies: (a) simulation of synthetic forecasts of meteorology to force HEFS; and (b) simulation of synthetic HEFS streamflow forecasts directly. We evaluate the synthetic forecasts using ensemble verification techniques and event‐based validation, finding good agreement with the actual ensemble forecasts. We then evaluate EFO policy performance using synthetic and actual forecasts over the hindcast period (1985–2010) and synthetic forecasts only over the pre‐hindcast period (1948–1984). Results show that the synthetic forecasts highlight important failure modes of the EFO policy under plausible forecast ensembles, but improvements are still needed to fully capture FIRO policy behavior under the actual forecast ensembles. Overall, the methodology advances a novel way to test FIRO policy robustness, which is key to building institutional support for FIRO. 

Abstract Stochastic Watershed Models (SWMs) are emerging tools in hydrologic modeling used to propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. One of the most promising uses of SWMs is uncertainty propagation for hydrologic simulations under climate change. However, a core challenge is that the historical predictive uncertainty may not correctly characterize the error distribution under future climate. For example, the frequency of physical processes (e.g., snow accumulation and melt) may change under climate change, and so too may the frequency of errors associated with those processes. In this work, we explore for the first time non‐stationarity in hydrologic model errors under climate change in an idealized experimental design. We fit one hydrologic model to historical observations, and then fit a second model to the simulations of the first, treating the first model as the true hydrologic system. We then force both models with climate change impacted meteorology and investigate changes to the error distribution between the models. We develop a hybrid machine learning method that maps model state variables to predictive errors, allowing for non‐stationary error distributions based on changes in the frequency of model states. We find that this procedure provides an internally consistent methodology to overcome stationarity assumptions in error modeling and offers an important advance for implementing SWMs under climate change. We test this method on three hydrologically distinct watersheds in California (Feather River, Sacramento River, Calaveras River), finding that the hybrid model performs best in larger and less flashy basins. 

State and federal governments use governance platforms to achieve central policy goals through distributed action at the local level. For example, California’s 2014 Sustainable Groundwater Management Act (SGMA) mandates local policy actors to work together to create new groundwater management institutions and plans. We argue that governance platforms entail a principal-agent problem where local decisions may deviate from central goals. We apply this argument to SGMA implementation, where local plans may respond more to local political economic conditions rather than address the groundwater problems prioritized by the state. Using a Structured Topic Model (STM) to analyze the content of 117 basin management plans, we regress each plan’s focus on core management reform priorities on local socio-economic and social-ecological indicators expected to shape how different communities respond to state requirements. Our results suggest that the focus of local plans diverges from problem conditions on issues like environmental justice and drinking water quality. This highlights how principal-agent logics of divergent preferences and information asymmetry can affect the design and implementation of governance platforms. 

Saltwater intrusion (SWI) into coastal freshwater systems is a growing concern in the face of climate change‐driven sea level rise and hydrologic variability. Saltwater contamination of surface freshwater in the coastal California Pajaro Valley exemplifies this concern, where surface water cannot be diverted for agriculture if it is too saline. Closures at the mouth of the Pajaro River Lagoon, a bar‐built estuary in the Pajaro Valley, are associated with SWI. Closures and SWI are driven by a combination of offshore climate, coastal hydrodynamics, estuarine dynamics, inland hydrology, and infrastructure and management. Here, we describe the Pajaro Valley coastal water system and identify the oceanic and inland hydrologic drivers of SWI using available observational data between 2012 and 2020. We use time series and exploratory statistical analyses of coastal total water levels (TWLs), slough stage and salinity, river discharge, and contextual knowledge from local water managers. We observe that wet season lagoon closure and SWI events follow high oceanic TWLs coupled with low stage and discharge in the inland freshwater network, revealing how both wave and inland flow conditions govern lagoon closures and coincident SWI. This study yields novel empirical findings and a methodology for connecting coastal oceanography, estuarine coupled hydro‐ and morpho‐dynamics, inland hydrology, and water management practices relevant to climate change adaptation in human‐modified coastal water systems.