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Climate vulnerability assessments rely on water infrastructure system models that imperfectly predict performance metrics under ensembles of future scenarios. There is a benefit to reduced complexity system representations to support these assessments, especially when large ensembles are used to better characterize future uncertainties. An important question is whether the total uncertainty in the output metrics is primarily attributable to the climate ensemble or to the systems model itself. Here we develop a method to address this question by combining time series error models of performance metrics with time‐varying Sobol sensitivity analysis. The method is applied to a reduced complexity multi‐reservoir systems model of the Sacramento‐San Joaquin River Basin in California to demonstrate the decomposition of flood risk and water supply uncertainties under an ensemble of climate change scenarios. The results show that the contribution of systems model error to total uncertainty is small (∼5%–15%) relative to climate based uncertainties. This indicates that the reduced complexity systems model is sufficiently accurate for use in the context of the vulnerability assessment. We also observe that climate uncertainty is dominated by the choice of general circulation model and its interactive effects with the representative concentration pathway (RCP), rather than the RCP alone. This observation has implications for how climate vulnerabilities should be interpreted.

Short‐term precipitation forecasts are critical to regional water management, particularly in the Western U.S. where atmospheric rivers can be predicted reliably days in advance. However, spatial error in these forecasts may reduce their utility when the costs of false positives and negatives differ greatly. Here we investigate whether deep learning methods can leverage spatial patterns in precipitation forecasts to (a) improve the skill of predicting the occurrence of precipitation events at lead times from 1 to 14 days, and (b) balance the tradeoff between the rate of false negatives and false positives by modifying the discrimination threshold of the classifiers. This approach is demonstrated for the Sacramento River Basin, California, using the Global Ensemble Forecast System (GEFS) v2 precipitation fields as input to convolutional neural network (CNN) and multi‐layer perceptron models. Results show that the deep learning models do not significantly improve the overall skill (F1 score) relative to the ensemble mean GEFS forecast with bias‐corrected threshold. However, additional analysis of the CNN models suggests they often correct missed predictions from GEFS by compensating for spatial error at longer lead times. Additionally, the deep learning models provide the ability to adjust the rate of false positives and negatives based on the ratio of costs. Finally, analysis of the network activations (saliency) indicates spatial patterns consistent with physical understanding of atmospheric river events in this region, lending additional confidence in the ability of the method to support water management applications.

The challenge of adapting water resources systems to uncertain hydroclimatic and socioeconomic conditions warrants a dynamic planning approach. Recent studies have designed policies with structures linking infrastructure and management actions to threshold values of indicator variables observed over time. Typically, one or more of these components are held fixed while the others are optimized, constraining the flexibility of policy generation. Here we develop a framework to address this challenge by designing and testing dynamic adaptation policies that combine indicators, actions, and thresholds in a flexible structure. The approach is demonstrated for a case study of northern California, where a mix of infrastructure, management, and operational adaptations are considered over time in response to an ensemble of nonstationary hydrology and water demands. We first identify a subset of non‐dominated policies that are robust to held‐out scenarios, and then analyze their most common actions and indicators compared to non‐robust policies. Results show that the robust policies are not differentiated by the actions they select, but show substantial differences in their indicator variables, which can be interpreted in the context of physical hydrologic trends. In particular, the most frequent statistical transformations of indicator variables highlight the balance between adapting quickly versus correctly. Additionally, we determine the indicators most frequently associated with each action, as well as the distribution of action timing across scenarios. This study presents a new and transferable problem framing for adaptation under uncertainty in which indicator variables, actions, and policy structure are identified simultaneously during the optimization.