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Abstract The risk of compound coastal flooding in the San Francisco Bay Area is increasing due to climate change yet remains relatively underexplored. Using a novel hybrid statistical-dynamical downscaling approach, this study investigates the impacts of climate change induced sea-level rise and higher river discharge on the magnitude and frequency of flooding events as well as the relative importance of various forcing drivers to compound flooding within the Bay. Results reveal that rare occurrences of flooding under the present-day climate are projected to occur once every few hundred years under climate change with relatively low sea-level rise (0.5 m) but would become annual events under climate change with high sea-level rise (1.0 to 1.5 m). Results also show that extreme water levels that are presently dominated by tides will be dominated by sea-level rise in most locations of the Bay in the future. The dominance of river discharge to the non-tidal and non-sea-level rise driven water level signal in the North Bay is expected to extend ~15 km further seaward under extreme climate change. These findings are critical for informing climate adaptation and coastal resilience planning in San Francisco Bay. 

Increasing exposure to coastal flood hazards will potentially induce an enormous socio‐economic toll on vulnerable communities. To accurately characterize the hazard, we must consider both natural water level variability and climate change‐induced sea‐level rise. In this study, we develop a paleo‐proxy‐based reconstruction of coastal flood events over the last 500 yr to capture natural water level variability and superimpose that reconstruction onto expected sea‐level rise to explore interannual and multidecadal variability in plausible future coastal flood risk. We first develop reconstructions of leading principal components (PCs) of sea surface temperature anomalies from 1500 CE onwards, using tree‐ring, coral, and sclerosponge chronology‐based El Niño Southern Oscillation reconstructions as predictors in a wavelet autoregression model. These reconstructions of PCs are then used in a stochastic water level emulator to develop ensemble simulations of hourly still water levels (SWLs) in the San Francisco Bay. The emulator accounts for multiple relevant processes, including monthly mean sea level (MMSL) anomalies, storm surge, and tide, all varying at different timescales. Accounting for natural variability in water levels over 1500–2000 CE increases coastal flood risk beyond that suggested by instrumental records alone. When superimposed on 0.22 m of sea‐level rise (approximately the amount experienced over the previous century), the simulations show that while high tides and large storm surges cause the smaller extreme SWLs, the larger extreme SWLs occur during concurrent high MMSL, high tides, and significant storm surges. Our findings thus highlight the need to consider natural water level variability for coastal adaptation and planning. 

Abstract 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. 

Abstract Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.