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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.

 

Abstract. The ability to adapt to social and environmental change is an increasinglycritical feature of environmental governance. However, an understandingof how specific features of governance systems influence how theyrespond to change is still limited. Here we focus on how system featureslike diversity, heterogeneity, and connectedness impact stability,which indicates a system's capacity to recover fromperturbations. Through a framework that combines agent-basedmodeling with “generalized”dynamical systems modeling, we model the stability of thousandsof governance structures consisting of groups of resource users and non-government organizations interacting strategically with the decision centers that mediate their access to a shared resource. Stabilizing factors include greater effortdedicated to venue shopping and a greater fraction of non-governmentorganizations in the system. Destabilizing factors include greaterheterogeneity among actors, a greater diversity of decision centers,and greater interdependence between actors. The results suggest thatwhile complexity tends to be destabilizing, there are mitigating factorsthat may help balance adaptivity and stability in complex governance. This study demonstrates the potential inapplying the insights of complex systems theory to managing complexand highly uncertain human–natural systems in the face of rapid socialand environmental change. 

Despite the growing focus on understanding how to build resilience, the interaction between resilience and equity, particularly in the context of power asymmetries like those in communities reliant on resource-based industries, or resource-based communities, is not well understood. Here we present a stylized dynamical systems model of asymmetric resource access and control in resource-based communities that links industrial resource degradation, community well-being, and migration in response to economic and resource conditions. The model reveals a mechanism of collapse due to these dynamics in which over-extraction and resource degradation trigger irreversible population decline. Regulating resource extraction can increase resilience (in the sense of persistence) while also shifting the sustainable equilibrium and the implications for equity. Resilience does not guarantee equity at equilibrium, and this misalignment is more pronounced in the transient interactions between short term equity and long term resilience. The misalignment between resilience and equity demonstrates how equity considerations change the policy design process in important ways.

 

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.