Dataset Information

Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay.

ABSTRACT: Rising sea levels increase the probability of future coastal flooding. Many decision-makers use risk analyses to inform the design of sea-level rise (SLR) adaptation strategies. These analyses are often silent on potentially relevant uncertainties. For example, some previous risk analyses use the expected, best, or large quantile (i.e., 90%) estimate of future SLR. Here, we use a case study to quantify and illustrate how neglecting SLR uncertainties can bias risk projections. Specifically, we focus on the future 100-yr (1% annual exceedance probability) coastal flood height (storm surge including SLR) in the year 2100 in the San Francisco Bay area. We find that accounting for uncertainty in future SLR increases the return level (the height associated with a probability of occurrence) by half a meter from roughly 2.2 to 2.7 m, compared to using the mean sea-level projection. Accounting for this uncertainty also changes the shape of the relationship between the return period (the inverse probability that an event of interest will occur) and the return level. For instance, incorporating uncertainties shortens the return period associated with the 2.2 m return level from a 100-yr to roughly a 7-yr return period (?15% probability). Additionally, accounting for this uncertainty doubles the area at risk of flooding (the area to be flooded under a certain height; e.g., the 100-yr flood height) in San Francisco. These results indicate that the method of accounting for future SLR can have considerable impacts on the design of flood risk management strategies.

SUBMITTER: Ruckert KL

PROVIDER: S-EPMC5370151 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay.

Ruckert Kelsey L KL Oddo Perry C PC Keller Klaus K

PloS one 20170328 3

Rising sea levels increase the probability of future coastal flooding. Many decision-makers use risk analyses to inform the design of sea-level rise (SLR) adaptation strategies. These analyses are often silent on potentially relevant uncertainties. For example, some previous risk analyses use the expected, best, or large quantile (i.e., 90%) estimate of future SLR. Here, we use a case study to quantify and illustrate how neglecting SLR uncertainties can bias risk projections. Specifically, we fo ...[more]

PMID: 28350884

Similar Datasets

Project description:BackgroundTidal marshes will be threatened by increasing rates of sea-level rise (SLR) over the next century. Managers seek guidance on whether existing and restored marshes will be resilient under a range of potential future conditions, and on prioritizing marsh restoration and conservation activities.MethodologyBuilding upon established models, we developed a hybrid approach that involves a mechanistic treatment of marsh accretion dynamics and incorporates spatial variation at a scale relevant for conservation and restoration decision-making. We applied this model to San Francisco Bay, using best-available elevation data and estimates of sediment supply and organic matter accumulation developed for 15 Bay subregions. Accretion models were run over 100 years for 70 combinations of starting elevation, mineral sediment, organic matter, and SLR assumptions. Results were applied spatially to evaluate eight Bay-wide climate change scenarios.Principal findingsModel results indicated that under a high rate of SLR (1.65 m/century), short-term restoration of diked subtidal baylands to mid marsh elevations (-0.2 m MHHW) could be achieved over the next century with sediment concentrations greater than 200 mg/L. However, suspended sediment concentrations greater than 300 mg/L would be required for 100-year mid marsh sustainability (i.e., no elevation loss). Organic matter accumulation had minimal impacts on this threshold. Bay-wide projections of marsh habitat area varied substantially, depending primarily on SLR and sediment assumptions. Across all scenarios, however, the model projected a shift in the mix of intertidal habitats, with a loss of high marsh and gains in low marsh and mudflats.Conclusions/significanceResults suggest a bleak prognosis for long-term natural tidal marsh sustainability under a high-SLR scenario. To minimize marsh loss, we recommend conserving adjacent uplands for marsh migration, redistributing dredged sediment to raise elevations, and concentrating restoration efforts in sediment-rich areas. To assist land managers, we developed a web-based decision support tool (www.prbo.org/sfbayslr).

Project description:Southern sea otters have been actively managed for their conservation and recovery since listing on the federal Endangered Species Act in 1977. Still, they remain constrained to a geographically small area on the central coast of California relative to their former coast-wide range, with population numbers far below those of the estimated optimal sustainable population size. Species managers have discussed reintroducing southern sea otters into parts of their historic range to facilitate sustained population growth and geographic range expansion. San Francisco Bay (SFB), historically home to several thousand sea otters, is one location identified as a candidate release site for these reintroductions. The return of sea otters to SFB could bring benefits to local ecosystem restoration and tourism, in addition to spurring sea otter population growth to meet recovery goals. However, this is a highly urbanized estuary, so sea otters could also be exposed to serious anthropogenic threats that would challenge a successful reintroduction. In light of these potential detriments we performed a spatially-explicit risk assessment to analyze the suitability of SFB for southern sea otter reintroduction. We looked at threats to sea otters specific to SFB, including: the impacts of vessel traffic from commercial shipping, high-speed ferries, and recreational vessels; environmental contaminants of methylmercury and polychlorinated biphenyls; major oil spills; and commercial fishing. Factors that influenced the relative threat imposed by each stressor included the spatio-temporal extent and intensity of the stressor and its mitigation potential. Our analysis revealed the complex spatial and temporal variation in risk distribution across the SFB. The type and magnitude of anthropogenic risk was not uniformly distributed across the study area. For example, the central SFB housed the greatest cumulative risk, where a high degree of vessel traffic and other stressors occurred in conjunction. The individual stressors that contributed to this risk score varied across different parts of the study area as well. Whereas vessel traffic, particularly of fast ferries, was a high scoring risk factor in in the north and central bay, in the south bay it was environmental contaminants that caused greater risk potential. To help identify areas within the study area that managers might want to target for release efforts, the spatially-explicit risk map revealed pockets of SFB that could provide both suitable habitat and relatively low overall risk. However in some cases these were adjacent or in close proximity to identified high-risk portions of habitat in SFB. This predictive suitability and risk assessment can be used by managers to consider the spatial distribution of potential threats, and risk abatement that may be necessary for sea otters to re-occupy their historic home range in SFB.

Project description:Understanding habitat associations is vital for conservation of at-risk marsh-endemic wildlife species, particularly those under threat from sea level rise. We modeled environmental and habitat associations of the marsh-endemic, Federally endangered salt marsh harvest mouse (Reithrodontomys raviventris, RERA) and co-occurrence with eight associated small mammal species from annual trap data, 1998-2014, in six estuarine marshes in North San Francisco Bay, California. Covariates included microhabitat metrics of elevation and vegetation species and cover; and landscape metrics of latitude-longitude, distance to anthropogenic features, and habitat patch size. The dominant cover was pickleweed (Salicornia pacifica) with 86% mean cover and 37 cm mean height, and bare ground with about 10% mean cover. We tested 38 variants of Bayesian network (BN) models to determine covariates that best account for presence of RERA and of all nine small mammal species. Best models had lowest complexity and highest classification accuracy. Among RERA presence models, three best BN models used covariates of latitude-longitude, distance to paved roads, and habitat patch size, with 0% error of false presence, 20% error of false nonpresence, and 20% overall error. The all-species presence models suggested that within the pickleweed marsh environment, RERA are mostly habitat generalists. Accounting for presence of other species did not improve prediction of RERA. Habitat attributes compared between RERA and the next most frequently captured species, California vole (Microtus californicus), suggested substantial habitat overlap, with RERA habitat being somewhat higher in marsh elevation, greater in percent cover of the dominant plant species, closer to urban areas, further from agricultural areas, and, perhaps most significant, larger in continuous size of marsh patch. Findings will inform conservation management of the marsh environment for RERA by identifying best microhabitat elements, landscape attributes, and adverse interspecific interactions.

Dataset Information

Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay.

Publications

Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets