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Flood and debris hazards are heightened following wildfires, but are challenging to quantify due to interdependence between fire frequency and severity, runoff and sediment fluxes during storms, and sedimentation that reduces infrastructure capacity. Herein we present a stochastic simulation framework to estimate compound flood and debris hazards from sequences of wildfires and rainstorms and the accumulation of sediment within flood infrastructure. Application of the framework to a hypothetical watershed representative of southern California shows that the present-day compound hazard may be up to 6 times greater than the marginal hazard posed by peak flows in the absence of wildfire, and that future compound hazards could be up to 11 times greater than the marginal hazard based on future increases in wildfire frequency. Numerous sensitivities are investigated, including infrastructure design and maintenance, which are shown to be crucial for moderating future increases in post-fire flood hazards.

As urbanization and climate change alter sediment fluxes, relative sea level, and coastal erosion around the world, management of sediment as a resource is increasingly important. Sediment is needed to enhance marsh accretion rates, raise the grade elevation of development, and build up beaches and dunes. Beneficial reuse of sediment refers to the repurposing of local sources of sediment for these applications, material typically available from dredging or sediment capture infrastructure, and represents a more sustainable approach compared to the status-quo involving transport to and from distant locations. However, in many locations, beneficial reuse remains a concept or is constrained to small-scale applications. In this paper, we draw on interviews with coastal sediment managers and regulators in Southern California to identify barriers to beneficial reuse and opportunities to overcome them. Interviewees reported numerous regulatory, technical, psychological, financial, and interorganizational barriers in their watersheds and regions. By highlighting these barriers, we aim to identify systemic changes that would make beneficial reuse a realistic and accessible option for Southern California and elsewhere. Most prominently, a more flexible regulatory framework that allows sediment management practices to adapt over time, pilot studies to understand how beneficial reuse works in various settings, and educational programs for regulators and the public could make beneficial reuse a more widespread approach.

Existing needs to manage flood risk in the United States are underserved by available flood hazard information. This contributes to an alarming escalation of flood impacts amounting to hundreds of billions of dollars per year and countless disrupted lives and affected communities. Making information about flood hazards useful for the range of decisions that dictate the consequences of flooding poses many challenges. Here, we describe collaborative flood modeling, whereby researchers and end-users at two coastal sites co-develop fine-resolution flood hazard models and maps responsive to decision-making needs. We find, first of all, that resident perception and awareness of flooding are enhanced more by fine-resolution depth contour maps than Federal Emergency Management Agency (FEMA) flood hazard classification maps and that viewing fine-resolution depth contour maps helps to minimize differences in flood perception across subgroups within the community, generating a shared understanding. We also find that collaborative flood modeling supports the engagement of a wide range of end-users in contemplating the risks of flooding and provides strong evidence that the co-produced knowledge can be readily adopted and applied for Flood Risk Management (FRM). Overall, collaborative flood modeling advances FRM by providing multiple points of entry for diverse groups of end-users to contemplate the spatial extent, intensity, timing, chance, and consequences of flooding, thus enabling the web of decision-making related to flooding to be better informed with the best available science. This transdisciplinary approach emphasizes vulnerability reduction and is complementary to FEMA Flood Insurance Rate Maps used for flood insurance administration.

Pluvial flash flooding (PFF) is a growing hazard facing cities around the world as a result of rapid urbanization and more intense precipitation from global warming, particularly for low-resourced settings in developing countries. We present collaborative modeling (CM) as an iterative process to meet diverse decision-making needs related to PFF through the co-production of flood hazard models and maps. CM resulted in a set of flood hazard maps accessible through an online viewer that end-users found useful and useable for understanding PFF threats, including debris blockages and barriers to mobility and evacuation. End-users of information included individuals concerned with general flood awareness and preparedness, and involved in infrastructure and emergency management, planning, and policy. CM also showed that rain-on-grid hydrodynamic modeling is needed to depict PFF threats in ways that are intuitive to end-users. These outcomes evidence the importance and transferability of public health rationale for community-based research and principles used here including recognizing community as a unit of identity, building on strengths of the community, and integrating knowledge for the benefit of all partners.

A method to link bivariate statistical analysis and hydrodynamic modeling for flood hazard estimation in tidal channels and estuaries is presented and discussed for the general case where flood hazards are linked to upstream riverine discharge Q and downstream ocean level, H. Using a bivariate approach, there are many possible combinations of Q and H that jointly reflect a specific return period, T, raising questions about the best choice as boundary forcing in a hydrodynamic model. We show, first of all, how possible Q and H values depend on whether the definition of T corresponds to the probability of exceedance of “H OR Q” or “H AND Q”. We also show that flood hazards defined by “OR” return periods are more conservative than “AND” return periods. Finally, we introduce a new composite water surface profile to represent the spatially distributed hazard for return period T. The composite profile synthesizes hydrodynamic model results from the “AND” hazard scenario and two scenarios based on traditional univariate analysis, a “Marginal Q” scenario and a “Marginal H” scenario.

Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but they do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies.

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998–2063) and mid-future (2018–2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950–2015) if adaptation measures are not implemented.

Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end-users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end-users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River Valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end-user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end-users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included: (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies.

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

The cumulative cost of frequent events (e.g., nuisance floods) over time may exceed the costs of the extreme but infrequent events for which societies typically prepare. Here we analyze the likelihood of exceedances above mean higher high water and the corresponding property value exposure for minor, major, and extreme coastal floods. Our results suggest that, in response to sea level rise, nuisance flooding (NF) could generate property value exposure comparable to, or larger than, extreme events. Determining whether (and when) low cost, nuisance incidents aggregate into high cost impacts and deciding when to invest in preventive measures are among the most difficult decisions for policymakers. It would be unfortunate if efforts to protect societies from extreme events (e.g., 0.01 annual probability) left them exposed to a cumulative hazard with enormous costs. We propose a Cumulative Hazard Index (CHI) as a tool for framing the future cumulative impact of low cost incidents relative to infrequent extreme events. CHI suggests that in New York, NY, Washington, DC, Miami, FL, San Francisco, CA, and Seattle, WA, a careful consideration of socioeconomic impacts of NF for prioritization is crucial for sustainable coastal flood risk management.

Nuisance flooding corresponds to minor and frequent flood events that have significant socioeconomic and public health impacts on coastal communities. Yearly averaged local mean sea level can be used as proxy to statistically predict the impacts of sea level rise (SLR) on the frequency of nuisance floods (NFs). In this study, we use generalized linear models (GLM) and Gaussian Process (GP) models combined to (i) estimate the frequency of NF associated with the change in mean sea level, and (ii) quantify the associated uncertainties via a novel and statistically robust approach. We calibrate our models to the water level data from 18 tide gauges along the coasts of United States, and after validation, we estimate the frequency of NF associated with the SLR projections in year 2030 (under RCPs 2.6 and 8.5), along with their 90% bands, at each gauge. The historical NF-SLR data are very noisy, and show large changes in variability (heteroscedasticity) with SLR. Prior models in the literature do not properly account for the observed heteroscedasticity, and thus their projected uncertainties are highly suspect. Among the models used in this study, the Negative Binomial Distribution GLM with GP best characterizes the uncertainties associated with NF estimates; on validation data (Formula presented.) 93% of the points fall within the 90% credible limit, showing our approach to be a robust model for uncertainty quantification.

Public participation geographic information systems (PPGIS) have been increasingly used to assess resident spatial knowledge of environmental hazards and to validate and supplement expert estimates of hazardous areas with local knowledge, but few studies have demonstrated methods for directly comparing local and expert knowledge of the spatial distribution of hazards. This study collected PPGIS digital sketch maps of flood-prone areas from 166 residents living adjacent to the Newport Bay Estuary in Southern California to examine variations in spatial knowledge of flood risk. First, we assessed agreement among participants and found that residents of areas with a higher percentage of homeowner, older, and higher income residents had greater agreement regarding areas at risk of flooding. Second, we introduced composite indices to assess the agreement between participant sketches of flood-prone areas with modeled estimates of the distribution of flood hazards, and found that the level of agreement between local and expert knowledge varied by the scale of analysis and by personal and contextual factors. Respondents with higher educational attainment, household income, and homeownership were associated with greater agreement between resident sketch maps and expert estimates of hazardous areas. Results inform spatial aspects of flood risk planning and communication by demonstrating how digital sketch maps can be used to identify potential shortcomings of expert hazard models, as well as hazardous areas where resident risk perception may be weak.

Mean sea level has risen tenfold in recent decades compared to the most recent millennia, posing a serious threat for population and assets in flood-prone coastal zones over the next century. An increase in the frequency of nuisance (minor) flooding has also been reported due to the reduced gap between high tidal datums and flood stage, and the rate of sea level rise (SLR) is expected to increase based on current trajectories of anthropogenic activities and greenhouse gases emissions. Nuisance flooding (NF), however nondestructive, causes public inconvenience, business interruption, and substantial economic losses due to impacts such as road closures and degradation of infrastructure. It also portends an increased risk in severe floods. Here we report substantial increases in NF along the coasts of United States due to SLR over the past decades. We then take projected near-term (2030) and midterm (2050) SLR under two representative concentration pathways (RCPs), 2.6 and 8.5, to estimate the increase in NF. The results suggest that on average, - 80 ± 10% local SLR causes the median of the NF distribution to increase by 55 ± 35% in 2050 under RCP8.5. The projected increase in NF will have significant socio-economic impacts and pose public health risks in coastal regions. Key Points Nuisance flooding substantially increases in a warming climate An ~80% increase in sea level by 2050 increases nuisance flooding by ~55% Increased nuisance flooding leads to socio-economic and public health impacts.

Porous shallow-water models (porosity models) simulate urban flood flows orders of magnitude faster than classical shallow-water models due to a relatively coarse grid and large time step, enabling flood hazard mapping over far greater spatial extents than is possible with classical shallow-water models. Here the errors of both isotropic and anisotropic porosity models are examined in the presence of anisotropic porosity, i.e., unevenly spaced obstacles in the cross-flow and along-flow directions, which is common in practical applications. We show that porosity models are affected by three types of errors: (a) structural model error associated with limitations of the shallow-water equations, (b) scale errors associated with use of a relatively coarse grid, and (c) porosity model errors associated with the formulation of the porosity equations to account for sub-grid scale obstructions. Results from a unique laboratory test case with strong anisotropy indicate that porosity model errors are smaller than structural model errors, and that porosity model errors in both depth and velocity are substantially smaller for anisotropic versus isotropic porosity models. Test case results also show that the anisotropic porosity model is equally accurate as classical shallow-water models when compared directly to gage measurements, while the isotropic model is less accurate. Further, results show the anisotropic porosity model resolves flow variability at smaller spatial scales than the isotropic model because the latter is restricted by the assumption of a Representative Elemental Volume (REV) which is considerably larger than the size of obstructions. These results point to anisotropic porosity models as being well-suited to whole-city urban flood prediction, but also reveal that point-scale flow attributes relevant to flood risk such as localized wakes and wave reflections from flow obstructions may not be resolved.