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.

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.

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 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.

The communication of information about natural hazard risks to the public is a difficult task for decision makers. Research suggests that newer forms of technology present useful options for building disaster resilience. However, how effectively these newer forms of media can be used to inform populations of the potential hazard risks in their communityremainsunclear. Thisresearch uses primary data from an in-person surveyof164 residents of Newport Beach, Californiaduring the spring of 2014 to ascertain thecurrent and preferred mechanisms through which individuals receive information on flood risks in their community. Factor analysis of survey data identified two predominant routes of dissemination forrisk information: older traditional media and newer social media sources. A logistic regression model was specified to identify predictors for choosing a particular communication route. This analysis revealed that ageis the central factor in predicting the sources people useto receive risk information. We follow the analysis by discussing this finding and its policy implications.

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.

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.

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 5 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, 10 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 15 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.

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