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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.

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.

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 community remains unclear. This research uses primary data from an in-person survey of 164 residents of Newport Beach, California during the spring of 2014 to ascertain the current 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 for risk 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 age is the central factor in predicting the sources people use to receive risk information. We follow the analysis by discussing this finding and its policy implications.