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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.

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.

Damage and disruption from flooding have rapidly escalated over recent decades. Knowing who and what is at risk, how these risks are changing, and what is driving these changes is of immense importance to flood management and policy. Accurate predictions of flood risk are also critical to public safety. However, many high‐profile research studies reporting risks at national and global scales rely upon a significant oversimplification of how floods behave—as a level pool—an approach known as bathtub modeling that is avoided in flood management practice due to known biases (e.g., >200% error in flood area) compared to physics‐based modeling. With publicity by news media, findings that would likely not be trusted by flood management professionals are thus widely communicated to policy makers and the public, scientific credibility is put at risk, and maladaptation becomes more likely. Here, we call upon researchers to abandon the practice of bathtub modeling in flood risk studies, and for those involved in the peer‐review process to ensure the conclusions of impact analyses are consistent with the limitations of the assumed flood physics. We document biases and uncertainties from bathtub modeling in both coastal and inland geographies, and we present examples of physics‐based modeling approaches suited to large‐scale applications. Reducing biases and uncertainties in flood hazard estimates will sharpen scientific understanding of changing risks, better serve the needs of policy makers, enable news media to more objectively report present and future risks to the public, and better inform adaptation planning. Plain Language Summary: Numerous studies of flood risks under climate change, such as changing sea levels and flood hydrology, assess exposure by assuming that projected water levels for oceans and rivers extend horizontally across the land surface. However, this represents a significant over‐simplification of flooding that can strongly bias estimates of flood exposure (e.g., a factor of two error). Of particular concern is that biased results sometimes feed into climate hype from the news media, which can undermine public trust in climate science. Data and models suited to more complete modeling of flooding are presented. Key Points: Estimates of flood risks can be strongly biased by bathtub hazard modelingPhysics‐based modeling reduces flood risk bias compared to bathtub modeling and is now feasible globallyShort‐format, high‐impact journals have contributed to "climate hype" stemming from biased bathtub modeling studies [ABSTRACT FROM AUTHOR]

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.

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national‐scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo‐Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo‐Drain estimates of population and property value exposed to 1%‐ and 5%‐annual‐chance hazards diverge at finer scales of governance, for example, by 4‐ to 18‐fold at the municipal scale. FSF and PRIMo‐Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national‐scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models. Plain Language Summary: Flooding presents a significant risk to human activities and development, and its impacts have been rapidly increasing over recent decades. However, government flood mapping in the U.S. has not kept pace with adaptation needs, and communities have now turned to other sources of information to inform planning and design decisions. This study examines the uncertainties of flood hazard data available from the First Street Foundation across Los Angeles, California, the second largest city in the U.S. With a comparision to two different models that more fully capture processes known to affect urban flooding, we show concerning levels of uncertainty in the First Street Foundation data at scales of municipalities and properties. These results highlight the need for more robust validation of urban flood hazard models, and caution against overliance of First Street Foundation data for urban flood management. Key Points: Flood risks are concentrated in urban areas, where national‐scale hazard models are less accurateFlood exposure estimates become increasingly uncertain at finer scales and may misrepresent the social distribution of riskRefined and validated urban flood models are needed to effectively and equitably manage increasingly severe flood risks [ABSTRACT FROM AUTHOR]