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Urbanization and climate change are contributing to severe flooding globally, damaging infrastructure, disrupting economies, and undermining human well-being. Approaches to make cities more resilient to floods are emerging, notably with the design of flood-resilient structures, but relatively little is known about the role of urban form and its complexity in the concentration of flooding. We leverage statistical mechanics to reduce the complexity of urban flooding and develop a mean-flow theory that relates flood hazards to urban form characterized by the ground slope, urban porosity, and the Mermin order parameter which measures symmetry in building arrangements. The mean-flow theory presents a dimensionless flood depth that scales linearly with the urban porosity and the order parameter, with different scaling for disordered square- and hexagon-like forms. A universal scaling is obtained by introducing an effective mean chord length representative of the unobstructed downslope travel distance for flood water, yielding an analytical model for neighborhood-scale flood hazards globally. The proposed mean-flow theory is applied to probe city-to-city variations in flood hazards, and shows promising results linking recorded flood losses to urban form and observed rainfall extremes. 

Abstract 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. 

Abstract Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5 m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1 m SLR, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2 m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales. 

Communities near the wildland urban interface (WUI) are exposed to a mix of three interconnected hazards (wildfire, flood, and mudslide), and understanding multi-hazard perceptions is critically important for emergency preparation and hazard mitigation—particularly given the WUI’s rapid expansion and intensifying environmental hazards. Based on a survey of residents living near recent burn scars in Southern California, we document cross-over effects in hazard perceptions, where resident experience with one hazard was associated with greater hazard rankings for other hazards. Additionally, for all three hazards analyzed we document perceptions of increasing hazard levels with increasing spatial scales (home, near-home, neighborhood, and community), providing evidence of spatial optimism, or the tendency to discount proximate hazards. This study stresses the importance of using a multi-hazard and multi-scale approach for understanding and responding to local level environmental hazards.