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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. A statistical mechanics approach unveils how urban layout influences flood hazards linking flood risk to factors like ground slope, porosity, building arrangement symmetry, and chord length, offering a scalable model applicable worldwide. [ABSTRACT FROM AUTHOR]

Cycles of wildfire and rainfall produce sediment‐laden floods that pose a hazard to development and may clog or overtop protective infrastructure, including debris basins and flood channels. The compound, post‐fire flood hazards associated with infrastructure overtopping and clogging are challenging to estimate due to the need to account for interactions between sequences of wildfire and storm events and their impact on flood control infrastructure over time. Here we present data sources and calibration methods to estimate infrastructure clogging and channel overtopping hazards on a catchment‐by‐catchment basis using the Post‐Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Publicly available data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post‐fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal where infrastructure is undersized to contain present‐day and future overtopping hazards based on current design standards. Model limitations and sources of uncertainty are also discussed. Plain Language Summary: Communities at the foot of the mountains face an especially dangerous type of flooding called "sediment‐laden floods." Many such communities in the southwestern U.S. are protected from water floods by flood infrastructure designed to trap sediment at the mouth of mountain canyons and convey only water flows safely past developed areas to a downstream water body. Sediment‐laden floods, which are more forceful and typically larger than water floods, are more likely to happen during storms over burned mountain canyons soon after a wildfire occurs. However, estimating the likelihood that sediment‐laden floods fill and overtop flood infrastructure is challenging since existing sediment‐laden flood models do not explicitly consider the role of flood infrastructure. Here we present the Post‐Fire Flood Hazard Model (PF2HazMo), a model that can estimate the likelihood of post‐fire floods on a canyon‐by‐canyon basis accounting for flood infrastructure. Environmental data collected following a major wildfire is used to apply PF2HazMo to three mountain canyons in southern California, and we find that it predicts the number of floods accurately relative to observed post‐fire flood channel overtopping events. Further, the model is used to predict the frequency of floods due to infrastructure overtopping under both present‐day and future wildfire scenarios. Key Points: Flood risks are heightened by clogging of infrastructure with sediment, which can occur from sequences of storms especially after wildfiresA framework for calibration and validation of a post‐fire infrastructure clogging and flood hazard model is presentedModel applications reveal whether infrastructure is adequately sized to meet design levels of protection [ABSTRACT FROM AUTHOR]

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]

Understanding the impact of digital, interactive flood hazard maps and flood control systems on public flood risk perception could enhance risk communication and management. This study analyzed a survey of residents living near California's Newport Bay Estuary and found that self-rated nonspatial perceptions of dread or concern over future flood impacts were positively associated with spatial awareness of flood-prone areas. Trust in flood control systems was associated with greater spatial flood hazard awareness but weaker nonspatial dread or concern, suggesting residents who witnessed and trust flood control systems developed a confident sense of flood-prone areas and that this confidence reduced the overall nonspatial sense of flood dread and concern. Viewing a flood hazard map eliminated differences in spatial hazard awareness between subgroups that existed prior to viewing a map, and viewing a map with estimated flood depth and greater spatial differentiation was associated with higher levels of postmap spatial awareness. [ABSTRACT FROM AUTHOR]

Highlights • Detailed flood simulation for a whole city is achieved with parallel computing and upscaling. • Hourly flood simulations are completed in a matter of seconds to minutes. • Subcritical, supercritical and transcritical flows and compound flooding can be simulated. Abstract Simulation of flood inundation at metric resolution is important for making hazard information useful to a wide range of end-users involved in flood risk management, and addressing the alarming increase in flood losses that have been observed over recent decades. However, high data volumes and computational demands make this challenging over large spatial extents comparable to the metropolitan areas of major cities where flood impacts are concentrated, especially for time-sensitive applications such as forecasting and repetitive simulation for uncertainty assessment. Additionally, several factors present difficulties for numerical solvers including combinations of steep and flat topography that promote transcritical flows, the need to resolve flow in relatively narrow features such as drainage channels and roadways in urban areas which channel flood water during extreme events, and the need to depict compound hazards resulting from the interaction of pluvial, fluvial and coastal flooding. A new flood inundation model is presented here to address these challenges. The Parallel Raster Inundation Model (PRIMo) solves the shallow-water equations on an upscaled grid that is far coarser than the underlying raster digital topographic model (DTM), and uses a subgrid modeling approach so that the solution benefits from DTM-scale topographic data. Additionally, an approximate Riemann solver is applied in an innovative way to integrate fluxes between cells, as needed to update the solution by the finite volume method, which makes the method applicable to subcritical, supercritical and transcritical flows. PRIMo is implemented using a two-dimensional domain decomposition approach to Single Process Multiple Data (SPMD) parallel computing, and overlapping communications and computations are implemented to yield ideal parallel scaling for well-balanced test cases. With both a subgrid model and ideal parallel scaling, the model can scale to meet the demands of any application. Several benchmarks are presented to demonstrate predictive skill and the potential for timely, whole-city, metric-resolution flooding simulations. Limitations of the methods and opportunities for improvements are also presented. [ABSTRACT FROM AUTHOR]

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, nonprofit, 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 [ABSTRACT FROM AUTHOR]

This research outlines a framework for the case-specific assessment of Green Infrastructure (GI) performance in mitigating flood hazard in small urban catchments. The urban hydrologic modeling tool (MUSIC) is coupled with a fine resolution 2D hydrodynamic model (BreZo) to test to what extent retrofitting an urban watershed with GI, rainwater tanks and infiltration trenches in particular, can propagate flood management benefits downstream and support intuitive flood hazard maps useful for communicating and planning with communities. The hydrologic and hydraulic models are calibrated based on current catchment conditions, then modified to represent alternative GI scenarios including a complete lack of GI versus a full implementation of GI. Flow in the hydrologic/hydraulic models is forced using a range of synthetic rainfall events with annual exceedance probabilities (AEPs) between 1–63% and durations from 10 min to 24 h. Flood hazard benefits mapped by the framework include maximum flood depths and extents, flow intensity (m 2 /s), flood duration, and critical storm duration leading to maximum flood conditions. Application of the system to the Little Stringybark Creek (LSC) catchment shows that across the range of AEPs tested and for storm durations equal or less than 3 h, presently implemented GI reduces downstream flooded area on average by 29%, while a full implementation of GI would reduce downstream flooded area on average by 91%. A full implementation of GI could also lower maximum flow intensities by 83% on average, reducing the drowning hazard posed by urban streams and improving the potential for access by emergency responders. For storm durations longer than 3 h, a full implementation of GI lacks the capacity to retain the resulting rainfall depths and only reduces flooded area by 8% and flow intensity by 5.5%. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Schubert, J.E.; Gallien, T.W.; Majd, M.S., and Sanders, B.F., 2015. Terrestrial laser scanning of anthropogenic beach berm erosion and overtopping. Anthropogenic berms are widely deployed to manage coastal flooding. The dynamic erosion of scraped berms exposed to waves and a rising tide in southern California was monitored with a terrestrial laser scanner (TLS) on three occasions in February and March of 2012. An improved characterization of initial berm geometry and the dynamics of berm erosion was pursued to accurately predict the onset and impact of coastal flooding associated with berm erosion and overtopping. TLS is shown to yield a digital terrain model (DTM) with a vertical accuracy of ca. 3 cm, indicating it is an excellent source of data for initializing mechanistic and/or empirical models that could be used to predict the onset and rate of wave overtopping. Minimum scan point spacings required to achieve this level of accuracy are investigated and reported. Additionally, a dimensionless water level representing the fractional submergence of the berm is identified as a good predictor of cumulative berm erosion under the test conditions. [ABSTRACT FROM AUTHOR]

Coastal flood risk in California is concentrated around urbanized embayments that are protected by infrastructure, such as levees, pumps, and flood walls, which pose a challenge to accurate flood prediction. A capability to predict coastal urban flooding at the parcel-scale (individual home or street) from high ocean levels (extreme high tides) is shown here by coupling a regional ocean forecasting system to an embayment-scale hydrodynamic model that incorporates detailed information about flood defenses. A unique flooding data set affords the rare opportunity to validate model predictions and allows us to identify model data that are essential for accurate forecasting. In particular, results show that flood defense height data are critical, and here, that information is supplied by a Real Time Kinematic Global Positioning System (RTK-GPS) survey, which yields ca. 1-cm, vertical root mean-squared error accuracy. Bathymetry surveys and aerial Light Detection and Ranging (LIDAR) data characterizing the embayment also prove essential. Moreover, hydrodynamic modeling of flood inundation is shown to significantly improve on planar surface models, which overestimate inundation, particularly when manipulated to account for run-up in a simplistic way. This is attributed to the transient nature of overtopping flows and motivates the need for dynamic, spatially-distributed overtopping models that are tailored to the urban environment. [ABSTRACT FROM AUTHOR]

Dam safety and flood risk management programs are dependent on damage predictions that are difficult to validate and subject to considerable uncertainty. The 1963 Baldwin Hills dam-break flood caused high-velocity flows exceeding 5 m/s and structural failure of 41 wood-framed residences built in the mid-1940s, 16 of which were completely washed out. The flood is revisited here to examine the predictive skill and variability of established structural damage models when coupled with a hydraulic flood model that predicts parcel-scale depths and velocities. Two-way coupling is introduced so that predictions of structural failure affect localized flood predictions, which in turn affects damage predictions, in contrast to one-way coupling where structural failure has no impact on flood predictions. Two damage states defined by structural failure (Level 2) and washout (Level 3) are considered, along with 10 different structural damage models. One damage model considers flood depth alone, while the remaining nine consider a combination of depth and velocity defined by a constant discharge, energy, or force. Two-way coupling is shown to yield predictions with 30% higher skill and 10% higher false alarms than one-way coupled models. Hence, there is a tradeoff between skill and false alarms that favors two-way coupling. Predictive skill is also shown to be sensitive to the structural damage classification and the damage model. Depth-based damage predictions yield low predictive skill as expected; however, across the nine velocity-based damage models, skill varies from 50 to 78% for structural failure and from 79 to 95% for washout. These results reveal a similar level of predictive uncertainty from the hydraulic model implementation, damage model, and damage classification. Results also point to flow force as a good predictor of both moderate (Level 2) and severe (Level 3) levels of damage. Through calibration, force thresholds of 0.75 and 9.5 m3/s2 are found to maximize model skill at 85 and 95% for Level 2 and 3 damage, respectively, and these values compare well with previously published thresholds for similar building types. Finally, the results reveal a model bias toward a relatively wide damage zone compared with the observations; therefore, the high skill predictions are accompanied by a high rate of false alarms. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: This work demonstrates a new process for communities to build flood resilience: collaborative flood modeling. Development of fine‐resolution flood models by scientists and engineers serves as a focal point for community stakeholders to build useful knowledge and tools to address flooding. A key issue shown here is that fine‐resolution flood visualizations help diverse community stakeholders build a more shared awareness about flooding, which creates conditions favorable to improved flood management. Key Points: Resident perception and awareness of flooding are enhanced more by fine‐resolution depth contour maps than FEMA mapsViewing fine‐resolution flood maps helps to minimize differences in flood perception across subgroups of a communityCollaborative flood modeling is a transdisciplinary process to coproduce actionable flood information and knowledge [ABSTRACT FROM AUTHOR]

Dual-grid models address the computational bottlenecks of large-scale (> 1 0 3 km 2) urban flood modeling with solution updates on a coarse grid that are informed by topographic data on a fine grid. However, dual-grid models may poorly resolve levees, leading to loss of accuracy. Here we present a grid edge classification method whereby specific edges of the coarse grid are flagged to gather nearby topographic data from the fine grid and create a contiguous physical barrier. The method relies on levee location data in a polyline format, and does not require levee height data since that information is stored on the fine grid. Using a 6804 km 2 model of the Los Angeles Metropolitan Region with 3 m topographic data and 987 km of levees, the proposed method is implemented and evaluated. Simulations using coarse grids of 15, 30 and 60 m capture flood extent consistent with fine-grid models based on a critical success index (CSI) of 90, 87 and 82%, respectively. Edge classification improves CSI up to 7 percentage points over a model with unclassified coarse grid edges, and reduces the false alarm ratio up to 10 percentage points. Differences in model performance across the study area are noted, including lower accuracy on urbanized alluvial fans. With compute costs that scale with the coarse grid, dual-grid models can efficiently realize more accurate large-scale models of urban flood hazards. • Spatial accuracy of large-scale flood model improved by 10% using levee polyline data. • Dual-grid model formulation supports high spatial accuracy with low compute costs. • Grid edge classification guides fine-grid data sampling to improve spatial accuracy. [ABSTRACT FROM AUTHOR]