Recent unprecedented events have highlighted that the existing approach to managing flood risk is inadequate for complex urban systems because of its overreliance on simplistic methods at ...coarse‐resolution large scales, lack of model physicality using loose hydrologic‐hydraulic coupling, and absence of urban water infrastructure at large scales. Distributed models are a potential alternative as they can capture the complex nature of these events through simultaneous tracking of hydrologic and hydrodynamic processes. However, their application to large‐scale flood mapping and forecasting remains challenging without compromising on spatiotemporal resolution, spatial scale, model accuracy, and local‐scale hydrodynamics. Therefore, it is essential to develop techniques that can address these issues in urban systems while maximizing computational efficiency and maintaining accuracy at large scales. This study presents a physically based but computationally efficient approach for large‐scale (area > 103 km2) flood modeling of extreme events using a distributed model called Interconnected Channel and Pond Routing. The performance of the proposed approach is compared with a hyperresolution‐fixed‐mesh model at 60‐m resolution. Application of the proposed approach reduces the number of computational elements by 80% and the simulation time for Hurricane Harvey by approximately 4.5 times when compared to the fixed‐resolution model. The results show that the proposed approach can simulate the flood stages and depths across multiple gages with a high accuracy (R2 > 0.8). Comparison with Federal Emergency Management Agency building damage assessment data shows a correlation greater than 95% in predicting spatially distributed flooded locations. Finally, the proposed approach can estimate flood stages directly from rainfall for ungaged streams.
Plain Language Summary
Climate change and land development or urbanization is expected to exacerbate both the intensity and frequency of extreme flooding worldwide. As the flood severity rises, there is a growing need to develop flood prediction and alert systems that provide fast and reliable forecasts. Currently, it is extremely difficult to identify how much, when, and where the flooding will occur, which can create uncertainty in evacuation planning and preparation. This study proposes a method to improve the urban flood prediction by incorporating more physicality into the numerical flood models, which enables a better estimation of the depth, location, and arrival time of flooding. The graphical elements used to construct the models result in a better representation of the real‐world physical features, thereby improving the accuracy of flood simulation. Moreover, the approach presented here also decreases the computation time required to simulate flooding, which is vital for providing timely forecasts. The proposed methods are tested across a large and complex urban system using the rainfall from Hurricane Harvey (2017) and validated using three additional flood events in Texas, United States.
Key Points
Fully distributed hydraulics‐hydrology, flexible surface mesh, and spatiotemporally adaptive hydraulic time stepping are incorporated
Unstructured mesh development is controlled by incorporating graphical features designed to maximize efficiency while maintaining accuracy
Proposed approach is 80% more efficient in simulating the flood hydrodynamics of Hurricane Harvey compared to a fixed‐resolution model
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This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel ...depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time.
Plain Language Summary
We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2 of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods.
Key Points
First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)
In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D models
By 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West
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This study analyzes the flash flood event of two ungauged ephemeral streams in Olympiada region (Chalkidiki, North Greece), which occurred at the 21–22 of November 2019. Aim of the study is to ...reconstruct the specific flash flood event, investigate the causes of flood generation mechanisms, evaluate the performance of SCS‐CN hydrological and HEC‐RAS hydraulic models, investigate the relation between extreme flash floods and human intervention, using the combination of ground and aerial observations obtained from the field survey and unmanned aerial vehicles (UAVs), respectively. The results of the specific discharge ranged between 9 and 11 m3 s−1 km2, values that are typical for flash flood events in Mediterranean region. The comparison between the observed and simulated values of flood extent showed sufficiently good performance of the hydraulic model (CSI = 82%). However, the statistical analysis of the observed and simulated flood depths displayed a flood depth overestimation by the applied model, despite that the values of the used statistic indexes are acceptable (RMSE = 0.35 m, SD = 0.53, NSE = 0.56, PBIAS = 11.26%). The model overestimation of flood depth was attributed to the DEM low resolution and quality. Ground and aerial observations depicted the alluvial fan activation, the alternation of flow paths and the huge sediment transport. Human intervention in main streams, urban sprawl, wet AMC and sediment transport were among the main factors that contributed to the flash flood generation. This integrated approach revealed the necessity of the constant evaluation and validation of hydrological and hydraulic models in small ungauged Mediterranean watersheds and ephemeral streams. The use of UAVs in combination with ground observations and hydraulic simulation could significantly contribute to the enhanced understanding of flash flood mechanisms, in the direction of flood risk mitigation, improvement of the planning efficiency of flood prevent measures, flood hazard estimation, evolution of flood warning systems and floodplain geomorphology analysis.
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This paper reports the development of a ∼30 m resolution two‐dimensional hydrodynamic model of the conterminous U.S. using only publicly available data. The model employs a highly efficient numerical ...solution of the local inertial form of the shallow water equations which simulates fluvial flooding in catchments down to 50 km2 and pluvial flooding in all catchments. Importantly, we use the U.S. Geological Survey (USGS) National Elevation Dataset to determine topography; the U.S. Army Corps of Engineers National Levee Database to explicitly represent known flood defenses; and global regionalized flood frequency analysis to characterize return period flows and rainfalls. We validate these simulations against the complete catalogue of Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) maps and detailed local hydraulic models developed by the USGS. Where the FEMA SFHAs are based on high‐quality local models, the continental‐scale model attains a hit rate of 86%. This correspondence improves in temperate areas and for basins above 400 km2. Against the higher quality USGS data, the average hit rate reaches 92% for the 1 in 100 year flood, and 90% for all flood return periods. Given typical hydraulic modeling uncertainties in the FEMA maps and USGS model outputs (e.g., errors in estimating return period flows), it is probable that the continental‐scale model can replicate both to within error. The results show that continental‐scale models may now offer sufficient rigor to inform some decision‐making needs with dramatically lower cost and greater coverage than approaches based on a patchwork of local studies.
Key Points
A 30 m resolution flood hazard model of the entire conterminous United States is built using publicly available data
Delineations of flood hazard are comprehensively validated against United States government agency benchmarks
Model performance is largely comparable to quality local models, offering cheaper hazard information with complete spatial coverage
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Reliable flood damage assessment is important for decision‐making in flood risk management. Flood damage assessment is often done with damage curves based only on water depth. These depth‐damage ...curves are usually developed based on data from a specific location and specific flood conditions. Such depth‐damage curves tend to be applied outside the scope of their validity. Validation studies show that in such cases depth‐damage curve are not very reliable, probably due to excluded influencing variables. The expectation is that the inclusion of more variables in a damage function will improve its transferability. We compare multi‐variable models based on Bayesian Networks and Random Forests developed on the basis of flood damage data sets from Germany and The Netherlands. The performance of the models is tested on a validation sub‐set of both countries' data. The models are also updated with data from the other country and then tested again. The results show that the German models (BN/RF‐FLEMOps) perform better in the Netherlands than the Dutch models (BN/RF‐Meuse) perform in Germany. This is probably because the FLEMOps models are based on more heterogeneous data than the Meuse models. The FLEMOps models, therefore, are better able to capture damages processes from other events and in other locations. Model performance improves via updating the models with data from the location to which the model is transferred to. The results show that there is high potential to develop improved damage models, by training multi‐variable models with heterogeneous data, for example from multiple flood events and locations.
Key Points
Multi‐variable flood damage models can be transferred between locations, provided the training data are similar
Flood damage collection efforts should focus on acquiring heterogeneous data, instead of collecting a large quantity of data only for a single event in one location
There is a high potential to develop more broadly applicable flood damage models, by training multi‐variable models with heterogeneous data from multiple flood events
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Flood susceptibility in the Lower Connecticut River Valley Region attributable to nonclimatic flood risk factors is mapped using a quantitative method using logistic regression. Flood risk factors ...considered include elevation, slope, curvature (concave, convex, or flat), distance to water, land cover, vegetative density, surficial materials, soil drainage, and impervious surface. Values of factors at point locations were correlated to whether a location was located within or outside of the U.S. Federal Emergency Management Agency 100‐year Special Flood Hazard Area (SFHA). The Lower Connecticut River Valley Region was divided into urban, rural, and coastal subregions to assess the differences in factor contributions to flood susceptibility between different region types; for each region flood risk factors were extracted from 4,000 points, of which an equal number were within or outside of the 100‐year SFHA. Logistic regression coefficients were obtained. It was found that elevation and distance to water have the greatest contribution to flood susceptibility in the urban and coastal subregions, whereas distance to water and surficial materials dominate in the rural subregion. The contribution of land use to flood susceptibility increased by over 200% between the rural and urban regions. Probabilities of flooding were computed using each regional logistic regression equation. Several areas classified as very high risk (80–100%) and high risk (60–80%) were located outside of the SFHA and included several types of infrastructure critical for human health, safety, and education. This study demonstrates the utility of logistic regression as an efficient methodology to map regional flood susceptibility.
Plain Language Summary
Flooding is one of the most severe and potentially devastating natural disasters that can occur. Floods can come in many forms, including river, coastal, and flash flooding. Whenever and wherever any of these types of flooding occur, long‐term planning and adaptation, preparedness, and response time are all critical factors in reducing the overall impacts. Awareness of areas that are currently prone and will remain prone to flooding in the future is essential to consider in both short‐term and long‐term planning. Such awareness comes from an understanding of a combination not only of regional climatic factors but also of nonclimate factors that relate to natural, physical, and development characteristics. The current study estimates the risk of flooding throughout the Lower Connecticut River Valley Region (LCRVR) based on site and regional characteristics not related to climate. Several methods were considered to estimate flood risk; the method that was finally selected for this study involves a statistical approach in which a data set having one or more independent variables that produce a binary value of no or yes (0 or 1, respectively) for the dependent variable is analyzed. The independent variables in this case include several nonclimate factors related to flood risk that could potentially affect the region and for which sufficient data were available and are referred to as flood risk factors. Flood risk factors considered include elevation, land slope, land curvature (concave, convex, or flat), distance to water body, land cover, density of vegetation, surface geology, ability of the soil to drain water, and the percent of impervious surface (e.g., pavement). The objective is to link each of the flood risk factors to the dependent variables, which in this case is the occurrence of flooding for a flood event that is estimated to occur on average once in every 100 years. It was found that the overall quality of recent satellite images of the LCRVR during large flood events was not sufficient for the current analysis; therefore, it was decided to use the U.S. Federal Emergency Management Agency 100‐year Special Flood Hazard Area (SFHA) to indicate areas where flood inundation would occur. The advantage of using the SFHA and the selected statistical modeling methodology is that they allow the contribution of each flood risk factor within the SFHA to be estimated and then applied to the entire study region to identify additional areas outside of the SFHA that have high flood risk. The LCRVR was divided into three subregions (urban, rural, and coastal) to accentuate the differences in the contributions of each flood risk factor to flood risk between an urban and a rural area and between inland and coastal areas; for each subregion 4,000 point locations were randomly chosen from which to extract data for each flood risk factor. An equal number of these points were selected in locations that were within and outside of the SFHA for each subregion. Site data for each flood risk factor were extracted and associated with a 1 if the location was within the SFHA and a 0 otherwise. The resulting relations between each flood risk factor and flood occurrence were analyzed so that regression coefficients could be estimated for each factor, the magnitude of which indicates the relative strength of each flood risk factor's influence on flooding in a subregion. It was found that elevation and distance to water have the most influence on flood risk in the urban and coastal subregions, whereas distance to water and surface geology dominate in the rural subregion. The contribution of elevation and land use were also found to increase the most between the rural and urban subregions. The coefficients for each subregion are then used to assign probabilities of flooding to all locations over a grid covering that subregion. The results for each subregion were combined to create an overall flood probability map of the LCRVR. Probabilities were classified very low risk (0–20%), low risk (20–40%), medium risk (40–60%), high risk (60–80%), and very high risk (80–100%). It was observed that several areas classified as very high risk and high risk were located outside of the SFHA. Several types of infrastructure critical for human health, safety, and education were finally overlaid on the flood risk map to identify those assets that are most vulnerable to the 100‐year flood and may therefore require additional flood risk mitigation.
Key Points
Elevation, distance to water, and surficial materials had the highest contributions to flood susceptibility throughout the study area
The contribution of elevation and land use to flood susceptibility increased substantially when comparing the urban to the rural subregion
Very high and high susceptible areas add over 6% of nonwater and wetland area to the SFHA, including 8% more developed area
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7.
Foreword Parsons, Melissa
Australian journal of emergency management,
04/2022, Volume:
37, Issue:
2
Journal Article
Peer reviewed
Open access
In this issue of the Australian Journal of Emergency Management, Andrew Gissing (Risk Frontiers) observes the aftermath of the recent flooding in Lismore through the lens of two decades of experience ...researching floods in the area. Flood risk, mitigation, urban planning, emergency services and community response intersect to provide both challenges, and solutions, for Lismore and similar flood-prone settlements.
Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have ...exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails.
Plain Language Summary
Statistical distributions are used to estimate the probability of flood peaks, which in turn is needed for risk management and the design of flood protection. Flood peak distributions often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic (light tailed behavior). This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior. In this review, we summarize the knowledge about the causes of heavy flood tails. To this end, we discuss the flood generation processes in the atmosphere, catchment, and river system, that tend to generate heavy‐tailed flood peak distributions.
Key Points
Heavy tail behavior of flood peak distributions may lead to surprise and high flood damage
We propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions
We review to which extent the current knowledge supports or contradicts these hypotheses
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Extreme hydrological phenomena are one of the most common causes of human life loss and material damage as a result of the manifestation of natural hazards around human communities. Climatic changes ...have directly impacted the temporal distribution of previously known flood events, inducing significantly increased frequency rates as well as manifestation intensities. Understanding the occurrence and manifestation behavior of flood risk as well as identifying the most common time intervals during which there is a greater probability of flood occurrence should be a subject of social priority, given the potential casualties and damage involved. However, considering the numerous flood analysis models that have been currently developed, this phenomenon has not yet been fully comprehended due to the numerous technical challenges that have arisen. These challenges can range from lack of measured field data to difficulties in integrating spatial layers of different scales as well as other potential digital restrictions.The aim of the current book is to promote publications that address flood analysis and apply some of the most novel inundation prediction models, as well as various hydrological risk simulations related to floods, that will enhance the current state of knowledge in the field as well as lead toward a better understanding of flood risk modeling. Furthermore, in the current book, the temporal aspect of flood propagation, including alert times, warning systems, flood time distribution cartographic material, and the numerous parameters involved in flood risk modeling, are discussed.
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