Past attempts to estimate rainfall-driven flood risk across the US either have incomplete coverage, coarse resolution or use overly simplified models of the flooding process. In this paper, we use a ...new 30 m resolution model of the entire conterminous US with a 2D representation of flood physics to produce estimates of flood hazard, which match to within 90% accuracy the skill of local models built with detailed data. These flood depths are combined with exposure datasets of commensurate resolution to calculate current and future flood risk. Our data show that the total US population exposed to serious flooding is 2.6-3.1 times higher than previous estimates, and that nearly 41 million Americans live within the 1% annual exceedance probability floodplain (compared to only 13 million when calculated using FEMA flood maps). We find that population and GDP growth alone are expected to lead to significant future increases in exposure, and this change may be exacerbated in the future by climate change.
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
Continental–global-scale flood hazard models simulate design floods, i.e. theoretical flood events of a given probability. Since they output phenomena unobservable in reality, large-scale models are ...typically compared to more localised engineering models to evidence their accuracy. However, both types of model may share the same biases and so not validly
illustrate their predictive skill. Here, we adapt an existing continental-scale design flood framework of the contiguous US to simulate historical flood events. A total of 35 discrete events are modelled and compared to observations of flood extent, water level, and inundated buildings. Model performance was highly variable, depending on the flood event chosen and validation data used. While all events were accurately replicated in terms of flood extent, some modelled water levels deviated substantially from those measured in the field. Despite this, the model generally replicated the observed flood events in the context of terrain data vertical accuracy, extreme discharge measurement uncertainties, and observational field data errors. This analysis highlights the continually improving fidelity of large-scale flood hazard models, yet also evidences the need for considerable advances in the accuracy of routinely collected field and high-river flow data in order to interrogate flood inundation models more comprehensively.
Flood-related events are the most damaging natural hazard in the United States, yet many households at risk do not have flood insurance. Using detailed policy- and claims-level data from the National ...Flood Insurance Program (NFIP), we conduct a holistic analysis of the market for publicly provided flood insurance in the U.S., focusing on not only high-risk areas subject to an incomplete mandate requiring the purchase of insurance, but also lower risk areas where no such mandate exists. We are able to better understand determinants of demand for insurance in a setting with voluntary purchase and low take-up and therefore provide a more complete analysis of the market for flood insurance in the U.S. than previous work. In addition to exploring correlates of demand for flood insurance, this paper provides quasi-experimental estimates of households’ willingness-to-pay for flood insurance and finds strong evidence to suggest the NFIP failing to utilize full information on flood risk leads to adverse selection in the program.
Abstract
Cities have historically developed close to rivers and coasts, increasing human exposure to flooding. That exposure is exacerbated by changes in climate and population, and by urban ...encroachment on floodplains. Although the mechanisms of how urbanization affects flooding are relatively well understood, there have been limited efforts to assess the magnitude of floodplain encroachment globally and how it has changed in both space and time. Highly resolved global datasets of both flood hazard and changes in urban area from 1985 to 2015 are now available, enabling the reconstruction of the history of floodplain encroachment at high spatial resolutions. Here we show that the urbanized area in floodplains that have an average probability of flooding of 1/100 years, has almost doubled since 1985. Further, the rate of urban expansion into these floodplains increased by a factor of 1.5 after the year 2000. We also find that urbanization rates were highest in the most hazardous areas of floodplains, with population growth in these urban floodplains suggesting an accompanying increase in population density. These results reveal the scope, trajectory and extent of global floodplain encroachment. With tangible implications for flood risk management, these data could be directly used with integrated models to assess adaptation pathways for urban flooding.
The execution of hydraulic models at large spatial scales has yielded a step change in our understanding of flood risk. Yet their necessary simplification through the use of coarsened terrain data ...results in an artificially smooth digital elevation model with diminished representation of flood defense structures. Current approaches in dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socioeconomic data. Here, we introduce a novel solution for application at scale. The geomorphometric characteristics of defense structures are sampled, and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source digital elevation model. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed U.S. levee crest heights. When incorporated into a continental‐scale hydrodynamic model based on LISFLOOD‐FP and compared to local flood models in Iowa (USA), median correspondence was 69% for high‐frequency floods and 80% for low‐frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted, and risk‐based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering‐grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved‐accuracy simulations of flood inundation.
Plain Language Summary
Traditional flood risk assessments are carried out using computer models built with local data, but their spatial coverage is impaired by how expensive and time‐consuming they are to produce. Recent advances in data availability, understanding of necessary physical process representation, and computational capacity have enabled hydraulic models of the entire globe to be built in an automated fashion at a fraction of the financial and human cost. However, their accuracy can be significantly impaired by a lack of information on flood defenses. As the model is built, elevation data are coarsened to reduce the number of calculations required to simulate flooding over such wide areas. This results in flood defense structures being smoothed out of the terrain information used in the model. Publicly available defense inventories are of insufficient coverage to ameliorate this issue. In this paper, a method is presented, which automatically detects levee‐like features in high‐resolution elevation data and accurately represents their heights during this necessary coarsening process. Simulating flood inundation over this “defended” topography results in high correspondence between local models and observations for test cases in the United States and Italy, with improvements particularly felt where a lack of defense information is the dominant source of error.
Key Points
Flood defense representation is presently poor in large‐scale flood models, impairing their ability to map flood hazard accurately
A new method is presented, which automatically identifies hydraulic structures in terrain data and accurately preserves their elevations
Hydraulic simulations where a lack of defense data is the dominant error show significant improvements in skill when incorporating this method
The flooding brought about by compound coastal flooding events can be devastating. Before, during, and immediately following these events, flood inundation maps (FIMs) can provide essential ...information to emergency management. However, there are a number of frameworks capable of estimating FIMs during flood events. In this article, we evaluate FIMs derived from three such frameworks in the context of Hurricane Harvey. Our analysis reveals that each of the three FIM frameworks provides different FIMs that differ in their level of accuracy. Each of the three FIMs also produces different exposure and consequence estimates because of their spatial differences. This investigation highlights the need for a centralized means of vetting and adjudicating multiple FIMs during compound flood events empowered by the ability to distribute FIMs as geographic information system (GIS) services and coalesce FIMs into a common operating picture. Furthermore, we provide evidence that the ability to produce multi-model estimates of FIMs to create probabilistic FIMs may provide a better product than the use of a lone FIM.
Typical flood models do not take into consideration the spatial structure of flood events, which can lead to errors in the estimation of flood risk at regional to continental scales. Large‐scale ...stochastic flood models can simulate synthetic flood events with a realistic spatial structure, although this method is limited by the availability of gauge data. Simulated discharge from global hydrological models has been successfully used to drive stochastic modeling in data‐rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally: synthetic flood events in different regions of the world (Australia, South Africa, South America, Malaysia, Thailand, and Europe) are simulated using both gauged and modeled discharge. By comparing them, we analyze how a model‐based approach can simulate spatial dependency in large‐scale flood modeling. The results show a promising performance of the model‐based approach, with errors comparable to those obtained over data‐rich sites: a model‐based approach simulates the joint occurrence of relative flow exceedances at two given locations similarly to when a gauge‐based statistical model is used. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data‐scarce regions and loss exceedance curves where exposure data are available.
Key Points
Large scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure even in data‐poor regions
Using a model‐based multivariate extreme model provides more robust dependence estimates than empirical distance decay functions
Modeled flow can be used in data poor‐regions to characterize dependence in large‐scale stochastic flood models and estimate flood risk
Because climate change will increase the frequency and intensity of precipitation extremes and coastal flooding, there is a clear need for an integrated hydrology and hydraulic system that has the ...ability to model the hydrologic conditions over a long period and the flow dynamic representations of when and where the extreme hydrometeorological events occur. This system coupling provides comprehensive information (flood wave, inundation extents, and depths) about coastal flood events for emergency management and risk minimization. This study provides an integrated hydrologic and hydraulic coupled modeling system that is based on the Coupled Routing and Excessive Storage (CREST) model and the Australia National University-Geophysics Australia (ANUGA) model to simulate flood. Forced by the near-real-time Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimates, this integrated modeling system was applied during the 2017 Hurricane Harvey event to simulate the streamflow, the flood extent, and the inundation depth. The results were compared with postevent high-water-mark survey data and its interpolated flood extent by the U.S. Geological Survey and the Federal Emergency Management Agency flood insurance claims, as well as a satellite-based flood map, the National Water Model (NWM), and the Fathom (LISFLOOD-FP) model simulated flood map. The proposed hydrologic and hydraulic model simulation indicated that it could capture 87% of all flood insurance claims within the study area, and the overall error of water depth was 0.91 m, which is comparable to the mainstream operational flood models (NWM and Fathom).
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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