Human exposure to floods continues to increase, driven by changes in hydrology and land use. Adverse impacts amplify for socially vulnerable populations, who disproportionately inhabit flood-prone ...areas. This study explores the geography of flood exposure and social vulnerability in the conterminous United States based on spatial analysis of fluvial and pluvial flood extent, land cover, and social vulnerability. Using bivariate Local Indicators of Spatial Association, we map hotspots where high flood exposure and high social vulnerability converge and identify dominant indicators of social vulnerability within these places. The hotspots, home to approximately 19 million people, occur predominantly in rural areas and across the US South. Mobile homes and racial minorities are most overrepresented in hotspots compared to elsewhere. The results identify priority locations where interventions can mitigate both physical and social aspects of flood vulnerability. The variables that most distinguish the clusters are used to develop an indicator set of social vulnerability to flood exposure. Understanding who is most exposed to floods and where, can be used to tailor mitigation strategies to target those most in need.
Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors. Here we introduce a high‐accuracy global DEM at 3″ ...resolution (~90 m at the equator) by eliminating major error components from existing DEMs. We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques. After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill‐valley structures, became clearly represented. We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.
Key Points
A high‐accuracy global digital elevation model (DEM) was developed by removing multiple height error components from existing DEMs
Landscape representation was improved, especially in flat regions where height error magnitude was larger than actual topography variation
The improved‐terrain DEM is helpful for any geoscience applications which are terrain dependent, such as flood inundation modelling
Plain Language Summary
Terrain elevation maps are fundamental input data for many geoscience studies. While very precise Digital Elevation Models (DEMs) based on airborne measurements are available in developed regions of the world, most areas of the globe rely on spaceborne DEMs which still include non‐negligible height errors for geoscience applications. Here we developed a new high accuracy map of global terrain elevations at 3" resolution (~90m at the equator) by eliminating multiple error components from existing spaceborne DEMs. The height errors included in the original DEMs were separated from actual topography signals and removed using a combination of multiple satellite datasets and filtering techniques. After error removal, global land areas mapped with ±2m or better accuracy increased from 39% to 58%. Significant improvements were found, especially in flat regions such as river floodplains. Here detected height errors were larger than actual topography variability, and following error removal landscapes features such as river networks and hill‐valley structures at last became clearly represented. The developed high accuracy topography map will expand the possibility of geoscience applications that require high accuracy elevation data such as terrain landscape analysis, flood inundation modelling, soil erosion analysis, and wetland carbon cycle studies.
A high-resolution global flood hazard model Sampson, Christopher C.; Smith, Andrew M.; Bates, Paul D. ...
Water resources research,
September 2015, Letnik:
51, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and ...economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data‐scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross‐disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high‐resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2‐D only variant and an independently developed pan‐European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next‐generation global terrain data sets will offer the best prospect for a step‐change improvement in model performance.
Key Points:
High‐resolution flood hazard model that employs globally available data sets
Quantitative assessment of model performance relative to benchmark local data
Performance adequate for certain real world applications in data‐scarce regions
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
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.
Abstract
Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there ...are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA’s 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.
Preclinical and clinical development of agents that inhibit cell-cycle progression have brought an understanding of the feasibility of targeting various cell-cycle regulators in patients with cancer. ...Small molecule inhibitors targeting key proteins that participate in cell-cycle progression including the cyclin-dependent kinases and checkpoint kinases induce cell-cycle arrest and apoptosis in neoplastic cells. Early phase I studies demonstrate targeted inhibitors can be administered safely in adult and pediatric cancer patients, but these agents generally show limited clinical benefits as single agents. In this review, we discuss biological mechanisms that support dual combination strategies of cell-cycle inhibition with chemotherapeutic agents that are anticipated to achieve rationally targeted therapies for cancer patients. The rationale for evaluating these combination strategies is that DNA damage renders tumors highly responsive to irreversible cell-cycle arrest therapy. This approach is predicted to generate less intensive therapies and to maximize the efficacy of individual agents against solid tumors and hematologic malignancies.
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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
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