Comparing earth observation and inundation models to map flood hazards

Hawker, Laurence; Neal, Jeffrey; Tellman, Beth; Ji, Liang; Schumann, Guy; Doyle, Colin; Sullivan, Jonathan A.; Savage, James; Tshimanga, Raphael M.

doi:10.1088/1748-9326/abc216

Cited by 25 publications

(26 citation statements)

References 64 publications

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“…Inter-model comparisons like this study do not answer the question of how well the individual flood layers agree with actual flood events. Future research can build upon this comparative study by conducting a validation analysis using flood delineation from satellite imagery, similar to the work by Bernhofen et al (2018) or Hawker et al (2020) but for a larger set of regions across the world (e.g. using the recent Global Flood Database; Tellman et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Average EarthEnv-DEM90 (Robinson et al, 2014) Precipitation (annual) Long-term (1950Long-term ( -2000 precipitation within the basin. Millimetres Annual average WorldClim v1.4 (Hijmans et al, 2005) Aridity index Ranges from 0 to 1: a value of 0 represents areas with no precipitation, and 1 represent areas where P ≥ PET. for the year 2015 (FAO UN, 2015).…”

Section: Decimetres Per Kilometrementioning

confidence: 99%

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Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?

Lindersson

Brandimarte

Mård

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Riverine flood risk studies often require the identification of areas prone to potential flooding. This modelling process can be based on either (hydrologically derived) flood hazard maps or (topography-based) hydrogeomorphic floodplain maps. In this paper, we derive and compare riverine flood exposure from three global products: a hydrogeomorphic floodplain map (GFPLAIN250m, hereinafter GFPLAIN) and two flood hazard maps (Flood Hazard Map of the World by the European Commission's Joint Research Centre, hereinafter JRC, and the flood hazard maps produced for the Global Assessment Report on Disaster Risk Reduction 2015, hereinafter GAR). We find an average spatial agreement between these maps of around 30 % at the river basin level on a global scale. This agreement is highly variable across model combinations and geographic conditions, influenced by climatic humidity, river volume, topography, and coastal proximity. Contrary to expectations, the agreement between the two flood hazard maps is lower compared to their agreement with the hydrogeomorphic floodplain map. We also map riverine flood exposure for 26 countries across the global south by intersecting these maps with three human population maps (Global Human Settlement population grid, hereinafter GHS; High Resolution Settlement Layer, hereinafter HRSL; and WorldPop). The findings of this study indicate that hydrogeomorphic floodplain maps can be a valuable way of producing high-resolution maps of flood-prone zones to support riverine flood risk studies, but caution should be taken in regions that are dry, steep, very flat, or near the coast.

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Section: Discussionmentioning

confidence: 99%

Section: Decimetres Per Kilometrementioning

confidence: 99%

Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?

Lindersson

Brandimarte

Mård

et al. 2021

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Moreover, we will add the snow water equivalent (SWE) data from SNODAS to characterize snowmelt-triggered flooding, which contributes to spring floods in northern CONUS and arid mountainous areas (Shen et al 2017b;Shen and Anagnostou 2017). Finally, we will attempt to utilize inundation map products from MODIS (Hawker et al 2019), Landsat (Jones 2019), Suomi-NPP (Li et al 2018), and passive microwave sensors (Du et al 2018) 1.…”

Section: Limitations and Future Developmentsmentioning

confidence: 99%

Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data

Yang

Shen

Yang

et al. 2022

Bulletin of the American Meteorological Society

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Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.

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“…The flood hazard maps based on rainfall events with 10, 25, 50 and 100 years were generated by widely and successfully applied LISFLOOD-FP flood model. This model is developed by the University of Bristol [19,20] which allows computation of water depth flows and 2-D water flow velocity in each cell of the raster grid across floodplains. It is based on a modified version of the momentum and continuity equations of full shallow water, neglecting only the convection force term, which can be assumed negligible [21].…”

Section: Flood Hazard Maps Using Lisflood Fp Modelmentioning

confidence: 99%

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

et al. 2021

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In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R2 and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area.

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Comparing earth observation and inundation models to map flood hazards

Cited by 25 publications

References 64 publications

Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?

Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?

Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

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