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

Abstract

Abstract. Riverine flood risk studies require the identification of areas prone to potential flooding. This 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 (GFPLAIN) and two flood hazard maps (JRC and GAR). We find an average spatial agreement between these maps of around 30% at 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 (GHS, 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.

A river ran through it: Floodplains as America’s newest relict landform

“…The limitations of these results include the application of the hydrogeomorphic floodplain model in areas with characteristics that can lead to lower model accuracy [e.g., dry, steep, flat areas or those near the coast] ( 19 , 21 ). Calibration of the floodplain model at the two-digit HUC basin level provides some mitigation.…”

“…Hydrogeomorphic models make efficient use of topographic data and are based on the natural depiction of floodplain topography resulting from recurring floods ( 20 ). The level of agreement between hydrogeomorphic models and other flood hazard models indicates the suitability of hydrogeomorphic modeling, especially in data-poor areas ( 21 ). However, one of the sources driving model disagreement and inaccuracy is infrastructure, including artificial levees ( 14 , 19 ).…”

Identification of Artificial Levees in the Contiguous United States

“…Previous studies have used FEMA floodplain maps for either calibration or validation (e.g., Annis et al., 2022; Nardi et al., 2018; Wing et al., 2017), as the level of agreement between hydrogeomorphic models and other flood models indicates the suitability of this type of application, especially in data‐poor areas (Lindersson et al., 2021). Lower model accuracy of hydrogeomorphic floodplains in certain areas (e.g., dry, steep, flat areas or those near the coast; Annis et al., 2022; Lindersson et al., 2021) is mitigated by our calibration at the 2‐digit HUC basin level. The GFPLAIN algorithm identifies geomorphic floodplains in two main steps: (a) terrain analysis of a DEM for basin drainage extraction and (b) floodplain delineation.…”

“…Valued for its continental coverage, the accuracy of FEMA maps varies and can be less accurate than local high quality flood models (Blessing et al., 2017). Previous studies have used FEMA floodplain maps for either calibration or validation (e.g., Annis et al., 2022; Nardi et al., 2018; Wing et al., 2017), as the level of agreement between hydrogeomorphic models and other flood models indicates the suitability of this type of application, especially in data‐poor areas (Lindersson et al., 2021). Lower model accuracy of hydrogeomorphic floodplains in certain areas (e.g., dry, steep, flat areas or those near the coast; Annis et al., 2022; Lindersson et al., 2021) is mitigated by our calibration at the 2‐digit HUC basin level.…”