Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece

Abstract

Floods are lethal and destructive natural hazards. The Mediterranean, including Greece, has recently experienced many flood events (e.g., Medicanes Zorbas and Ianos), while climate change results in more frequent and intense flood events. Accurate flood mapping in river areas is crucial for flood risk assessment, planning mitigation measures, protecting existing infrastructure, and sustainable planning. The accuracy of results is affected by all simplifying assumptions concerning the conceptual and numerical model implemented and the quality of geospatial data used (Digital Terrain Models—DTMs). The current research investigates flood modelling sensitivity against geospatial data accuracy using the following DTM resolutions in a mountainous river sub-basin of Thessaly’s Water District (Greece): (a) open 5 m and (b) 2 m data from Hellenic Cadastre (HC) and (c) 0.05 m data from an Unmanned Aerial Vehicle (UAV) topographical mission. RAS-Mapper and HEC-RAS are used for 1D (steady state) hydraulic simulation regarding a 1000-year return period. Results include flood maps and cross section-specific flow characteristics. They are analysed in a graphical flood map-based empirical fashion, whereas a statistical analysis based on the correlation matrix and a more sophisticated Machine Learning analysis based on the interpretation of nonlinear relationships between input–output variables support and particularise the conclusions in a quantifiable manner.

Bias in Flood Hazard Grid Aggregation

“…More prevalent is the use of scale transfers via coarse hydrodynamic models, where the friction term is calibrated to observations yielding coarse WSH grids directly from aggregated or coarse DEM grids. Such scaling issues in coarse hydrodynamic models have been studied extensively (Banks et al., 2015; Ghimire & Sharma, 2021; Mohanty et al., 2020; Muthusamy et al., 2021; Saksena & Merwade, 2015; Xafoulis et al., 2023). To evaluate the transferability between the aggregation routines considered here and hydrodynamic modeling (i.e., whether aggregation behavior might serve as an analog for coarse hydrodynamic modeling behavior) Text S3 in Supporting Information presents a brief comparison of the aggregation routines against a depth calibrated hydrodynamic model built on a DEM resampled to the coarse resolution ( s 2 = 32 m) for a separate case study where more data is available.…”

“…Because of the computational demands of such models, resolution has been extensively studied and found to be one of the parameters of most importance for accuracy (Alipour et al., 2022; Fewtrell et al., 2008; Papaioannou et al., 2016; Savage et al., 2016). Focusing on the relationship between model resolution and inundation area, many studies of fluvial floods find a positive inundation area and flood depth bias at coarser resolutions (Banks et al., 2015; Ghimire & Sharma, 2021; Mohanty et al., 2020; Muthusamy et al., 2021; Saksena & Merwade, 2015; Xafoulis et al., 2023) while studies of urban flooding are less conclusive (Fewtrell et al., 2008). For the underlying terrain model grids or digital elevation models (DEM), the resampling method used to generate the coarse analogs is often found to be of little significance (Muthusamy et al., 2021; Saksena & Merwade, 2015) except at high resolutions when buildings are present in the fine DEM (Fewtrell et al., 2008).…”

Modern Developments in Flood Modelling

“…The paper "Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece" [12] by Nikolaos Xafoulis, Yiannis Kontos, Evangelia Farsirotou, Spyridon Kotsopoulos, Konstantinos Perifanos, Nikolaos Alamanis, Dimitrios Dedousis and Konstantinos Katsifarakis investigated flood modelling sensitivity against geospatial data accuracy using the following DTM resolutions in a mountainous river sub-basin of Thessaly's Water District (Greece): (a) open 5 m and (b) 2 m data from Hellenic Cadastre (HC) and (c) 0.05 m data from a topographical mission using an unmanned aerial vehicle (UAV). RAS-Mapper and HEC-RAS were used for 1D (steady state) hydraulic simulation regarding a 1000-year return period.…”

Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates

“…Despite hydrological models having recently made great progress in mapping water pathways during flood events [2][3][4], accurately modeling the evolution of floods on the ground in urban areas at the temporal and spatial scales, requiring resolving single-home or finer spatial scale issues, is still problematic. The discharge of water depends, indeed, on many endogenous (e.g., fluid properties) and exogenous (e.g., street material, roughness, slope, etc.)…”