Abstract
AbstractAccurate terrain representation is critical to estimating flood risk in urban areas. However, all current global elevation data sets can be regarded as digital surface models in urban areas as they contain building artifacts that cause artificial blocking of flow pathways. By taking surveyed terrain and LIDAR data as “truth,” the vertical error in three popular global DEMs (SRTM 1″, MERIT DEM, and TDM90) was analyzed in six European cities and an Asian city, with RMSE found to be 2.32–5.98 m. To increase the utility of global DEM data for flood modeling, a Random Forest model was developed to correct building artifacts in the MERIT DEM using factors from widely available public datasets, including satellite night‐time lights, global population density, and OpenStreetMap buildings. The proposed correction reduced the vertical errors of MERIT by 15%–67%, despite not using data samples from the target city in training the model. When training data from the target city was included error reduction improved by between 57 and 76 percentage points. The resulting Urban Corrected MERIT DEM improved simulated inundation depth by 18% over original MERIT in a hydrodynamic model of flooding in the UK city of Carlisle, although it did not outperform TDM90 at this site. We conclude that the proposed method has the potential to generate a bare‐earth global DEM in urban areas with improved terrain representation, although in data scarce regions this requires more complete OpenStreetMap building information. In the future, the method should be applied to TDM90.
Highlights
In the context of climate change and urban development, urban flooding issues are becoming more prevalent (Ford et al, 2019), highlighting the need for accurate flood mapping in these areas
Built-up areas mostly show positive errors (i.e., global digital elevation models (DEMs) (GDEMs) elevations are higher than the reference digital terrain model (DTM))
As forest makes up more than 15% of the land area within the Berlin city boundary, it was excluded from the GDEM error histograms and statistical analysis for this city
Summary
In the context of climate change and urban development, urban flooding issues are becoming more prevalent (Ford et al, 2019), highlighting the need for accurate flood mapping in these areas. Precise representation of terrain is of great significance for estimating flood risk (Sampson et al, 2015; Schumann & Bates, 2018; Yamazaki, Sato, et al, 2014) and LIDAR surveys can provide high accuracy DEM data with vertical error of a few tens of centimeters, publicly available LIDAR data are limited to a handful of developed countries. Free-to-access spaceborne global DEMs (GDEMs) based on radar interferometry and photogrammetry are still the only viable data source for flood inundation simulation in many urban regions of the world. All such data sets are digital surface models (DSMs) in urban areas (Gamba et al, 2002) due to the reflection of radar and optical signals from ground objects such as buildings.
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