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<p>Increasingly frequent and extreme precipitation events are heightening flood hazard exposure, intensifying the need for efficient and reliable flood risk modeling. Such modeling relies critically on accurate Digital Elevation Models (DEMs), yet raw DEMs often contain artificial obstructions—primarily bridges and culverts—that incorrectly disrupt modeled water flow. While high-resolution elevation data from Light Detection and Ranging (LiDAR) helps address some of these challenges, LiDAR cannot resolve elevations beneath bridges and culverts, necessitating further corrections. Traditional manual methods of DEM correction are labor-intensive and impractical at large scales. To address this issue, we introduce DEMend (Digital Elevation Model mender), a streamlined, automated tool designed to efficiently detect and correct hydrological obstructions in DEMs. DEMend leverages widely available stream and road network datasets and employs locally weighted regression techniques to statistically adjust and smooth terrain elevations. Applied to Northern Virginia’s Accotink watershed, DEMend rapidly identified and corrected 119 artificial obstructions, markedly enhancing stream alignment and facilitating efficient flood modeling workflows. Packaged as an ArcGIS toolbox, DEMend significantly reduces manual preprocessing efforts, offering flood modelers a practical and scalable solution to rapidly improve DEM suitability for accurate flood risk assessments.</p>

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DEMend: Automating Hydrological Correction of Digital Elevation Models for Enhanced Urban Flood Modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Zeeshan Khalid,
  • Andre de Souza de Lima,
  • P. J. Ruess,
  • Arslaan Khalid,
  • Tyler Miesse,
  • Diana Veronez,
  • Celso M. Ferreira,
  • James L. Kinter

摘要

Increasingly frequent and extreme precipitation events are heightening flood hazard exposure, intensifying the need for efficient and reliable flood risk modeling. Such modeling relies critically on accurate Digital Elevation Models (DEMs), yet raw DEMs often contain artificial obstructions—primarily bridges and culverts—that incorrectly disrupt modeled water flow. While high-resolution elevation data from Light Detection and Ranging (LiDAR) helps address some of these challenges, LiDAR cannot resolve elevations beneath bridges and culverts, necessitating further corrections. Traditional manual methods of DEM correction are labor-intensive and impractical at large scales. To address this issue, we introduce DEMend (Digital Elevation Model mender), a streamlined, automated tool designed to efficiently detect and correct hydrological obstructions in DEMs. DEMend leverages widely available stream and road network datasets and employs locally weighted regression techniques to statistically adjust and smooth terrain elevations. Applied to Northern Virginia’s Accotink watershed, DEMend rapidly identified and corrected 119 artificial obstructions, markedly enhancing stream alignment and facilitating efficient flood modeling workflows. Packaged as an ArcGIS toolbox, DEMend significantly reduces manual preprocessing efforts, offering flood modelers a practical and scalable solution to rapidly improve DEM suitability for accurate flood risk assessments.