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http://hdl.handle.net/20.500.11765/13711
Pluvial flooding: high-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms
Title: | Pluvial flooding: high-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms |
Authors: | Mediero Orduña, Luis; Soriano, Enrique; Oria Iriarte, Peio
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Keywords: | Pluvial floods; Safer_RAIN; Flood hazard mapping; Rapid flood model; Urban areas; Urban areas |
Issue Date: | 2022 |
Publisher: | Elsevier |
Citation: | Journal of Hydrology. 2022 (608), 127649 |
Publisher version: | https://doi.org/10.1016/j.jhydrol.2022.127649 |
Abstract: | Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T-year design storm generates the T-year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ~ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2). |
URI: | http://hdl.handle.net/20.500.11765/13711 |
ISSN: | 0022-1694 |
Appears in Collections: | Artículos científicos 2019-2022 |
Files in This Item:
File | Description | Size | Format | ||
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![]() | JH_2022_mediero.pdf | 29,92 MB | Adobe PDF | ![]() View/Open | |
![]() | Supplementar_material... | 1,53 MB | Adobe PDF | ![]() View/Open |
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