Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding.

Autor: Mukherjee K; School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom., Rivas Casado M; School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom., Ramachandran R; School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom., Leinster P; School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Sep 24; Vol. 19 (9), pp. e0310753. Date of Electronic Publication: 2024 Sep 24 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0310753
Abstrakt: Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Mukherjee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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