Published 2024
| Version v1
Journal article
A review of flash‐floods management: From hydrological modeling to crisis management
Contributors
Others:
- Laboratoire des Sciences des Risques (LSR) ; IMT - MINES ALES (IMT - MINES ALES) ; Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
- Hydrosciences Montpellier (HSM) ; Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
- Étude des Structures, des Processus d'Adaptation et des Changements de l'Espace (ESPACE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)
- Région Occitanie
- ANR-21-LCV1-0001,Hydr.IA,Laboratoire de Prévision Hydrologique par Intelligence Artificielle(2021)
Description
In a context of climate change, flash‐floods are expected to increase in frequency. Considering their devastating impacts, it is primordial to safeguard the exposed population and infrastructure. This is the responsibility of crisis managers but they face difficulties due to the rapidity of these events. The focus of this study was to characterize the extent of the link between hydrologists and crisis managers. It also aimed to determine the limiting and the fostering factors to an effective integration of forecasting in crisis management during flash‐floods. This was achieved through an extensive and methodological study of available literature in selected platforms. The models encountered were characterized on multiple levels including the physical, geographical and crisis management level. The results revealed a limited link between the two involved parties with limiting factors such as the complexity of the modeling approach, the insufficient projection in the implications of operationality of the models proposed and the financial aspect. On the other hand, acknowledging the threat of flash‐floods and conducting cost–benefit‐analysis were pinpointed as fostering factors. This study showed to reconsider the forecasting methods employed, particularly, the integration of machine learning, and the needs of end‐user in these applications in a crisis management context.
Abstract
International audienceAdditional details
Identifiers
- URL
- https://imt-mines-ales.hal.science/hal-04602210
- URN
- urn:oai:HAL:hal-04602210v1
Origin repository
- Origin repository
- UNICA