Flood scenarios sampling effect on annualised flood damage estimation

Frédéric Grelot (), Jean-Stéphane Bailly () and David Dorchies ()
Additional contact information
Frédéric Grelot: UMR G-EAU - Gestion de l'Eau, Acteurs, Usages - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - BRGM - Bureau de Recherches Géologiques et Minières - IRD - Institut de Recherche pour le Développement - AgroParisTech - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement
Jean-Stéphane Bailly: UMR LISAH - Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème - IRD - Institut de Recherche pour le Développement - AgroParisTech - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement, AgroParisTech
David Dorchies: UMR G-EAU - Gestion de l'Eau, Acteurs, Usages - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - BRGM - Bureau de Recherches Géologiques et Minières - IRD - Institut de Recherche pour le Développement - AgroParisTech - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement

Post-Print from HAL

Abstract: Annualised average damage (AAD) is a widely used indicator, both in research and in operational use, for two purposes: the evaluation of the flood exposure of a territory and the estimation of the effectiveness of flood prevention policies. The AAD synthesizes rich information resulting from the combination of hydrological (relationship between the rarity and intensity of events), hydraulic (spatial extent and intensity of floods), geographical (location and characteristic of stakes), and vulnerability (potential damage) modelling. By construction, the ADD allows to follow the evolution of hydrology or land use, whether they are due to the evolution of the climate, of the society or to flood prevention policies. As hydraulic modelling is costly to calibrate, in practice, the AAD is usually estimated on the basis of a set of specific flood scenarios. The objective of our presentation is to discuss the influence of the choice of these scenarios (flood sampling) according to the expected use of the AAD (exposure diagnosis vs. project effectiveness). To do so, we build a digital experiment that mime the sampling of floods encountered in practice while keeping full control of the key parameters in the estimation of the AAD. This digital experiment is made up of a stochastic and parametric generator of flood scenarios (hydrograms at floodplain inlets), a Saint-Venant 1.5D-network hydraulic model, whose spatial representation directly arises from a digital terrain model, and a collection of spatially arranged stakes, whose vulnerability is represented in the form of multivariate damage functions (height and duration of submersion). This controlled digital experiment allows the evaluation of various types of policies, alone or combined: diking system, upstream reservoir, adaptation or relocation of the stakes, in a stationary or non-stationary climatic context. Based on the simulation of a 10,000-year chronicle of flood events, we calculate a reference AAD (empirical average as an unbiased estimator of AAD mathematical expectation). This reference allows us to discuss the accuracy of the estimation of the AAD from a set of flood scenarios (sampling effect), and ultimately, the strategies adopted for the choice of flood scenarios (sampling design).

Date: 2022-05-29
References: Add references at CitEc
Citations:

Published in IAHS-AISH Scientific Assembly 2022, IAHS, May 2022, Montpellier, France. pp.IAHS2022-40, ⟨10.5194/iahs2022-40⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04006613

DOI: 10.5194/iahs2022-40

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().