Contribution to improve modelling and observation of flood impacts on agricultural assets (MOOM-agri) - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2022

Contribution to improve modelling and observation of flood impacts on agricultural assets (MOOM-agri)

Modéliser pour Observer, Observer pour Modéliser appliqué au secteur agricole (MOOM-agri)

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Abstract

In the context of climate change, farms are expected to face more frequent extreme events such as heat peaks, droughts, frost, hail or floods. In order to comprehend and compile data on these subjects, observation through on field surveys is essential. Nevertheless, this method is time consuming and costly which is why modelling has been developed to help predict or evaluate farm vulnerability linked with climate change. The so-ii observatory studies two watersheds (Lez-Mosson and Or) near the city of Montpellier. Subject to a Mediterranean climate, the types of flood hazards it faces are multiple (runoff, overflow, marine submersion or rising water tables). The flooding hazard is the natural hazard that generates the most damage in the world. This observatory focuses on floods but faces many other climatic challenges such as high temperatures and water stress. Short term-damage is often well covered but the evaluation of long-term damage, resilience and vulnerability of farms facing extreme events is less studied as it is difficult to collect data and understand the mechanisms behind these issues. Observation on the long-term will be facilitated with the observatory and setting up networks of flood observers. In this report, we propose a methodology that we are currently implementing to jointly and recursively improve the observation and modelling of flood impacts on agricultural systems. In particular, we wish to define more precisely the diversity of flood impacts and long-term damage on farms by taking into account the development trajectory of farms. To this end, we combine two complementary approaches: observation and modelling.
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Dates and versions

hal-03911224 , version 1 (22-12-2022)

Identifiers

  • HAL Id : hal-03911224 , version 1

Cite

Maxime Modjeska, Pauline Bremond, Nina Graveline, Frédéric Grelot, Laure Hossard. Contribution to improve modelling and observation of flood impacts on agricultural assets (MOOM-agri). UMR G-EAU (INRAE). 2022. ⟨hal-03911224⟩
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