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SurEau: a mechanistic model of plant water relations under extreme drought

Abstract : Abstract Key message A new process-based model, SurEau , is described. It predicts the risk of xylem hydraulic failure under drought. Context The increase in drought intensity due to climate change will accentuate the risk of tree mortality. But very few process-based models are currently able to predict this mortality risk. Aims We describe the operating principle of a new mechanistic model SurEau that computes the water balance, water relations, and hydraulics of a plant under extreme drought. Methods SurEau is based on the formalization of key physiological processes of plant response to water stress. The hydraulic and hydric functioning of the plant is at the core of this model, which focuses on both water flows (i.e., hydraulic) and water pools (i.e., hydric) using variable hydraulic conductances. The model considers the elementary flow of water from the soil to the atmosphere through different plant organs that are described by their symplasmic and apoplasmic compartments. For each organ, the symplasm is described by a pressure-volume curve and the apoplasm by its vulnerability curve to cavitation. The model is evaluated on mature oak trees exposed to water stress. Results On the tested oak trees, the model captures well the observed soil water balance, water relations, and level of embolism. A sensitivity analysis reveals that the level of embolism is strongly determined by air VPD and key physiological traits such as cuticular transpiration, resistance to cavitation, and leaf area. Conclusion The process-based SurEau model offers new opportunities to evaluate how different species or genotypes will respond to future climatic conditions.
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Contributor : Nelly Lucas <>
Submitted on : Thursday, June 24, 2021 - 8:49:29 AM
Last modification on : Tuesday, July 13, 2021 - 3:54:47 AM

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Hervé Cochard, François Pimont, Julien Ruffault, Nicolas Martin-Stpaul. SurEau: a mechanistic model of plant water relations under extreme drought. Annals of Forest Science, Springer Nature (since 2011)/EDP Science (until 2010), 2021, 78 (2), ⟨10.1007/s13595-021-01067-y⟩. ⟨hal-03269429⟩



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