bioRxivEcologyPreprint
Rooting for water: Bridging Plant Hydraulics and Ecohydrology to Predict Drought Stress
Arsène DrueliD, Julien RuffaultiD, Hervé CochardiD, Miquel De CáceresiD, Nicolas DelpierreiD, Jean‐Marc LimousiniD, Maurizio MencucciniiD, Matthias CuntziD, Laurine ChiriD, Rémi PatiniD, Jérôme OgéeiD, Renaud DecarsiniD, Sylvain DelzoniD, Claude DoussaniD, Joannès GuillemotiD, Émilie JoetzjeriD, Maximilian LarteriD, François PimontiD, Albert Olioso, Guillaume SimioniiD, Léa VeuilleniD, Gregor RickertiD, Ilhan Özgen‐XianiD, Nicolas K Martin-StPauliD
Abstract
Forests regulate climate and sustain biodiversity, but their resilience is increasingly threatened by drought-induced hydraulic failure, when xylem water transport is impaired by embolism. A critical but poorly constrained determinant of this risk largely due to limited observations of deep root water uptake is the amount of root-accessible (sub)soil water storage (SR), which governs land-atmosphere exchanges during prolonged dry periods. While ecohydrological theory has long suggested a balance between soil water storage in the rooting zone, vegetation growth and drought tolerance, this concept has not yet been integrated into predictive models of hydraulic failure. Here we propose a novel, process-based inversion framework that integrates ecohydrological optimality theory with principles of plant hydraulic to infer SR. Using the SurEau plant hydraulic model, we estimate the SR value that balances the costs of soil exploration with the avoidance of drought-induced hydraulic damage. Applied first to a well-instrumented Mediterranean Quercus ilex forest, the inferred SR closely matched independent neutron probe and eddy covariance measurements and accurately reproduced observed drought responses, including leaf water potential and sap flow dynamics. We then scaled the approach across European forests remotely-sensed data, to assess spatially explicit SR estimates and associated hydraulic failure risk. Across more than 20 species and sites, inferred SR and drought stress metrics aligned with field observations and outperformed estimates based on conventional soil databases or remote-sensing-only approaches. By linking plant physiology and ecohydrological theory within a scalable inversion framework, this approach improves predictions of forest drought risk under climate change.
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