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Generalising Tree-Level Sap Flow Across the European Continent
  • +3
  • Ralf Loritz,
  • Chen Huan Wu,
  • Daniel Klotz,
  • Martin Gauch,
  • Frederik Kratzert,
  • Maoya Bassiouni
Ralf Loritz
Karlsruhe Institute of Technology

Corresponding Author:[email protected]

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Chen Huan Wu
Karlsruhe Institute of Technology
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Daniel Klotz
Johannes Kepler University Linz
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Martin Gauch
Google Research
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Frederik Kratzert
Google Research
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Maoya Bassiouni
University of California, Berkeley
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Abstract

Sap flow observations provide a basis for estimating transpiration and understanding forest water use dynamics and plant-climate interactions. This study developed a continental modeling approach using Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe based on the SAPFLUXNET database. We developed models with varying levels of training sets to evaluate performance in unseen conditions. The average Kling-Gupta Efficiency was 0.77 for gauged models trained on 50 % of time series across all forest stands and was 0.52 for ungauged models trained on 50 % of the forest stands. Continental models matched or exceeded performance of specialized and baseline models for all genera and forest stands, demonstrating the potential of LSTMs to generalize hourly sap flow across tree, climate, and forest types. This work highlights hence the potential of deep learning models to generalize sap flow for enhancing tree to continental ecohydrological investigations.
20 Nov 2023Submitted to ESS Open Archive
20 Nov 2023Published in ESS Open Archive