Generalizing Tree–Level Sap Flow Across the European Continent

Abstract

Abstract Sap flow offers key insights about transpiration dynamics and forest‐climate interactions. Accurately simulating sap flow remains challenging due to measurement uncertainties and interactions between global and local environmental controls. Addressing these complexities, this study leveraged Long Short‐Term Memory networks (LSTMs) with SAPFLUXNET to predict hourly tree‐level sap flow across Europe. We built models with diverse training sets to assess performance under previously unseen conditions. The average Kling‐Gupta Efficiency was 0.77 for models trained on 50% of time series across all forest stands, and 0.52 for models trained on 50% of the forest stands. Continental models not only matched but surpassed the performance of specialized and baselines for all genera and forest types, showcasing the capacity of LSTMs to effectively generalize across tree genera, climates, and forest ecosystems given minimal inputs. This study underscores the potential of LSTMs in generalizing state‐dependent ecohydrological processes and bridging tree level measurements to continental scales.

Keywords

• sap flow
• LSTMs
• deep learning
• ecohydrology
• transpiration
• vegetation dynamics