Generalising Tree–Level Sap Flow Across the European Continent using LSTMs

In this presentation, we explore the application of Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe, utilizing the comprehensive SAPFLUXNET database. This study emphasizes the potential of deep learning in estimating transpiration and understanding forest water use dynamics and plant-climate interactions. By developing LSTM models with varied training sets, we assess their capability to perform in previously unencountered conditions. Our research reveals that these models achieve an average Kling-Gupta Efficiency of 0.77 when trained on 50% of the time series across all forest stands, and 0.52 for models trained on 50% of the forest stands without prior gauging. These continental-scale models not only meet but often exceed the performance of specialized and baseline models across all tree genera and forest types. In this submission, we will discuss the methodologies employed, the challenges faced, and the insights gained from this research. The presentation will also highlight the broader implications of this study for ecohydrological investigations, particularly the enhanced capacity of deep learning models to generalize sap flow data, thereby improving our understanding of ecohydrology from individual trees to a continental scale.