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