EcoPro-LSTM_𝑣0: A Memory-based Machine Learning Approach to Predicting Ecosystem Dynamics across Time Scales in Mediterranean Environments

Abstract. Climate change is anticipated to alter the global water and carbon cycles, but the spatiotemporal effects of these climate-induced shifts remain poorly understood. Of particular relevance are the variations in rainfall intensity and frequency affecting the carbon and water cycles from daily to interannual time scales. Yet, the current models fail to reproduce these processes as capturing the complex interactions and interrelated dependencies at different timescales (daily to seasonal) requires the simultaneous estimation of multiple interconnected ecological processes. To address this challenge, here, we introduce initial version of our ecosystem process modelling using Long Short-Term Memory approach (EcoPro-LSTM_𝑣0) which uses a temporal multitask deep learning model designed to predict ecosystem responses, focusing on critical terrestrial variables, including ecosystem respiration (RECO), gross primary productivity (GPP), evapotranspiration (ET), and surface soil water content (SWC). Our approach leverages the capabilities of LSTM networks to capture the interdependencies of those processes across time scales. LSTMs excel at time-series prediction because they can learn long-term relationships and patterns in data. We trained and tested our model using long-term data from FLUXNET2015 Mediterranean sites (at hourly and daily time-steps), mainly in the USA and Europe, known for their ecological diversity and significance. We demonstrate our model's outperforming against state-of-the-art data products and test the robustness of our model and findings through k-fold cross-validation. We also showcase the model's interpretability in revealing how short- and long-term atmospheric drivers, like precipitation, influence GPP in Mediterranean climates. This model and accompanying insights can help better understand and manage ecosystems under climate change, especially in response to changing extreme events.