Hydrological Modeling with AI in Forests

Dear Colleagues,

Forests are dynamic regulators of the terrestrial water and energy cycles, influencing interception, soil moisture, evapotranspiration, and groundwater recharge across spatial and temporal scales. Capturing these interactions remains a major scientific challenge due to their inherent nonlinearity, spatial heterogeneity, and sensitivity to climatic and land-use changes. Recent advances in Machine Learning (ML) and data-driven modeling offer transformative opportunities to explore, quantify, and predict hydrological processes in forest systems. From soil moisture and interception dynamics to streamflow generation and ecohydrological feedbacks, ML enables the integration of multi-source data—ranging from in situ sensors to remote sensing and climate reanalysis—into predictive and diagnostic frameworks. Emerging approaches, such as physics-informed learning, explainable AI, and hybrid modeling, are bridging the gap between empirical prediction and process understanding. This Special Issue welcomes contributions that leverage ML to improve hydrological prediction, enhance process representation, and advance the interpretability and generalizability of models applied to forest systems. By integrating hydrology, ecology, and data science, this collection aims to foster robust and transparent ML applications that support sustainable forest management, climate adaptation, and water resources planning in a changing world.

Dr. André Ferreira Rodrigues
Dr. Carlos Rogério Mello
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

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