Hydrological Modeling with AI in Forests
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Hydrology".
Deadline for manuscript submissions: 18 June 2026 | Viewed by 6
Special Issue Editors
Interests: canopy interception; soil water dynamics; nutrient and water cycles in forest systems; evapotranspiration; extreme events; forest resilience; urban forests
Special Issues, Collections and Topics in MDPI journals
Interests: hydrological modeling; water resources management; environmental science; soil physics; hydrology; environmental impact assessment; water balance; climate change impacts on hydrology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning
- forest water resources
- resilience
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