Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Forests
Special Issues
Hydrological Modeling with AI in Forests
share announcement

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 585

Special Issue Editors

Dr. André Ferreira Rodrigues

E-Mail Website
Guest Editor
Department of Hydraulics and Water Resources, School of Engineering, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
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
Dr. Carlos Rogério Mello

E-Mail Website
Guest Editor
Water Resources Department, Engineering School, Federal University of Lavras, Lavras 37200-000, MG, Brazil
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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 7795 KB  
Article
AI-Based Modeling of Post-Fire Evapotranspiration Using Vegetation Recovery Indicators: Application to the 2022 Chongqing Burned Areas
by Ziyan Zhao and Rongfei Zhang
Forests 2026, 17(4), 410; https://doi.org/10.3390/f17040410 - 25 Mar 2026
Viewed by 345
Abstract
The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field [...] Read more.
The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day−1, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article
(This article belongs to the Special Issue Hydrological Modeling with AI in Forests)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop