Hydrological Modeling in the Age of AI and Remote Sensing

Dear Colleagues,

Traditional hydrological models, which provide mathematical representations of physical and hydrological processes, have become indispensable tools for advancing scientific understanding and supporting decision-making in water resources sectors. They are widely employed to simulate varied hydrologic and environmental dynamics, ranging from physical processes such as streamflow, groundwater, and river morphology to resource assessments including water availability and quality, as well as the occurrence of extreme hydroclimatic events such as floods and droughts. Despite their central role, the reliability of hydrological modeling is constrained by multiple sources of uncertainty, including the availability and quality of input data, calibration and validation strategies, evaluation metrics, and the underlying model structure. These challenges are further amplified under nonstationary climate conditions, which reduce model robustness across diverse environmental settings, making it increasingly difficult for hydrologists to accurately predict water resources and manage water-related risks. In recent decades, remote sensing has emerged as a transformative source of hydrologically relevant information, offering spatially continuous, temporally frequent, and globally accessible observations of key hydrologic variables. These products are particularly valuable in data‑sparse and ungauged regions, and they enable hydrologists to benchmark and evaluate model performance across diverse hydroclimatic conditions at regional to global scales. Concurrently, rapid advances in artificial intelligence (AI), including machine learning and deep learning, have opened new opportunities for addressing long-standing challenges, especially in extracting nonlinear and multiscale patterns from large datasets, improving data assimilation and parameter estimation, bridging spatial and temporal scale gaps, and emulating process-based modeling frameworks. When combined with remote sensing, AI can substantially enhance the value of satellite‑derived datasets, and it represents a promising frontier for hydrological modeling. This synergy offers the potential to reduce uncertainties, improve predictive accuracy, and support more reliable assessments of water resources systems under ongoing environmental and climatic change.

This Special Issue will showcase recent advances in hydrologic modeling enabled by remote sensing and AI-driven methods. We welcome contributions spanning method development, comparative and benchmarking studies, and application-oriented research across catchment to global scales. Studies that integrate multi-source observations (e.g., precipitation, soil moisture, snow, evapotranspiration, inundation extent) with hydrological models, data assimilation, and machine learning frameworks are particularly encouraged. We also welcome research that improves model reliability through uncertainty quantification, interpretability, and reproducible evaluation, as well as case studies that demonstrate operational or decision-relevant value for water-resources management and hazard forecasting.

Both original research articles and comprehensive review papers are welcome. We also encourage articles that introduce high-quality datasets (e.g., remote sensing products, model outputs, benchmarking datasets) with clear documentation and potential for broad reuse in hydrology. Submissions may address, but are not limited, to the following themes:

• AI–remote sensing integration for hydrological modeling;
• Uncertainty quantification and model interpretability in AI‑based hydrology;
• Surrogate modeling of hydrological simulations based on deep learning;
• Development of remote sensing datasets for hydrological AI;
• Multi‑sensor data fusion and data assimilation for hydrological applications;
• Physics‑informed, machine learning, and deep learning models for hydrology;
• Hydrological modeling under climate change using AI and remote sensing;
• Flood and drought predictions using AI and remote sensing;
• Intercomparison and benchmarking of remote sensing products and AI methods for hydrological modeling.

Dr. Jiao Wang
Dr. Lu Zhuo
Dr. Siyu Zhu
Dr. Mengye Chen
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.

• hydrological modeling
• remote sensing
• artificial intelligence
• deep learning
• machine learning
• hybrid modeling
• uncertainty analysis
• physics-informed AI
• hydrodynamic and flood modeling
• drought forecasting
• earth observation data
• benchmark datasets