Integrated Hydrologic Modeling and Data-Driven Decision Support
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 20 March 2027 | Viewed by 53
Editors
Interests: integrat-ed hydrological modeling; uncertainty quantification; data-driven analysis; water resources management; sustainable water systems
Special Issues, Collections and Topics in MDPI journals
Interests: hydrological analysis; data-driven analysis; water resources management; remote sensing data; big data mining; GIS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydrologic systems are facing increasingly complex challenges as climate patterns become more variable and land use continues to change. These changes lead to more frequent flash floods, unprecedented droughts, drying rivers, and growing water quality concerns, such as contaminant transport and elevated nutrient levels. The aim of this Special Issue is to publish original research, review papers, and case studies on advanced hydrologic modeling and data-driven decision support for water resources planning and management.
Main themes include, but are not limited to, the following:
(1) Integration of physical process-based and data-driven models: Studies on how machine learning, data-driven analysis, and statistical approaches can enhance the parameterization, calibration, uncertainty quantification, and predictive capability of physical hydrologic models, while demonstrating how process understanding improves the interpretability and reliability of data-driven models.
(2) Integrated surface water, groundwater, and water quality modeling: Studies representing interactions among surface water, groundwater, water quality, ecosystems, and management practices across watershed, regional, or urban systems, including real-world applications of integrated modeling and hybrid physical data-driven approaches.
(3) Uncertainty analysis, scenario assessment, and decision support: Probabilistic frameworks, ensemble simulations, optimization, and decision-support methodologies that support adaptive water resources management under changing climate, land use, and management conditions.
Dr. Seonggyu Park
Dr. Heechan Han
Guest Editors
Manuscript Submission Information
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Keywords
- integrated hydrologic modeling
- process-based modeling
- data-driven analysis
- decision support
- uncertainty analysis
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