Advancing Data-Driven Approaches in Landscape Hydrology: Innovations and Applications
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".
Deadline for manuscript submissions: 25 November 2025 | Viewed by 50
Special Issue Editors
Interests: landscape hydrology; stormwater management; urban planning; landscape architecture; green infrastructure; nature-based solutions
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning in hydrology; spatiotemporal data mining; remote sensing applications; streamflow and runoff prediction; climate change impact assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the era of big data and artificial intelligence, data-driven approaches are revolutionizing landscape hydrology by enhancing our ability to analyze, model, and predict hydrological processes across diverse spatial and temporal scales. This Special Issue aims to showcase cutting-edge research that integrates advanced computational methods, remote sensing, machine learning, and hydrological modeling to address key challenges in water resources management, flood risk assessment, climate change adaptation, and sustainable landscape planning.
We welcome contributions that explore novel methodologies, interdisciplinary frameworks, and real-world applications in the following areas:
- Machine learning and AI applications in hydrological modeling;
- Remote sensing and GIS-based hydrological analysis;
- Hydroinformatics and big data analytics in water resource management;
- Climate change impacts on landscape hydrology;
- Nature-based solutions for flood and drought mitigation;
- Coupled human-nature systems in hydrological research.
By bringing together innovative research and practical applications, this Special Issue aims to foster the development of more resilient and sustainable hydrological systems in the face of global environmental change.
Dr. Mo Wang
Dr. Rana Muhammad Adnan Ikram
Dr. Shiqi Zhou
Guest Editors
Manuscript Submission Information
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Keywords
- data-driven hydrology
- machine learning in hydrology
- hydroinformatics
- climate change adaptation
- nature-based solutions
- GIS-based hydrological modeling
- flood and drought mitigation
- sustainable water management
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