Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 15244
Special Issue Editors
Interests: remote sensing; GIS; water cycle; hydrological model; precipitation extremes; floods; droughts; spatial analysis; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; GIS; machine learning; waterlogging; flash floods; hydrological model; precipitation extremes; digital twin watershed; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of environment; GIS; quantitative remote sensing; wetland vulnerability; coastal monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; urbanization and water environment; geospatial analysis; water pollution and public health; agriculture and water resources
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Driven by global change and population pressure, droughts, floods and water pollution have been the most serious hydrological hazards that can lead to crop losses and economic havoc in many areas, affecting more people globally than any other natural hazard. As droughts, floods and water pollution are complex hydrological systems, they deserve a multidisciplinary monitoring effort in order to carry out appropriate and timely hazard assessments. Recently, various spatiotemporal data (e.g., remote sensing, big data, in-situ monitoring, etc.) have been widely applied to obtain a synoptic and punctual view over basin-scale monitored areas. The application of art-to-state spatiotemporal data can potentially provide an extra contribution to hydrological hazards of drought, flood and water pollution assessment and monitoring, for instance, in terms of the accuracy of results, the amount of information obtained, temporal availability, etc.
We are seeking contributions that integrate the application of spatiotemporal data such as remote sensing, Big data, etc., with a particular focus on and reference to drought, flood or water pollution monitoring and hazard assessment. In particular, contributions on various droughts or flood monitoring indexes from different spatiotemporal data resources are also welcome and encouraged. The investigative approach characterized by the integration of disciplines at different scales of vision and precision represents a modern challenge to strive for a more complete understanding of drought, flood and water pollution processes and, therefore, a better hazard evaluation.
Dr. Yaohuan Huang
Prof. Dr. Yesen Liu
Dr. Runhe Shi
Dr. Hongyan Ren
Prof. Dr. Yuanyuan Liu
Guest Editors
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Keywords
- droughts
- floods
- water pollution
- waterlogging
- remote sensing observation
- big data
- digital twin watershed
- hazard assessment
- machine learning
- public health
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