Advances in Water Resource Monitoring and Modelling: Water Quantity and Quality Issues
A special issue of Resources (ISSN 2079-9276).
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 44982
Special Issue Editor
Interests: applied ecology; water quality; biogeochemistry; environmental monitoring and modelling; management of water resources; climate change impact on surface waters; sustainability; ecosystem services
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in environmental monitoring and modelling can help in the management of water resources. The availability of water is being put at risk by pressures acting at both the local (e.g., nutrient/pollutant loads) and the global (e.g., climate change) scale. The increasing pressure on water resources can be either quantitative or qualitative threats. Monitoring of the water resource thus has to cover both of these aspects. In recent years, there has been an increase in data availability, including both in situ and remote-sensing data. If, on the one hand, the availability of these big datasets is increasing our knowledge on aquifers, on the other hand, the management of these data implies the development of advanced tools to validate, store and process these data. The use of advanced mathematical and statistical models is a central aspect of this rapidly evolving field of research. Models, in fact, are fundamental tools for predicting water availability and quality, in relation to different potential uses (e.g., irrigational, drinking supply), and under both local and global stresses. This Special Issue deals with advances in all of the different fields of research related to monitoring and modelling water research, including: in situ sensor and sensor network development, advances in remote sensing data acquisition, data storage and validation, statistical and process-based model implementation. Contributions dealing with methods to merge modelled and measured data (e.g., data assimilation techniques) or data driven computational methods (e.g., machine learning) are also welcome.
Dr. Diego Copetti
Guest Editor
Manuscript Submission Information
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Keywords
- sensors
- sensors network
- remote sensing
- process based models
- statistical models
- machine learning methods
- data assimilation techniques
- water quantity
- water quality
- water management
- sustainability
- Long term trends
- Integrated analitical approaches
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