Rainfall-Runoff Prediction for Water Resource Management

Dear Colleagues,

Rainfall-runoff is critical in the assessment and planning of water resources. Due to the scarcity of measurements, particularly in developing countries. Modelling, statistical, or regionalization techniques are required to assess the spatial and temporal variability of Rainfall-runoff. This Special Issue welcomes contributions that will assist the scientific community and technicians in fostering knowledge on rainfall-runoff prediction for sustainable water resource management at various spatial scales, from hillslope to catchment scales, while explicitly taking climate and the peculiarities of arid or hyper-humid areas into account. To provide decision makers with reliable quantile predictions, novel approaches are required to predict runoff at any cross section of natural or controlled rivers, from hourly to daily to annual time scales. Integrations with climate models are also possible in order to forecast rainfall and runoff in real time for civil protection purposes, or to have long-term forecasts to support water resource management and dam operations.

Rainfall and runoff must be given special consideration for sustainable water resource management under climate change conditions, with a particular emphasis on countries where rainfall is expected to decrease over the next century. At the same time, researchers must continue to focus on extreme rainfall changes and their impact on rainfall-runoff.

This open-access Special Issue invites high-quality and innovative scientific articles on the use of remote sensing techniques and data from any platform (ground sensing, satellite, aircraft, drones, etc.) to study critical water-related issues. Potential topics include, but are not limited to, those listed below:

• Rainfall-runoff modeling;
• Water resources management;
• Deep learning application in water resources;
• Big data analytics in flood forecasting;
• Machine learning approaches for rainfall-runoff modelling;
• Impact of climate change on urban flood;
• Data-driven approaches for flood mapping and modeling;
• Novel methods for improving flood research.

Dr. Subodh Chandra Pal
Guest Editor

Manuscript Submission Information

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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. Water 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 2600 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.

• rainfall-runoff
• flash flood
• climate change
• data fusion
• data mining
• sensor networks
• hydrologic model
• hydraulic model
• machine learning
• GIS