Special Issue "Rainfall-Runoff Prediction for Water Resource Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (28 April 2023) | Viewed by 1390

Special Issue Editor

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Guest Editor
Department of Geography, The University of Burdwan, Bardhaman, India
Interests: remote sensing; hazards modelling; water resources management; climate change; land use change; geomorphology; soil water dynamics; extreme events (flood, landslide, drought); forest health and modelling

Special Issue Information

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

Published Papers (1 paper)

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Regionalization of IDF Curves by Interpolating the Intensity and Adjustment Parameters: Application to Boyacá, Colombia, South America
Water 2023, 15(3), 561; https://doi.org/10.3390/w15030561 - 31 Jan 2023
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Intensity, duration and frequency (IDF) curves are necessary tools for the design and construction of hydraulic projects. However, the pluviographic records needed to determine the IDF curves do not exist or are scarce. This research presents the regionalization of the IDF curves for [...] Read more.
Intensity, duration and frequency (IDF) curves are necessary tools for the design and construction of hydraulic projects. However, the pluviographic records needed to determine the IDF curves do not exist or are scarce. This research presents the regionalization of the IDF curves for the department of Boyacá, Colombia, which is made up of 16 municipalities including the provincial capital, Tunja. For the regionalization, the adjustment parameters (u and α) of the IDF curve stations in the study area were used. In the case of regionalization by the parameters found for the construction of the IDF curves, estimation methods with ordinary moments means and maximum likelihood were used. The regionalization and interpolation of the data were performed with Arcgis software. The resulting isoline maps were made in the case of regionalization intensities, and each map is associated with a different return period and duration to construct the IDF curves in the studied area. In the case of the regionalization maps, the parameters associated with each individual parameter were performed last. The results show that the use of IDF curve data is more accurate and reduces errors in the design. With the methods proposed in this study, IDF curves can be constructed for any site of interest that does not have rainfall stations. Full article
(This article belongs to the Special Issue Rainfall-Runoff Prediction for Water Resource Management)
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