Advances in Data-Driven Water Cycling Analyses

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 174

Special Issue Editors

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Guest Editor
1. Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK S4S 0A2, Canada
Interests: environmental statistics; non-deterministic optimization; hydroclimatic modeling; water resource management; energy systems management; environmental systems management

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Guest Editor
Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Pkwy, Regina, SK, Canada
Interests: hydrological and environmental systems simulation; climate modeling and downscaling; resources, environmental, and economic systems optimization; synchrotron-based environmental chemistry and biochemistry
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Special Issue Information

Dear Colleagues,

Massive water-related data (e.g., hydrologic observations, climatic re-analyses, satellite images, environmental monitoring, socioeconomic statistics, and experimental analyses) are available in the current era of big data. Numerous data-driven methods, algorithms, approaches, and software are being continuously developed or employed to quantitatively analyze said data. The analyses are helpful for revealing complicated mechanisms of water cycling, e.g., its associations with climate, energy, agriculture, environment, geology, ecology, health, social economy, technological advancement, engineering construction, and other related systems at large scales. They can also help to identify the optimal policy, technological, or engineering solutions for addressing diverse water crises all over the world under natural and anthropogenic impacts.

This Special Issue focuses on emerging advances in data-driven water-related studies, summarizes recent findings of water cycling obtained from extensive data analyses, discusses existing gaps and challenges in both methodology and applications, and exploring the reformation of this field in the next phase from the perspective of sustainable development of global communities.

Prof. Dr. Guanhui Cheng
Prof. Dr. Gordon Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.


  • water
  • data-driven
  • modeling
  • optimization
  • sustainable development

Published Papers

There is no accepted submissions to this special issue at this moment.
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