Hydroinformatics in Hydrology

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1968

Special Issue Editors


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Guest Editor
Civil & Environmental Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: hydroinformatics; geographic information systems; hydrologic information systems; environmental modelling; decision support systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: remote sensing; geochemical data; transport processes; water quality; data fusion
Special Issues, Collections and Topics in MDPI journals
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: machine learning; hydrologic modeling; geographic information systems; optimization; distributed/parallel computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Food and Resource Economics Department, University of Florida, Gainesville, FL, USA
Interests: hydroinformatics; hydrology; disaster impact analysis; Web GIS; geovisualization; data management

Special Issue Information

Dear Colleagues,

Recent years have witnessed a massive increase in the volume and quality of water data available to aid water resource decision makers, managers, and scientists. This has been accompanied by exponential growth in both desktop and cloud computing data storage and computational capabilities. As a result, there are now abundant opportunities to drastically change how water data are collected, managed, disseminated, and analyzed. Such changes would ultimately exert significant positive impacts on water science, engineering, and management. Indeed, we are at the beginning of a new era in water data science, one which promises to bring with it many new and interesting technological and scientific challenges and opportunities. This Special Issue of Water is intended to bring together some of the latest research on hydroinformatics for water data management and analysis. We are seeking submissions in a wide range of topics, including data collection and analysis tools and technologies, hydrologic information systems, distributed hydrologic modeling and simulation, open water data initiatives, big data in hydrology, geographic information technologies in water data, and related areas.

Prof. Dr. Daniel P. Ames
Dr. Gustavious Paul Williams
Dr. Huidae Cho
Dr. Xiaohui Qiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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.

Keywords

  • hydroinformatics
  • hydrologic information systems
  • water data management
  • distributed hydrologic modeling
  • water resources software
  • cloud computing in water resources
  • open water data initiatives
  • hydrologic data collection technologies
  • open water data analysis and modeling
  • big data in hydrology

Published Papers (1 paper)

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10 pages, 558 KiB  
Technical Note
Reproducibility Starts at the Source: R, Python, and Julia Packages for Retrieving USGS Hydrologic Data
by Timothy O. Hodson, Laura A. DeCicco, Jayaram A. Hariharan, Lee F. Stanish, Scott Black and Jeffery S. Horsburgh
Water 2023, 15(24), 4236; https://doi.org/10.3390/w15244236 - 09 Dec 2023
Viewed by 1178
Abstract
Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database [...] Read more.
Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users. Full article
(This article belongs to the Special Issue Hydroinformatics in Hydrology)
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