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Water
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Hydroinformatics in Hydrology
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Hydroinformatics in Hydrology

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

Deadline for manuscript submissions: 25 March 2025 | Viewed by 5460

Special Issue Editors

Prof. Dr. Daniel P. Ames

E-Mail Website
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
Dr. Gustavious Paul Williams

E-Mail Website
Guest Editor
Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: remote sensing; water quality; statistical analysis; aquatic chemistry
Special Issues, Collections and Topics in MDPI journals
Dr. Huidae Cho

E-Mail Website
Guest Editor
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: smart infrastructure; water resources engineering; hydrologic modeling; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohui Qiao

E-Mail Website
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

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Published Papers (4 papers)

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Research

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22 pages, 6029 KiB  
Article
Transforming Hydrology Python Packages into Web Application Programming Interfaces: A Comprehensive Workflow Using Modern Web Technologies
by Sarva T. Pulla, Hakan Yasarer and Lance D. Yarbrough
Water 2024, 16(18), 2609; https://doi.org/10.3390/w16182609 - 14 Sep 2024
Viewed by 781
Abstract
The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow [...] Read more.
The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow leverages modern web technologies and containerization to streamline the deployment process. The workflow was applied to three distinct models: a GRACE downscaling model, a synthetic time series generator, and a MODFLOW groundwater model. The implementation process for each model was completed in approximately 15 min with a reliable internet connection, demonstrating the efficiency of the approach. The resulting APIs provide standardized interfaces for model execution, progress tracking, and result retrieval, facilitating integration with various applications. This workflow significantly reduces barriers to model deployment and usage, potentially broadening the user base for sophisticated hydrological tools. The approach aligns hydrological modeling with contemporary software development practices, opening new avenues for collaboration and innovation. While challenges such as performance scaling and security considerations remain, this work provides a blueprint for making complex hydrological models more accessible and operational, paving the way for enhanced research and practical applications in hydrology. Full article
(This article belongs to the Special Issue Hydroinformatics in Hydrology)
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31 pages, 10340 KiB  
Article
Research on Coupling Knowledge Embedding and Data-Driven Deep Learning Models for Runoff Prediction
by Yanling Li, Junfang Wei, Qianxing Sun and Chunyan Huang
Water 2024, 16(15), 2130; https://doi.org/10.3390/w16152130 - 27 Jul 2024
Viewed by 1004
Abstract
Accurate runoff prediction is crucial for watershed water resource management, flood prevention, and hydropower station scheduling. Data-driven models have been increasingly applied to runoff prediction tasks and have achieved impressive results. However, existing data-driven methods may produce unreasonable predictions due to the lack [...] Read more.
Accurate runoff prediction is crucial for watershed water resource management, flood prevention, and hydropower station scheduling. Data-driven models have been increasingly applied to runoff prediction tasks and have achieved impressive results. However, existing data-driven methods may produce unreasonable predictions due to the lack of prior knowledge guidance. This study proposes a multivariate runoff prediction model that couples knowledge embedding with data-driven approaches, integrating information contained in runoff probability distributions as constraints into the data-driven model and optimizing the existing loss function with prior probability density functions (PDFs). Using the main stream in the Yellow River Basin with nine hydrological stations as an example, we selected runoff feature factors using the transfer entropy method, chose a temporal convolutional network (TCN) as the data-driven model, and optimized model parameters with the IPSO algorithm, studying univariate input models (TCN-UID), multivariable input models (TCN-MID), and the coupling model. The results indicate the following: (1) Among numerous influencing factors, precipitation, sunshine duration, and relative humidity are the key feature factors driving runoff occurrence; (2) the coupling model can effectively fit the extremes of runoff sequences, improving prediction accuracy in the training set by 6.9% and 4.7% compared to TCN-UID and TCN-MID, respectively, and by 5.7% and 2.8% in the test set. The coupling model established through knowledge embedding not only retains the advantages of data-driven models but also effectively addresses the poor prediction performance of data-driven models at extremes, thereby enhancing the accuracy of runoff predictions. Full article
(This article belongs to the Special Issue Hydroinformatics in Hydrology)
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13 pages, 2078 KiB  
Article
Analyzing the Vertical Recharge Mechanism of Groundwater Using Ion Characteristics and Water Quality Indexes in Lake Hulun
by Hengshuai Gao, Sheng Zhang, Wenbao Li and Yulong Tao
Water 2024, 16(12), 1756; https://doi.org/10.3390/w16121756 - 20 Jun 2024
Viewed by 664
Abstract
The water level of Lake Hulun has changed dramatically in recent years. The interannual interaction between groundwater and lake water is an important factor affecting Lake Hulun’s water level. Vertical recharge between groundwater and the lake is particularly important. Based on an analysis [...] Read more.
The water level of Lake Hulun has changed dramatically in recent years. The interannual interaction between groundwater and lake water is an important factor affecting Lake Hulun’s water level. Vertical recharge between groundwater and the lake is particularly important. Based on an analysis of differences between the hydrogeochemical and water quality characteristics of the spring water, the lake water, and the surrounding groundwater, the source and recharge mechanism of the spring water in the vertical recharge lake are determined. The results show that spring water is exposed at the bottom of Lake Hulun, and there are obvious differences between spring water and lake water in lake ice thickness, ion characteristics, and water quality characteristics. For example, the ice thickness at the spring site is only 6.8% of the average ice thickness of the lake, and there is a triangular area directly above the spring water area that is not covered by ice; the ion contents of the spring water at the lake bottom were less than 50% of those in the lake water; and the NH4+-N content of the spring water at the lake bottom was only 3.0% of the mean content of the lake water. In addition, the total nitrogen (TN), dissolved oxygen (DO), and NH4+-N contents of the spring water at the lake bottom all fall outside the range of contents of the surrounding groundwater. In general, the source of the spring water at the lake bottom is not recharged by the infiltration recharge of the phreatic aquifer but by the vertical recharge of the confined aquifer. Additionally, the Lake Hulun basin may be supplied with confined water through basalt channels while it is frozen. The vertical groundwater recharge mechanism may be that spring water at the lake bottom is first supplied by the deep, confined aquifer flowing through the fault zone to the loose-sediment phreatic aquifer under the lake, and finally interacts with the lake water through the phreatic aquifer. Full article
(This article belongs to the Special Issue Hydroinformatics in Hydrology)
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Other

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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 - 9 Dec 2023
Cited by 1 | Viewed by 1733
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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