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Advances in Hydroinformatics for Water Data Management and Analysis

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 41479

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Special Issue Editors

Prof. Dr. Daniel P. Ames

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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

Prof. Min Chen

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Guest Editor

Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
Interests: geographic modeling and simulation; virtual geographic environments; geographic information system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shaowen Wang

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Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: geographic information science and systems (GIS); cyberGIS; geospatial data science; hydroinformatics; spatial analysis and modeling

Dr. Anthony Castronova

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Guest Editor

Consortium of Universities for the Advancement of Hydrologic Science, Inc., Cambridge, MA 02140, USA
Interests: hydroinformatics; hydrologic modeling; integrated modeling; reproducible science; cloud-computing

Special Issue Information

Dear Colleagues,

Recent years have witnessed a massive increase in the volume and quality of water data available to aid water resources 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 abundant opportunities to drastically change how water data are collected, managed, disseminated, and analyzed—which should ultimately have significant positive impacts on water science, engineering, and management. Indeed, we are at the beginning of a new era in water data science, which brings 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. Toward this goal, 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. Daniel P. Ames
Prof. Min Chen
Prof. Shaowen Wang
Dr. Anthony Castronova
Guest Editors

Manuscript Submission Information

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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 (10 papers)

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Research

21 pages, 4674 KiB

Open AccessArticle

Assessing 1D Hydrodynamic Modeling of Júcar River Behavior in Mancha Oriental Aquifer Domain (SE Spain)

by Iordanka Dountcheva, David Sanz, Philip Penchev, Eduardo Cassiraga, Vassil Galabov and Juan José Gómez-Alday

Water 2023, 15(3), 485; https://doi.org/10.3390/w15030485 - 25 Jan 2023

Viewed by 2328

Abstract

In times of population growth, climate change, and increasing water scarcity around the world, it is important to take an objective look at water, a fundamental resource for life. Hydrodynamic modeling makes possible the research of different aspects of the water cycle and the evaluation of different hydrological and hydrogeological forecasting scenarios in the short and medium terms. The present research offers a more detailed scope at the hydrodynamic processes and their space-time distributions on a UE pilot in the Júcar River Basin, providing a calibrated and validated hydrodynamic model of 121 km river reach for 45 years period (1974–2019) on a daily scale. The obtained information is about discharge and water depths along the Júcar River reach within the hydrogeological boundaries of the Mancha Oriental Aquifer (MOA). The river–aquifer interactions have been represented as dynamic boundary conditions expressed as a difference between observed discharges measured in 3 gauging stations. The obtained calibration error performance evaluations of observed and simulated values cover two periods, according to observed data availability from gauging station 08036 with resulting R² for both discharges and water depths over 0.96. The model validation results were obtained for a different gauge 08132 and the determination coefficients R² also perform very well with value of 0.90. The model developed might be useful for decision making in water resources management and can be used to generate simulated time series of water depths, levels, discharges, and velocities in reaches where gauging measurements are not available with a desired space-time resolution (from meter/second to kilometer/month). Estimation of critical discharge value (1.973 m³s⁻¹) for system equilibrium, based on the balance between losing and gaining sub-reaches of the river, is also made with a statistical significance at 95% for hydrologic years 2007–2010, period influenced by restrictions in groundwater withdrawals. The results of the present research are important for the proper and objective management of the scarce water resources on a watershed scale in Júcar River Basin, a complex case study representing semiarid climate, growing anthropogenic pressures, and complex river–aquifer interactions. The used approach of dynamic representation of the river–aquifer interactions as distributed source boundary condition in the one-dimensional hydrodynamic model might be applied in another study case on similar scale. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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15 pages, 5372 KiB

Open AccessArticle

Building and Validating Multidimensional Datasets in Hydrology for Data and Mapping Web Service Compliance

by J. Enoch Jones, Riley Chad Hales, Karina Larco, E. James Nelson, Daniel P. Ames, Norman L. Jones and Maylee Iza

Water 2023, 15(3), 411; https://doi.org/10.3390/w15030411 - 19 Jan 2023

Cited by 1 | Viewed by 2364

Abstract

Multidimensional, georeferenced data are used extensively in hydrology, meteorology, and water science and engineering. These data are produced, shared, and used by diverse organizations globally. Conventions have been developed to standardize the metadata and format of these datasets to ensure compatibility with current and future software and web services. However, the most common conventions are complex and difficult to implement correctly, resulting in datasets that are unusable for many applications due to a lack of compliance with the conventions. We have developed a method and software module for programmatically assigning metadata and guiding the dataset creation, validating, and cleaning process, so that convention-compliant datasets can be consistently and repeatably created by people with a limited knowledge of file formats and data standards. These datasets can then be used in any application that supports the particular standard. Specifically, this paper examines the process of building multidimensional, georeferenced netCDF datasets that are compliant with the NetCDF Climate and Forecast Conventions. We present a new free and open-source Python package called cfbuild that helps to automate the process of building or updating datasets, making them sufficiently compliant with the Climate and Forecast Conventions and the Attribute Conventions for Data Discovery so that they can be reliably served using a THREDDS Data Server and shared via OPeNDAP. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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18 pages, 5053 KiB

Open AccessArticle

Impact of Data Temporal Resolution on Quantifying Residential End Uses of Water

by Camilo J. Bastidas Pacheco, Jeffery S. Horsburgh and Arle S. Beckwith, Jr.

Water 2022, 14(16), 2457; https://doi.org/10.3390/w14162457 - 9 Aug 2022

Cited by 4 | Viewed by 2625

Abstract

Residential water end-use events (e.g., showers, toilets, faucets, etc.) can be derived from high temporal resolution (<1 min) water metering data. Past studies have collected data at different temporal resolutions (e.g., 4 s, 5 s, or 10 s) without assessing the impact of the temporal aggregation interval on end-use event features (e.g., volume, flowrate, duration) due to the unavailability of data at a sufficient temporal resolution to enable such analyses. We recorded the time between every magnetic pulse generated by a magnetically driven residential water meter’s measurement element (full pulse resolution) using a new, open-source datalogging device and collected data for two residential homes in Utah, USA. We then examined water use events without temporally aggregating data and compared to the same data aggregated at different time intervals to evaluate how temporal resolution of the data affects our ability to identify end-use events, calculate features of individual events, and classify events by end use. Our results show how collecting full pulse resolution data can provide more accurate estimates of event occurrence, timing, and features along with producing more discriminative event features that can only be estimated from full pulse resolution data to make event classification easier and more accurate. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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25 pages, 4174 KiB

Open AccessArticle

Rainfall Runoff Balance Enhanced Model Applied to Tropical Hydrology

by Arisvaldo Vieira Méllo Júnior, Lina Maria Osorio Olivos, Camila Billerbeck, Silvana Susko Marcellini, William Dantas Vichete, Daniel Manabe Pasetti, Ligia Monteiro da Silva, Gabriel Anísio dos Santos Soares and João Rafael Bergamaschi Tercini

Water 2022, 14(12), 1958; https://doi.org/10.3390/w14121958 - 18 Jun 2022

Cited by 6 | Viewed by 3918

Abstract

The integrative and comprehensive analysis considering the spatial and temporal representation of the hydrological process, such as the distribution of rainfall, land cover and land use, is a challenge for the water resources management. In tropical areas, energy availability throughout the year defines the rainfall distribution and evapotranspiration rate according to vegetation heterogeneity. To quantify water balance in tropical areas including these heterogeneities in the soil-vegetation-atmosphere relationship, we developed a fully distributed hydrological model called the Rainfall Runoff Balance Enhanced Model (RUBEM). The model was developed under a physics-based process structure, using remote sensing data to represent soil-water balance patterns, such as evapotranspiration, interception, baseflow, lateral flow, recharge, and runoff. The calibration procedure was based on nine global parameters. RUBEM could represent the spatio-temporal heterogeneities (soil, land use and land cover (LULC), topography, vegetation, and climate) in three basins in a tropical area. The results showed good adherence between the processes governing the soil-vegetation-atmosphere relationship according to the humidity indicator and the runoff coefficient. Overall, RUBEM can be used to help improve the management and planning of integrated water resources under climate, land use, and land cover changes in tropical regions. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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14 pages, 8785 KiB

Open AccessArticle

Baseflow Separation Using the Digital Filter Method: Review and Sensitivity Analysis

by Taeuk Kang, Sangho Lee, Namjoo Lee and Youngkyu Jin

Water 2022, 14(3), 485; https://doi.org/10.3390/w14030485 - 7 Feb 2022

Cited by 17 | Viewed by 7185

Abstract

The baseflow separation method based on a digital filter is a simple method for separating the baseflow from streamflow. Appropriate estimation of filter parameters is required to use the digital filter method for analysis. We carried out sensitivity analysis on four digital filter methods: Lyne–Hollick (LH), Chapman, Chapman and Maxwell (CM), and exponentially weighted moving average (EWMA). Furthermore, appropriate filter parameters were suggested for each method in this study. By applying them to 25 stage stations in the Nakdong River in the Republic of Korea, the four methods were evaluated. The results of the evaluation showed that the Chapman and CM methods had problems separating the baseflow during the dry seasons. The EWMA and LH methods were able to achieve reliable baseflow separation of the outcomes by selecting appropriate the filter parameters. Thus, the EWMA and LH methods can be used easily and reasonably among the digital filter methods that have one filter parameter. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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14 pages, 20933 KiB

Open AccessArticle

Water Extraction from Fully Polarized SAR Based on Combined Polarization and Texture Features

by Jikang Wan, Jiayi Wang and Min Zhu

Water 2021, 13(23), 3332; https://doi.org/10.3390/w13233332 - 24 Nov 2021

Cited by 9 | Viewed by 2762

Abstract

Given the limited features (for example, the backscattering coefficient threshold range) of single-channel Synthetic Aperture Radar (SAR) images, it is difficult to distinguish ground objects similar to the backscattering coefficients of water bodies. In this paper, two representative research areas are selected (Yancheng Coastal wetland and Shijiu Lake), and the fully polarized SAR data based on Gaofen-3 are used to extract water bodies using the method of polarization decomposition and gray level co-occurrence matrix. Firstly, the multi-dimensional features of ground objects are extracted, and then the redundancy processing of multi-dimensional features is carried out by the separability index, which effectively solves the misclassification of non-water bodies and water bodies and improves the accuracy of water body extraction. The comparison between the results of full-polarization extraction and single-polarization extraction shows that both full-polarization and single-polarization extraction can extract water information, but the extraction accuracy of the full-polarization method can reach 94.74% in the area with complex wetland features, which can effectively compensate for the lack of precision of the single-polarization method. Although multi-dimensional features can be extracted from fully polarimetric SAR data, data redundancy may exist. Therefore, using the Separability index (SI) to process multi-dimensional features can effectively solve the problem of feature redundancy and improve classification accuracy. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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18 pages, 8561 KiB

Open AccessFeature PaperArticle

A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics

by Fei Guo, Jing Yang, Hu Li, Gang Li and Zhuo Zhang

Water 2021, 13(19), 2759; https://doi.org/10.3390/w13192759 - 5 Oct 2021

Cited by 15 | Viewed by 2897

Abstract

Groundwater is an important water resource, and groundwater level (GWL) forecasting is a useful tool for supporting the sustainable management of water resources. Existing studies have shown that GWLs can be accurately predicted by combining an artificial neural network model with meteorological and hydrological factors. However, GWL data are typically geographic spatiotemporal series data, and current studies have considered only the spatial distance factor when predicting GWLs. In karst aquifers, the GWL is affected by the developmental degree of the karst, topographic factors, structural features, and other factors; considering only the spatial distance is not enough, and the real spatial connectivity characteristics need to be considered. Thus, in this paper, we proposed a new method for forecasting GWLs in karst aquifers while considering connectivity characteristics using a neural network prediction model. The connectivity of a karst aquifer was analyzed by a multidimensional feature clustering method based on the distance index and hydrogeological characteristics recorded at observation wells, and a convolutional long short-term memory (ConvLSTM) conjunction model was constructed. The proposed approach was validated through GWL simulations and predictions in karst aquifers in Jinan, China, and four experiments were conducted for comparison. The experimental results show that the proposed method provided the most consistent results with the measured observation well data among the analyzed methods. These findings demonstrate that the proposed method, which considers connectivity characteristics in karst aquifers, has a higher simulation accuracy than other methods. This method is therefore effective and provides a new idea for the real-time prediction of the GWLs of karst aquifers. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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21 pages, 9447 KiB

Open AccessArticle

A Barotropic Tide Model for Global Ocean Based on Rotated Spherical Longitude-Latitude Grids

by Fuqiang Lu, Milan Konecny, Min Chen and Tomas Reznik

Water 2021, 13(19), 2670; https://doi.org/10.3390/w13192670 - 27 Sep 2021

Cited by 4 | Viewed by 3027

Abstract

Ocean modeling and simulation are important for understanding the dynamic processes in the geophysical system, and the simulation of tidal dynamics is of great significance for understanding the dynamic evolution of the ocean. However, there are some problems in existing simulations, including lack of specific standards to produce a desirable discrete spherical mesh for global ocean modelling. Many global ocean numerical models based on conventional longitude-latitude (LL) coordinates suffer from the “pole problem” in regions adjacent to the North Pole due to the convergence of meridians, which seriously hinders global ocean simulations. In this paper, a new longitude-latitude spherical grid coupled with rotated coordinate mapping is proposed to overcome the problem. In the design of the numerical model, for spatial approximation, the finite volume method on staggered C grid is proposed to solve the two-dimensional tidal wave equations for the global ocean. For temporal integration, the third-order Adams-Bashforth method is used to explicitly extrapolate the value on the next time interval half layer, and then the fourth-order implicit Adams-Moulton method is used to correct the water level. Finally, the constructed model is used to simulate the dynamics of two-dimensional tidal waves in the global ocean, and the co-tidal maps of two major diurnal tide and semidiurnal tide components are shown. The results demonstrate that the proposed model can support the simulation of tidal dynamics in the global ocean, especially for the Arctic Ocean. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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19 pages, 19390 KiB

Open AccessFeature PaperArticle

The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data

by Riley Chad Hales, Everett James Nelson, Gustavious P. Williams, Norman Jones, Daniel P. Ames and J. Enoch Jones

Water 2021, 13(15), 2066; https://doi.org/10.3390/w13152066 - 29 Jul 2021

Cited by 7 | Viewed by 4621

Abstract

Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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17 pages, 14109 KiB

Open AccessFeature PaperArticle

Water Data Explorer: An Open-Source Web Application and Python Library for Water Resources Data Discovery

by Giovanni Romero Bustamante, Everett James Nelson, Daniel P. Ames, Gustavious P. Williams, Norman L. Jones, Enrico Boldrini, Igor Chernov and Jorge Luis Sanchez Lozano

Water 2021, 13(13), 1850; https://doi.org/10.3390/w13131850 - 2 Jul 2021

Cited by 8 | Viewed by 7326

Abstract

We present the design and development of an open-source web application called Water Data Explorer (WDE), designed to retrieve water resources observation and model data from data catalogs that follow the WaterOneFlow and WaterML Service-Oriented Architecture standards. WDE is a fully customizable web application built using the Tethys Platform development environment. As it is open source, it can be deployed on the web servers of international government agencies, non-governmental organizations, research teams, and others. Water Data Explorer provides uniform access to international data catalogs, such as the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Hydrologic Information System (HIS) and the World Meteorological Organization (WMO) Hydrological Observing System (WHOS), as well as to local data catalogs that support the WaterOneFlow and WaterML standards. WDE supports data discovery, visualization, downloading, and basic data interpolation. It can be customized for different regions by modifying the user interface (i.e., localization), as well as by including pre-defined data catalogs and data sources. Access to WDE functionality is provided by a new open-source Python package called “Pywaterml” which provides programmable access to WDE methods to discover, visualize, download, and interpolate data. We present two case studies that access the CUAHSI HIS and WHOS catalogs and demonstrate regional customization, data discovery from WaterOneFlow web services, data visualization of time series observations, and data downloading. Full article

(This article belongs to the Special Issue Advances in Hydroinformatics for Water Data Management and Analysis)

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