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18 pages, 5195 KiB  
Article
Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data
by Zunguang Zhou, Baohong Lu, Zhengfang Jiang and Yirui Zhao
Sustainability 2024, 16(19), 8437; https://doi.org/10.3390/su16198437 - 27 Sep 2024
Viewed by 219
Abstract
The Huaihe River Basin is an important ecological function conservation area in China, and it is also an important production area for national food, energy, minerals, and manufacturing. The groundwater storage and groundwater drought in this region are of great significance for ecological [...] Read more.
The Huaihe River Basin is an important ecological function conservation area in China, and it is also an important production area for national food, energy, minerals, and manufacturing. The groundwater storage and groundwater drought in this region are of great significance for ecological maintenance and water resources management. In this study, based on GRACE data and GLDAS data, a dynamic calculation method for groundwater storage in the Huaihe River Basin was developed, and a groundwater drought index (GRACE-GDI) was derived. By coupling GRACE-GDI with run theory, the quantitative identification of groundwater drought events, as well as their duration, intensity, and other characteristics within the basin, was achieved. The spatiotemporal changes in groundwater storage and groundwater drought in the Huaihe River Basin were analyzed using the developed method. The results showed that GRACE data are highly applicable in the Huaihe River Basin and is capable of capturing the spatiotemporal variations in groundwater storage in this region. Over the study period, mainly affected by rainfall, the terrestrial water storage and surface water storage in the Huaihe River Basin showed a decreasing trend, while groundwater storage showed a slight increasing trend. The duration of groundwater drought events in the basin ranged from 78 to 152 months, with an intensity of 82.77 to 104.4. The duration of drought gradually increased from north to south, while the intensity increased from south to north. Full article
(This article belongs to the Section Sustainable Water Management)
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<p>Location map of the study area (Huaihe River Basin).</p>
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<p>Identification of drought variables through the run theory with multiple thresholds. (The horizontal axis is time, the vertical axis is the drought index GRACE-GDI, duration represents the duration of a drought from the beginning to the end, intensity is the area of red shadow, <span class="html-italic">X</span><sub>1</sub>, <span class="html-italic">X</span><sub>2</sub> and <span class="html-italic">X</span><sub>0</sub> represent the threshold values of the three GRACE-GDI).</p>
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<p>Trends in terrestrial water storage anomalies.</p>
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<p>Magnitude of change in terrestrial water storage anomalies.</p>
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<p>Trends in surface water storage anomalies.</p>
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<p>Magnitude of change in surface water storage anomalies.</p>
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<p>Mean value of water storage anomalies in the basin. (<b>a</b>) shows the average of land and surface storage anomalies, while (<b>b</b>) shows the average of groundwater reserves storage anomalies.</p>
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<p>Correlation between groundwater storage changes and measured groundwater levels.</p>
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<p>Trends in groundwater storage anomalies.</p>
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<p>Magnitude of change in groundwater storage anomalies.</p>
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<p>Groundwater drought characterization variables. (<b>a</b>) shows the total number of groundwater droughts, (<b>b</b>) shows the total duration of groundwater droughts, and (<b>c</b>) shows the total intensity of groundwater droughts.</p>
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<p>Maximum duration and intensity of groundwater drought. (<b>a</b>) shows the maximum duration of groundwater drought, and (<b>b</b>) shows the maximum intensity of groundwater drought.</p>
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<p>Average duration and intensity of groundwater droughts. (<b>a</b>) shows the average duration of groundwater drought, and (<b>b</b>) shows the average intensity of groundwater drought.</p>
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16 pages, 1523 KiB  
Article
Detection and Measurement of Bacterial Contaminants in Stored River Water Consumed in Ekpoma
by Imokhai T. Tenebe, Eunice O. Babatunde, Nkpa M. Ogarekpe, Joshua Emakhu, Egbe-Etu Etu, Onome C. Edo, Maxwell Omeje and Nsikak U. Benson
Water 2024, 16(18), 2696; https://doi.org/10.3390/w16182696 - 23 Sep 2024
Viewed by 448
Abstract
This study was conducted in Ekpoma, a town dependent on rainwater and river water from nearby areas because of a lack of groundwater sources, and the physicochemical and bacteriological (heterotrophic plate count [HPC], total coliform count [TCC], and fecal coliform count [FCC]) properties [...] Read more.
This study was conducted in Ekpoma, a town dependent on rainwater and river water from nearby areas because of a lack of groundwater sources, and the physicochemical and bacteriological (heterotrophic plate count [HPC], total coliform count [TCC], and fecal coliform count [FCC]) properties of 123 stored river water samples grouped into five collection districts (EK1 to EK5). The results were compared with regulatory standards and previous regional studies to identify water quality trends. While most physicochemical properties met drinking water standards, 74% of samples had pH values > 8.5. Twenty-seven samples were fit for drinking, with EK4 having the highest number of bacterio-logically unsuitable samples. Ten bacterial species were identified, with Gram-negative short-rod species such as Escherichia coli, Klebsiella pneumoniae, and Salmonella typhimurium being predominant. HPC values varied from 367 × 10⁴ to 1320 × 10⁴ CFU/mL, with EK2 (2505 × 10⁴ CFU/mL) and EK5 (1320 × 10⁴ CFU/mL) showing particularly high counts. The TCC values ranged from 1049 × 10⁴ to 4400 × 10⁴ CFU/mL, and the FCC values from 130 × 10⁴ to 800 × 10⁴ CFU/mL, all exceeding WHO limits (1.0 × 102 CFU/mL). Historical data show no improvement in water quality, emphasizing the need for individuals to treat water properly before consumption. The findings provide baseline data for local water authorities and serve as a wake-up call for adequate water treatment, storage interventions, and community education on water security. Additionally, this study offers a practical process for improving the quality of water stored in similar regions. Full article
(This article belongs to the Special Issue Monitoring and Remediation of Contaminants in Soil and Water)
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<p>Map showing sampling sites in Ekpoma (adapted from [<a href="#B27-water-16-02696" class="html-bibr">27</a>]).</p>
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<p>A typical water storage well in Ekpoma.</p>
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<p>Bacterial species occurring in stored river water samples from different districts.</p>
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37 pages, 14338 KiB  
Article
Archaeological Excavation, Protection, and Display Engineering Design Practice: A Case Study in the Ruins of the Imperial City of the Minyue Kingdom
by Shihui Zhou, Lei Zhang, Yile Chen, Liang Zheng, Nengzhong Lei and Jiali Zhang
Coatings 2024, 14(9), 1220; https://doi.org/10.3390/coatings14091220 - 21 Sep 2024
Viewed by 607
Abstract
The Han Dynasty Ruins in Chengcun Village of Wuyishan City, also known as the Ruins of the Imperial City of the Minyue Kingdom, are located on the hilly slope southwest of Chengcun Village, Xingtian Town, Wuyishan City, Fujian Province, China. These are ruins [...] Read more.
The Han Dynasty Ruins in Chengcun Village of Wuyishan City, also known as the Ruins of the Imperial City of the Minyue Kingdom, are located on the hilly slope southwest of Chengcun Village, Xingtian Town, Wuyishan City, Fujian Province, China. These are ruins of a Han Dynasty city. Wuyi Mountain’s World Cultural and Natural Heritage Committee declared it a World Heritage Site in 1999. It is also the only imperial city site from the Han Dynasty that has been declared a World Heritage Site in China, and it is the most well-preserved large-scale imperial city site from the Middle Ages on the Pacific Rim. This study used comprehensive archaeological techniques, including archaeological excavation work, site information recording, erosion situation analysis, and geological surveys, to design and implement protective engineering projects in response to existing problems. In this study, the researchers conducted a geological survey of the study area to analyze the topography, rock and soil distribution characteristics, groundwater storage conditions, and geotechnical engineering conditions. At the same time, they explored the preservation status of the site, including the preservation status of the East Gate and the East City Wall, and they analyzed the causes of damage. Finally, the investigation and analysis results guided the design of a site display project, which included safeguarding against collapse and erosion, treating trees and shrubs, and designing the exhibition project for the East Gate. This study provides some practical reference for the excavation and archaeological work of the royal city in the surrounding areas. At the same time, in terms of the technical process of the project, it is also hoped to provide ideas for international ancient city excavation, display, and protection projects. Full article
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<p>Map of Mount Wuyi with minor boundary modifications inscribed (image source: UNESCO World Heritage Center, <a href="https://whc.unesco.org/en/list/911/maps/" target="_blank">https://whc.unesco.org/en/list/911/maps/</a>, (accessed on 15 August 2024).</p>
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<p>Location analysis (image source: drawn by the author).</p>
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<p>General plan of the Ruins of the Imperial City of the Minyue Kingdom (image source: drawn by the author).</p>
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<p>A plan involving the dimensioning and segmentation of the layout of the city wall (image source: drawn by the author).</p>
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<p>Typical erosion of the soil remains (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Typical collapse of soil remains (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Typical growth of trees and shrubs in the soil remains (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>The displayed bricks and stones are partially cracked or missing (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Plan of the East Gate site, a and b in the figure represent the positions of the sections. (image source: drawn by the author).</p>
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<p>The photographs show the plan of the East City Gate ruins and the doorway, taken from west to east (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>East and west elevations of the East Gate site (image source: drawn by the author).</p>
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<p>Sectional drawing (please refer to <a href="#coatings-14-01220-f009" class="html-fig">Figure 9</a> for the details of cuts a-a and b-b) (image source: drawn by the author).</p>
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<p>Aerial photo of the remains of the East City Wall (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Eastern facade of Section A of the East City Wall (image source: drawn by the author).</p>
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<p>Western facade of Section A of the East City Wall (image source: drawn by the author).</p>
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<p>Surveying and mapping status of Section A of the East Gate site (image source: drawn by the author).</p>
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<p>Surveying and mapping status of Section B of the East Gate site (image source: drawn by the author).</p>
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<p>East facade of Section B of the East City Wall (image source: drawn by the author).</p>
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<p>West facade of Section B of the East City Wall (image source: drawn by the author).</p>
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<p>Surveying and mapping status of Section C of the East Gate site (image source: drawn by the author).</p>
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<p>East facade of Section C of the East City Wall, the red outline indicates the main area (image source: drawn by the author).</p>
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<p>West facade of Section C of the East City Wall, the red outline indicates the main area (image source: drawn by the author).</p>
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<p>Section D of the East City Wall (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Analysis of collapse treatment measures (image source: drawn by the author).</p>
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<p>Plan of city wall drainage and temporary protection measures for the East Gate (image source: drawn by the author).</p>
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<p>Environmental improvement of the lotus pond east of the East City Gate (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Rendering of the design scheme for the East City Gate Relic Exhibition Project (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Rendering of the design scheme for the East City Gate Relic Exhibition Project (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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<p>Rendering of the design scheme for the East City Gate Relic Exhibition Project (image source: provided by the excavation team for the Ruins of the Imperial City of the Minyue Kingdom).</p>
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31 pages, 29333 KiB  
Article
VARS and HDMR Sensitivity Analysis of Groundwater Flow Modeling through an Alluvial Aquifer Subject to Tidal Effects
by Javier Samper, Brais Sobral, Bruno Pisani, Alba Mon, Carlos López-Vázquez and Javier Samper-Pilar
Water 2024, 16(17), 2526; https://doi.org/10.3390/w16172526 - 5 Sep 2024
Viewed by 538
Abstract
Groundwater flow and transport models are essential tools for assessing and quantifying the migration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced [...] Read more.
Groundwater flow and transport models are essential tools for assessing and quantifying the migration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream of the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analyzed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity, and hydraulic head calibration metrics. Input parameters include aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductivities, and boundary inflows and conductances. VARS, HDMR, and graphical methods agree to identify the most influential parameters, which for most of the outputs are the hydraulic conductivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations. Full article
(This article belongs to the Section Hydrogeology)
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<p>Flowchart of the methodology used in this study.</p>
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<p>(<b>a</b>) Location of the study area; (<b>b</b>) enlargement showing the model domain, the Sabiñánigo reservoir, the Sardas landfill, the Gállego River course, and the INQUINOSA (Sabiñánigo, Spain) former production site. The arrows along the Gállego River course indicate the flow direction.</p>
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<p>Cross-sectional geological profile of the Sabiñánigo reservoir and the Gállego River alluvial plain as reported by Sobral et al. [<a href="#B38-water-16-02526" class="html-bibr">38</a>]. Alluvial deposits include a shallow silt layer (green) and a deep layer of sand and gravel.</p>
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<p>2D finite element mesh, monitoring wells, material zones, boundary conditions, and GSA input parameters (<b>top plot</b>) and enlargement showing the area downstream of the Sardas landfill (<b>bottom plot</b>). The confined storage coefficient (S<sub>S</sub>) is the same in the four material zones. The sands and gravels are assumed to be confined in the alluvial (r<sub>c</sub>), except in the wooded areas (r<sub>u</sub>). Unconfined areas are shown with a back-hashed polygon.</p>
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<p>Map showing the reservoir tail area (hashed blue polygon) where aquifer/reservoir fluxes were calculated at times t1, t2, and t3, the monitoring wells whose piezometric data were used to calculate the calibration metrics, monitoring wells ST1C, PS19B, SPN1, and PS16C (where the average Darcy velocity is computed).</p>
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<p>Measured reservoir hydrograph and piezometric heads in well ST1C from 18–20 September 2020. The computed piezometric heads in monitoring wells ST1C, PS19B, and SPN1 and the aquifer/reservoir fluxes are analyzed at the following times: (1) t1, 18 September 2020, 20:00 (low reservoir water level), (2) t2, 18 September 2020, 22:30 (peak reservoir water level) and (3) t3, 19 September 2020, 04:30 (descending reservoir water level).</p>
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<p>Scatterplots of the computed piezometric heads in wells ST1Ct2 (<b>upper left plot</b>), PS19Bt2 (<b>upper right plot</b>), SPN1t2 (<b>lower left plot</b>), and Qt2 (<b>lower right plot</b>) versus the vertical hydraulic conductivity of the silting sediments in the former river course (Kvs1). The sample of 16384 points was generated with a Sobol sequence. The clouds of plots are shown for the following three ranges of percentiles, p, of the specific storage coefficient (S<sub>S</sub>): (1) p &lt; 30%; (2) 30% &lt; p &lt; 70%, and (3) p &gt; 70%.</p>
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<p>CUSUNORO curves of computed head in wells ST1C and PS19B at times t1, t2 and t3; and well SPN1 at times t1 and t2.</p>
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<p>CUSUNORO curves of the computed head in well SPN1 at time t3, MAEg, NRMSEg, NSEg, Q<sub>t1</sub>, Q<sub>t2</sub>, Q<sub>t3</sub>, and q<sub>av</sub>.</p>
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<p>IVARS<sub>50</sub> indexes of input parameters as a function of the number of star centers for MAEg (<b>upper plot</b>), and robustness of ranking as a function of the number of star centers (<b>bottom plot</b>).</p>
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<p>IVARS<sub>50</sub> indexes of input parameters as a function of the number of star centers for the average Darcy velocity (q<sub>av</sub>) (<b>upper plot</b>), and robustness of ranking as a function of the number of star centers (<b>bottom plot</b>).</p>
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<p>Sample variograms of the computed heads in monitoring wells ST1C and PS19B at times t1, t2, and t3 and monitoring well SPN1 at times t1 and t2. Only the variograms of the five most influential parameters are shown in the plots.</p>
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<p>Sample variograms of the computed head in well SPN1 at time t3, MAEg, NRMSEg, NSEg, Q<sub>t1</sub>, Q<sub>t2</sub>, Q<sub>t3</sub>, and q<sub>av</sub>. Only the variograms of the five most influential parameters are shown in the plots.</p>
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<p>VARS-TO, IVARS<sub>50</sub>, and VARS-ABE indexes for the computed heads in wells ST1C and PS19B at times t1, t2, and t3 and well SPN1 at times t1 and t2.</p>
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<p>VARS-TO, IVARS<sub>50</sub>, and VARS-ABE indexes for the computed head in well SPN1 at time t3, MAEg, NRMSEg, NSEg, Q<sub>t1</sub>, Q<sub>t2</sub>, Q<sub>t3</sub>, and q<sub>av</sub>.</p>
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<p>IVARS<sub>50</sub> sensitivity indexes for computed heads in wells ST1C (<b>top left plot</b>), PS19B (<b>top right plot</b>), and SPN1 (<b>bottom left plot</b>) and aquifer/reservoir flow (<b>bottom right plot</b>) at times t1, t2, and t3.</p>
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<p>IVARS<sub>50</sub> sensitivity indexes for calibration metrics MAEg, NRMSEg, and NSEg.</p>
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17 pages, 24301 KiB  
Article
Hydrodynamic Model of the Area of the Żelazny Most Mining Waste Storage Facility to Reconstruct the Migration of Saline Groundwater
by Jacek Gurwin, Marek Wcisło, Stanisław Staśko, Sebastian Buczyński, Magdalena Modelska, Tomasz Olichwer and Robert Tarka
Water 2024, 16(17), 2431; https://doi.org/10.3390/w16172431 - 28 Aug 2024
Viewed by 514
Abstract
This paper presents the construction of a numerical three-dimensional model of the area of the Żelazny Most Mining Waste Storage Facility (MWSF). In the study area, the difficult geological conditions associated with glaciotectonics are accompanied by a complex hydrotechnical system of sediment deposition [...] Read more.
This paper presents the construction of a numerical three-dimensional model of the area of the Żelazny Most Mining Waste Storage Facility (MWSF). In the study area, the difficult geological conditions associated with glaciotectonics are accompanied by a complex hydrotechnical system of sediment deposition and sedimentary water drainage. In order to effectively reflect the water flow paths, a detailed schematization was carried out, using 700,000 boreholes and more than 300 hydrogeological cross-sections. In addition, numerous drainage sections, streams, and ditches were included to reliably assess the amount of saline water entering the underlying aquifers. This research was supported by magnetic resonance sounding (MRS) studies of the reservoir’s sediments. The MWSF is currently being expanded, so the work primarily focuses on illustrating changes in the hydrodynamic field resulting from the inclusion of the new southern section. Models of similar facilities have been implemented before, but in the current one, the combination of meticulous analysis of the hydro-structural system, the water balance, a significant amount of data, the size of the facility, and the use of an unstructured discretization grid in the calculations is undoubtedly innovative and will be an important contribution to the development of analogous solutions around the world. Full article
(This article belongs to the Special Issue Groundwater Monitoring, Assessment and Modelling)
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<p>Location map of Żelazny Most Mining Waste Storage Facility (<b>a</b>), with satellite image (<b>b</b>).</p>
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<p>The variation in the hydraulic conductivity of sediments within the Żelazny Most landfill, specifically in the cross-section transitioning from the western dam to the eastern dam [<a href="#B30-water-16-02431" class="html-bibr">30</a>] (with permission from KGHM company).</p>
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<p>Hydrogeological schematization on an exemplary XIXaS cross-section from the hydrogeological documentation [<a href="#B38-water-16-02431" class="html-bibr">38</a>] (with permission from KGHM company), along with schematic lines of the available geological cross-sections against the range of the numerical model.</p>
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<p>Thickness maps of the layers of the hydrogeological model.</p>
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<p>Spatial distribution of the hydraulic conductivity of the 3rd (<b>a</b>) and 5th (<b>b</b>) layers of the hydrogeological model.</p>
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<p>Maps of the bottom of layer 2 (<b>a</b>) and layer 3 (<b>b</b>) in the model grid.</p>
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<p>Discretization grid with introduced boundary conditions.</p>
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<p>Results of MRS studies.</p>
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<p>Computed vs. observed head values.</p>
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<p>Head contour map according to calibration state for 2019.</p>
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<p>Head contour map from model forecast simulation for 2026.</p>
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25 pages, 3325 KiB  
Article
Effects of Paddy Rain-Flood Storage on Rice Growth Physiological Indices and Nitrogen Leaching under Organic Planting in Erhai Lake Basin
by Qingsheng Liu, Qiling Lu, Liudong Zhang, Shufang Wang, Aiqing Zou, Yong Su, Jun Sha, Ying Wang and Lihong Chen
Plants 2024, 13(17), 2381; https://doi.org/10.3390/plants13172381 - 26 Aug 2024
Viewed by 593
Abstract
In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments [...] Read more.
In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments were set up: conventional flooding irrigation (CK) and controlled irrigation (C), with three replicates for each treatment. In 2023, aiming to enhance the utilization rate of rainwater resources and reduce the direct discharge of dry-farming tailwater from upstream into Erhai Lake. The paddy field was used as an ecological storage basin, and the water storage depth of the paddy field was increased compared to the depth of 2022. Combined with the deep storage of rainwater, the dry-farming tailwater was recharged into the paddy field to reduce the drainage. In 2023, two water treatments, flooding irrigation with deep storage and controlled drainage (CKCD) and water-saving irrigation with deep storage and controlled drainage (CCD) were set up, and each treatment was set up with three replicates. The growth and physiological index of rice at various stages were observed. Nitrogen leaching of paddy field in surface water, soil water, and groundwater under different water treatments after tillering fertilizer were observed. The research results show that the combined application of organic and inorganic fertilizers under organic planting can provide more reasonable nutrient supply for rice, promote dry matter accumulation and other indices, and also reduce the concentration of NH4+-N in surface water. Compared with CK, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of C are increased by 4.8%, 4.1%, 20.9%, and 9.7%, respectively. Compared with CKCD, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of CCD are increased by 6.5%, 3.8%, 19.6%, and 21.9%, respectively. The yield in 2023 is 19% higher than that in 2022. Treatment C can increase the growth indicators and reduce the net photosynthetic rate to a certain extent, while CCD rain-flood storage can alleviate the inhibition of low irrigation lower limit on the net photosynthetic rate of rice. Both C and CCD can reduce nitrogen loss and irrigation amount in paddy fields. CCD can reduce the tailwater in the Gusheng area of the Erhai Lake Basin to Erhai Lake, and also can make full use of N, P, and other nutrients in the tailwater to promote the formation and development of rice. In conclusion, the paddy field rain-flood storage methodology in the Erhai Lake Basin can promote various growth and physiological indicators of rice, improve water resource utilization efficiency, reduce direct discharge of tailwater into Erhai Lake, and decrease the risk of agricultural non-point source pollution. Full article
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<p>Site location.</p>
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<p>Precipitation and average air temperature in rice season (Gusheng Village, China). Date is presented as month/day.</p>
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<p>Schematic diagram of deep storage and emission reduction in paddy fields (Gusheng Village, China).</p>
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<p>Maps of plant length and weight of dry matter on the ground during the reproductive period. TS, PIS, HFS, MRS, and RS represent tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage. Different letters in subfigures (<b>a</b>,<b>b</b>) indicate statistical significances at the <span class="html-italic">p</span> = 0.05 level within the same measurement date. “**” indicate that the significance test of 0.05 level has been passed, respectively.</p>
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<p>Root-to-shoot ratio and leaf area index at each growth stage. TS, PIS, HFS, MRS, and RS represent tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage. Different letters in subfigure indicate statistical significances at the <span class="html-italic">p</span> = 0.05 level within the same measurement date.</p>
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<p>The correlations between various growth indicators. Yield, DM, R/S, LAI, and NOGPS represent the rice yield, the weight of dry matter accumulation, the root-to-shoot ratio, the leaf area index, and the number of grains per spike. “*” indicates that the significance test of the 0.05 level has been passed.</p>
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<p>Diurnal variation in net photosynthetic rate at each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the net photosynthetic rate of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the net photosynthetic rate of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Diurnal variation in stomatal conductance at each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the stomatal conductance of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the stomatal conductance of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Diurnal variation in transpiration rate in each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the transpiration rate of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the transpiration rate of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Changes in surface water nitrogen concentration after fertilization (Gushengcun, China). (<b>a</b>): surface water NH<sub>4</sub><sup>+</sup>-N concentration in 2022, (<b>b</b>): surface water NH<sub>4</sub><sup>+</sup>-N concentration in 2023.</p>
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<p>Changes in soil water nitrogen concentration after fertilization (Gushengcun, China): (<b>a</b>): (0–20 cm, 2022), (<b>b</b>): (20–40 cm, 2022), (<b>c</b>): (0–20 cm, 2023), and (<b>d</b>): (20–40 cm, 2023).</p>
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<p>Changes in groundwater nitrogen concentration after fertilization (Gushengcun, China). (<b>a</b>): groundwater NH<sub>4</sub><sup>+</sup>-N concentration in 2022, (<b>b</b>): groundwater NH<sub>4</sub><sup>+</sup>-N concentration in 2023.</p>
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23 pages, 37832 KiB  
Article
CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models
by Jae Young Seo and Sang-Il Lee
Remote Sens. 2024, 16(17), 3122; https://doi.org/10.3390/rs16173122 - 24 Aug 2024
Viewed by 466
Abstract
Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory [...] Read more.
Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Study area: (<b>a</b>) Locations of South Korea. (<b>b</b>) Provinces of South Korea. (<b>c</b>) Soil texture classification and groundwater observation wells.</p>
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<p>A flow chart of the methodology used in this study.</p>
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<p>Structure of data-driven models: (<b>a</b>) XGBoost, (<b>b</b>) CNN-LSTM.</p>
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<p>Concept of entropy weight.</p>
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<p>Model predictions of groundwater storage anomalies (GWSAs) along with in situ observations.</p>
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<p>Density scatterplots comparing predictions of XGBoost and CNN-LSTM with in situ groundwater storage anomaly (GWSA) observations during the test period.</p>
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<p>Spatial distribution of root mean square error (RMSE) and correlation coefficient (<span class="html-italic">r</span>) for XGBoost and CNN-LSTM predictions vs. in situ groundwater storage anomalies (GWSA) using inverse distance weighting (IDW).</p>
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<p>Groundwater storage anomaly (GWSA) trend of (<b>a</b>) XGBoost and (<b>b</b>) CNN-LSTM. Median GWSA of (<b>c</b>) XGBoost and (<b>d</b>) CNN-LSTM. (<b>e</b>) Bicarbonate concentration.</p>
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<p>CO<sub>2</sub> emissions due to groundwater storage depletion estimated by (<b>a</b>) XGBoost and (<b>b</b>) CNN-LSTM. (<b>c</b>) Annual CO<sub>2</sub> emissions. (<b>d</b>) Annual precipitation in South Korea.</p>
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<p>Weights of six indicators determined using the entropy weight method.</p>
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<p>Groundwater storage depletion vulnerability maps prepared using the entropy–TOPSIS method for each administrative region ((<b>a</b>) XGBoost; (<b>b</b>) CNN-LSTM) and province ((<b>c</b>) XGBoost; (<b>d</b>) CNN-LSTM).</p>
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<p>Co-occurrence of CO<sub>2</sub> emissions and groundwater storage depletion vulnerability (the legends show the number of corresponding administrative regions).</p>
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20 pages, 12936 KiB  
Article
Dynamic Changes and Influencing Factors Analysis of Groundwater Icings in the Permafrost Region in Central Sakha (Yakutia) Republic under Modern Climatic Conditions
by Miao Yu, Nadezhda Pavlova, Jing Zhao and Changlei Dai
Atmosphere 2024, 15(9), 1022; https://doi.org/10.3390/atmos15091022 - 23 Aug 2024
Viewed by 391
Abstract
In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in [...] Read more.
In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in this field has become urgently necessary. This study aims to analyze the impacts of various factors on the scale of icing formation using Landsat satellite data, Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, Global Land Data Assimilation System (GLDAS) data, and field observation results. The results showed that the surface area of icings in the study area showed an overall increasing trend from 2002 to 2022, with an average growth rate of 0.06 km2/year. Suprapermafrost water and intrapermafrost water are the main sources of icings in the study area. The total Groundwater Storage Anomaly (GWSA) values from October to April showed a strong correlation with the maximum icing areas. Icings fed by suprapermafrost water were influenced by precipitation in early autumn, while those fed by intrapermafrost water were more affected by talik size and distribution. Climate warming contributed to the degradation of the continuous permafrost covering an area of 166 km2 to discontinuous permafrost, releasing additional groundwater. This may also be one of the reasons for the observed increasing trend in icing areas. This study can provide valuable insights into water resource management and infrastructure construction in permafrost regions. Full article
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<p>Location of the study area.</p>
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<p>Icings identification process.</p>
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<p>GWSA calculation process for icings’ extents in Central Yakutia, Russia.</p>
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<p>Dynamic changes in the surface area of icings on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022.</p>
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<p>Probability of the occurrence of icings in Central Yakutia from 2002 to 2022.</p>
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<p>Dynamic changes in GWSA on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022.</p>
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<p>Spatial variation characteristics of interannual GWSA on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia during the icing growth period (from the October of the current year to the May of the following year) from 2002 to 2022 (<b>A</b>–<b>T</b>) (a. Buluus, b. Keturen, c. Byatei, d. Mendensky, e. Unugestyakh, f. Muocmakh, g. Ulakhan-Taryn, h. Dzholokh, i. Eruu, and j. Yutelir).</p>
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<p>Spatial variation characteristics of interannual GWSA on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia during the icing growth period (from the October of the current year to the May of the following year) from 2002 to 2022 (<b>A</b>–<b>T</b>) (a. Buluus, b. Keturen, c. Byatei, d. Mendensky, e. Unugestyakh, f. Muocmakh, g. Ulakhan-Taryn, h. Dzholokh, i. Eruu, and j. Yutelir).</p>
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<p>Changes in monthly average and minimum air temperatures and the duration of icing growth on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022 (the solid line represents monthly average temperatures, scattered points represent monthly minimum temperatures during the icing growth period, and shaded areas represent the icing growth period).</p>
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<p>Changes in the monthly precipitation and monthly average atmospheric freezing index on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022 (the solid line represents monthly precipitation, scattered points represent monthly average atmospheric freezing index during the icing growth period, and shaded areas represent the icing growth period).</p>
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<p>Heatmap depicting the correlation between the maximum surface area of icings on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022, and the mean GWSAs for different periods (redder shades indicate a higher correlation and bluer shades indicate a lower or even negative correlation).</p>
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<p>Heatmap depicting the correlation between the maximum surface area of icings and precipitation, freezing duration, and negative accumulated temperatures on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia, from 2002 to 2022 (redder shades indicate a higher correlation and bluer shades indicate a lower or even negative correlation).</p>
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<p>Permafrost coverage (<b>A</b>,<b>B</b>) and annual trends in permafrost occurrence probability (<b>C</b>) from 2002 to 2019 on the Bestyakh Terrace on the northeast side of the Lena River in Central Yakutia, Russia (a. Buluus, b. Keturen, c. Byatei, d. Mendensky, e. Unugestyakh, f. Muocmakh, g. Ulakhan-Taryn, h. Dzholokh, i. Eruu, j. Yutelir).</p>
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23 pages, 9046 KiB  
Article
Evaluation of Wetland Area Effects on Hydrology and Water Quality at Watershed Scale
by Dipesh Nepal, Prem Parajuli, Ying Ouyang, Filip To, Nuwan Wijewardane and Vivek Venishetty
Resources 2024, 13(8), 114; https://doi.org/10.3390/resources13080114 - 22 Aug 2024
Viewed by 857
Abstract
Change in land use and land cover (LULC) is crucial to freshwater ecosystems as it affects surface runoff, groundwater storage, and sediment and nutrient transport within watershed areas. Ecosystem components such as wetlands, which can contribute to the reduction of water pollution and [...] Read more.
Change in land use and land cover (LULC) is crucial to freshwater ecosystems as it affects surface runoff, groundwater storage, and sediment and nutrient transport within watershed areas. Ecosystem components such as wetlands, which can contribute to the reduction of water pollution and the enhancement of groundwater recharge, are altered by LULC modifications. This study evaluates how wetlands in the Big Sunflower River Watershed (BSRW) have changed in recent years and quantified their impacts on streamflow, water quality, and groundwater storage using the Soil and Water Assessment Tool (SWAT). The model was well calibrated and validated prior to its application. Our study showed that the maximum increase in wetland areas within the sub-watersheds of interest was 26% from 2008 to 2020. The maximum changes in reduction due to the increase in wetland areas were determined by 2% for streamflow, 37% for total suspended solids, 13% for total phosphorus (TP), 4% for total nitrogen (TN), and the maximum increase in shallow groundwater storage by 90 mm from 2008 to 2020 only in the selected sub-basins. However, the central part of the watershed experienced average declines of groundwater levels up to 176 mm per year due to water withdrawal for irrigation or other uses. This study also found that restoration of 460 to 550 ha of wetlands could increase the reduction of discharge by 20%, sediment by 25%, TN by 18%, and TP by 12%. This study highlights the importance of wetland conservation for water quality improvement and management of groundwater resources. Full article
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<p>Location map of the Big Sunflower River Watershed showing locations of weather stations, land use, land cover, elevation, and soil classification.</p>
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<p>General steps used in the SWAT modeling process for wetland simulation.</p>
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<p>Watershed map showing groundwater monitoring wells, model calibration, and validation subbasins.</p>
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<p>Percentage of wetlands in watershed sub-basins 15 and 33 in the years 2008, 2014, and 2020.</p>
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<p>Simulated and measured streamflow during calibration and validation at Sunflower gauge station.</p>
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<p>Simulated and measured TSS loads during calibration and validation at Sunflower gauge station.</p>
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<p>Simulated and measured TP loads during calibration and validation at Sunflower gauge station.</p>
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<p>Simulated and measured TN loads during calibration and validation at Sunflower gauge station.</p>
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<p>Simulated vs. observed seasonal groundwater level changes from 2008 to 2020 during model: (<b>a</b>) calibration, and (<b>b</b>) validation.</p>
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<p>Effects of wetlands on annual shallow groundwater change.</p>
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<p>Twelve-year (2008–2020) variation in groundwater levels at watershed.</p>
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18 pages, 8874 KiB  
Article
Groundwater Level Prediction for Landslides Using an Improved TANK Model Based on Big Data
by Yufeng Zheng, Dong Huang, Xiaoyi Fan and Lili Shi
Water 2024, 16(16), 2286; https://doi.org/10.3390/w16162286 - 13 Aug 2024
Viewed by 662
Abstract
Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is [...] Read more.
Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is developed based on the influence of rainfall intensity, terrain, and geological conditions on the groundwater level in order to effectively predict the groundwater level evolution of rainfall landslides. A trapezoidal structure is used instead of the traditional rectangular structure to define the nonlinear change in a water level section to accurately estimate the storage of groundwater in rainfall landslides. Furthermore, big data are used to extract effective features from large-scale monitoring data. Here, we build prediction models to accurately predict changes in groundwater levels. Monitoring data of the Taziping landslide are taken as the reference for the study. The simulation results of the traditional TANK model and the improved TANK model are compared with the actual monitoring data, which proves that the improved TANK model can effectively simulate the changing trend in the groundwater level with rainfall. The study can provide a reliable basis for predicting and evaluating the change in the groundwater state in rainfall-type landslides. Full article
(This article belongs to the Special Issue Assessment of the Rainfall-Induced Landslide Distribution)
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<p>Geomorphological map of the Taziping landslide.</p>
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<p>Layout of monitoring equipment for the Taziping landslide. (<b>a</b>) The type and location of the monitoring instruments are described in the floor plan; (<b>b</b>) the upstream, middle, and downstream areas of the monitoring instrument layout and the distribution of different lithologies in the strata are introduced in the section drawing.</p>
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<p>Layout of monitoring equipment for the Taziping landslide. (<b>a</b>) The type and location of the monitoring instruments are described in the floor plan; (<b>b</b>) the upstream, middle, and downstream areas of the monitoring instrument layout and the distribution of different lithologies in the strata are introduced in the section drawing.</p>
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<p>Sample training definition flow chart.</p>
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<p>Schematic diagram of the multi-layer TANK model.</p>
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<p>Geological profile of the Taziping landslide.</p>
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<p>Taziping strata distribution (red dotted line) and the model improvement basis. The red dotted line range indicates the water storage form of the TANK model to be set according to the inverted trapezoidal display of the Taziping strata. The red line represents the landslide boundary.</p>
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<p>Improved TANK model diagram. By changing the water storage method and rainfall accumulation method of the TANK model, the inverted trapezoidal shape is used to realize a more physically meaningful water storage model that is first fast and then slow. The red wireframe indicates the shape of the model and the basis for the number of layouts.</p>
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<p>Rainfall intensity and pore water pressure from 2013 to 2016. The orange box area shows the rainfall intensity and pore water pressure during the flood season. The unit of time is days, the unit of rainfall intensity is millimeters per hour, and the unit of pore water pressure is KPa.</p>
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<p>Comparison of the water level predicted by the traditional TANK model and the observed level. The red curve represents the forecast result, the black curve represents the observed water table height, and the histogram represents the rainfall intensity. The red area is the non-flood season, and the comparison of the situation under the condition of little or no rain is convenient for comparison with the prediction results of the improved TANK model.</p>
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<p>Comparison between the prediction of the groundwater level and the observed level by the improved TANK model over 3 years. The red curve represents the forecast result, the black curve represents the observed water table height, and the histogram represents the rainfall intensity. The yellow area is the non-flood season, which compares the situation with little or no rainfall, which is convenient to compare with the prediction results of the traditional TANK model in order to observe the improved effect.</p>
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<p>Results of the training and prediction of rainfall model samples for short−, medium−, and long-term forecasts every 3 h. The black curve represents the observed groundwater level, the green curve represents the prediction results of the traditional TANK model, the red curve represents the prediction results of the improved TANK model, and the histogram represents the hourly rainfall intensity.</p>
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<p>Results of the training and prediction of rainfall model samples for short−, medium−, and long-term forecasts every 3 h. The black curve represents the observed groundwater level, the green curve represents the prediction results of the traditional TANK model, the red curve represents the prediction results of the improved TANK model, and the histogram represents the hourly rainfall intensity.</p>
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<p>Rainfall model training outcomes for the short-, medium-, and long-term forecasts every 24 h. The black curve represents the observed groundwater level, the green curve represents the prediction results of the traditional TANK model, the red curve represents the prediction results of the improved TANK model, and the histogram represents the hourly rainfall intensity.</p>
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<p>Rainfall model training outcomes for the short-, medium-, and long-term forecasts every 24 h. The black curve represents the observed groundwater level, the green curve represents the prediction results of the traditional TANK model, the red curve represents the prediction results of the improved TANK model, and the histogram represents the hourly rainfall intensity.</p>
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<p>The flood season prediction results of TANK model were improved in 2015. The two graphs are a 3-h cumulative forecast and a 24-h hourly forecast, respectively. The black curve represents the observed groundwater level, the red curve represents the 3-day improved TANK model prediction results, the purple curve represents the 7-day improved TANK model prediction results, and the orange curve represents the 14-day improved TANK model prediction results.</p>
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18 pages, 6817 KiB  
Article
Effect of Water Tank Size and Supply on Greenhouse-Grown Kidney Beans Irrigated by Rainwater in Cold and Arid Regions of North China
by Mengmeng Sun, Jizong Zhang, Zhihui Wang, Jingxin Ran, Yunjie Han, Jianheng Zhang, Huibin Li and Lifeng Zhang
Agronomy 2024, 14(8), 1767; https://doi.org/10.3390/agronomy14081767 - 12 Aug 2024
Viewed by 473
Abstract
In response to water scarcity in the Bashang area of northwest Hebei Province, a cold and arid region in north China, and to address the diminishing groundwater levels caused by pumping irrigation, this study investigated the impact of rainwater tank size and water [...] Read more.
In response to water scarcity in the Bashang area of northwest Hebei Province, a cold and arid region in north China, and to address the diminishing groundwater levels caused by pumping irrigation, this study investigated the impact of rainwater tank size and water supply on kidney beans production in greenhouses under various precipitation scenarios to determine the production potential and development strategies for regional precipitation resources. Under the background of average annual precipitation, kidney bean yield increased with increasing reservoir volume and shorter irrigation cycles. Under a 4-day irrigation cycle, the water demand satisfaction rate of kidney beans reached 100% water demand when the rainwater tank size was 15.7 m3. Against the wide variation in multi-year regional precipitation from 1992 to 2023, the annual effect of rainwater harvest was simulated using precipitation data collected 20 years with an 80% precipitation guarantee rate. The average minimum yield reduction rate obtained was 9.4%, and the corresponding minimum rainwater tank size was 29.5 m3. By superimposing the rainwater harvested in the shed and nonshed areas, the volume of the reservoir without yield reduction could be reduced to 20.0 m3. The sum of discharged and inventory water was much greater than the water scarcity in each water supply situation. Simulating and analyzing the effect of the relationship between rainwater tank size and water supply on rainwater harvesting in regional farmland by year provides important data affecting the construction of regional rainwater storage facilities and water supply efficiency. To achieve a high, stable yield of kidney beans grown in a greenhouse with shed film and shed area rainwater harvesting in north China, 2.6 m3 supplementary groundwater irrigation is still needed during the annual growing season. Full article
(This article belongs to the Section Farming Sustainability)
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<p>Location of the research area.</p>
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<p>Greenhouse rainwater harvesting, storage, and utilization system.</p>
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<p>Rainwater tank size under the average daily rainfall of 25 years and reference irrigation scheme scenario-water supply effect.</p>
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<p>Water demand satisfaction rate of the 8.0 m<sup>3</sup> rainwater tank of the kidney bean greenhouse.</p>
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<p>Influence of irrigation cycle (irrigation frequency) on water supply for an 8.0 m<sup>3</sup> rainwater tank.</p>
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21 pages, 9141 KiB  
Article
Heavy Metal Groundwater Transport Mitigation from an Ore Enrichment Plant Tailing at Kazakhstan’s Balkhash Lake
by Dauren Muratkhanov, Vladimir Mirlas, Yaakov Anker, Oxana Miroshnichenko, Vladimir Smolyar, Timur Rakhimov, Yevgeniy Sotnikov and Valentina Rakhimova
Sustainability 2024, 16(16), 6816; https://doi.org/10.3390/su16166816 - 8 Aug 2024
Cited by 1 | Viewed by 707
Abstract
Sustainable potable groundwater supply is crucial for human development and the preservation of natural habitats. The largest endorheic inland lake in Kazakhstan, Balkhash Lake, is the main water resource for the arid southeastern part of the country. Several ore enrichment plants that are [...] Read more.
Sustainable potable groundwater supply is crucial for human development and the preservation of natural habitats. The largest endorheic inland lake in Kazakhstan, Balkhash Lake, is the main water resource for the arid southeastern part of the country. Several ore enrichment plants that are located along its shore have heavy metal pollution potential. The study area is located around a plant that has an evident anthropogenic impact on the Balkhash Lake aquatic ecological system, with ten known heavy metal toxic hotspots endangering fragile habitats, including some indigenous human communities. This study assessed the risk of heavy metal contamination from tailing dump operations, storage ponds, and related facilities and suggested management practices for preventing this risk. The coastal zone risk assessment analysis used an innovative integrated groundwater numerical flow and transport model that predicted the spread of groundwater contamination from tailing dump operations under several mitigation strategies. Heavy metal pollution prevention models included a no-action scenario, a filtration barrier construction scenario, and two scenarios involving the drilling of drainage wells between the pollution sources and the lake. The scenario assessment indicates that drilling ten drainage wells down to the bedrock between the existing drainage channel and the lake is the optimal engineering solution for confining pollution. Under these conditions, pollution from tailings will not reach Lake Balkhash during the forecast period. The methods and tools used in this study to enable mining activity without environmental implications for the region can be applied to sites with similar anthropogenic influences worldwide. Full article
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<p>The study site location map and the Balkhash Industrial Area aerial photo display industrial objects included in the model’s schematization where the orange line is an interface with water bodies, the purple line is the tailing storage interface, black lines are barriers, and green lines are drains. The figure was prepared by Corel Draw with a base experimental site image taken from Google Earth.</p>
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<p>Hydrogeological cross-section along lines A–B (<a href="#sustainability-16-06816-f001" class="html-fig">Figure 1</a>). 1—upper-middle Quaternary lacustrine aquifer; 2—Pliocene aquitard of Pavlodar formations; 3—Miocene aquitard of Argyn formations; 4—Meso-Cenozoic water-bearing formations; 5—Carboniferous aquifer; 6—Paleozoic zone of fractured intrusive rocks; 7—tectonic faults; 8—groundwater level; 9—upper Quaternary technogenic aquifer, bulk soil; 10—sands with gravel inclusions; 11—crushed stones; 12—loams; 13—clays; 14—granites; 15—syenite porphyries; 16—dacite porphyries; 17—fractured rock; 18—well. Numbers: on top—well number, bottom—well depth, m; on the left in the numerator—mineralization, g/L; in the denominator—temperature, °C; on the right: in the numerator—well flow rate, L/s; in the denominator—drawdown, m. Shading corresponds to the chemical composition of groundwater in the sampled interval with the predominance of chloride and sulfate anions.</p>
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<p>The conceptual working process applied for the Balkhash Lake contamination risk assessment (<b>a</b>) and model application steps (<b>b</b>).</p>
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<p>Model calibration results.</p>
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<p>(<b>a</b>) Path lines of particles released from the source of pollution by the area tracked with MODPATH and (<b>b</b>) heavy metal spatial distribution in groundwater for ten years after contaminant release without a change in hydrogeological conditions (legend in <a href="#sustainability-16-06816-t001" class="html-table">Table 1</a>).</p>
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<p>Spatial distribution of heavy metals in groundwater from 14 drainage wells drilled between the drainage channel and Lake Balkhash (<b>a</b>) and for the scenario of drilling ten drainage wells between the drainage channel and Lake Balkhash (<b>b</b>).</p>
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<p>Spatial distribution of heavy metals in groundwater for the scenario of boundary construction between the drainage channel and Lake Balkhash.</p>
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<p>Spatial distribution of heavy metals in groundwater for the scenario of drainage wells drilled between the tailings pond drainage channel and the lake.</p>
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<p>Results of heavy metal concentration in groundwater monitoring wells over time.</p>
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<p>Sampling points on a map of heavy metal halo distribution in groundwater at the time of sampling in 2020.</p>
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<p>Calibration graph of the observed and calculated heavy metal concentrations at the sampling points.</p>
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<p>Relative sensitivity coefficients concerning different input parameters for a ±50% change in each parameter.</p>
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24 pages, 5024 KiB  
Review
Advances in Geochemical Monitoring Technologies for CO2 Geological Storage
by Jianhua Ma, Yongzhang Zhou, Yijun Zheng, Luhao He, Hanyu Wang, Lujia Niu, Xinhui Yu and Wei Cao
Sustainability 2024, 16(16), 6784; https://doi.org/10.3390/su16166784 - 7 Aug 2024
Viewed by 1326
Abstract
CO2 geological storage, as a large-scale, low-cost, carbon reduction technology, has garnered widespread attention due to its safety. Monitoring potential leaks is critical to ensuring the safety of the carbon storage system. Geochemical monitoring employs methods such as gas monitoring, groundwater monitoring, [...] Read more.
CO2 geological storage, as a large-scale, low-cost, carbon reduction technology, has garnered widespread attention due to its safety. Monitoring potential leaks is critical to ensuring the safety of the carbon storage system. Geochemical monitoring employs methods such as gas monitoring, groundwater monitoring, tracer monitoring, and isotope monitoring to analyze the reservoir’s storage state and secondary changes after a CO2 injection. This paper summarizes the recent applications and limitations of geochemical monitoring technologies in CO2 geological storage. In gas monitoring, the combined monitoring of multiple surface gasses can analyze potential gas sources in the storage area. In water monitoring, pH and conductivity measurements are the most direct, while ion composition monitoring methods are emerging. In tracer monitoring, although artificial tracers are effective, the environmental compatibility of natural tracers provides them with greater development potential. In isotope monitoring, C and O isotopes can effectively reveal gas sources. Future CO2 geological storage project monitoring should integrate various monitoring methods to comprehensively assess the risk and sources of CO2 leakage. The incorporation of artificial intelligence, machine learning technologies, and IoT monitoring will significantly enhance the accuracy and intelligence of numerical simulations and baseline monitoring, ensuring the long-term safety and sustainability of CO2 geological storage projects. Full article
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<p>(<b>a</b>) Temperature correction diagram of the infrared spectrum sensor (modified from [<a href="#B30-sustainability-16-06784" class="html-bibr">30</a>]); (<b>b</b>) the variations in the relative concentration of CO<sub>2</sub> and O<sub>2</sub> (modified from [<a href="#B33-sustainability-16-06784" class="html-bibr">33</a>]); (<b>c</b>) processes defined by relationships between O<sub>2</sub> and CO<sub>2</sub> [<a href="#B36-sustainability-16-06784" class="html-bibr">36</a>].</p>
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<p>The ratios between SF<sub>6</sub> and Kr are plotted with the empty circle mark depending on the elapsed time (min) (modified from [<a href="#B63-sustainability-16-06784" class="html-bibr">63</a>]). An open system means that the tracer gas can leave the water, while a closed system means that the tracer stays in the water continuously.</p>
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<p>Risks and monitoring requirements at different stages of CCS projects.</p>
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<p>The flow of the simulated annealing algorithm.</p>
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<p>PSNR for all indicators. Error bars represent the standard deviation of PSNR value indicators (modified from [<a href="#B37-sustainability-16-06784" class="html-bibr">37</a>]).</p>
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<p>Plot showing the loss of CO<sub>2</sub> from some 2009 well gasses calculated from the difference between measured <sup>3He</sup>/<sup>4</sup>He and <sup>40</sup>Ar/<sup>4</sup>He ratios and those predicted from the mixing lines [<a href="#B86-sustainability-16-06784" class="html-bibr">86</a>].</p>
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<p>(<b>a</b>) Model of a push–pull test (modified from [<a href="#B64-sustainability-16-06784" class="html-bibr">64</a>]); (<b>b</b>) distribution model of underground CO<sub>2</sub> after 10 years, predicted by numerical simulation [<a href="#B100-sustainability-16-06784" class="html-bibr">100</a>]; (<b>c</b>) natural CO<sub>2</sub> leakage points on Panarea Island (modified from [<a href="#B98-sustainability-16-06784" class="html-bibr">98</a>]).</p>
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<p>Application of different types of machine learning algorithms in CCS engineering (modified from [<a href="#B110-sustainability-16-06784" class="html-bibr">110</a>]).</p>
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<p>CO<sub>2</sub> monitoring concentrations on different monitoring dates [<a href="#B125-sustainability-16-06784" class="html-bibr">125</a>].</p>
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<p>Frame diagram of the Internet of Things.</p>
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11 pages, 4957 KiB  
Communication
A ‘Nuclear Bomb’ or Just ‘a Joke’? Groundwater Models May Help Communicate Nuanced Risks to the Great Salt Lake
by Matthew D. LaPlante, Piyush Dahal, Shih-Yu Simon Wang, Kirsti Hakala and Avik Mukherjee
Water 2024, 16(16), 2221; https://doi.org/10.3390/w16162221 - 6 Aug 2024
Viewed by 672
Abstract
The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned [...] Read more.
The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed. Full article
(This article belongs to the Topic Hydrology and Water Resources Management)
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<p>(<b>Left</b>) The three major sub-basins that flow into the Great Salt Lake. (<b>Right</b>) From the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information, the 9-month Standardized Precipitation Index, depicting the deviation of observed precipitation from the climatological average (using the base period 1895–2014). The Y-axis is the spatial percent of land in the three combined basins falling under each of the classifications from the U.S. Drought Monitor, from D1 (moderate drought) to D4 (exceptional drought).</p>
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<p>(<b>a</b>) From the USGS National Water Information System, the locations of Utah’s actively monitored wells. The brown box represents the domain used for analysis. (<b>b</b>) Great Salt Lake normalized annual average observed elevation (blue line), observed groundwater levels from NWIS (brown line), and CESM2/CLM-estimated total water storage (green line).</p>
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<p>(<b>a</b>) A simulation of the Great Salt Lake’s elevation (GSLE), derived from the CESM2 Community Land Model (CLM) hydrologic variable of Total Water Storage (green box and whisker plot) under the Shared Socioeconomic Pathway known as SSP370, a “middle of the road” scenario for greenhouse gas emissions and resultant warming. The data are computed every 30 years, with 100 runs each using quantile mapping, and shown in overlapping increments of 15 years. The top (bottom) of each whisker represents the 99th percentile near-maximum (minimum) modeled outcomes; the top (bottom) of each box represents the 75th (25th) percentile; and the line within the box represents the median value. The gray line is the annual average elevation of the Great Salt Lake and the dark, medium, and light blue dotted lines represent the GSLE at 4211, 4188, and 4180 feet, respectively. (<b>b</b>) The Great Salt Lake at its observed historic high of 4211 feet in 1986 (dark blue), observed historic low of 4188 feet in 2022 (medium blue), and a hypothetical future low of 4180 feet (light blue).</p>
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<p>(<b>a</b>) Ensemble and mean of past (light blue/blue) and future (orange/red) modeled precipitation from CLM, with historical ERA-5 reanalysis (black). (<b>b</b>) Same, for temperature. (<b>c</b>) Same, for evapotranspiration. (<b>d</b>) Same, for snow depth. (<b>e</b>) Same, for runoff. (<b>f</b>) CLM ensemble and mean of past and future modeled TWS.</p>
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<p>From [<a href="#B24-water-16-02221" class="html-bibr">24</a>], GSL water year level for the period 1429–2005, reconstructed using a within-basin network of seven tree-ring chronologies. The bold line is a 15-year cubic smoother applied to the reconstructed time-series with 50% frequency cutoff to accentuate the quasi-decadal variability indicated by the wavelet analysis. The yellow circles, which have been added to the original figure, indicate possible periods in which lake elevation may have rivaled or exceeded the observational record low from 2022.</p>
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11 pages, 5748 KiB  
Article
The Influence of Groundwater Migration on Organic Matter Degradation and Biological Gas Production in the Central Depression of Qaidam Basin, China
by Jixian Tian, Qiufang He, Zeyu Shao and Fei Zhou
Water 2024, 16(15), 2163; https://doi.org/10.3390/w16152163 - 31 Jul 2024
Viewed by 596
Abstract
For insight into the productive and storage mechanisms of biogas in the Qaidam Basin, efforts were made to investigate the groundwater recharge and the processes of hydrocarbon generation by CDOM-EEM (fluorescence excitation-emission matrix of Chromophoric dissolved organic matter) spectrum, hydrogen and oxygen isotopes, [...] Read more.
For insight into the productive and storage mechanisms of biogas in the Qaidam Basin, efforts were made to investigate the groundwater recharge and the processes of hydrocarbon generation by CDOM-EEM (fluorescence excitation-emission matrix of Chromophoric dissolved organic matter) spectrum, hydrogen and oxygen isotopes, and geochemical characters in the central depression of the Qaidam Basin, China. The samples contain formation water from three gas fields (TN, SB, and YH) and surrounding surface water (fresh river and brine lake). The results indicate that modern precipitation significantly controls the salinity distribution and organic matter leaching in the groundwater system of the central depression of the Qaidam Basin. Higher salinity levels inhibit microbial activity, which leads to organic matter degradation and to gas generation efficiency being limited in the groundwater. The inhabitation effect is demonstrated by the notable negative correlation between the extent of organic matter degradation and its concentration with hydrogen and oxygen isotopes. The conclusion of this study indicated that modern precipitation emerges as a crucial factor affecting the biogas production and storage in the Qaidam Basin by influencing the ultimate salinity and organic matter concentration in the formation, which provides theoretical insight for the maintenance of modern gas production wells and the assessment of gas production potential. Full article
(This article belongs to the Section Hydrogeology)
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<p>The schematic hydrogeology (<b>a</b>) and Quaternary Stratigraphic Column map (<b>b</b>) of Qaidam Basin.</p>
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<p>Stable isotope distribution of hydrogen and oxygen in the formation and surface water of central depression in Qaidam Basin (The data of brine lake and river water are cited from Li et al. [<a href="#B11-water-16-02163" class="html-bibr">11</a>]).</p>
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<p>The organic matter contents and CDOM index variation in the surface and formation water of Qaidam Basin.</p>
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<p>Fitting graphs of organic matter content in formation water, water isotopes, and CDOM spectral index in the central depression area of the Qaidam Basin; (<b>a</b>) Linear fit of total organic carbon (TOC) content with hydrogen (filled blue rhombus) and oxygen isotope (filled purple triangle) content; (<b>b</b>) Fitting of total organic carbon (TOC) content with humification index (HIX, filled green circle) and spectral index of aromaticity (SUV254, filled square).</p>
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