Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 

Topic Editors

School of Life Sciences, Technical University of Munich, Munich, Germany
Department of Earth Sciences and Program of Environmental Studies, University of California, Santa Barbara, CA 93106, USA

Advances in Hydrogeological Research

Abstract submission deadline
closed (31 October 2024)
Manuscript submission deadline
31 December 2024
Viewed by
13475

Topic Information

Dear Colleagues,

Advances in hydrogeological research can lead to the better management of water resources and identify a roadmap to address future challenges. The hydrogeologist community has developed interdisciplinary approaches in terms of concepts, models, and techniques as well as tools at different scales (from the laboratory to the field). The aim is to highlight isotope methods, qualitative and quantitative models, vulnerability, adsorption–desorption, diffusion mechanisms, fractionation, analytical development, emerging contaminants, nanoparticles, nanoplastics, colloids, aquatic ecology, remediation, treatment, climate impacts, etc. Our goal is to repair or propose state-of-the-art technologies based on interdisciplinary and multidisciplinary hydrogeological approaches.

Prof. Dr. Karl Auerswald
Prof. Dr. Jordan Clark
Topic Editors

Keywords

  • hydrology
  • groundwater
  • soil erosion
  • water resource management
  • hydrogeology
  • surface waters
  • stable isotopes

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Geosciences
geosciences
2.4 5.3 2011 26.2 Days CHF 1800 Submit
Hydrology
hydrology
3.1 4.9 2014 18.6 Days CHF 1800 Submit
Remote Sensing
remotesensing
4.2 8.3 2009 24.7 Days CHF 2700 Submit
Sustainability
sustainability
3.3 6.8 2009 20 Days CHF 2400 Submit
Water
water
3.0 5.8 2009 16.5 Days CHF 2600 Submit

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (12 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
19 pages, 4082 KiB  
Review
A Systematic Review of the Current State of Numerical Groundwater Modeling in American Countries: Challenges and Future Research
by Baltazar Leo Lozano Hernández, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Alfredo Ramos Leal, Eliseo Hernández Pérez, Joel García Pazos and Oscar Guadalupe Almanza Tovar
Hydrology 2024, 11(11), 179; https://doi.org/10.3390/hydrology11110179 - 25 Oct 2024
Viewed by 653
Abstract
In arid and semi-arid regions, groundwater is often the only available water source. However, overexploitation and pollution have led to a decrease in groundwater quantity and quality. Therefore, the proper management of groundwater resources is essential to promote sustainable development. Numerical simulation models [...] Read more.
In arid and semi-arid regions, groundwater is often the only available water source. However, overexploitation and pollution have led to a decrease in groundwater quantity and quality. Therefore, the proper management of groundwater resources is essential to promote sustainable development. Numerical simulation models (NSMs) have emerged as a valuable tool to address these challenges due to their ability to accurately and efficiently model groundwater systems. This study provides a comprehensive systematic review to evaluate the current knowledge on using numerical groundwater flow models for planning and water resource management in countries in the American region. A total of 166 research articles were published between the years 2000 and 2024. We analyzed and summarized details such as the study regions, numerical simulation methods and applied software, performance metrics, modeling units, modeling limitations, and prediction scenarios. In addition, we discuss alternatives to address the constraints and difficulties and suggest recommendations for future research. The continued research, improvement, and development of numerical groundwater models are essential to ensure the sustainability of groundwater resources. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Process flowchart for the selection of articles using the methodology adapted of De León Pérez et al. [<a href="#B25-hydrology-11-00179" class="html-bibr">25</a>].</p>
Full article ">Figure 2
<p>Trends in publication on numerical modeling of groundwater flow over time.</p>
Full article ">Figure 3
<p>Tree map with top ten journals with highest numbers of records observed.</p>
Full article ">Figure 4
<p>Geographic distribution of the 166 articles published per country.</p>
Full article ">Figure 5
<p>VOSviewer authors’ keyword co-occurrence.</p>
Full article ">Figure 6
<p>Performance metrics employed for calibrating and validating hydrogeological models. (<b>A</b>) Combination of PMs used and (<b>B</b>) pie chart of the PMs used. Performance metrics: root mean square error (RMSE), coefficient of determination (R<sup>2</sup>), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), correlation coefficient (R), normalized root mean square error (NRMSE), percentage of bias (PBIAS), mean error (ME), Kling–Gupta efficiency (KGE), modified Nash–Sutcliffe efficiency (mNSE), standard deviation of measured data (RSR), and normalized objective function (NOF).</p>
Full article ">Figure 7
<p>Study units in the reviewed articles. (<b>A</b>) Fundamental units for hydrogeological numerical modeling of groundwater. (<b>B</b>) Aquifer types conceptualized in the studies.</p>
Full article ">
21 pages, 21987 KiB  
Article
Spatiotemporal Variation Characteristics of Extreme Precipitation in the Mid–Lower Reaches of the Yangtze River Basin Based on Precipitation Events
by Yixin Zhang, Peng Li, Guoce Xu, Zhanbin Li, Zhou Wang, Yueming Rao, Zifan Liu, Yiting Chen and Bin Wang
Sustainability 2024, 16(21), 9197; https://doi.org/10.3390/su16219197 - 23 Oct 2024
Viewed by 506
Abstract
In addition to greater precipitation on extreme days of precipitation, preceding and succeeding precipitation (PSP) is often an objective component of flooding in the mid–lower reaches of the Yangtze River Basin (MLRYRB). In this study, focused on the temporal distribution pattern of precipitation, [...] Read more.
In addition to greater precipitation on extreme days of precipitation, preceding and succeeding precipitation (PSP) is often an objective component of flooding in the mid–lower reaches of the Yangtze River Basin (MLRYRB). In this study, focused on the temporal distribution pattern of precipitation, the concept of an extreme precipitation event (EPE), defined as a consecutive precipitation event having at least one daily precipitation extreme, is proposed to consider PSP in an extreme event. We analyzed the spatiotemporal variation of four types of EPEs based on daily data obtained from 130 monitoring stations covering 1960–2019. Extreme precipitation increased significantly over the last 60 years (p < 0.01). The frequency and precipitation amount of single-day EPEs accounted for only 13% and 21%, respectively, while multi-day continuous EPE types that are associated with PSP accounted for 87% and 79%, respectively, confirming the connotations of EPEs. The front and late EPEs under the 100-year return level reached 250 mm and 230 mm, respectively. Furthermore, climate warming could lead to significant increases in the frequency of single-day and late EPEs, particularly in the southern region. The EPE concept may be helpful in exploring disaster-causing processes under extreme weather, and it provides a theoretical basis for deriving the precipitation hazard chain, which is more applicable to basins with long precipitation durations. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the study area and spatial distribution of the meteorological stations.</p>
Full article ">Figure 2
<p>Identification process of extreme precipitation events (EPEs).</p>
Full article ">Figure 3
<p>Spatial distribution of precipitation in different climate zones during the 1960–2019 period in the mid–lower reaches of the Yangtze River Basin.</p>
Full article ">Figure 4
<p>Temporal variation in extreme precipitation in the mid–lower reaches of the Yangtze River Basin.</p>
Full article ">Figure 5
<p>Cumulative frequency and monthly precipitation for different extreme precipitation event (EPE) types during the 1960–2019 period.</p>
Full article ">Figure 6
<p>(<b>a</b>) Cumulative frequency of the extreme precipitation event (EPE) types during the 1960–2019 period. (<b>b</b>) Cumulative precipitation percentages of the extreme precipitation event (EPE) types during the 1960–2019 period.</p>
Full article ">Figure 7
<p>Spatial distribution of extreme precipitation event (EPE) types during the 1960–2019 period.</p>
Full article ">Figure 8
<p>Precipitation amount and frequency trends in extreme precipitation events (EPEs) during the 1960–2019 period.</p>
Full article ">Figure 9
<p>Spatial variation trends in precipitation, frequency, and duration of extreme precipitation events (EPEs) during the 1960–2019 period.</p>
Full article ">Figure 10
<p>Trends in the precipitation, frequency, and duration of extreme precipitation events (EPEs) in (<b>a</b>) the entire basin, (<b>b</b>) in the northern region (Zone I), and (<b>c</b>) in the southern region of the basin (Zone II).</p>
Full article ">Figure 11
<p>Concentrations of extreme precipitation events (EPEs).</p>
Full article ">Figure 12
<p>Return levels of precipitation for extreme precipitation events (EPEs) at (<b>a</b>) 20 years, (<b>b</b>) 50 years, and (<b>c</b>) 100 years.</p>
Full article ">Figure 13
<p>Wavelet coherence analysis of extreme precipitation event types and temperature. The thick black contour indicates correlations above the 95% significance level against the noise.</p>
Full article ">
20 pages, 6033 KiB  
Article
Identification of Anthropogenic and Natural Inputs of Sulfate into River System of Carbonate Zn-Pb Mining Area in Southwest China: Evidence from Hydrochemical Composition, δ34SSO4 and δ18OSO4
by Kailiang Zhang, Zeming Shi, Xiaoyan Ding, Liquan Ge, Maolin Xiong, Qingxian Zhang, Wanchang Lai and Liangquan Ge
Water 2024, 16(16), 2311; https://doi.org/10.3390/w16162311 - 16 Aug 2024
Viewed by 655
Abstract
The release of pollutants from lead-zinc mining areas poses a significant threat to the environment, making pollution tracing crucial for environmental protection. However, the complexity of carbonate mining areas makes tracing these pollutants challenging. This study used δ34SSO4 and δ [...] Read more.
The release of pollutants from lead-zinc mining areas poses a significant threat to the environment, making pollution tracing crucial for environmental protection. However, the complexity of carbonate mining areas makes tracing these pollutants challenging. This study used δ34SSO4 and δ18OSO4 isotopes combined with the Stable Isotope Mixing Models in R (SIMMR) to assess anthropogenic sulfate sources in the Daliangzi mining area. The river water types were mainly Ca2+-Mg2+-HCO3, and SO42, which are significantly influenced by dolomite dissolution. The δ34SSO4 values ranged from 6.47‰ to 17.96‰ and the δ18OSO4 values ranged from −5.66‰ to 13.98‰. The SIMMR results showed that evaporite dissolution in tributaries, driven by gypsum, contributed 31% of sulfate, while sulfide oxidation, sewage, and atmospheric deposition contributed 19%, 18%, and 24%, respectively. The tailings pond near Xincha Creek has a higher sulfate release potential than the processing plant near Cha Creek. In the mainstream, sulfide oxidation contributed 25%, primarily from mine drainage. Anthropogenic sources, including sulfide oxidation, fertilizers, and sewage, made up about 50% of the total sulfate, with sulfide oxidation accounting for half of this input. The strong correlation between the Zn and SO42 concentrations (R2 = 0.82) and between the Zn and the contribution from the sulfide oxidation (R2 = 0.67) indicates their co-release during sulfide oxidation, making SO42 a proxy for tracing Zn sources. This study highlights the utility of δ34SSO4 and δ18OSO4 with SIMMR in tracing anthropogenic inputs and underscores the significant impact of mining on river systems and the sulfur cycle. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Geological map and sampling points. (<b>a</b>) The location of Daliangzi Zn-Pb mining area; (<b>b</b>) Stratigraphy and structure of the study area; (<b>c</b>) The locations of the mainstream, tributaries, and water samples collected in the Daqiao River Basin. Note: The red points represent mine water, the gray points represent tailings leachate, the orange points represent main stream water samples, and the white points represent tributary water samples.</p>
Full article ">Figure 2
<p>Piper diagram of different types of water sample in Daliangzi mining area.</p>
Full article ">Figure 3
<p>Correlations between sulfate and (<b>a</b>) Ca<sup>2</sup><sup>+</sup>, (<b>b</b>) Na<sup>+</sup> + K<sup>+</sup>, (<b>c</b>) Cl<sup>−</sup>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math>, (<b>e</b>) Mg<sup>2</sup><sup>+</sup>, and (<b>f</b>) TDS.</p>
Full article ">Figure 4
<p>Graphs of (<b>a</b>) non-gypsum-source Ca<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math> and (<b>b</b>) Mg<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math>; (<b>c</b>) non-carbonate source of Ca<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>SO</mi> </mrow> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>The relations between (<b>a</b>) δ<sup>34</sup>S<sub>SO4</sub> and δ<sup>18</sup>O<sub>SO4</sub> and (<b>b</b>) δ<sup>34</sup>S<sub>SO4</sub> and δ<sup>18</sup>O<sub>SO4</sub>-1/[SO<sub>4</sub><sup>2</sup><sup>−</sup>].</p>
Full article ">Figure 6
<p>The spatial variation of (<b>a</b>) SO<sub>4</sub><sup>2</sup><sup>−</sup>, (<b>b</b>) Cl<sup>−</sup>, (<b>c</b>) δ<sup>34</sup>S, and (<b>d</b>) δ<sup>18</sup>O characteristics.</p>
Full article ">Figure 7
<p>(<b>a</b>) Sulfate contributions at tributaries and (<b>b</b>) their correlations with each other.</p>
Full article ">Figure 8
<p>(<b>a</b>) Sulfate contributions at mainstream and (<b>b</b>) their correlations with each other.</p>
Full article ">Figure 9
<p>Correlations between Zn and (<b>a</b>) SO<sub>4</sub><sup>2</sup><sup>−</sup> and (<b>b</b>) contribution of sulfide oxidation.</p>
Full article ">
22 pages, 6377 KiB  
Article
LithoSFR Model for Mapping Groundwater Potential Zones Using Remote Sensing and GIS
by Amin Shaban, Nasser Farhat, Mhamad El-Hage, Batoul Fadel, Ali Sheib, Alaa Bitar and Doha Darwish
Water 2024, 16(14), 1951; https://doi.org/10.3390/w16141951 - 10 Jul 2024
Cited by 2 | Viewed by 1163
Abstract
Groundwater is a significant source of water supply, especially with depleted and quality-deteriorated surface water. The number of drilled boreholes for groundwater has been increased, but erroneous results often occur while selecting sites for digging boreholes. This makes it necessary to follow a [...] Read more.
Groundwater is a significant source of water supply, especially with depleted and quality-deteriorated surface water. The number of drilled boreholes for groundwater has been increased, but erroneous results often occur while selecting sites for digging boreholes. This makes it necessary to follow a science-based method indicating potential zones for groundwater storage. The LithoSFR Model is a systematic approach we built to create an indicative map with various categories for potential groundwater sites. It is based mainly on retrieved geospatial data from satellite images and from available thematic maps, plus borehole data. The geospatial data were systematically manipulated in a GIS with multi-criteria applications. The novelty of this model includes the empirical calculation of the level each controlling factor (i.e., weights and rates), as well as the LithoSFR Model, adopting new factors in its design. This study was applied on a representative Mediterranean region, i.e., Lebanon. Results showed that 44% of the studied region is characterized by a very high to high potentiality for groundwater storage, mainly in areas with fractured and karstified carbonate rocks. The obtained results from the produced map were compared with datasets which were surveyed from representative boreholes to identify the discharge in the dug boreholes, and then to compare them with the potential zones in the produced map The reliability of the produced map exceeded 87%, making it a significant tool to identify potential zones for groundwater investment. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location map of the study area.</p>
Full article ">Figure 2
<p>Flow chart showing the framework of the LithoSFR Model for geospatial data manipulation.</p>
Full article ">Figure 3
<p>The lithological map of the study area.</p>
Full article ">Figure 4
<p>Lineament map of the study area.</p>
Full article ">Figure 5
<p>Lineament density map of the study area.</p>
Full article ">Figure 6
<p>Streams in the study area and their connectivity points.</p>
Full article ">Figure 7
<p>Stream density map of the study area.</p>
Full article ">Figure 8
<p>Connectivity density map of the study area.</p>
Full article ">Figure 9
<p>Slope map of the study area.</p>
Full article ">Figure 10
<p>Recharge rates of the area.</p>
Full article ">Figure 11
<p>Groundwater potential map for the study area.</p>
Full article ">Figure 12
<p>Simple linear regression showing the relationship between wells’ productivity and the mapped GWP zones.</p>
Full article ">
21 pages, 8588 KiB  
Article
Human Activities Have Altered Sediment Transport in the Yihe River, the Longest River Originating from Shandong Province, China
by Jiayuan Liu, Shuwei Zheng, Jinkuo Lin, Mengjie Zhao, Yanan Ma, Banghui Chen, Fei Wen, Zhijie Lu and Zijun Li
Sustainability 2024, 16(13), 5396; https://doi.org/10.3390/su16135396 - 25 Jun 2024
Viewed by 1087
Abstract
Climate change and human activities affect regional sediment transport and ecological environment construction. Investigating sediment transport and its influencing factors in the Yihe River Basin (YHRB) will provide guidance for regional soil and water conservation and sustainable development. We analyzed the chronological changes, [...] Read more.
Climate change and human activities affect regional sediment transport and ecological environment construction. Investigating sediment transport and its influencing factors in the Yihe River Basin (YHRB) will provide guidance for regional soil and water conservation and sustainable development. We analyzed the chronological changes, cycles, spatial distribution and influencing factors using Mann–Kendall (M-K) trend analysis, wavelet analysis, and the Pettitt mutation point (PMP) test, then quantified the role of precipitation and human activities in sediment transport changes. The results showed that annual precipitation decreased marginally, whereas sediment load has noticeably declined. Four precipitation cycles were observed: 4–8a, 9–14a, 16–19a, and 20–28a, where 9–14a was dominant; sediment transport cycles were tracked: 3–5a, 9–15a, and 30a, where 30a was dominant with a decreasing trend. The sediment load was higher in the central, northern, and southwestern sub-basins of the YHRB, while it was lower in the southeast. The contribution of human activities and precipitation changes to sediment transport was 73.14% and 26.86% in transitional phase I (1965–1980) and 71.97% and 28.03% in transitional phase II (1981–2020), respectively. Hydraulic engineering construction, water resource development, land-use changes, and soil and water conservation measures intercepted precipitation and sediment, making them the primary factor affecting sediment transport changes in the YHRB. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Distribution of the water system, rain gauge stations, and hydrological station in the YHRB. (<b>a</b>,<b>b</b>) Locations of Shandong Province and the YHRB; (<b>c</b>) Dem, sub-basin, and locations of the hydrological and rain gauge stations of the YHRB.</p>
Full article ">Figure 2
<p>Changes in average monthly precipitation and sediment load in the YHRB.</p>
Full article ">Figure 3
<p>Changes in the annual precipitation and annual sediment load in the YHRB from 1956 to 2020.</p>
Full article ">Figure 4
<p>Wavelet analysis of precipitation in the YHRB from 1956 to 2020. (<b>a</b>) Wavelet-transformed real part plot of annual precipitation; (<b>b</b>) wavelet variance plot of annual precipitation.</p>
Full article ">Figure 5
<p>Wavelet analysis of sediment load in the YHRB from 1956 to 2020. (<b>a</b>) Wavelet-transformed real part plot of annual sediment load; (<b>b</b>) wavelet variance plot of annual sediment load.</p>
Full article ">Figure 6
<p>Changes in the spatial distribution of sediment transport modulus in the YHRB.</p>
Full article ">Figure 7
<p>Annual total reservoir capacity change of the YHRB from 1956 to 2020.</p>
Full article ">Figure 8
<p>Temporal variations in annual water consumption in the YHRB from 1984 to 2020 (artificial ecological and environmental recharge water consumption refers to the transfer of water to ecosystems damaged by the failure to meet minimum ecological water demand through the adoption of engineering or non-engineering measures to replenish their ecosystem water consumption, to curb the destruction of ecosystem structure and loss of function, and to gradually restore the ecosystem’s original ability to self-regulate function; industrial and domestic water consumption refers to the amount of water extracted and does not include the amount of water reused).</p>
Full article ">Figure 9
<p>Land-use structures in the YHRB in 1975, 1995, and 2020.</p>
Full article ">Figure 10
<p>Characteristics of land-use transfer in the YHRB in 1975, 1995, and 2020.</p>
Full article ">Figure 11
<p>Temporal variations in the cumulative area of soil and water conservation measures in the YHRB from 1972 to 2017 (dammed land refers to the arable land formed by damming in the gully and blocking the soil washed down from the mountain).</p>
Full article ">Figure 12
<p>DCCs of annual sediment load and precipitation in the YHRB.</p>
Full article ">Figure 13
<p>Test of abrupt points of annual sediment transport in the YHRB. (<b>a</b>) Test of abrupt points of annual sediment transport from 1956 to 2020 using the PMP test; (<b>b</b>) test of abrupt points of annual sediment transport from 1956 to 1980 using the PMP test.</p>
Full article ">Figure 14
<p>The characteristics of inter-annual variation in sediment load in the YHRB from 1956 to 2020.</p>
Full article ">
12 pages, 2736 KiB  
Brief Report
Identifying a Minimum Time Period of Streamflow Recession Records to Analyze the Behavior of Groundwater Storage Systems: A Study in Heterogeneous Chilean Watersheds
by Víctor Parra, Enrique Muñoz, José Luis Arumí, Yelena Medina and Robert Clasing
Water 2024, 16(12), 1741; https://doi.org/10.3390/w16121741 - 20 Jun 2024
Viewed by 834
Abstract
Aquifers are complex systems that present significant challenges in terms of characterization due to the lack or absence of watershed-scale hydrogeological information. An alternative to address the need to characterize watershed-scale aquifer behavior is recession flow analysis. Recession flows are flows sustained by [...] Read more.
Aquifers are complex systems that present significant challenges in terms of characterization due to the lack or absence of watershed-scale hydrogeological information. An alternative to address the need to characterize watershed-scale aquifer behavior is recession flow analysis. Recession flows are flows sustained by groundwater release from the aquifer. Aquifer behavior can be characterized using recession flow records available from gauging stations, and therefore an indirect measure of aquifer behavior is obtained through watershed-scale recession flow records and analysis. This study seeks to identify the minimum time period necessary to characterize the behavior of groundwater storage systems in watersheds with different geological, morphological, and hydrological characteristics. To this end, various watersheds in south-central Chile underwent recession flow analysis, with eight time periods considered (2, 3, 4, 5, 10, 15, 20, and 25 years). The results indicate that 25 years of records are sufficient for the characterization of watershed-scale aquifer behavior, along with the representation of the groundwater storage-release (S-Q) process in watersheds with different geological, morphological, and hydrological characteristics. Additionally, the results show that an initial characterization of the groundwater system behavior in watersheds with different geological characteristics can be carried out with two years of records. This information could be important for practical engineering and the study of groundwater systems in watersheds with limited hydrological and hydrogeological information. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Locations of the watersheds used in the study area and their hydrological and geomorphological characteristics. The predominant slope map (<b>a</b>), degree of permeability (<b>b</b>), and aridity index (<b>c</b>) of each watershed are shown.</p>
Full article ">Figure 2
<p>Cluster characteristics. The mean elevation (<b>a</b>), mean slope (<b>b</b>), degree of permeability (<b>c</b>), and aridity index (<b>d</b>) of each cluster are also shown.</p>
Full article ">Figure 3
<p>Boxplots with values of recession parameter <span class="html-italic">b</span> obtained for different watershed groups (clusters). w1, w2, w3, w4, w5, w6, w7, and w8 correspond to moving windows of 2 years, 3 years, 4 years, 5 years, 10 years, 15 years, 20 years, and 25 years, respectively.</p>
Full article ">Figure 4
<p>Median slope <span class="html-italic">b</span> values of each cluster obtained from the different time windows. The dashed line represents the limit value of <span class="html-italic">b</span> for fast drainage (<span class="html-italic">b</span> &gt; 1.5) and slow drainage processes (<span class="html-italic">b</span> &lt; 1.5). w1, w2, w3, w4, w5, w6, w7, and w8 correspond to moving windows of 2 years, 3 years, 4 years, 5 years, 10 years, 15 years, 20 years, and 25 years, respectively.</p>
Full article ">Figure 5
<p>The correlation matrix between each watershed’s median slope <span class="html-italic">b</span> values from the 8 time windows.</p>
Full article ">Figure 6
<p>Median slope <span class="html-italic">b</span> values of two-year combinations. The dashed line represents the limit value of <span class="html-italic">b</span> for fast drainage (<span class="html-italic">b</span> &gt; 1.5) and slow drainage processes (<span class="html-italic">b</span> &lt; 1.5).</p>
Full article ">
23 pages, 4462 KiB  
Article
Synergic Origin and Evolution of TDS, Mg and Fluoride in Groundwater as Relative to Chronic Kidney Disease of Unknown Etiology (CKDu) in Sri Lanka
by K. S. G. S. Priyadarshanee, Zhonghe Pang, E. A. N. V. Edirisinghe, H. M. T. G. A. Pitawala, J. D. C. Gunasekara, W. M. G. S. Wijesooriya, Yinlei Hao, Yifan Bao and Jiao Tian
Water 2024, 16(11), 1606; https://doi.org/10.3390/w16111606 - 4 Jun 2024
Cited by 1 | Viewed by 902
Abstract
The rural population in the Dry Zone of Sri Lanka is largely affected by Chronic Kidney Disease of Unknown etiology (CKDu). According to the multidisciplinary research carried out so far, quality of groundwater is considered one of the possible causative factors for CKDu. [...] Read more.
The rural population in the Dry Zone of Sri Lanka is largely affected by Chronic Kidney Disease of Unknown etiology (CKDu). According to the multidisciplinary research carried out so far, quality of groundwater is considered one of the possible causative factors for CKDu. Therefore, assessment of the quality of groundwater being used for drinking and its evolution mechanism is the key to identifying the linkage between CKDu and drinking water. This study aimed to perform a detailed investigation on groundwater sources using isotopic, chemical, and hydrogeological methods in the CKDu-endemic (site A) and the control area (sedimentary formation—site B) in the Malwathu Oya basin and the control areas in the Malala Oya basin (site C) selected for a systematic comparison. Our investigation shows that elevated levels of TDS, magnesium, and fluoride in the shallow groundwater affected by climatic, geochemical, and hydrogeological processes may contribute to the CKDu in the Dry Zone of Sri Lanka. All the groundwater samples analysed have exceeded the hardness threshold. Prominent Mg hardness proportion together with excess F in the CKDu endemic area may produce nephrotoxic MgF2 complexes that may trigger renal damage. In contrast, NaF complexes in the CKDu control area leads to reduction of F toxicity in the human body. Elevated F and Mg2+ are found in site A, low F and high Mg2+ in site B, and either combinations of low F and low Mg2+, high F and low Mg2+, or low F with high Mg2+ in site C. TDS, hardness, Mg2+, Na+, and F are formed with different mechanisms in the three selected areas. The primary process that regulates the evolution of groundwater types and contents in sites A and C is the weathering of silicates. Similarly, in site A, carbonate dissolution and reverse ion exchange are quite strong. Cation exchange and evaporite dissolution are more pronounced in site C. Shallow groundwaters are evapo-concentrated, hence their quality deteriorates more significantly than the deep groundwater in the CKDu endemic area. Dilution decreases the ion content in site A while evaporite dissolution increases it in site C after the rainy season. Evaporation and seawater mixing affect the quality of groundwater in site B. It is also found that a statistically significant difference exists in the F/Na+, F/Mg2+, and F/Ca2+ between the endemic and control areas. Intensive rock weathering combined with desorption has added excess F to the groundwater in site A, while cation exchange and fluorite dissolution are contributing factors in site C. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malwathu Oya basin (site A and site B); (<b>b</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malala Oya basin (site C).</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malwathu Oya basin (site A and site B); (<b>b</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malala Oya basin (site C).</p>
Full article ">Figure 2
<p>The plot showing the F<sup>−</sup> and Mg<sup>2+</sup> variation of groundwater during the dry season.</p>
Full article ">Figure 3
<p>A Piper trilinear plot illustrating the hydro-geochemical facies of groundwater during the wet and dry seasons.</p>
Full article ">Figure 4
<p>Gibb’s plot illustrating the hydro-chemical facies of groundwater during the wet and dry season.</p>
Full article ">Figure 5
<p>Major ion relationships of (<b>a</b>) Cl<sup>−</sup> vs. Na<sup>+</sup> (meq/L) (<b>b</b>) Mg<sup>2+</sup> vs. Ca<sup>2+</sup> (meq/L) (<b>c</b>) HCO<sub>3</sub><sup>−</sup> vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>) (meq/L) (<b>d</b>) Mg<sup>2+</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup> (<b>e</b>) HCO<sub>3</sub><sup>−</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup> and (<b>f</b>) (Na<sup>+</sup>/K<sup>+</sup>)-Cl<sup>−</sup> vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>)-(HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>) of groundwater in the CKDu endemic and control area.</p>
Full article ">Figure 6
<p>δ<sup>2</sup>H vs. δ<sup>18</sup>O relationship of groundwater in (<b>a</b>,<b>b</b>). CKDu endemic Malawthu Oya (<b>d</b>,<b>e</b>). CKDu control Malala Oya, and (<b>c</b>,<b>f</b>) relationship of D-excess and TDS in both dry and wet seasons.</p>
Full article ">Figure 7
<p>Pearson’s correlation analysis among water quality parameters of groundwater in CKDu endemic (<b>a</b>,<b>c</b>) and control areas (<b>b</b>,<b>d</b>).</p>
Full article ">
20 pages, 9443 KiB  
Article
Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis
by José Rafael Irigoyen-Campuzano, Diana Barraza-Barraza, Mélida Gutiérrez, Luis Arturo Torres-Castañón, Liliana Reynoso-Cuevas and María Teresa Alarcón-Herrera
Hydrology 2024, 11(6), 76; https://doi.org/10.3390/hydrology11060076 - 31 May 2024
Viewed by 903
Abstract
The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na+, K+, Ca2+, Mg2+, F, Cl [...] Read more.
The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na+, K+, Ca2+, Mg2+, F, Cl, SO42−, NO3, HCO3, As, pH and electrical conductivity in 40 groundwater samples collected from shallow and deep wells, where As and F are contaminants of concern. The effectiveness of each hierarchical and k-means clustering method in explaining solute concentrations within the aquifer and the co-occurrence of arsenic and fluoride was tested by comparing two datasets containing samples from 40 and 36 wells, the former including ionic balance outliers (>10%). When tested without outliers, cluster quality improved by about 5.4% for k-means and 7.3% for HCA, suggesting that HCA is more sensitive to ionic balance outliers. Both algorithms yielded similar clustering solutions in the outlier-free dataset, aligning with the k-means solution for all 40 samples, indicating that k-means was the more robust of the two methods. k-means clustering resolved fluoride and arsenic concentrations into four clusters (K1 to K4) based on variations in Na+, Ca2+, As, and F. Cluster K2 was a Na-HCO3 water type with high concentrations of As and F. Clusters K1, K3, and K4 exhibited a Ca-HCO3, Na-Ca-HCO3, and Ca-Na-HCO3 water types, respectively, with decreasing As and F concentrations following the order K2 > K3 > K1 > K4. The weathering of evaporites and silicates and Na-Ca ion exchange with clays were the main processes controlling groundwater geochemistry. The dissolution of felsic rocks present in the aquifer fill is a likely source of As and F, with evaporation acting as an important concentration factor. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Study area, the main exploitation zone, in the eastern part of the Valle del Guadiana aquifer.</p>
Full article ">Figure 2
<p>Piper diagram of the 40 sampled sites. Red points were omitted in 30-sample subset.</p>
Full article ">Figure 3
<p>Principal component analysis validation for grouping tendency: (<b>a</b>) four groups were identified in the original 40-well dataset; (<b>b</b>) three groups were identified in the depurated 34-well dataset.</p>
Full article ">Figure 4
<p>Dendrogram of sampling sites constructed using Ward’s method and Euclidean distances.</p>
Full article ">Figure 5
<p>Clusters of sampling sites constructed using the k-means clustering algorithm. Concentration is expressed as the median value of the group.</p>
Full article ">Figure 6
<p>Piper diagram color-coded by cluster. (<b>left</b>): clusters from k-means algorithm; (<b>right</b>): clusters from hierarchical clustering algorithm. (Note 1: Cluster boundaries are arbitrarily drawn to highlight the difference in the clusterization pattern. Note 2: Group colors in this figure were assigned for clusters visualization).</p>
Full article ">Figure 7
<p>Comparative Stiff diagrams of the groups formed by the two algorithms tested with the depurated 34 well dataset (error in ionic balance &lt; 10%).</p>
Full article ">Figure 8
<p>Comparative Piper diagrams of the groups formed by k-means and HCA before and after the removal of ionic balance outliers (Note: Group colors in this figure were assigned for clusters visualization only).</p>
Full article ">Figure 9
<p>Graphic hydrogeochemical process assessment: (<b>a</b>–<b>d</b>) bivariate plots; (<b>e</b>,<b>f</b>) Na+-normalized plot for HCO<sub>3</sub><sup>−</sup>.</p>
Full article ">Figure 10
<p>Chloroalkaline indices of the 40 sampled wells.</p>
Full article ">Figure 11
<p>(<b>a</b>) Saturation indices for calcite and fluorite as a function of fluoride concentration; (<b>b</b>) saturation indices for calcite and fluorite classified by cluster.</p>
Full article ">Figure 12
<p>Effect of Na<sup>+</sup>/Ca<sup>2+</sup> ratio in fluoride concentration.</p>
Full article ">Figure 13
<p>Spatial distributions of arsenic (<b>left</b>) and fluoride (<b>right</b>) in Valle del Guadiana, May–June 2022.</p>
Full article ">
18 pages, 6866 KiB  
Article
Temperature Effects in AMSR2 Soil Moisture Products and Development of a Removal Method Using Data at Ascending and Descending Overpasses
by Minjiao Lu, Kim Oanh Hoang and Agampodi Deva Thisaru Nayanathara Kumarasiri
Remote Sens. 2024, 16(9), 1606; https://doi.org/10.3390/rs16091606 - 30 Apr 2024
Viewed by 1074
Abstract
Soil moisture is among the most essential variables in hydrology and earth science. Many satellite missions, such as AMSR-E/2, have been launched to observe it in broader spatial coverage to overcome the shortage of in situ observations. However, the satellite soil moisture products [...] Read more.
Soil moisture is among the most essential variables in hydrology and earth science. Many satellite missions, such as AMSR-E/2, have been launched to observe it in broader spatial coverage to overcome the shortage of in situ observations. However, the satellite soil moisture products have been reported to comprise errors caused by the so-called “temperature effects” widely observed in dielectrically measured in situ volumetric soil water content (SWC). In this work, we confirmed the existence of these errors in AMSR2 soil moisture products. A new algorithm was developed to remove these errors using satellite data at ascending and descending overpasses. The application of this algorithm to both satellite and in situ data of SWC and soil temperature at the Mongolia site shows that the difference between SWC values at ascending and descending overpasses caused by temperature effects is effectively removed. We assess the impact of this removal method on satellite data by comparing it with in situ data, utilizing metrics such as the correlation coefficient and other widely adopted evaluation methods. It is shown that the difference between the original and corrected in situ SWC is much smaller than that between AMSR2 and in situ SWC, either corrected or not. The results indicate that the metric values between the corrected AMSR2 and in situ SWC, after removing apparent differences caused by temperature effects, slightly improved compared to those between the original AMSR2 and in situ SWC. Though these findings imply that the removed errors may not be the most dominant, considering the current significant difference between AMSR2 and in situ SWC, the removal makes the ascending and descending data have close characteristics. It may allow using data at both ascending and descending overpasses and double the temporal resolution of AMSR2 SWC data. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Ratios of temperature (<math display="inline"><semantics> <msub> <mi>R</mi> <mi>T</mi> </msub> </semantics></math>) and SWC (<math display="inline"><semantics> <msub> <mi>R</mi> <mi>θ</mi> </msub> </semantics></math>) in three categories, Ap/D: ratio of value at previous ascending overpass to that at descending overpass, D/D: ratio of value at descending overpass to itself, Af/D: ratio of value at following ascending overpass to that at descending overpass. The red diamond is the average ratio of temperature of all ADA triples and the blue circle is the average ratio of SWC of ADA triples. The red square shows (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>A</mi> <mi>m</mi> </mrow> </msub> </semantics></math>), the average ratios of temperature in category Ap/D and Af/D; and the blue square shows (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>θ</mi> <mo>,</mo> <mi>A</mi> <mi>m</mi> </mrow> </msub> </semantics></math>), the average ratios of SWC in category Ap/D and Af/D. The yellow double-head arrow line indicates the differences between two ratios of SWC in categories Ap/D and Af/D which may be caused by factors other than temperature, and the blue double-head arrow line indicates the difference caused by temperature effects (TEs).</p>
Full article ">Figure 2
<p>Study area in Mongolia. The green and red dots are automatic stations for soil hydrology (ASSHs) and automatic weather stations (AWSs).</p>
Full article ">Figure 3
<p>The variety among nine stations in the Mongolia CVS, presenting their respective average values alongside the standard deviation over a five-day period. Panel (<b>a</b>) displays the soil water content values, while panel (<b>b</b>) showcases the soil temperature readings.</p>
Full article ">Figure 4
<p>The bi-hourly in situ soil temperature, in situ SWC and AMSR2 SWC and AMSR2 LST at ascending and descending overpasses averaged over CVS in Mongolia (<b>a</b>). The blue dashed line and orange dotted line represent the in situ SWC (<math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>I</mi> <mi>n</mi> <mi>S</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math>) and soil temperature (<math display="inline"><semantics> <msub> <mi>T</mi> <mi>S</mi> </msub> </semantics></math>) at 3 cm depth, respectively. The red and green stars stand for AMSR2 LST at ascending (<math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>F</mi> <mo>,</mo> <mi>A</mi> </mrow> </msub> </semantics></math>) and descending (<math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>F</mi> <mo>,</mo> <mi>D</mi> </mrow> </msub> </semantics></math>) overpasses, and the red and green pluses are AMSR2 SWC at ascending (<math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>A</mi> <mi>M</mi> <mi>S</mi> <mi>R</mi> <mn>2</mn> <mo>,</mo> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>) and descending overpasses (<math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>A</mi> <mi>M</mi> <mi>S</mi> <mi>R</mi> <mn>2</mn> <mo>,</mo> <mi>D</mi> </mrow> </msub> </mrow> </semantics></math>), respectively. (<b>b</b>,<b>c</b>) The ADA triangles of temperature and SWC of in situ and satellite data. The red lines are the temperature and the blue lines are the SWC in both.</p>
Full article ">Figure 5
<p>Estimation of temperature effect coefficient from in situ data (<b>a</b>) and AMSR2 data (<b>b</b>). The blue dots are the original data and the red circles are the outliers determined in the recursive regression.</p>
Full article ">Figure 6
<p>The bi-hourly in situ soil temperature, in situ SWC and the original and corrected AMSR2 SWC and AMSR2 LST at ascending and descending overpasses averaged over the CVS in Mongolia (<b>a</b>). In addition to <a href="#remotesensing-16-01606-f004" class="html-fig">Figure 4</a>a, a red line representing the corrected in situ SWC (<math display="inline"><semantics> <msub> <mrow> <mi>θ</mi> <mo>′</mo> </mrow> <mrow> <mi>I</mi> <mi>n</mi> <mi>S</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> </mrow> </msub> </semantics></math>) at 3 cm depth and the red and green circles showing the corrected AMSR2 SWC at ascending (<math display="inline"><semantics> <msub> <mrow> <mi>θ</mi> <mo>′</mo> </mrow> <mrow> <mi>A</mi> <mi>M</mi> <mi>S</mi> <mi>R</mi> <mn>2</mn> <mo>,</mo> <mi>A</mi> </mrow> </msub> </semantics></math>) and descending (<math display="inline"><semantics> <msub> <mrow> <mi>θ</mi> <mo>′</mo> </mrow> <mrow> <mi>A</mi> <mi>M</mi> <mi>S</mi> <mi>R</mi> <mn>2</mn> <mo>,</mo> <mi>D</mi> </mrow> </msub> </semantics></math>) overpasses are added. The green ADA triangles of the corrected in situ and the AMSR2 SWC are also added in both (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 7
<p>Accumulated absolute difference between <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>A</mi> <mi>m</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>D</mi> </msub> </semantics></math> of in situ SWC (<b>a</b>) and AMSR2 SWC (<b>b</b>). The blue and red lines are the data before and after the correction.</p>
Full article ">
16 pages, 8348 KiB  
Article
Trends in Concentration and Flux of Total Suspended Matter in the Irrawaddy River
by Zhuoqi Zheng, Difeng Wang, Dongyang Fu, Fang Gong, Jingjing Huang, Xianqiang He and Qing Zhang
Remote Sens. 2024, 16(5), 753; https://doi.org/10.3390/rs16050753 - 21 Feb 2024
Viewed by 1125
Abstract
Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, [...] Read more.
Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, based on the existing measured the total suspended matter flux (FTSM) and discharge data for the Irrawaddy River, an inversion model of the total suspended matter concentration (CTSM) is constructed for the Irrawaddy River, and the CTSM and FTSM from 1990 to 2020 are estimated using the L1 products of Landsat-8 OLI/TIRS and Landsat-5 TM. The results show that over the last 30 years, the FTSM of the Irrawaddy River decreased at a rate of 3.9 Mt/yr, which is significant at the 99% confidence interval. An increase in the vegetation density of the Irrawaddy Delta has increased the land conservation capacity of the region and reduced the inflow of land-based total suspended matter (TSM). The FTSM of the Irrawaddy River was estimated by fusing satellite data and data measured at hydrological stations. The research method employed in this paper provides a new supplement to the existing hydrological data for large rivers. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) The location of the Irrawaddy River (blue line) and the basin (black line). (<b>B</b>) The location of the satellite image of the selected inversion modeling area (red box). (<b>C</b>) The location of the satellite image used to estimate the F<sub>TSM</sub> (red line).</p>
Full article ">Figure 2
<p>Comparison of the ratios between the near-infrared and blue light wavebands determined using Landsat and Sentinel data.</p>
Full article ">Figure 3
<p>Number of available remote sensing images in each year and season.</p>
Full article ">Figure 4
<p>Seasonal average C<sub>TSM</sub> trends in the Irrawaddy River calculated based on empirical data from the literature. The blue histogram shows the specific values of the seasonal average C<sub>TSM</sub>, and the red stacking curve shows the cumulative percentage of C<sub>TSM</sub> by season.</p>
Full article ">Figure 5
<p>Establishment and verification of the inversion model. There are 40 matching points in total. We used 30 points for modeling, and the remaining 10 points were used as validation datasets. The solid red line represents the 95% confidence interval.</p>
Full article ">Figure 6
<p>Distribution trends in the Irrawaddy River C<sub>TSM</sub> in (<b>A</b>) winter, (<b>B</b>) spring, (<b>C</b>) summer, and (<b>D</b>) autumn.</p>
Full article ">Figure 7
<p>Annual C<sub>TSM</sub> trend change in the Irrawaddy River. The blue circles represent the inversion results from Landsat, the red circles represent the in situ data results, the black circles represent the inversion results from Sentinel-2, the blue dotted line represents the linear regression of the inversion results from 1990 to 2020, and the red dotted line represents the linear regression of the in situ data from 1990 to 1996.</p>
Full article ">Figure 8
<p>The correction results (<b>left</b>) of and the corrected discharge changes (<b>right</b>) in the GloFAS discharge data based on the measured discharge data of the Pyay hydrologic station. The red dotted line is the discharge data measured from Pyay, the blue solid line is the corrected discharge data from the GloFAS, and the gray dotted line is the uncorrected discharge data from the GloFAS.</p>
Full article ">Figure 9
<p>Annual F<sub>TSM</sub> changes in the Irrawaddy River. The red circles refer to the measured F<sub>TSM</sub> data for Pyay station, the blue circles refer to the inverted F<sub>TSM</sub> data based on Landsat data, and the black circles refer to the inverted F<sub>TSM</sub> data based on Sentinel-2 data. The black dotted line is the linear fit of the change in the Irrawaddy River from 1966 to 2020.</p>
Full article ">Figure 10
<p>Results of the correlation analysis between the F<sub>TSM</sub> and discharge (<b>left</b>) and between the F<sub>TSM</sub> and C<sub>TSM</sub> (<b>right</b>) in the Irrawaddy River from 1990 to 2020.</p>
Full article ">Figure 11
<p>Precipitation and discharge in the Irrawaddy River.</p>
Full article ">Figure 12
<p>Results of land classification in the Irrawaddy Basin.</p>
Full article ">Figure 13
<p>The results of land transfer in the middle and upper reaches of the Irrawaddy River basin (red) and the lower reaches of the delta (yellow) represent the percentage of land area transferred from column elements to row elements; N represents no element transfer.</p>
Full article ">Figure 14
<p>(<b>Left</b>): land types dominated by human activity (yellow columns, which indicate artificial surface and cultivated land); land types dominated by vegetation (green columns, which indicate forests, shrubs, grasslands, and wetlands); and changes in F<sub>TSM</sub> over the three periods. (<b>Right</b>): the correlation between the area covered by land types dominated by human activity (red dots, indicating artificial surface and cultivated land) and the area covered by land types dominated by vegetation (green dots, indicating forests, shrubs, grasslands, and wetlands) and the change in F<sub>TSM</sub>.</p>
Full article ">
22 pages, 4184 KiB  
Article
A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction
by Futo Ueda, Hiroto Tanouchi, Nobuyuki Egusa and Takuya Yoshihiro
Water 2024, 16(4), 607; https://doi.org/10.3390/w16040607 - 18 Feb 2024
Cited by 1 | Viewed by 1713
Abstract
River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model [...] Read more.
River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Overview of the prediction procedure.</p>
Full article ">Figure 2
<p>Observatories in relation to the prediction site (from the Geospatial Information Authority of Japan [<a href="#B32-water-16-00607" class="html-bibr">32</a>]).</p>
Full article ">Figure 3
<p>Location of observatories used for pre-training (from the Geospatial Information Authority of Japan [<a href="#B32-water-16-00607" class="html-bibr">32</a>]).</p>
Full article ">Figure 4
<p>Area of radar rainfall data (from the Geospatial Information Authority of Japan [<a href="#B32-water-16-00607" class="html-bibr">32</a>]).</p>
Full article ">Figure 5
<p>Creating the flow–distance matrix from the surface flow–direction matrix.</p>
Full article ">Figure 6
<p>CNN operations.</p>
Full article ">Figure 7
<p>LSTM structure.</p>
Full article ">Figure 8
<p>Proposed model for river water-level prediction incorporating CNN and LSTM structures.</p>
Full article ">Figure 9
<p>The sequence of our transfer learning: (<b>a</b>) pre-training with other river data, (<b>b</b>) re-training with the prediction river data.</p>
Full article ">Figure 10
<p>Loss-function values in pre-training.</p>
Full article ">Figure 11
<p>The average prediction accuracy.</p>
Full article ">Figure 12
<p>Hydrographs for the highest-peak periods. (<b>a</b>) Model C with upstream measurements; (<b>b</b>) Model F incorporating transfer learning without using upstream measurements.</p>
Full article ">Figure 13
<p>Hydrographs of Models D and F for 3 h ahead forecast. (<b>a</b>) The highest-peak periods with Model D (without transfer learning). (<b>b</b>) The second highest-peak periods with Model D (without transfer learning). (<b>c</b>) The third highest-peak periods with Model D (without transfer learning). (<b>d</b>) The highest-peak periods with Model F (with transfer learning). (<b>e</b>) The second highest-peak periods with Model F (with transfer learning). (<b>f</b>) The third highest-peak periods with Model F (with transfer learning).</p>
Full article ">Figure 14
<p>Prediction accuracy of Models D and F with various numbers of training periods.</p>
Full article ">Figure 15
<p>Hydrographs for 1 h ahead forecast (3rd highest period case).</p>
Full article ">Figure 16
<p>Hydrographs for 1 h ahead forecast (5th highest period case).</p>
Full article ">Figure 17
<p>The effect of the flow–distance matrix in transfer learning.</p>
Full article ">
22 pages, 7815 KiB  
Article
Quantitative Groundwater Modelling under Data Scarcity: The Example of the Wadi El Bey Coastal Aquifer (Tunisia)
by Hatem Baccouche, Manon Lincker, Hanene Akrout, Thuraya Mellah, Yves Armando and Gerhard Schäfer
Water 2024, 16(4), 522; https://doi.org/10.3390/w16040522 - 6 Feb 2024
Viewed by 1595
Abstract
The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a [...] Read more.
The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a novel physiography-based method that uses soil texture-dependent potential infiltration coefficients and monthly rainfall data. The developed transient flow model was then applied to compute the temporal variation in the groundwater level in 34 observation wells from 1973 to 2020, taking into account the time series of spatially variable groundwater recharge, artificial groundwater recharge from 5 surface infiltration basins, pumping rates on 740 wells, and internal prescribed head cells to mimic water exchange between the wadis and aquifer. The quantified deviations in the computed hydraulic heads from measured water levels are acceptable because the database used to construct a scientifically sound and reliable groundwater model was limited. Further work is required to collect field data to quantitatively assess the local inflow and outflow rates between surface water and groundwater. The simulation of 12 climate scenarios highlighted a bi-structured north—south behaviour in the hydraulic heads: an increase in the north and a depletion in the south. A further increase in the pumping rate would, thus, be severe for the southern part of the Wadi El Bey aquifer. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
Show Figures

Figure 1

Figure 1
<p>Location of the study site (<b>a</b>) and schematic view of the hydrogeological N—S cross section of the Grombalia unconfined aquifer [<a href="#B21-water-16-00522" class="html-bibr">21</a>] (<b>b</b>), 3D view of the numerical model with location of wadis (yellow dots) (<b>c</b>), location of observation wells, wadi points, and artificial recharge basins implemented in the groundwater flow model (<b>d</b>), and boundary conditions of the numerical model (plan view): prescribed water heads (blue circles) (<b>e</b>), and 740 pumping wells (yellow dots) (<b>f</b>).</p>
Full article ">Figure 2
<p>Hydraulic head variation evaluated for the 36 wadi points: (<b>a</b>) Group 1, (<b>b</b>) Group 2, (<b>c</b>) Group 3, and (<b>d</b>) Group 4. Notably, in the legend, the wadi points are abbreviated as WP.</p>
Full article ">Figure 3
<p>Spatial distribution of both drainable porosity (<b>a</b>) and isotropic hydraulic conductivity (<b>b</b>).</p>
Full article ">Figure 4
<p>Groundwater recharge adopted in the transient flow model: (<b>a</b>) location of the selected seven zones and time-dependent (monthly variable) groundwater recharge for zones (<b>b</b>) RN Perm 4, (<b>c</b>) Perm 25 North, (<b>d</b>) Perm 25 South, (<b>e</b>) RN Perm 35, (<b>f</b>) RN Perm 40, (<b>g</b>) RN Perm 80 North, and (<b>h</b>) RN Perm 80 South. Note: <span class="html-italic">t</span> = 0 d and <span class="html-italic">t</span> = 17,503 d correspond to 1 January 1973 and 31 December 2020, respectively.</p>
Full article ">Figure 5
<p>Hydraulic head distribution computed at steady-state flow conditions for 1972 (<b>a</b>) and compared to manually interpolated isolines obtained from field observations (dark lines) and isolines computed with MODFLOW (red lines [<a href="#B27-water-16-00522" class="html-bibr">27</a>]) (<b>b</b>).</p>
Full article ">Figure 6
<p>Locations of the 70 chosen virtual observation points (<b>a</b>) and cross-plot of our computed hydraulic heads against the “observed” hydraulic heads obtained in the previous numerical studies of Hammami [<a href="#B27-water-16-00522" class="html-bibr">27</a>] and Gaaloul et al. [<a href="#B14-water-16-00522" class="html-bibr">14</a>] (<b>b</b>). The blue line shown in <a href="#water-16-00522-f006" class="html-fig">Figure 6</a>b represents the 1:1 line.</p>
Full article ">Figure 7
<p>Pumping rate applied for each of the 740 wells from 1973 to 2020 (<b>a</b>) and infiltration rate implemented in the model for each of the five artificial recharge basins active between 1990 and 2015 (<b>b</b>).</p>
Full article ">Figure 8
<p>Transient flow model: starting values of the hydraulic head on 1 January 1973 (<b>a</b>), and computed hydraulic heads on 31 December 1983 (<b>b</b>), 31 December 1993 (<b>c</b>), 31 December 2003 (<b>d</b>), 31 December 2013 (<b>e</b>), and 31 December 2020 (<b>f</b>). The 25 m contour line is highlighted as a reference for the mean groundwater level.</p>
Full article ">Figure 9
<p>Effect of wadis on the transient water balance: (<b>a</b>) cumulative water volume computed (in+/out−) across the fixed head boundaries of the domain and across fixed head nodes located on all 36 Wadi points and those of Oued El Bey-El Melah (Wadi Points 1, 4, 6, 8, 10, 15, 19, 23, 28, 31, 32, and 33; <a href="#water-16-00522-f001" class="html-fig">Figure 1</a>d); (<b>b</b>) inflow and outflow rates computed over the fixed head nodes along Oued El Bey-El Melah. The cumulative inflow water volume across the fixed head boundaries of the total domain (<a href="#water-16-00522-f009" class="html-fig">Figure 9</a>a) corresponds entirely to the water volume coming from the 36 Wadi points. Note: <span class="html-italic">t</span> = 0 d and <span class="html-italic">t</span> = 17,503 d correspond to 1 January 1973 and 31 December 2020, respectively.</p>
Full article ">Figure 10
<p>Cross-plot of the computed hydraulic heads against observed water heads at (<b>a</b>) starting values of hydraulic head on 1 January 1973 and computed hydraulic heads for 31 December 1983 (<b>b</b>); 31 December 1993 (<b>c</b>); 31 December 2003 (<b>d</b>); 31 December 2013 (<b>e</b>); and 31 December 2020 (<b>f</b>). The blue lines represent the 1:1 lines; the scatterplot is qualified by the mean error (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>E</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>), root mean square error (RMS), and standard deviation (σ), with all the error metrics expressed in metres.</p>
Full article ">Figure 11
<p>Hydraulic heads computed from 1973 to 2020 compared to those measured at the four selected observation wells, placed along a north—south longitudinal section in the study site, starting in the north: (<b>a</b>) 2379, (<b>b</b>) 12,406, (<b>c</b>) 8588, and (<b>d</b>) 11,419. The locations of the four observation wells are shown in <a href="#water-16-00522-f001" class="html-fig">Figure 1</a>d. Note: <span class="html-italic">t</span> = 0 d and <span class="html-italic">t</span> = 17,503 d correspond to 1 January 1973 and 31 December 2020, respectively.</p>
Full article ">Figure 12
<p>Monthly zonal groundwater recharge (<span class="html-italic">GR</span>) expressed in meter per day for eight distinct zones (see <a href="#water-16-00522-f004" class="html-fig">Figure 4</a>a) used in climate scenario 1 based on RCP 4.5 (<b>a</b>–<b>h</b>) and RCP 8.5 (<b>i</b>–<b>p</b>). Notably, the groundwater recharges in zones ‘RN35’ and ‘RN80’ have been divided into two subzones: one located in the north and one in the south.</p>
Full article ">Figure 13
<p>Map of the hydraulic heads computed with climate scenario 1 based on RCP 4.5 at different time stages: starting values on 1 January 2021 (<b>a</b>); 31 December 2040 (<b>b</b>); 31 December 2060 (<b>c</b>); 31 December 2098 (<b>d</b>). The 25 m contour line is highlighted as a reference for the mean groundwater level.</p>
Full article ">Figure 14
<p>Results of the time-dependent hydraulic head calculations (from January 2021 to December 2098) under climate scenario 1 for RCP 4.5 (<b>a</b>) and RCP 8.5 (<b>b</b>) at the four selected observation wells.</p>
Full article ">Figure 15
<p>Histogram of the hydraulic head differences calculated for RCPs 4.5 (<b>a</b>) and 8.5 (<b>b</b>) from a long-term perspective with regard to baseline hydraulic heads in 2021.</p>
Full article ">
Back to TopTop