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Water, Volume 16, Issue 4 (February-2 2024) – 111 articles

Cover Story (view full-size image): Gran Canaria, due to its condition as an island, has an isolated energy system (IES) and depends on itself for energy production, which is obtained from (a) wind and solar energy, 19%, and (b) energy produced in the existing thermal power plants, 81%. A solution must be found to the current production system, which is already partially obsolete and must be renewed and/or dismantled, which means a change in the production cycle. In addition, incorporating a pumped hydroelectric energy storage (PHES) "Chira-Soria" plant into the Gran Canaria electricity system means another, even more important, change in the dynamics that has followed until now. This plant, which is hydraulically stabilized by a seawater desalination plant (SWRO), incorporates energy storage by storing water at high altitudes to be turned under suitable conditions. View this paper
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14 pages, 12543 KiB  
Article
Evaluation of Various Forms of Geothermal Energy Release in the Beijing Region, China
by Zebin Luo, Mingbo Yang, Xiaocheng Zhou, Guiping Liu, Jinlong Liang, Zhe Liu, Peixue Hua, Jingchen Ma, Leyin Hu, Xiaoru Sun, Bowen Cui, Zhiguo Wang and Yuxuan Chen
Water 2024, 16(4), 622; https://doi.org/10.3390/w16040622 - 19 Feb 2024
Viewed by 1277
Abstract
The energy inside the Earth can not only be released outward through earthquakes and volcanoes but also can be used by humans in the form of geothermal energy. Is there a correlation between different forms of energy release? In this contribution, we perform [...] Read more.
The energy inside the Earth can not only be released outward through earthquakes and volcanoes but also can be used by humans in the form of geothermal energy. Is there a correlation between different forms of energy release? In this contribution, we perform detailed seismic and geothermal research in the Beijing area. The results show that the geothermal resources in Beijing belong to typical medium-low temperature geothermal resources of the sedimentary basin, and some areas are controlled by deep fault activities (e.g., Xiji geothermal well (No. 17)). The heat sources are upper mantle heat, radioactive heat in granite, and residual heat from magma cooling. The high overlap of earthquakes and geothermal field locations and the positive correlation between the injection water and earthquakes indicate that the exploitation and injection water will promote the release of the earth’s energy. The energy releases are partitioned into multiple microearthquakes, avoiding damaging earthquakes (ML ≥ 5) due to excessive energy accumulation. Therefore, the exploitation of geothermal resources may be one way to reduce destructive earthquakes. Furthermore, the use of geothermal resources can also reduce the burning of fossil energy, which is of great significance in dealing with global warming. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) A simple map of China. (<b>b</b>) Schematic map showing the distribution of geothermal fields and location of sampling points in the Beijing area, modified after Liu et al. [<a href="#B43-water-16-00622" class="html-bibr">43</a>]. 1: Yanqing, 2: Xiaotangshan, 3: Houshayu, 4: Northwest district, 5: Tianzhu, 6: Lishui, 7: Southeast district, 8: Shuangqiao, 9: Liangxiang and 10: Fengheying geothermal field. The size of the symbol of the earthquake label indicates the magnitude of the earthquake.</p>
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<p>Piper diagram of geothermal waters in Beijing. These waters are Na∙Ca∙Mg-HCO<sub>3</sub>, Na-SO<sub>4,</sub> and Na-Cl types.</p>
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<p>δD and δ<sup>18</sup>O (relative to V-SMOW) values for waters collected from the Beijing area. The GMWL is a global meteoric water line [<a href="#B49-water-16-00622" class="html-bibr">49</a>]. The LMWL is a local meteoric water line [<a href="#B45-water-16-00622" class="html-bibr">45</a>]. Arrows indicate enhanced water-rock reactions.</p>
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<p>(Na<sup>+</sup> + K<sup>+</sup>)/(HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>)meq/L versus (Ca<sup>2+</sup> + Mg<sup>2+</sup>)/(HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>) meq/L (<b>a</b>) and Rb/Ni ppm versus Sr/Ni ppm (<b>b</b>) for geothermal waters of Beijing area. Group 1 is characterized by the reaction of carbonate rock with water, while groups 2 and 3 are characterized by the reaction of silicate rock with water.</p>
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<p>Na<sup>+</sup> versus Cl<sup>−</sup> for Beijing area geothermal waters.</p>
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<p>Mg<sup>2+</sup>/Ca<sup>2+</sup> versus Na<sup>+</sup>/Ca<sup>2+</sup> for geothermal waters of the Beijing area. The Dolomite, silicate, and limestone areas are from [<a href="#B50-water-16-00622" class="html-bibr">50</a>].</p>
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<p>Characteristics of gas composition with time in Xiji (No. 17) geothermal well. Data of 19 January 2022 from Yang et al. [<a href="#B40-water-16-00622" class="html-bibr">40</a>].</p>
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<p>The water cycle model of the geothermal waters and gases in the Beijing area. The geothermal water in the Beijing area originated from atmospheric precipitation. The precipitation flows into the ground along the fault and reacts with the surrounding rock while being heated. Eventually, upwell along the fault to form hot springs.</p>
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<p>Earthquake records from 1970 in the Beijing area. The triangle shows the distribution of seismic stations, and their locations are from the China Earthquake Administration.</p>
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<p>Temporal variations of earthquake frequency (time) (<b>a</b>), energy release (J) (<b>b</b>), and geothermal production (10<sup>4</sup> m<sup>3</sup>) (<b>c</b>). The conversion formula of magnitude and energy: lgE = 4.8 + 1.5 M, E is energy (J). M is magnitude (<span class="html-italic">M</span><sub>L</sub> &gt; 2), earthquake data from the China Earthquake Administration. Geothermal production data from the Beijing Hydrogeological Engineering Team (2014–2019 are estimates).</p>
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24 pages, 27091 KiB  
Article
Projected Increase in Compound Drought and Hot Days over Global Maize Areas under Global Warming
by Yan He, Yanxia Zhao, Yihong Duan, Xiaokang Hu and Jiayi Fang
Water 2024, 16(4), 621; https://doi.org/10.3390/w16040621 - 19 Feb 2024
Viewed by 1538
Abstract
Compound drought and hot events can lead to detrimental impacts on crop yield with grave implications for global and regional food security. Hence, an understanding of how such events will change under unabated global warming is helpful to avoid associated negative impacts and [...] Read more.
Compound drought and hot events can lead to detrimental impacts on crop yield with grave implications for global and regional food security. Hence, an understanding of how such events will change under unabated global warming is helpful to avoid associated negative impacts and better prepare for them. In this article, we comprehensively analyze the projected changes in compound drought and hot days (CDHDs) occurring within the maize-growing season of 2015–2100 over dynamic global maize areas using 10 downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) models and four socio-economic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results demonstrate a notable increase in the frequency and severity of CDHDs over global maize areas under all four SSPs, of which SSP5-8.5 has the fastest rise, followed by SSP3-7.0, SSP2-4.5 and SSP1-2.6. By the end of 21st century, the global average frequency and severity of CDHDs will reach 18~68 days and 1.0~2.6. Hotspot regions for CDHDs are mainly found in southern Africa, eastern South America, southern Europe and the eastern USA, where drought and heat show the most widespread increases. The increase in CDHDs will be faster than general hot days so that almost all increments of hot days will be accompanied by droughts in the future; therefore, compound dry and hot stresses will gradually become the predominant form of dry and heat stress on maize growth. The results can be applied to optimize adaptation strategies for mitigating risks from CDHDs on maize production worldwide. Full article
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Graphical abstract

Graphical abstract
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<p>Global maize-planting areas in 2010 (<b>a</b>), maize production in 2010 (<b>b</b>), the proportion of maize production in total food crop production (<b>c</b>), population in 2022 (<b>d</b>) and food calorie demand from maize (<b>e</b>). Data for (<b>a</b>–<b>c</b>) were obtained from SPAM 2010 (the latest high-resolution data on global scale, <a href="https://mapspam.info/data/" target="_blank">https://mapspam.info/data/</a>, accessed on 1 June 2023). Data for (<b>d</b>) were obtained from LandScan (<a href="https://landscan.ornl.gov/" target="_blank">https://landscan.ornl.gov/</a>, accessed on 1 June 2023). (<b>e</b>) is from Erenstein et al. (2022) [<a href="#B42-water-16-00621" class="html-bibr">42</a>].</p>
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<p>Planting date and maturity date of rainfed maize (<b>a</b>,<b>b</b>) and irrigated maize (<b>c</b>,<b>d</b>) at global scale, provided by GGCMI Phase 3 crop calendar dataset.</p>
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<p>Methodology of identifying CDHDs in maize-growing seasons.</p>
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<p>Global average total precipitation (<b>a</b>), total PET (<b>b</b>), average SPEI (<b>c</b>), drought events (<b>d</b>), hot days (<b>e</b>) and HDDs (<b>f</b>) in each maize-growing season during the historical period (1951–2014) and future period (2015–2100) (under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Solid lines represent 10-GCM ensemble averages; the shades represent the range between 25th and 75th percentiles of the 10-GCM ensemble. Trends are calculated based on MK-TSA, significance level: <span class="html-italic">p</span> &lt; 0.1 *, <span class="html-italic">p</span> &lt; 0.05 **, <span class="html-italic">p</span> &lt; 0.01 ***.</p>
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<p>Spatial distribution of trends in total precipitation (<b>a</b>), total PET (<b>b</b>) and drought events (<b>c</b>) in maize-growing season during 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Trends are calculated based on MK-TSA; blank regions indicate no maize-planting; gray indicates trends are not significant; other colors indicate trends are significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Spatial distribution of trends in hot days (<b>a</b>) and HDDs (<b>b</b>) in maize-growing season during 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Trends are calculated based on MK-TSA; blank regions indicate no maize planting; gray indicates trends are not significant; other colors indicate trends are significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Global average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>t</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> </mrow> </semantics></math>(<b>e</b>) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>a</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> (<b>i</b>) in each maize-growing season during historical period (1951–2014) and future period (2015–2100) (under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Solid lines represent 10-GCM ensemble averages; the shades represent the range between 25th and 75th percentiles of 10-GCM ensemble. Trends are calculated based on MK-TSA, significance level: <span class="html-italic">p</span> &lt; 0.1 *, <span class="html-italic">p</span> &lt; 0.05 **, <span class="html-italic">p</span> &lt; 0.01 ***. And the average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> (<b>b</b>–<b>d</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>t</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> </mrow> </semantics></math> (<b>f</b>–<b>h</b>) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>a</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> (<b>j</b>–<b>l</b>) in near, mid- and long-term future.</p>
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<p>Spatial distribution of trends in <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>t</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> (<b>b</b>) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>a</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> (<b>c</b>) in maize-growing season during 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Trends are calculated based on MK-TSA; blank regions indicate no maize planting; gray indicates trends are not significant; other colors indicate trends are significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Spatial distribution and the probability density function (PDF) of average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> in near future (<b>a</b>,<b>d</b>), mid-future (<b>b</b>,<b>e</b>) and far future (<b>c</b>,<b>f</b>) under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</p>
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<p>Spatial distribution and the probability density function (PDF) of average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi>a</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> in near future (<b>a</b>,<b>d</b>), mid-future (<b>b</b>,<b>e</b>) and far future (<b>c</b>,<b>f</b>) under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</p>
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<p>Global average drought events with and without CDHDs and the proportion of drought events with CDHDs in total drought events (<b>a</b>); global average CDHDs and general hot days (hot days that do not coincide with drought events), and the proportion of CDHDs in total hot days (<b>b</b>), during 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Trends in proportion are calculated based on MK-TSA, significance level: <span class="html-italic">p</span> &lt; 0.1 *, <span class="html-italic">p</span> &lt; 0.05 **, <span class="html-italic">p</span> &lt; 0.01 ***.</p>
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<p>Global average general hot days (hot days that do not coincide with drought events) and CDHDs at different levels of daily maximum temperature (Tmax) during 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 (<b>a</b>–<b>d</b>). Trends in general hot days and CDHDs at different levels of Tmax are calculated based on MK-TSA, significance level: <span class="html-italic">p</span> &lt; 0.1 *, <span class="html-italic">p</span> &lt; 0.05 **, <span class="html-italic">p</span> &lt; 0.01 *** (<b>e</b>,<b>f</b>).</p>
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16 pages, 4355 KiB  
Article
Systematic Assessment on Waterlogging Control Facilities in Hefei City of Anhui Province in East China
by Hao Hu, Yankun Liu, Jiankang Du, Rongqiong Liu, Banglei Wu and Qingwei Zeng
Water 2024, 16(4), 620; https://doi.org/10.3390/w16040620 - 19 Feb 2024
Viewed by 1142
Abstract
Both the renovation of rainwater pipes and the addition of sponge city facilities in the low-terrain residences of urban fringes were rarely systematically simulated using the Storm Water Management Model (SWMM). With the waterlogging prevention project in an old residential quarter at a [...] Read more.
Both the renovation of rainwater pipes and the addition of sponge city facilities in the low-terrain residences of urban fringes were rarely systematically simulated using the Storm Water Management Model (SWMM). With the waterlogging prevention project in an old residential quarter at a fringe of Hefei city being an example, this study used the SWMM to simulate the effect of the renovation of rainwater pipes and sponge city facilities under different return periods. The results showed the key nodes on the main pipes met the drainage requirements based on water depth analysis after renovation below the 20-year return period, and the reduction rate of the maximum water depth at the key node J5 was the greatest, with 87.7%. The four flow parameters (the average flow rate, the peak flow rate, the total discharge, and the percentage of water flow frequency) for the two outlets (PFK1 and PFK2) all improved after renovation under five return periods (2, 5, 10, 20, and 50 years [a]). The addition of sponge city facilities effectively reduced the amount of rainwater runoff from 28.68% to 14.78% during 2 a to 50 a, and the maximum reduction rate of water depth, being 61.15%, appeared in J5 under 20 a. The curve integral area of the depth over the elapsed time was innovatively used to indirectly express the accumulated rainwater volume through the rainwater well. This study verified that the SWMM model can be well applied to old low-terrain residential quarters in urban fringes and broadened the application scenario of the model. Full article
(This article belongs to the Special Issue Urban Flood Mitigation and Sustainable Stormwater Management)
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Figure 1

Figure 1
<p>The area of this study, which is located in the fringe of Hefei city, near the suburban Feixi under the jurisdiction of the city, in Anhui Province in east China.</p>
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<p>Flow rates of the main trunk pipes for the 20 a return period before renovation over the elapsed time (<b>A</b>) and those after renovation (<b>B</b>). CMS means cubic meter per second, which equates cubic meter per second (m<sup>3</sup>/s).</p>
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<p>Longitudinal section of main trunk pipes under the 2 a and 20 a return periods: (<b>A</b>) Longitudinal section of the main trunk pipes under the 2 a return period before renovation; (<b>B</b>) Longitudinal section of main trunk pipes under the 2 a return period after renovation; (<b>C</b>) Longitudinal section of the main trunk pipes under the 20 a return period before renovation; (<b>D</b>) Longitudinal section of the main trunk pipes under the 20 a return period after renovation. Dark blue line represents the rainwater level of the rainwater wells; light grey line represents the elevation of the top of the rainwater wells along the route; and light blue shaded areas represents the rainwater filling situation in the rainwater wells and rainwater pipes.</p>
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<p>Maximum discharge of rainwater pipe GQ32.</p>
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<p>Maximum rainwater depth of rainwater wells at key nodes under different return periods: (<b>A</b>) 2 a, (<b>B</b>) 5 a, (<b>C</b>) 10 a, (<b>D</b>) 20 a, and (<b>E</b>) 50 a.</p>
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<p>Rainwater depths at key nodes changed over time under the 20 a return period before (<b>A</b>) and after (<b>B</b>) renovation; rainwater depths at key nodes changed over time under 50 a return period before (<b>C</b>) and after (<b>D</b>) renovation.</p>
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<p>Four flow parameters of two discharge outlets (PFK1 and PFK2) before and after renovation: (<b>A</b>) Percentage of water flow frequency; (<b>B</b>) Average flow rate; (<b>C</b>) Peak flow rate; (<b>D</b>) Total discharge.</p>
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<p>Maximum water depths of rainwater wells at key nodes with or without sponge city facilities under different return periods: (<b>A</b>) 2 a, (<b>B</b>) 5 a, (<b>C</b>) 10 a, (<b>D</b>) 20 a, and (<b>E</b>) 50 a.</p>
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<p>Changes in the rainwater depths of the rainwater wells at key nodes after renovation with or without sponge city facilities over the time: (<b>A</b>) 20 a without sponge city facilities; (<b>B</b>) 20 a with sponge city facilities; (<b>C</b>) 50 a without sponge city facilities; (<b>D</b>) 50 a with sponge city facilities.</p>
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<p>Reduction rates in the four flow parameters of two discharge outlets (PFK1 and PFK2) with addition of sponge city facilities compared to those without: (<b>A</b>) Average flow rate reduction; (<b>B</b>) Peak flow reduction; (<b>C</b>) Total discharge reduction; (<b>D</b>) Percentage of water flow frequency reduction.</p>
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17 pages, 1360 KiB  
Article
Extensive Study of Electrocoagulation-Based Adsorption Process of Real Groundwater Treatment: Isotherm Modeling, Adsorption Kinetics, and Thermodynamics
by Forat Yasir AlJaberi
Water 2024, 16(4), 619; https://doi.org/10.3390/w16040619 - 19 Feb 2024
Cited by 2 | Viewed by 1453
Abstract
In this study, several adsorption models were studied to predict the adsorption kinetics of turbidity on an electro-generated adsorbent throughout the electrocoagulation remediation of real groundwater. A new design for an electrocoagulation reactor consisting of a finned anode positioned concentrically in a tube-shaped [...] Read more.
In this study, several adsorption models were studied to predict the adsorption kinetics of turbidity on an electro-generated adsorbent throughout the electrocoagulation remediation of real groundwater. A new design for an electrocoagulation reactor consisting of a finned anode positioned concentrically in a tube-shaped cathode was fabricated, providing a significant active area compared to its immersed volume. This work completed a previous electrochemical study through a deep investigation of adsorption technology that proceeded throughout the electrocoagulation reactor under optimal operating conditions, namely a treatment period of 2–30 min, a 2.3-Ampere current, and a stirring speed of 50 rpm. The one-, two-, and three-parameter adsorption models investigated in this study possess significant regression coefficients: Henry (R2 = 1.000), Langmuir (R2 = 0.9991), Freundlich (R2 = 0.9979), Temkin (R2 = 0.9990), Kiselev (R2 = 0.8029), Harkins–Jura (R2 = 0.9943), Halsey (R2 = 0.9979), Elovich (R2 = 0.9997), Jovanovic (R2 = 0.9998), Hill–de Boer (R2 = 0.8346), Fowler–Guggenheim (R2 = 0.8834), Dubinin–Radushkevich (R2 = 0.9907), Sips (R2 = 0.9834), Toth (R2 = 0.9962), Jossens (R2 = 0.9998), Redlich–Peterson (R2 = 0.9991), Koble–Carrigan (R2 = 0.9929), and Radke–Prausnitz (R2 = 0.9965). The current behavior of the adsorption–electrocoagulation system follows pseudo-first-order kinetics (R2 = 0.8824) and the Bangham and Burt mass transfer model (R2 = 0.9735). The core findings proved that an adsorption-method-based electrochemical cell has significant outcomes, and all the adsorption models could be taken into consideration, along with other kinetic and thermodynamics investigations as well. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>The EC reactor and electrode configuration.</p>
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<p>Classification of adsorption models.</p>
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<p>Outcomes of Van’t Hoff plot for the removal of turbidity by adsorption-based electrocoagulation system.</p>
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15 pages, 5201 KiB  
Article
Daily Runoff Prediction with a Seasonal Decomposition-Based Deep GRU Method
by Feifei He, Qinjuan Wan, Yongqiang Wang, Jiang Wu, Xiaoqi Zhang and Yu Feng
Water 2024, 16(4), 618; https://doi.org/10.3390/w16040618 - 19 Feb 2024
Cited by 4 | Viewed by 1759
Abstract
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is [...] Read more.
Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R2) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models. Full article
(This article belongs to the Special Issue Advanced Technologies for Water Quality Monitoring and Prediction)
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<p>The structure of the GRU unit.</p>
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<p>The structure of DNN.</p>
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<p>The flow chart of the proposed method.</p>
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<p>Average rainfall and runoff of the Qingjiang Shuibuya station.</p>
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<p>Seasonal decomposition chart of runoff.</p>
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<p>The autocorrelation plot of runoff data.</p>
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<p>Comparison of prediction results and error release chart for different models.</p>
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<p>Scatter plots of prediction results for different models.</p>
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<p>Taylor diagram of the proposed model and the comparison models.</p>
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<p>Comparison chart showing the prediction errors of different models (all indicators have been normalized).</p>
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<p>Comparison chart of prediction indicators for different hyperparameter models (all indicators have been normalized).</p>
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20 pages, 9212 KiB  
Article
Case Study of Contaminant Transport Using Lagrangian Particle Tracking Model in a Macro-Tidal Estuary
by Bon-Ho Gu, Seung-Buhm Woo, Jae-Il Kwon, Sung-Hwan Park and Nam-Hoon Kim
Water 2024, 16(4), 617; https://doi.org/10.3390/w16040617 - 19 Feb 2024
Cited by 1 | Viewed by 1321
Abstract
This study presents a comprehensive analysis of contaminant transport in estuarine environments, focusing on the impact of tidal creeks and flats. The research employs advanced hydrodynamic models with irregular grid systems and conducts a detailed residual current analysis to explore how these physical [...] Read more.
This study presents a comprehensive analysis of contaminant transport in estuarine environments, focusing on the impact of tidal creeks and flats. The research employs advanced hydrodynamic models with irregular grid systems and conducts a detailed residual current analysis to explore how these physical features influence the movement and dispersion of contaminants. The methodology involves simulating residual currents and Lagrangian particle trajectories in both ‘Creek’ and ‘No Creek’ cases, under varying tidal conditions. The results indicate that tidal creeks significantly affect particle retention and transport, with notable differences observed in the dispersion patterns between the two scenarios. The ‘Creek’ case demonstrates enhanced material retention along the creek pathways, while the ‘No Creek’ case shows broader dispersion, potentially leading to increased sedimentation in open sea areas. The discussion highlights the implications of these findings for sediment dynamics, contaminant transport, and estuarine ecology, emphasizing the role of tidal creeks in modulating flow and material transport. The research underlines the necessity of incorporating detailed environmental features in estuarine models for accurate contaminant transport prediction and effective estuarine management. This study contributes to a deeper understanding of estuarine hydrodynamics and offers valuable insights for environmental policy and management in coastal regions. Full article
(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)
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<p>Map of Gyeonggi Bay (GGB) with observation sites. The red triangles represent tidal stations operated by the Korea Hydrographic and Oceanographic Agency (KHOA), utilized for model validation. The yellow lines in the blue box indicate Acoustic Doppler Current Profiler (ADCP) shipborne measurements.</p>
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<p>(<b>a</b>) The unstructured mesh grid of Gyeonggi Bay (GGB) includes the tidal flat marked with a red box. (<b>b</b>) The depth contour focuses on the tidal flats. The model domain for this study is divided into two scenarios: (<b>c</b>) Creek and (<b>d</b>) No Creek cases. The ‘Creek’ case incorporates the tidal channel between Ganghwa Island and Yeongjong Island, representing the rapidly changing depths with high resolution. The ‘No Creek’ case, on the other hand, grids the intertidal zone areas with uniform high resolution, without considering the tidal channel.</p>
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<p>The 1:1 correlation plots of the ‘Creek’ and ‘No Creek’ cases against observational data (OBS) for the four major tidal constituents (M<sub>2</sub>, S<sub>2</sub>, K<sub>1</sub>, O<sub>1</sub>). (<b>a</b>) The ‘Creek’ case with amplitudes and phases closely clustered around the 1:1 line, indicating a high level of agreement between the model result (FVCOM) and OBS, particularly for M<sub>2</sub> and O<sub>1</sub> constituents. (<b>b</b>) The ‘No Creek’ case, which shows a slightly wider distribution of data points but still maintains a substantial alignment with the OBS, indicating reliable model predictions. Both panels collectively highlight the model’s adeptness at capturing tidal dynamics.</p>
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<p>Spatial distribution of residual current differences between the ‘Creek’ and ‘No Creek’ cases during (<b>a</b>) spring tide (15–16 June 2009) and (<b>b</b>) neap tide (8–9 June 2009). Each row represents a different vertical layer (surface, bottom, and depth-averaged), and each column corresponds to a different method of residual current calculation (Lagrangian, Eulerian, and Stokes drift) applied between Ganghwa Island and Yeongjong Island. The red boxes indicate the tidal flats. The contour color gradients indicate the velocity magnitude of the residual currents difference (m/s).</p>
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<p>Comparison of residual volume transport in the ‘Creek’ and ‘No Creek’ cases, calculated based on ADCP data from [<a href="#B11-water-16-00617" class="html-bibr">11</a>] using the method of [<a href="#B13-water-16-00617" class="html-bibr">13</a>]. The bar charts represent the residual volume transport (m<sup>3</sup>/s) across two transects (Line 1 and Line 2) during neap and spring tides. Each panel shows the results derived from the Lagrangian, Eulerian, and Stokes drift.</p>
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<p>Variation in salinity stratification (unit: psu) across the surface and bottom layers during (<b>a</b>) spring tide and (<b>b</b>) neap tide for the ‘Creek’ and ‘No Creek’ cases. Darker shading areas indicate stronger stratification, which is particularly evident during neap tides, suggesting significant spatial–temporal stratification effects due to tidal creek inclusion in the ‘Creek’ case. This highlights the role of tidal creek-induced residuals in altering material transport and estuarine circulation patterns.</p>
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<p>Lagrangian particle trajectories over a two-day period for the ‘Creek’ and ‘No Creek’ cases during (<b>b</b>) neap and (<b>c</b>) spring tide, differentiated by color to indicate starting regions within (<b>a</b>) the model domain. Green represents particles influenced by freshwater sources, red indicates particles on the tidal flat, and yellow marks particles originating from the southern part of the tidal channel. The brown contours delineate bathymetry, with darker shades indicating depths exceeding 20 m.</p>
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<p>Longitudinal Lagrangian particle trajectories for the ‘Creek’ and ‘No Creek’ cases during (<b>b</b>) neap tide and (<b>c</b>) spring tide. (<b>a</b>) Particles were initialized along the tidal creek direction, with the green, red, and yellow colors representing different starting regions.</p>
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<p>Long-term Lagrangian particle trajectories over 40 days in the (<b>a</b>) ‘Creek’ and (<b>b</b>) ‘No Creek’ cases. The sequence from left to right represents the spread of particles initially located at the tidal channel (yellow), middle tidal flat (green), and upper tidal flat (red).</p>
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<p>Trajectories of representative particles from group ‘A’ originating from the initial positions used in <a href="#water-16-00617-f007" class="html-fig">Figure 7</a> (panels (<b>a</b>,<b>c</b>)) and group ‘B’ from <a href="#water-16-00617-f008" class="html-fig">Figure 8</a> (panels (<b>b</b>,<b>d</b>)), shown during neap and spring tides. The solid lines track particle paths within the Creek case, while the dotted lines show movements in the No Creek case, demonstrating the contrasting dispersion influenced by the presence or absence of tidal creek structures.</p>
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<p>Comparative analysis of surface residual currents between ‘Creek’ and ‘No Creek’ cases. This figure illustrates a side-by-side comparison of the surface residual currents for the ‘Creek’ and ‘No Creek’ scenarios within the study’s estuarine environment. The top panel demonstrates the Lagrangian residual transport velocities, indicating the alignment of the ‘Creek’ case with observed data (OBS) and highlighting the model’s accuracy in simulating surface currents under the influence of tidal creeks. The middle panel depicts Eulerian transport velocities, indicating minimal discrepancy between the two experimental setups, reflecting the models’ capability to capture the estuary’s general flow patterns. The bottom panel presents the Stokes drift velocities, highlighting a pronounced difference in the directionality of the residuals, evidencing the significant impact of creek morphology on surface current characteristics. This appendix figure supplements the main text’s discussion, providing an extended validation of the models’ hydrodynamic simulations, specifically focusing on the surface layer that predominantly governs particle trajectories in the estuarine system.</p>
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16 pages, 21151 KiB  
Article
Comprehensive Risk Assessment Framework for Flash Floods in China
by Qing Li, Yu Li, Lingyun Zhao, Zhixiong Zhang, Yu Wang and Meihong Ma
Water 2024, 16(4), 616; https://doi.org/10.3390/w16040616 - 19 Feb 2024
Viewed by 2001
Abstract
Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. [...] Read more.
Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters. Full article
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<p>2017–2021 flash flood disaster distribution in China.</p>
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<p>Research approach.</p>
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<p>Distribution map of flash flood risk drivers. (<b>a</b>–<b>h</b>) represent the distribution maps of rainfall, elevation, slope, landform, soil, vegetation, land use, and population density factors, overlaid with the disaster points. Note: The black triangle represents a flash flood event.</p>
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<p>Flash flood driving factor. (<b>a</b>–<b>d</b>) represent FFPI values corresponding to slope, soil, vegetation, and land use, respectively.</p>
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<p>Distribution of flash flood potential index.</p>
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<p>Probability distribution of flash flood risk.</p>
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15 pages, 5078 KiB  
Article
The Impact of Tides and Monsoons on Tritium Migration and Diffusion in Coastal Harbours: A Simulation Study in Lianyungang Haizhou Bay, China
by Yangxin Zhang, Jiangmei Zhang, Tuantuan Liu, Xinghua Feng, Tengxiang Xie and Haolin Liu
Water 2024, 16(4), 615; https://doi.org/10.3390/w16040615 - 19 Feb 2024
Cited by 1 | Viewed by 1194
Abstract
Many nuclear power plants have been built along China’s coasts, and the migration and diffusion of radioactive nuclides in coastal harbours is very concerning. In this study, considering the decay and free diffusion of radioactive nuclides, a local hydrodynamic model based on the [...] Read more.
Many nuclear power plants have been built along China’s coasts, and the migration and diffusion of radioactive nuclides in coastal harbours is very concerning. In this study, considering the decay and free diffusion of radioactive nuclides, a local hydrodynamic model based on the FVCOM was built to investigate the migration and diffusion of the radioactive nuclide tritium in Haizhou Bay, China. This model was calibrated according to the observed tidal level and flow velocity and direction, which provide an accurate background. This study aimed to evaluate the impact of tides and monsoons on the migration path and concentration variations in tritium over time. The results demonstrated that the simulated flow field can reflect real-life receiving waters. The distribution of the tritium concentration is affected by the flow field, which is related to the tides. Moreover, more severe radioactive contamination was exhibited in winter than in summer because monsoons may have hindered the migration and diffusion of tritium within the harbour. Given the poor hydrodynamic conditions and slow water exchange in the open ocean in Haizhou Bay, the diffusion rate of radioactive nuclides outside the bay area was higher than that within it. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Pattern computing region.</p>
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<p>(<b>a</b>) Depth of simulated region. (<b>b</b>) Grid design of simulated region.</p>
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<p>Regions of Haizhou Bay.</p>
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<p>Calculated and measured water levels in Lanshangang.</p>
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<p>Calculated and measured water levels in Lianyungang.</p>
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<p>Verification of tidal current. (<b>A</b>) Comparison of measured and simulated values of tidal flow velocity at station 1; (<b>B</b>) Comparison of measured and simulated values of tidal flow direction at station 1; (<b>C</b>) Comparison of measured and simulated values of tidal flow velocity at station 2; (<b>D</b>) Comparison of measured and simulated values of tidal flow direction at station 2.</p>
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<p>Activity concentration distribution of radioactive nuclide tritium over time in case 1. Distribution of tritium at (<b>a</b>) 2 h, (<b>b</b>) 7 h, (<b>c</b>) 1 day, (<b>d</b>) 3 days, (<b>e</b>) 6 days, (<b>f</b>) 10 days, (<b>g</b>) 31 days, and (<b>h</b>) 50 days after release.</p>
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<p>Activity concentration distribution of radioactive nuclide tritium over time in case 2. Distribution of tritium at (<b>a</b>) 2 h, (<b>b</b>) 7 h, (<b>c</b>) 1 day, (<b>d</b>) 3 days, (<b>e</b>) 6 days, (<b>f</b>) 10 days, (<b>g</b>) 31 days, and (<b>h</b>) 50 days after release.</p>
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<p>Activity concentration distribution of radioactive nuclide tritium over time in case 3. Distribution of tritium at (<b>a</b>) 2 h, (<b>b</b>) 7 h, (<b>c</b>) 1 day, (<b>d</b>) 3 days, (<b>e</b>) 6 days, (<b>f</b>) 10 days, (<b>g</b>) 31 days, and (<b>h</b>) 50 days after release.</p>
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<p>Activity concentration distribution of radioactive nuclide tritium over time in case 4. Distribution of tritium at (<b>a</b>) 2 h, (<b>b</b>) 7 h, (<b>c</b>) 1 day, (<b>d</b>) 3 days, (<b>e</b>) 6 days, (<b>f</b>) 10 days, (<b>g</b>) 31 days, and (<b>h</b>) 50 days after release.</p>
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<p>A schematic view of spreading channels of tritium with the radioactive water in Haizhou Bay, Lianyungang. The times represent the averaged time it would take to reach the located region after discharging from the open boundary. The thick solid arrows indicate main channels in the surface mixed layer. The thin solid arrows show the spreading directions on the surface mixed layer.</p>
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20 pages, 6650 KiB  
Article
Seasonal Study of the Kako River Discharge Dynamics into Harima Nada Using a Coupled Atmospheric–Marine Model
by Valentina Pintos Andreoli, Hikari Shimadera, Hiroto Yasuga, Yutaro Koga, Motoharu Suzuki and Akira Kondo
Water 2024, 16(4), 614; https://doi.org/10.3390/w16040614 - 19 Feb 2024
Viewed by 1153
Abstract
This study developed a coupled atmospheric–marine model using the COAWST model system for the Harima Nada area between spring 2010 and winter 2011 to evaluate the seasonal influence of the Kako River’s discharge in the sea. The Kako River is one of the [...] Read more.
This study developed a coupled atmospheric–marine model using the COAWST model system for the Harima Nada area between spring 2010 and winter 2011 to evaluate the seasonal influence of the Kako River’s discharge in the sea. The Kako River is one of the largest rivers in southwest Japan, contributing almost half of the freshwater discharged in the Harima Nada region in the Seto Inland Sea. Validation was conducted for the entire period, showing a good performance for the atmospheric and marine variables selected. Multiple experiments injecting an inert tracer in the Kako River estuary were performed to simulate the seasonal river water distribution from the estuary into the sea and to analyze the seasonal differences in concentration patterns and mean residence times in Harima Nada. Because the study area is shallow, the results were evaluated at the surface and 10 m depth layers and showed significant seasonal differences in tracer distribution, circulation patterns, and mean residence times for the region. On the other hand, differences seemed to not be significant during the same season at different depths. The obtained results also agreed with the area’s natural water circulation, showing that the Kako River waters tend to distribute towards the west coast of Harima Nada in the warmer seasons but shift towards the east in winter. The influence of the Kako River in the center of the study area is seasonal and strongly dependent on the direction of the horizontal velocities more than their magnitude. The mean residence times varied seasonally from approximately 30 days in spring to 12 days in fall. The magnitude of the horizontal velocity was found to be maximum during summer when circulation patterns at the surface and 10 m depth in the central part of Harima Nada also seem to promote the strongest horizontal and vertical mixes. Full article
(This article belongs to the Special Issue Hydrodynamics in Coastal Areas)
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<p>Calculation domains for WRF and ROMS (<b>a</b>) and Harima Nada region (<b>b</b>). JMA stations selected for SSH evaluation are (A) Takamatsu, (B) Kobe, (C) Sumoto, (D) Osaka, (E) Matsuyama, and (F) Kochi.</p>
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<p>Coupled model layout and flowchart.</p>
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<p>Calculated and observed meteorological average values in the Harima Nada area.</p>
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<p>Salinity and temperature at four different observatories in Harima Nada.</p>
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<p>Comparison of relative SSH values with respect to the annual average and their correlation (R) and mean average errors (MAEs) in different JMA coastal observatories inside the ROMS domain.</p>
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<p>Seasonal ((<b>A</b>) spring, (<b>B</b>) summer, (<b>C</b>) autumn, and (<b>D</b>) winter) tracer distribution and average horizontal velocity in Harima Nada at the surface (<b>1</b>) and 10 m layers (<b>2</b>). The white areas in the 10 m layer correspond to places of lower bathymetry.</p>
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<p>Tracer concentration and remain fraction values at the surface layer for different points of the control domain.</p>
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15 pages, 4881 KiB  
Article
Experimental Study on the Morphology of Snow Crystal Particles and Its Influence on Compacted Snow Hardness
by Shengbo Hu, Zhijun Li, Peng Lu, Qingkai Wang, Jie Wei and Qiuming Zhao
Water 2024, 16(4), 613; https://doi.org/10.3390/w16040613 - 19 Feb 2024
Viewed by 1264
Abstract
In their natural state, snow crystals are influenced by the atmosphere during formation and multiple factors after landing, resulting in varying particle sizes and unstable particle morphologies that are challenging to quantify. The current research mainly focuses on the relationship between the porosity [...] Read more.
In their natural state, snow crystals are influenced by the atmosphere during formation and multiple factors after landing, resulting in varying particle sizes and unstable particle morphologies that are challenging to quantify. The current research mainly focuses on the relationship between the porosity of compacted snow samples or qualitatively describes snow crystals and their macroscopic physical properties, ignoring that the significant differences in the morphology of snow crystals also affect their physical properties. To quantitatively evaluate the morphology of snow crystals, we employed optical microscopy to obtain digital images of snow crystals in Harbin, utilizing the Sobel and Otsu algorithms to determine the equivalent particle size and fractal dimension of individual snow particles. In addition, the hardness of snow with a density of 0.4 g/cm3 was measured through a penetration test, with an analysis of its correlation relative to particle size and fractal dimension. The results indicated the fractal dimension as an effective parameter for characterizing particle shape, which decreased rapidly over time and then fluctuated within the range of 1.10 to 1.15. During the initial period, natural snow crystals broke down rapidly, leading to an increase in the percentage of natural snow crystals with an equivalent particle size of 0.2–0.4 mm up to 51.86%. After three days, the sintering effect between snow crystals was enhanced, resulting in an even distribution of the equivalent particle size. Finally, multiple linear regression analysis showed a positive correlation between compacted snow hardness and fractal dimension, with a negative correlation between compacted snow hardness and equivalent particle size. These findings offer valuable technical support and data reference for exploring the relationship between snow’s mechanical properties and its microscopic particle shape. Full article
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<p>Snow and test equipment. (<b>a</b>) Diagram of the stored natural snow waiting to metamorphose; (<b>b</b>) Microscope; (<b>c</b>) Electronically controlled penetrometer.</p>
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<p>Extraction of snow crystal edge contour lines. The original images of (<b>a</b>) a plate-like snow crystal, (<b>b</b>) a dendritic snow crystal, and (<b>c</b>) a hexagonal dendritic snow crystal. Automatically extracted images of (<b>d</b>) a plate-like snow crystal, (<b>e</b>) a dendritic snow crystal, and (<b>f</b>) a hexagonal dendritic snow crystal. Manual extraction of (<b>g</b>) a plate-like snow crystal, (<b>h</b>) a dendritic snow crystal, and (<b>i</b>) a hexagonal dendritic snow crystal. In (<b>g</b>–<b>i</b>), only the single required snow crystal was extracted manually.</p>
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<p>Equivalent particle size distribution. (<b>a</b>) Equivalent particle size distribution of natural snow crystals in different periods; snow crystal equivalent particle size distribution on (<b>b</b>) day 1, (<b>c</b>) day 3, (<b>d</b>) day 15.</p>
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<p>Grading curves of snow crystals. In different periods, (<b>a</b>) the relationships between equivalent particle size and cumulative mass percentage of natural snow crystals, and (<b>b</b>) the uniformity coefficients and curvature coefficients of snow crystals. In (<b>b</b>), the red area represented <span class="html-italic">C<sub>u</sub></span> &gt; 5 and the blue area represented <span class="html-italic">C<sub>c</sub></span> = 1–3. A good distribution of particle size was indicated when <span class="html-italic">C<sub>u</sub></span> &gt; 5 and <span class="html-italic">C<sub>c</sub></span> = 1–3.</p>
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<p>Comparative weight distribution of particles. (<b>a</b>) The grading curve of snow crystal on the 10th day and its corresponding well-graded curve; (<b>b</b>) The weight distribution of snow crystal on the 10th day.</p>
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<p>Variation curves of snow crystal morphology parameters. The curves of (<b>a</b>) equivalent particle size and (<b>b</b>) fractal dimension of snow crystals.</p>
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<p>The curves of snow hardness, equivalent particle size, and the fractal dimension of a compacted snow crystal.</p>
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<p>The composite correlation plot of compacted snow hardness with fractal dimension, equivalent particle size, and its fitted surface.</p>
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14 pages, 3225 KiB  
Communication
Metabolic Rates of Rainbow Trout Eggs in Reconstructed Salmonid Egg Pockets
by Rudy Benetti, Tobia Politi, Marco Bartoli and Nerijus Nika
Water 2024, 16(4), 612; https://doi.org/10.3390/w16040612 - 19 Feb 2024
Viewed by 1188
Abstract
In situ evaluations of the metabolic rates (i.e., respiration and excretion) of salmonid eggs are mostly indirect, focusing on the sampling of hyporheic water from wild or artificial nests. Comparatively, experimental studies carried out under controlled, laboratory conditions are less abundant due to [...] Read more.
In situ evaluations of the metabolic rates (i.e., respiration and excretion) of salmonid eggs are mostly indirect, focusing on the sampling of hyporheic water from wild or artificial nests. Comparatively, experimental studies carried out under controlled, laboratory conditions are less abundant due to methodological difficulties. This study presents a novel experimental setup aimed to address this issue and enable the measurement of oxygen and dissolved inorganic nitrogen fluxes in simulated rainbow trout (O. mykiss) egg pockets. The experimental setup consists of reconstructed egg pockets in cylindrical cores under flow-through conditions. Live and dead eyed-stage eggs were incubated in a natural, sterilised gravel substrate. Hyporheic water circulation was ensured using peristaltic pumps, with the possibility to collect and analyse inflowing and outflowing water for chemical analyses. Microcosm incubations, with closed respirometry of eggs in water alone, were also carried out in order to infer the importance of microbial respiration in the simulated egg pockets. The results show an increasing trend in oxygen demand, due to the development of biofilm in the reconstructed egg pockets and increased egg respiration rates. Moreover, egg pockets showed positive ammonium net fluxes connected with the advancing developmental egg stage, while nitrate removal peaked during the last phase of the experiment, mainly due to the formation of oxic-hypoxic interfaces, leading to couple nitrification–denitrification processes. The suggested approach enables to test a number of in situ situations, including the effects of extreme hydrological conditions, sediment clogging and sudden changes in water chemistry or temperature on the survival and metabolic performances of nests, at different egg development stages. Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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<p>The photos summarise the sequence of actions for the mesocosm apparatus and the measurements: (<b>a</b>) preparation of the cores with spawning gravel, (<b>b</b>) egg pocket construction and laying of eggs, (<b>c</b>) falcon tube sampled with the needle oxygen logger, and (<b>d</b>) chamber used to incubate eggs for the microcosm assay.</p>
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<p>Mesocosm setup in which reconstructed trout nests were incubated. The aquarium was laid in one pool of the RAS. Arrows indicate the water circulation direction.</p>
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<p>Boxplot portraying the (<b>a</b>) DO demand (mg O<sub>2</sub> egg<sup>−1</sup> h<sup>−1</sup>) and (<b>b</b>) N-NH<sub>4</sub><sup>+</sup> excretion rates (μg N-NH<sub>4</sub><sup>+</sup> egg<sup>−1</sup> h<sup>−1</sup>) of rainbow trout eggs at the three main developmental stages: eyed egg (294–310 dd), nearly hatched alevin with yolk sack (350–358 dd) and prior to the emergence time (swim up, 454–462 dd). The three eggs stages were incubated in closed glass chambers (see the text for more details).</p>
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<p>Line plots depicting the (<b>a</b>) oxygen (mg O<sub>2</sub> mesocosm<sup>−1</sup> h<sup>−1</sup>) demand, (<b>b</b>) ammonium (mg N-NH<sub>4</sub><sup>+</sup> mesocosm<sup>−1</sup> h<sup>−1</sup>) and (<b>c</b>) nitrates fluxes (mg N-NO<sub>3</sub><sup>−</sup> mesocosm<sup>−1</sup> h<sup>−1</sup>) for the simulated salmonid egg pockets (cores), for the whole incubation period until the complete alevin yolk sack absorption. Different colours represent the two main stages “eyed egg” and “alevin”, while different geometries stand for the live (L) and live and dead (L + D) setups and the control (bare sediments). Boxplots of 100-egg respiration and excretion data from the microcosm incubation are inserted for comparison. Please note that the boxplot position along the degree day axis is representative of the degree day intervals in which fluxes were measured from the microcosm experiments.</p>
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4 pages, 169 KiB  
Editorial
Pathogen Detection and Identification in Wastewater
by Guangming Jiang, Ryo Honda and Sudipti Arora
Water 2024, 16(4), 611; https://doi.org/10.3390/w16040611 - 19 Feb 2024
Viewed by 1817
Abstract
The COVID-19 pandemic has renewed research needs for the detection and monitoring of various pathogens in urban wastewater systems including sewerage systems and wastewater treatment or recycling plants [...] Full article
(This article belongs to the Special Issue Pathogen Detection and Identification in Wastewater)
30 pages, 410 KiB  
Review
Challenges to Water Resource Management: The Role of Economic and Modeling Approaches
by Ariel Dinar
Water 2024, 16(4), 610; https://doi.org/10.3390/w16040610 - 18 Feb 2024
Cited by 8 | Viewed by 7270
Abstract
The field of water management is continually changing. Water has been subject to external shocks in the form of climate change and globalization. Water management analysis is subject to disciplinary developments and inter-disciplinary interactions. Are these developments well-documented in the literature? Initial observations [...] Read more.
The field of water management is continually changing. Water has been subject to external shocks in the form of climate change and globalization. Water management analysis is subject to disciplinary developments and inter-disciplinary interactions. Are these developments well-documented in the literature? Initial observations in the interdisciplinary literature suggest that results are fragmented, implying that a state-of-the-art review is needed. This paper aims to close such a gap by reviewing recent developments in water economics that address increasing perceptions of water scarcity by looking first at changes in the supply and quality of water and then at the impacts of climate change on water supply extremes. Among responses to such challenges, this paper identifies changes to water use patterns by including and co-managing water from different sources, including surface and groundwater, reclaimed wastewater, and desalinated water. Technological advancements are also among the resources that address water challenges. Water challenges are also reflected in the management of internationally shared water. A recent surge in scientific work identified international treaties as a significant contributor to international water management. This paper reviews recently employed economic approaches, such as experimental economics, game theory, institutional economics, and valuation methods. And, finally, it explores modeling approaches, including hydro-economic and computable general equilibrium models, that are being used to deal with water challenges. Full article
21 pages, 10109 KiB  
Article
Design of a Seawater Desalination System with Two-Stage Humidification and Dehumidification Desalination Driven by Wind and Solar Energy
by Kaijie Huang, Chengjun Qiu, Wenbin Xie, Wei Qu, Yuan Zhuang, Kaixuan Chen, Jiaqi Yan, Gao Huang, Chao Zhang and Jianfeng Hao
Water 2024, 16(4), 609; https://doi.org/10.3390/w16040609 - 18 Feb 2024
Viewed by 1808
Abstract
The paper presents a wind–photovoltaic-thermal hybrid-driven two-stage humidification and dehumidification desalination system for remote island regions lacking access to electricity and freshwater resources. By conducting an analysis of the wind and solar energy resources at the experimental site, a suitable wind power station [...] Read more.
The paper presents a wind–photovoltaic-thermal hybrid-driven two-stage humidification and dehumidification desalination system for remote island regions lacking access to electricity and freshwater resources. By conducting an analysis of the wind and solar energy resources at the experimental site, a suitable wind power station and photovoltaic power station are constructed. The performance of the wind–solar complementary power generation system is then evaluated based on factors such as output power, seawater desalination load power, battery compensation output, system energy consumption, and water production costs. A variable step gradient disturbance method based on the power–duty ratio is proposed for tracking the maximum power point (MPPT) of wind power generation. The output power of the photovoltaic power generation system is optimized, employing a fuzzy logic control (FLC) method to track the MPPT of photovoltaic power generation. This approach effectively addresses the issues of slow speed and low accuracy encountered by traditional MPPT algorithms in tracking the maximum power point (MPP) of both photovoltaic and wind power generations. In order to ensure that the desalination system can operate stably under different weather conditions, eight working modes are designed, and a programmable logic controller (PLC) is used to control the system, which provides a guarantee for stable water production. Experimental results demonstrate that the system exhibits stable performance, achieving a maximum water output of 80.63 Kg/h and daily water yield is 751.32 Kg, the cost of desalination equipment is 1.4892 USD/t. Full article
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<p>Solar heat collection device.</p>
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<p>Two-stage humidification and dehumidification desalination schematic diagram.</p>
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<p>Water yields with and without return coils at different seawater spray temperatures.</p>
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<p>Wind speed frequency distribution.</p>
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<p>Wind speed and wind turbine power generation changing curve over time.</p>
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<p>Seawater temperature and power generation with irradiance curve.</p>
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<p>Relationship between output power and rotating speed of a wind turbine generator.</p>
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<p>Equivalent circuit diagram of wind power generation.</p>
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<p>The flowchart of the MPPT algorithm.</p>
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<p>MATLAB/Simulink model of the wind power generation module with MPPT.</p>
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<p>Performance of a power duty cycle gradient perturbation MPPT.</p>
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<p>(<b>a</b>) Characteristic curves at different temperatures; (<b>b</b>) Characteristic curves at different irradiances.</p>
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<p>MPPT control block diagram.</p>
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<p>MATLAB/Simulink model of the solar PV module with FLC MPPT method.</p>
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<p>Performance of FLC MPPT.</p>
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<p>Control system structure diagram.</p>
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<p>Schematic diagram of energy distribution.</p>
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<p>Schematic diagram of the working mode of the power supply device.</p>
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<p>(<b>a</b>) Solar collectors and desalination devices; (<b>b</b>) Two-stage humidification and dehumidification desalination device.</p>
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<p>Wind speed and solar irradiance curves on the test day.</p>
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<p>Wind and solar hybrid supply system operation curves.</p>
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<p>Relationship between output power of power supply system and water yield on test day.</p>
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20 pages, 3088 KiB  
Article
Assessment of Effluent Wastewater Quality and the Application of an Integrated Wastewater Resource Recovery Model: The Burgersfort Wastewater Resource Recovery Case Study
by Sekato Maremane, Gladys Belle and Paul Oberholster
Water 2024, 16(4), 608; https://doi.org/10.3390/w16040608 - 18 Feb 2024
Cited by 2 | Viewed by 1767
Abstract
Rivers in Africa have experienced dire pollution as a result of the poor management of wastewater effluent emanating from water resource recovery facilities (WRRFs). An integrated wastewater resource recovery model was developed and applied to identify ideal wastewater resource recovery technologies that can [...] Read more.
Rivers in Africa have experienced dire pollution as a result of the poor management of wastewater effluent emanating from water resource recovery facilities (WRRFs). An integrated wastewater resource recovery model was developed and applied to identify ideal wastewater resource recovery technologies that can be used to recover valuable resources from a mixture of wastewater effluents in a case study in the Burgersfort WRRF in the Limpopo province, South Africa. This novel model incorporates the process of biological nutrient removal (BNR) with an extension of conventional methods of resource recovery applicable to wastewater. The assessment of results of effluent quality from 2016 to 2022 revealed that ammonia, chemical oxygen demand, total coliform, fecal coliform, and Escherichia coli levels were critically non-compliant with the permissible effluent guidelines, indicating a stable upward trend in terms of concentrations, and scored a very bad wastewater quality index rating. All variables assessed showed a significant loading, except for orthophosphates, and significant correlations were observed among the variables. The results of the integrated wastewater resource recovery model revealed a high probability of reclaiming recoverable resources such as nutrients, sludge, bioplastics, biofuel, metals, and water from wastewater, which have economic, environmental, and social benefits, thereby improving the effluent quality of a WRRF. Full article
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<p>Aerial map of the Burgersfort wastewater treatment works and the surrounding land uses (Map by Owolabi S based on Google Maps data).</p>
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<p>Integrated wastewater resource recovery model (Authors’ own, 2023): Blue arrows represents water movement from one unit to the other; Brown arrows indicate movement of sludge from one unit to the other, while green arrows show movement of gases from one unit to the other.</p>
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<p>Ammonia sequential Mann–Kendall plot.</p>
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<p>Chemical oxygen demand sequential Mann–Kendall plot.</p>
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<p>Total coliforms sequential Mann–Kendall plot.</p>
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<p>Fecal coliform sequential Mann–Kendall plot.</p>
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<p><span class="html-italic">E. coli</span> sequential Mann–Kendall plot.</p>
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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)
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<p>Overview of the prediction procedure.</p>
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<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>
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<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>
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<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>
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<p>Creating the flow–distance matrix from the surface flow–direction matrix.</p>
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<p>CNN operations.</p>
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<p>LSTM structure.</p>
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<p>Proposed model for river water-level prediction incorporating CNN and LSTM structures.</p>
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<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>
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<p>Loss-function values in pre-training.</p>
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<p>The average prediction accuracy.</p>
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<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>
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<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>
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<p>Prediction accuracy of Models D and F with various numbers of training periods.</p>
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<p>Hydrographs for 1 h ahead forecast (3rd highest period case).</p>
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<p>Hydrographs for 1 h ahead forecast (5th highest period case).</p>
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<p>The effect of the flow–distance matrix in transfer learning.</p>
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19 pages, 3518 KiB  
Article
Study on the Influence of Water Erosion on the Bearing Capacity and Function of the High Pile Foundation of the Wharf
by Yashi Yang, Peng Zhang, Lingjun Wu and Qian Zhang
Water 2024, 16(4), 606; https://doi.org/10.3390/w16040606 - 18 Feb 2024
Viewed by 1259
Abstract
High-pile foundation is a common form of deep foundation commonly used in ocean environments, such as docks and bridge sites. Aiming at the problem of bearing capacity of high pile foundations, this paper proposes the calculation of bearing capacity and the analysis of [...] Read more.
High-pile foundation is a common form of deep foundation commonly used in ocean environments, such as docks and bridge sites. Aiming at the problem of bearing capacity of high pile foundations, this paper proposes the calculation of bearing capacity and the analysis of scour depth of high pile foundations under the action of scour based on the modified p-y curve. In this paper, three kinds of scour mechanisms—natural evolution scour, general scour, and local scour—are described; and the calculation methods of scour widely used at present are compared and analyzed. The solution of the vertical stress of soil around the pile under local scour is solved and applied to the β method to solve the lateral resistance of the pile under local scour. The local erosion is equivalent to the whole erosion, and the expression of the ultimate soil resistance before and after the equivalent is calculated, respectively, according to the principle that the ultimate soil resistance at a certain point above the equivalent pile end remains unchanged. The distance from the equivalent soil surface to the pile end can be obtained simultaneously, and then the equivalent erosion depth, p-y curve of sand at different depths, and high pile bearing capacity can be obtained. Finally, it is found that the bending moment of a single pile body varies along the pile body in the form of a parabola, and the maximum bending moment of the pile body is below the mud surface and increases with the increase in horizontal load. When the scouring depth is 30 m, the horizontal load is 25 KN, and the maximum bending moment of the pile body is about 150 N·m. The data with a relative error greater than 10% accounted for only 16.6% of the total data, and the error between the calculated value and the measured value was small. The formula can predict the erosion depth more accurately. Full article
(This article belongs to the Special Issue Effects of Groundwater and Surface Water on the Natural Geo-Hazards)
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<p>Wave load and its spectrum.</p>
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<p>(<b>a</b>) General scouring depth (<b>b</b>) Local scour depth (<b>c</b>) Vortex flow field structure. Erosion structure diagram of a high pile foundation.</p>
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<p>Failure model and load/unload curve.</p>
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<p>Model structure and layout.</p>
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<p>Determination of water flow velocity.</p>
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<p>Changes in river bed erosion and deposition.</p>
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<p>Pile bending moment distribution.</p>
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<p>Fitting and formula verification.</p>
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18 pages, 7770 KiB  
Article
An Optimization Study of Advanced Fenton Oxidation Methods (UV/Fenton–MW/Fenton) for Treatment of Real Epoxy Paint Wastewater
by Esra Billur Balcioglu Ilhan, Fatih Ilhan, Ugur Kurt and Kaan Yetilmezsoy
Water 2024, 16(4), 605; https://doi.org/10.3390/w16040605 - 18 Feb 2024
Cited by 3 | Viewed by 1893
Abstract
The use of various advanced oxidation methods in the treatment of wastewater has become the subject of many studies published in recent years. In particular, it is exceedingly significant to compare these treatment methods for industrial wastewater to reduce environmental effects and optimize [...] Read more.
The use of various advanced oxidation methods in the treatment of wastewater has become the subject of many studies published in recent years. In particular, it is exceedingly significant to compare these treatment methods for industrial wastewater to reduce environmental effects and optimize plant operations and economics. The present study is the first to deal with the treatability of real epoxy paint wastewater (EPW) using MW- and UV-assisted Fenton processes within an optimization framework. A three-factor, three-level Box–Behnken experimental design combined with response surface methodology (RSM) was conducted for maximizing the chemical oxygen demand (COD) and color removal efficiencies of ultraviolet (UV)/Fenton and microwave (MW)/Fenton processes in the treatment of the real epoxy paint wastewater (EPW, initial COD = 4600 ± 90 mg/L, initial color = 114 ± 4 Pt-Co), based on 15 different experimental runs. Three independent variables (reaction time ranging from 20 to 60 min (UV) and from 5 to 15 min (MW), power ranging from 20 to 40 W (UV) and from 300 to 600 W (MW), and H2O2/Fe2+ ratio ranging from 0.2 to 0.6 (for both UV and MW)) were consecutively coded as A, B, and C at three levels (−1, 0, and 1), and four second-order polynomial regression equations were then derived to estimate the responses (COD and color removals) of two distinct systems. The significance of the independent model components and their interrelations were appraised by means of a variance analysis with 99% confidence limits (α = 0.01). The standardized differences of the independent variables and the consistency between the actual and predicted values were also investigated by preparing normal probability residual plots and experiment-model plots for all processes. The optimal operating conditions were attained by solving the quadratic regression models and analyzing the surface and contour plots. UV/Fenton and MW/Fenton processes, which constitute combined Fenton processes, were performed using advanced oxidation methods, while Fenton processes were utilized as the standard method for wastewater treatment. When UV/Fenton and MW/Fenton processes were applied separately, the COD removal efficiencies were determined to be 96.4% and 95.3%, respectively. For the color parameter, the removal efficiencies after the application of both processes were found to exceed 97.5%. While these efficiencies were achieved in 1 h with a 38 W UV unit, they were achieved in 15 min with a MW power of 570 W. According to the RSM-based regression analysis results, the R2 values for both processes were greater than 0.97 and p values were less than 0.003. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Pictorial diagram of the treatment process used for the advanced oxidation of EPW.</p>
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<p>Normal probability residual plot and predicted vs. actual plot based on chemical oxygen demand (COD) removal efficiency values for the UV/Fenton process.</p>
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<p>Normal probability residual plot and predicted vs. actual plot based on color removal efficiency values for the UV/Fenton process.</p>
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<p>Surface and contour plots generated to illustrate the removal of pollutants, specifically addressing COD and color, through the UV/Fenton process.</p>
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<p>Normal probability residual plot and predicted vs. actual plot based on chemical oxygen demand (COD) removal efficiency values for the MV/Fenton process.</p>
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<p>Normal probability residual plot and predicted vs. actual plot based on color removal efficiency values for the MV/Fenton process.</p>
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<p>Surface and contour plots generated to illustrate the removal of pollutants, specifically addressing COD and color, through the MW/Fenton process.</p>
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23 pages, 18125 KiB  
Article
Stability Analysis of a Rocky Slope with a Weak Interbedded Layer under Rainfall Infiltration Conditions
by Yizhou Zhuang, Xiaoyao Hu, Wenbin He, Danyi Shen and Yijun Zhu
Water 2024, 16(4), 604; https://doi.org/10.3390/w16040604 - 18 Feb 2024
Cited by 3 | Viewed by 1595
Abstract
Landslides not only cause great economic and human life losses but also seriously affect the safe operation of infrastructure such as highways. Rainfall is an important condition for inducing landslides, especially when a fault and weak interlayer exist on the slope, which can [...] Read more.
Landslides not only cause great economic and human life losses but also seriously affect the safe operation of infrastructure such as highways. Rainfall is an important condition for inducing landslides, especially when a fault and weak interlayer exist on the slope, which can easily transform into a landslide and cause instability under the action of rainfall. To explore the effects of a soft interlayer, a fault, and extreme rainfall on slope stability, this paper takes the landslide on the right side of the G104 Jinglan Line in Shengzhou City, Shaoxing City, Zhejiang Province, China, as an example. The cause, failure mechanism, and characteristics of the landslide are analyzed through field investigation and borehole exploration in the landslide area. The slope is simulated by numerical analysis, and the stability of the landslide under natural conditions and extreme rainstorm conditions is calculated using the strength reduction method. The stability of the slope before and after treatment is compared, and the effectiveness of the treatment measures is verified by combining the field monitoring data. At the same time, the complex geological structure and rainfall are considered to have been the main factors leading to the G104 landslide. Near the fault, the weak interlayer of the landslide was easily disturbed, the deformation trend of the deep displacement was consistent with rainfall, and the axial force of the anti-slide piles at the weak interlayer was correspondingly large. For a wedge rock slope, “excavation unloading” and “prestressed anchor + prestressed anchor cable + anti-slide pile” are effective treatments. This paper reveals the effects of a weak interlayer, a fault, and strong rainfall on a rocky high slope, providing predictions of instability modes and time evolution patterns for similar complex geological slopes under rainfall infiltration conditions and providing references for their treatment measures. Full article
(This article belongs to the Section Urban Water Management)
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<p>The overall workflow.</p>
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<p>Study area and landslide: (<b>a</b>) location of the study area; (<b>b</b>) photo of the slope.</p>
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<p>A small amount of groundwater seeps from the retaining wall.</p>
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<p>Plan view of slope drilling location.</p>
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<p>Schematic diagram of a typical engineering geological section.</p>
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<p>Landslide deformation area.</p>
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<p>Equatorial projection.</p>
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<p>Slope deformation diagrams: (<b>a</b>) panoramic view of landslide deformation; (<b>b</b>) shear exit area; (<b>c</b>) right crack area; (<b>d</b>) back edge area; (<b>e</b>) mid-slope tensile crack area; (<b>f</b>) left crack area.</p>
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<p>Excavation and unloading solution.</p>
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<p>Slope reinforcement scheme.</p>
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<p>Photo of the slope after reinforcement.</p>
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<p>Detailed layout of monitoring instruments.</p>
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<p>Deep displacement deformation time course curve: (<b>a</b>) inclinometer hole 1 from 13 April 2022 to 14 July 2022; (<b>b</b>) inclinometer hole 1 from 4 August 2022 to 31 May 2023; (<b>c</b>) inclinometer hole 2.</p>
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<p>Curve of anti-slide pile force with depths.</p>
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<p>Cable tension variation diagram.</p>
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<p>Calculation model: (<b>a</b>) original slope; (<b>b</b>) excavation; (<b>c</b>) support.</p>
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<p>Maximum shear strain case under natural conditions.</p>
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<p>Maximum shear strain case under rainfall conditions.</p>
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<p>Safety factors for each step of excavation under natural conditions.</p>
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<p>Maximum shear strain case under slope treatment conditions.</p>
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<p>Safety factors for each step of excavation under rainstorm conditions.</p>
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<p>Maximum shear strain case under the combination of slope treatment and rainstorm conditions.</p>
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<p>The deep displacement of each hole: (<b>a</b>) ZK4, (<b>b</b>) ZK7, (<b>c</b>) ZK5, (<b>d</b>) ZK6.</p>
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<p>The weak layer location.</p>
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25 pages, 24325 KiB  
Article
The Investigation of the Response Mechanism of SST and Chlorophyll to Super Typhoon “Rey” in the South China Sea
by Shichao Wang, Jun Song, Junru Guo, Yanzhao Fu, Yu Cai and Linhui Wang
Water 2024, 16(4), 603; https://doi.org/10.3390/w16040603 - 18 Feb 2024
Viewed by 1399
Abstract
As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data [...] Read more.
As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data and focusing on Typhoon Rey No. 22’s transit event in 2021, this study quantitatively analyzes typhoon-induced energy input through heat pumping and cold suction at both surface and subsurface levels of the ocean. Additionally, it explores the response characteristics and feedback mechanisms of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the South China Sea to typhoon events. The research results show that the SST variation along the typhoon track displayed an asymmetric pattern, with a more pronounced warming on the right side and a cold anomaly lasting for 3–5 days on the left side. The subsurface warm anomaly dominated on the right side, showing a maximum temperature difference of 1.54 °C, whereas Ekman suction-induced upwelling led to cooling effects both at the subsurface and surface level on the left side, resulting in a maximum temperature difference of −3.28 °C. During the typhoon event, there was a significant decrease in sea surface heat flux, reaching 323.36 W/m2, accompanied by corresponding changes in SST due to processes such as upwelling, seawater mixing, and air–sea heat transfer dynamics where anomalies arising from oceanic dynamic processes and exchange through sea surface heat flux contributed equally. Furthermore, strong suction-induced upwelling during the typhoon influenced chlorophyll concentration within the central and western regions of the South China Sea (13.5° N–16.5° N, 111° E–112.5° E), resulting in significant enhancement and reaching its peak value at approximately 0.65 mg/L. The average chlorophyll concentration increased by approximately 0.31 mg/L. Full article
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<p>Chart of the track and intensity change of Typhoon Rey.</p>
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<p>The South China Sea is divided into eight research zones along the path of Typhoon Rey.</p>
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<p>Selected profile breakpoint location in South China Sea research area.</p>
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<p>Wind field and wind speed distribution during the passage of Typhoon Rey over the South China Sea (16–21 December 2021).</p>
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<p>Distribution map of regional wind curl over the South China Sea during Typhoon Rey.</p>
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<p>Ekman pumping intensity distribution map in the South China Sea research area.</p>
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<p>Net heat flux of the sea surface in the study area during Typhoon Rey.</p>
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<p>Net heat flux of the sea surface in the study area during Typhoon Rey.</p>
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<p>The sequence diagram of net heat flux change in the profile of the study area.</p>
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<p>SST distribution in the South China Sea research area during Typhoon Rey.</p>
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<p>SST distribution in the South China Sea research area during Typhoon Rey.</p>
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<p>SST variation distribution in the South China Sea during Typhoon Rey.</p>
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<p>SST variation distribution in the South China Sea during Typhoon Rey.</p>
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<p>The temperature curve of the R5, R6, and R7 zones with time was studied.</p>
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<p>R5, R6, and R7 Ekman pumping strength change line chart in the study area.</p>
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<p>The SST distribution profile varied with temperature in the study area.</p>
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<p>The SST distribution profile varied with temperature in the study area.</p>
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<p>Time variation distribution of the depth of the mixed layer.</p>
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<p>The variation of heat flux in the profile position led to the varied distribution of SST.</p>
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<p>Distribution of SST changes caused by upwelling.</p>
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<p>The distribution of sea surface temperature variation caused by the variation of mixed layer depth.</p>
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<p>Distribution of Chl-a concentration in the South China Sea during Typhoon Rey.</p>
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<p>Distribution of Chl-a concentration in the South China Sea during Typhoon Rey.</p>
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<p>Profile of Chl-a concentration in the South China Sea research area.</p>
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31 pages, 5658 KiB  
Article
Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions
by Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar
Water 2024, 16(4), 602; https://doi.org/10.3390/w16040602 - 18 Feb 2024
Cited by 1 | Viewed by 1284
Abstract
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind [...] Read more.
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R2), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day−1) and RMSE (0.326 mm day−1) were also obtained. Full article
(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)
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<p>The location of weather stations used for the study.</p>
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<p>Seasonal variation of evapotranspiration with respect to the elevation.</p>
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<p>Architecture of a modular feed-forward neural network (MFFNN). Dotted circle represent an example of a detailed module with different kind of layers.</p>
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<p>Artificial neural network flowchart.</p>
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<p>Annual reference evapotranspiration calculated with different methods for the Jendouba, Kairouan and Kélibia weather stations.</p>
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<p>Monthly reference evapotranspiration calculated with different methods for the Jendouba (<b>a</b>), Kairouan (<b>b</b>), and Kélibia (<b>c</b>) weather stations.</p>
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<p>Neural network accuracy under two hiding layers configuration (HL) and different number of neurons during the ETo modeling process in the Jendouba region. Black lines represent the variation of errors with the neurons number and pink lines represent the best configuration.</p>
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<p>The FAO-56 Penman Monteith and the modular feed-forward estimated ETo values of the Jendouba weather station during the testing stage.</p>
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<p>The FAO-56 Penman–Monteith and the modular feed-forward estimated ETo values of the Kairouan weather station during the testing stage.</p>
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<p>The FAO-56 Penman–Monteith and the modular feed-forward estimated ETo values of the Kélibia weather station during the testing period.</p>
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<p>Relationship between average monthly estimates ETo estimated with the MFF-10 model and average monthly observed ETo for the weather stations of Jendouba, Kélibia, and Kairouan.</p>
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<p>Relationship between EToPM and ETo_MFF-10 during the production stage for the Béja, Le Kef, Sidi Bouzid, Siliana, Bizerte, and Tunis weather stations.</p>
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<p>Comparison of mean absolute error (MAE) and root mean square error (RMSE) for the Béja, Le Kef, Sidi Bouzid, Siliana, Bizerte, and Tunis weather stations.</p>
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22 pages, 42889 KiB  
Article
Hydrogeochemistry and Water Quality Index for Groundwater Sustainability in the Komadugu-Yobe Basin, Sahel Region
by Abdulrahman Shuaibu, Robert M. Kalin, Vernon Phoenix, Limbikani C. Banda and Ibrahim Mohammed Lawal
Water 2024, 16(4), 601; https://doi.org/10.3390/w16040601 - 18 Feb 2024
Cited by 4 | Viewed by 1873
Abstract
The assessment of hydrochemical characteristics and groundwater quality is crucial for environmental sustainability in developing economies. This study employed hydrogeochemical analysis, geospatial analysis, and groundwater quality index to assess hydrogeochemical processes and quality of groundwater in the Komadugu-Yobe basin. The pH, total dissolved [...] Read more.
The assessment of hydrochemical characteristics and groundwater quality is crucial for environmental sustainability in developing economies. This study employed hydrogeochemical analysis, geospatial analysis, and groundwater quality index to assess hydrogeochemical processes and quality of groundwater in the Komadugu-Yobe basin. The pH, total dissolved solids (TDS), and electrical conductivity (EC) were assessed in situ using a handheld portable electrical conductivity meter. The concentrations of the major cations (Na+, Ca2+, Mg2+, and K+), were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES). The major anions (chloride, fluoride, sulfate, and nitrate) were analyzed via ion chromatography (IC). Total alkalinity and bicarbonate were measured in situ using a HACH digital alkalinity kit by the titrimetric method. Hydrochemical results indicate some physicochemical properties of the groundwater samples exceeded the maximum permissible limits as recommended by the World Health Organization guidelines for drinking water. Gibbs diagrams indicate rock–water interaction/rock weathering processes are the dominant mechanisms influencing the groundwater chemistry. Groundwater is predominantly Ca2+-Mg2+-HCO3 water type, constituting 59% of the groundwater samples analyzed. The groundwater quality index (GWQI) depicted 63 and 27% of the groundwater samples as excellent and good water types for drinking purposes, respectively. This study further relates the interaction between geology, hydrochemical characteristics, and groundwater quality parameters. The results are essential to inform a sustainable management strategy and protection of groundwater resources. Full article
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<p>Groundwater sampling location, geology type, and electrical conductivity (EC) concentration in Komadugu-Yobe basin.</p>
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<p>Spatial distribution of groundwater quality parameter in Komadugu-Yobe basin. (<b>a</b>) pH; (<b>b</b>) TDS.</p>
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<p>Spatial distribution of groundwater quality parameter in Komadugu−Yobe basin. (<b>a</b>) TH; (<b>b</b>) Ca<sup>2+</sup>; (<b>c</b>) Mg<sup>2+</sup>; (<b>d</b>) Na<sup>+</sup>; (<b>e</b>) K<sup>+</sup>.</p>
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<p>Spatial distribution of groundwater quality parameters in Komadugu−Yobe basin. (<b>a</b>) HCO<sub>3</sub><sup>−</sup>; (<b>b</b>) Cl<sup>−</sup>; (<b>c</b>) SO<sub>4</sub><sup>2−</sup>; (<b>d</b>) NO<sub>3</sub><sup>−</sup>; (<b>e</b>) F<sup>−</sup>.</p>
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<p>Gibbs plots showing the dominant geochemical mechanisms in KYB, (<b>left</b>): anions ratio and (<b>right</b>): cations ratio vs. TDS (mg/L). ED: evaporation dominance. RWD: rock weathering dominance. PD: precipitation dominance.</p>
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<p>Piper diagram showing various water types in Komadugu−Yobe basin.</p>
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<p>Chada plot showing groundwater evolution in Komadugu−Yobe basin.</p>
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<p>Spatial distribution of groundwater quality index in Komadugu−Yobe basin.</p>
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<p>Groundwater quality index (GWQI) classification in Komadugu−Yobe basin.</p>
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8 pages, 7483 KiB  
Communication
Water Supply and Firefighting: Early Lessons from the 2023 Maui Fires
by Robert B. Sowby and Braxton W. Porter
Water 2024, 16(4), 600; https://doi.org/10.3390/w16040600 - 18 Feb 2024
Cited by 1 | Viewed by 2658
Abstract
Even though drinking water utilities are not meant to fight wildfires, they quickly become stakeholders, if not first responders, when their resources are needed for firefighting. The August 2023 wildfires on the island of Maui, Hawaii, USA, have highlighted weaknesses at this intersection. [...] Read more.
Even though drinking water utilities are not meant to fight wildfires, they quickly become stakeholders, if not first responders, when their resources are needed for firefighting. The August 2023 wildfires on the island of Maui, Hawaii, USA, have highlighted weaknesses at this intersection. While attention has focused on the wildfire causes or water quality impacts afterward, few studies have analyzed the response. We review this extreme case to support disaster-response lessons for water utilities and to guide further research and policy. First, emergency water releases were not available in a timely manner. Second, fire and wind toppled power lines, causing power outages that inhibited pumping water. Third, many structures were a total loss despite water doused on them, consuming valuable water. Finally, water was lost through damaged premise plumbing in burned structures, further reducing system pressure. These conditions emphasize that water utilities need to access emergency water supplies quickly, establish reliable backup electricity, coordinate with firefighters on priority water uses, and shut valves in burned areas to preserve water. While further research will certainly follow, we present these early lessons as starting points. Full article
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<p>Overview of Maui fires.</p>
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<p>Fire damage in Lahaina (photo by State Farm via Flickr. CC BY 2.0).</p>
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<p>A typical Hawaiian fire hydrant (photo by NAVFAC via Flickr. CC BY 2.0).</p>
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17 pages, 4996 KiB  
Article
Advantages of the Event Method for the Simulation of Water Quality in Pressurised Water Systems
by Marta Hervás, Fernando Martínez-Alzamora, Pilar Conejos and Joan Carles Alonso
Water 2024, 16(4), 599; https://doi.org/10.3390/w16040599 - 18 Feb 2024
Cited by 1 | Viewed by 1073
Abstract
In this paper, several methods for the calculation of water quality evolution in drinking water distribution networks are analysed. The Lagrangian Time-Driven method has been implemented in the Epanet simulation software since version 2.0. In version 2.2, some improvements were implemented to deal [...] Read more.
In this paper, several methods for the calculation of water quality evolution in drinking water distribution networks are analysed. The Lagrangian Time-Driven method has been implemented in the Epanet simulation software since version 2.0. In version 2.2, some improvements were implemented to deal with mass imbalances (Lagrangian Time-Driven improved method). However, it sometimes presents inaccuracies in calculations, especially when there are short-length pipes. To solve this problem, the implementation of the Lagrangian Event-Driven method is proposed, which provides more accurate quality results. In order to detail the differences and similarities of the results of the different methods and to determine under what conditions the results provided by Epanet are sufficiently adjusted, two practical examples have been carried out, one of them on a hydraulic model of a real network. Full article
(This article belongs to the Section Urban Water Management)
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<p>Schematic of the simulation by the Lagrangian Time-Driven method, LTD (Epanet 2.0).</p>
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<p>Schematic of the simulation by the Lagrangian Time-Driven Improved method, LTDI (Epanet 2.2).</p>
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<p>Steps of the implemented LED method.</p>
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<p>Schematic of the simulation by the Lagrangian Event-Driven method, LED (step 1).</p>
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<p>Schematic of the simulation by the Lagrangian Event-Driven method, LED (step 2).</p>
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<p>Linear pipe divided into 10 sections.</p>
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<p>Results of the quality simulation with Epanet 2.0 for ∆t<sub>Q</sub> = 6 min, at 24 min and 60 min.</p>
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<p>Results of the quality simulation with Epanet 2.0 for ∆t<sub>Q</sub> = 12 min, at 24 min and 60 min.</p>
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<p>Results of the quality simulation with Epanet 2.2 for ∆t<sub>Q</sub> = 6 min, at 24 min and 60 min.</p>
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<p>Results of the quality simulation with Epanet 2.2 for ∆t<sub>Q</sub> = 12 min, at 24 min and 60 min.</p>
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<p>Simplified (<b>left</b>) and detailed (<b>right</b>) models of a real network.</p>
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<p>Contaminant injection evolution.</p>
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<p>Comparison of the results obtained with the LED and LTD (Epanet 2.0) methods.</p>
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<p>Comparison of the results obtained with the LED and LTDI (Epanet 2.2) methods.</p>
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21 pages, 6364 KiB  
Article
Making Rivers, Producing Futures: The Rise of an Eco-Modern River Imaginary in Dutch Climate Change Adaptation
by Lotte de Jong, Gert Jan Veldwisch, Lieke Anna Melsen and Rutgerd Boelens
Water 2024, 16(4), 598; https://doi.org/10.3390/w16040598 - 18 Feb 2024
Cited by 1 | Viewed by 1890
Abstract
In the field of climate change adaptation, the future matters. River futures influence the way adaptation projects are implemented in rivers. In this paper, we challenge the ways in which dominant paradigms and expert claims monopolise the truth concerning policies and designs of [...] Read more.
In the field of climate change adaptation, the future matters. River futures influence the way adaptation projects are implemented in rivers. In this paper, we challenge the ways in which dominant paradigms and expert claims monopolise the truth concerning policies and designs of river futures, thereby sidelining and delegitimising alternative river futures. So far, limited work has been performed on the power of river futures in the context of climate change adaptation. We conceptualised the power of river futures through river imaginaries, i.e., collectively performed and publicly envisioned reproductions of riverine socionatures mobilised through truth claims of social life and order. Using the Border Meuse project as a case study, a climate change adaptation project in a stretch of the river Meuse in the south of the Netherlands, and a proclaimed success story of climate adaptation in Dutch water management, we elucidated how three river imaginaries (a modern river imaginary, a market-driven imaginary, and an eco-centric river imaginary) merged into an eco-modern river imaginary. Importantly, not only did the river futures merge, but their aligned truth regimes also merged. Thus, we argue that George Orwell’s famous quote, “who controls the past, controls the future: who controls the present, controls the past” can be extended to “who controls the future, controls how we see and act in the present, and how we rediscover the past”. Full article
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<p>Front covers of the river’s future plans demonstrate a romantic view that represents a wild and natural river as it presumably was in the past. The vegetation is an eye-catching feature. The illustrations were made by (<b>a</b>) Natuur en Milieu Gelderland [<a href="#B39-water-16-00598" class="html-bibr">39</a>] and (<b>b</b>) Bureau Stroming and reused with the owner’s permission [<a href="#B41-water-16-00598" class="html-bibr">41</a>,<a href="#B45-water-16-00598" class="html-bibr">45</a>].</p>
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<p>Pictures shared internally and posted by the Anglers Association on their Facebook account and to create allies for a more fish-friendly river and to visualise a lack of focus on fish ecology around the Grensmaas. The photo on the left (<b>a</b>) was taken by Henk Houben and reused with the permission of the owner David Vertegaal, Sportvisserij Nederland. The photo on the right (<b>b</b>) was taken by Thijs Belgers and reused with the owner’s permission [<a href="#B49-water-16-00598" class="html-bibr">49</a>].</p>
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<p>Visualisation of a living river with thriving underwater life in the new living river vision of the World Wildlife Foundation. This vision includes fish as an important indicator of life and indicates that, in addition to fish, macro- and microfauna in the river exist and are considered to be valuable for the underwater reserves. The rewilding image is extended from only having trees on land in the original plans (<a href="#water-16-00598-f001" class="html-fig">Figure 1</a>) to including underwater wildlife in this image. The illustration includes tekst in Dutch arguing why river wood is crucial for underwater life and was made by Jeroen Helmer, ARK Rewilding Nederland and reused with the owner’s permission [<a href="#B51-water-16-00598" class="html-bibr">51</a>].</p>
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<p>Timeline of visions, studies, and events that shaped an eco-modern river imaginary.</p>
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<p>Illustration of the dynamics of the river imaginaries over time, highlighting important events in the Border Meuse. In the floods of 1993 and 1995, three imaginaries moved toward each other and started negotiating toward an eco-modern river imaginary. Yet, during this time, the fish-friendly imaginary split from the eco-centric imaginary. After the 2021 floods, the eco-centric imaginary split off from the modern and market-driven imaginaries.</p>
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26 pages, 6287 KiB  
Article
Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan
by Nuaman Ejaz, Aftab Haider Khan, Muhammad Shahid, Kifayat Zaman, Khaled S. Balkhair, Khalid Mohammed Alghamdi, Khalil Ur Rahman and Songhao Shang
Water 2024, 16(4), 597; https://doi.org/10.3390/w16040597 - 17 Feb 2024
Viewed by 1770
Abstract
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of [...] Read more.
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs. Full article
(This article belongs to the Section Hydrology)
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<p>(<b>a</b>) Topographical elevation in Pakistan based on the Shuttle Radar Topography Model (SRTM), (<b>b</b>) classification of Pakistan into four climatic zones showing meteorological stations and their serial numbers in each zone (GMS, HMS, AMS, and HAMS represent meteorological stations in glacial, humid, arid, and hyper-arid zones, respectively), and (<b>c</b>) mean annual precipitation variation across Pakistan.</p>
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<p>Spatial distributions of temporally averaged DCBA weights for the four merging members during 2000–2015.</p>
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<p>Spatial distributions of RPCA weights for the four merging members during 2000–2015.</p>
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<p>Spatial distribution maps in the glacial zone: (<b>a</b>) MAE, (<b>b</b>) RMSE for the DCBA (<b>left</b> side); (<b>a</b>) MAE, (<b>b</b>) RMSE for the RCPA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the glacial zone: (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the DCBA (<b>left</b> side); (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the RPCA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the humid zone: (<b>a</b>) MAE, (<b>b</b>) RMSE for the DCBA (<b>left</b> side); (<b>a</b>) MAE, (<b>b</b>) RMSE for the RCPA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the humid zone: (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the DCBA (<b>left</b> side); (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the RPCA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the arid zone: (<b>a</b>) MAE, (<b>b</b>) RMSE for the DCBA (<b>left</b> side); (<b>a</b>) MAE, (<b>b</b>) RMSE for the RCPA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the arid zone: (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the DCBA (<b>left</b> side); (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the RPCA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the hyper-arid zone: (<b>a</b>) MAE, (<b>b</b>) RMSE for the DCBA (<b>left</b> side); (<b>a</b>) MAE, (<b>b</b>) RMSE for the RCPA (<b>right</b> side).</p>
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<p>Spatial distribution maps in the hyper-arid zone: (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the DCBA (<b>left</b> side); (<b>a</b>) CC, (<b>b</b>) SD, and (<b>c</b>) Theil’s U for the RPCA (<b>right</b> side).</p>
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17 pages, 4457 KiB  
Article
Environmental Impact Analysis and Carbon Emission Reduction Pathways by Upgrading Wastewater Treatment Plant: A Case Study of Upgrading Project at a Wastewater Treatment Plant in Dongguan, China
by Yunxia Lu, Hao An, Chao Li and Changmin Liu
Water 2024, 16(4), 596; https://doi.org/10.3390/w16040596 - 17 Feb 2024
Cited by 1 | Viewed by 2514
Abstract
The potential environmental impact and increased operational costs associated with the upgrading and renovation of sewage treatment plants are acknowledged. This study employs the upgrading and expansion project of a municipal sewage plant in Dongguan City, Guangdong Province, as a case study. Utilizing [...] Read more.
The potential environmental impact and increased operational costs associated with the upgrading and renovation of sewage treatment plants are acknowledged. This study employs the upgrading and expansion project of a municipal sewage plant in Dongguan City, Guangdong Province, as a case study. Utilizing the principles and methods of the Life Cycle Assessment (LCA), a comprehensive assessment of the environmental benefits during the upgrading and renovation process of the sewage treatment plant, is conducted and targeted solutions are proposed. The research findings indicate that upgrading and renovating sewage treatment plants can significantly augment the adverse environmental effects of such facilities. Therefore, this study strategically proposes measures such as the utilization of clean energy, sludge resource utilization, and recycled water use as carbon emission reduction pathways. Through calculations, it is demonstrated that the utilization of clean energy and sludge resource can respectively reduce electricity consumption by 12.41% and 59.06%. Concurrently, recycled water use can lead to a reduction of 68.65% in carbon emissions, thereby markedly enhancing positive environmental outcomes. Full article
(This article belongs to the Special Issue Control and Treatment of Emerging Contaminants in Water Ecosystems)
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<p>Environmental impact classification of wastewater treatment.</p>
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<p>Weight distribution for different environmental impacts.</p>
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<p>Process flow chart of the assessed plant: (<b>a</b>) Phase I and Phase I-upgrading, (<b>b</b>) Phase II, (<b>c</b>) Phase III.</p>
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<p>Annual evaluation monitoring results for water quality indicators (2018–2022): (<b>a</b>) BOD<sub>5</sub>, (<b>b</b>) COD, (<b>c</b>) NH<sub>3</sub>-N, (<b>d</b>) SS, (<b>e</b>) TN, (<b>f</b>) TP.</p>
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<p>Proportion of the environmental impacts in different phases: (<b>a</b>) Phase I, (<b>b</b>) Phase I-upgrading, (<b>c</b>) Phase II, (<b>d</b>) Phase III. P1: Globe Warming Potential, P2: Acidification Potential, P3: Landfill Space Consumption, P4: Eutrophication Potential, P5: Water Quality Impact, P6: Human Health Impact, P7: Other Impact, P8: non-Renewable Resource Consumption, P9: Renewable Resource Consumption.</p>
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<p>The normalized results of the environmental impacts in different phases: (<b>a</b>) Phase I, (<b>b</b>) Phase I-upgrading, (<b>c</b>) Phase II, (<b>d</b>) Phase III. P1: Globe warming potential, P2: acidification potential, P3: landfill space consumption, P4: eutrophication potential, P5: water quality impact, P6: human health impact, P7: other impact, P8: non-renewable resource consumption, P9: renewable resource consumption.</p>
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<p>Comparison of the environmental impacts between the characterization and normalization results: (<b>a</b>) Phase I, (<b>b</b>) Phase I-upgrading, (<b>c</b>) Phase II, (<b>d</b>) Phase III.</p>
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<p>Comparison of quantitative environmental impact results at different stages.</p>
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17 pages, 7506 KiB  
Article
Study on the Hydrodynamic Performance of Swing-Type Flapping Hydrofoil Bionic Pumps Affected by Foil Camber
by Qizong Sun, Ertian Hua, Liying Sun, Linfeng Qiu, Yabo Song and Mingwang Xiang
Water 2024, 16(4), 595; https://doi.org/10.3390/w16040595 - 17 Feb 2024
Viewed by 1113
Abstract
The flapping hydrofoil bionic pump is an innovative hydrodynamic device that utilizes flapping hydrofoil technology. Flapping hydrofoil bionic pumps are crucial in addressing issues like inadequate river hydropower and limited water purification capabilities in flat river network regions. Optimizing the foil characteristics is [...] Read more.
The flapping hydrofoil bionic pump is an innovative hydrodynamic device that utilizes flapping hydrofoil technology. Flapping hydrofoil bionic pumps are crucial in addressing issues like inadequate river hydropower and limited water purification capabilities in flat river network regions. Optimizing the foil characteristics is essential for enhancing the hydrodynamic efficiency of the flapping hydrofoil bionic pump. This study investigates the impact of foil camber parameters on the hydrodynamic performance of swing-type asymmetric flapping bionic pumps. The NACA series standard foils with varying cambers are analyzed using the overlapping grid technology and finite volume method. The thrust coefficient, flow rate, pumping efficiency, and flow field structure of the flapping hydrofoil bionic pump are examined under pressure inlet conditions with the foil camber. The findings indicate that increasing the foil’s curvature within a specific range can greatly enhance the maximum values of thrust coefficient, propulsive efficiency, and pumping efficiency of the flapping hydrofoil bionic pump. Specifically, when the foil curvature is 6%c, the maximum value of the instantaneous thrust coefficient of the flapping hydrofoil bionic pump is significantly improved by 31.25% compared to the symmetric foil type under the condition of an oscillating frequency of f = 1 HZ. The flapping hydrofoil bionic pump achieves its maximum pumping efficiency when the oscillation frequency is within the range of f ≤ 2.5 Hz. This efficiency is 11.7% greater than that of the symmetric foil, and it occurs when the foil curvature is 8%c. Within the frequency range of f > 2.5 Hz, the flapping hydrofoil bionic pump that has a foil curvature of 6%c exhibits the highest enhancement in pumping efficiency. It achieves a maximum increase of 12.8% compared to the symmetric foil type. Nevertheless, the average head was less than 0.4 m, making it suitable for ultra-low-head applications. Full article
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<p>Schematic of 2-degree-of-freedom flapping hydrofoil motion.</p>
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<p>Schematic diagram of two-dimensional rigid biomimetic flapping hydrofoil mechanical model.</p>
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<p>Profile of foil and its main parameters.</p>
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<p>Foils with different cambers.</p>
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<p>Computational domain mesh and grid division.</p>
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<p>Grid number independence verification.</p>
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<p>Time step independence verification.</p>
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<p>Comparison between the numerical simulation outcomes and the experimental data in the previous study [<a href="#B32-water-16-00595" class="html-bibr">32</a>].</p>
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<p>The time-varying curves of the instantaneous thrust coefficient for flapping hydrofoils with different foil curvatures.</p>
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<p>The time-varying curves of the instantaneous lift coefficient for flapping hydrofoils with different foil cambers.</p>
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<p>Foils with varying curvatures experience pressure clouds at the moment when the thrust coefficient reaches its maximum value: (<b>a</b>) NACA0012; (<b>b</b>) NACA4412; (<b>c</b>) NACA8412.</p>
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<p>Vorticity maps at different times in one period for hydrofoils with different foil curvatures.</p>
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<p>Velocity clouds of flapping hydrofoils with different foil curvatures at the same moment of flapping: (<b>a</b>) NACA0012; (<b>b</b>) NACA 2412; (<b>c</b>) NACA 4412; (<b>d</b>) NACA 6412; (<b>e</b>) NACA 8412.</p>
Full article ">Figure 13 Cont.
<p>Velocity clouds of flapping hydrofoils with different foil curvatures at the same moment of flapping: (<b>a</b>) NACA0012; (<b>b</b>) NACA 2412; (<b>c</b>) NACA 4412; (<b>d</b>) NACA 6412; (<b>e</b>) NACA 8412.</p>
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<p>Curves of mean flow velocity and mean flow rate with varying frequency for flapping hydrofoils with different foil curvatures: (<b>a</b>) average flow velocity; (<b>b</b>) average flow rate.</p>
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<p>Curve of mean head versus frequency for flapping hydrofoils with different foil curvatures.</p>
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<p>Propulsive efficiency and pumping efficiency versus frequency curves for flapping hydrofoils with different curvatures: (<b>a</b>) propulsion efficiency; (<b>b</b>) pumping efficiency.</p>
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16 pages, 6673 KiB  
Article
Correlation between Turbidity and Inherent Optical Properties as an Initial Recognition for Backscattering Coefficient Estimation
by Kamila Haule, Maria Kubacka, Henryk Toczek, Barbara Lednicka, Bogusław Pranszke and Włodzimierz Freda
Water 2024, 16(4), 594; https://doi.org/10.3390/w16040594 - 17 Feb 2024
Viewed by 1450
Abstract
Seawater turbidity is a common water quality indicator measured in situ and estimated from space on a regular basis. However, it is rarely correlated with the inherent optical properties of seawater, which convey information about seawater composition. In this study, we show a [...] Read more.
Seawater turbidity is a common water quality indicator measured in situ and estimated from space on a regular basis. However, it is rarely correlated with the inherent optical properties of seawater, which convey information about seawater composition. In this study, we show a simple application of the turbidimeter’s weighting function in the estimation of the backscattering coefficient of a model inorganic suspension in seawater. First, we introduce a method to measure the instrument’s weighting function which describes the sensor’s angular response in terms of scattering angles. The determination of the sensor-specific weighting function led us to characterize its angular sensitivity to the presence of suspended particles. The highest sensitivity for the Seapoint turbidimeter is in the range of 114°–128° (containing 25% of the total signal). Next, we describe the correlations between turbidity and the scattering and backscattering coefficients on the example of the model of inorganic particle suspension using the calculations based on Mie theory. The correlations are analyzed for narrow size fractions of the particle size distribution of silica in the range of 0.59–190 µm. We established that there is a good linear correlation (characterized by the coefficient of determination r2 = 0.979) between the part of the scattering coefficient measured by the turbidimeter and the backscattering coefficient of all size fractions of the model inorganic suspension. Full article
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<p>The design of the test stand for measuring the angular weighting function of the turbidimeter.</p>
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<p>The spatial distribution of turbidity caused by light scattering from the rod placed in front of the turbidimeter in MilliQ water.</p>
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<p>Visualization of contribution to the turbidimeter signal from different scattering angles <span class="html-italic">θ</span>.</p>
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<p>The angular weighting function <span class="html-italic">γ</span>(<span class="html-italic">θ</span>) of the Seapoint turbidimeter.</p>
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<p>The simulated results of (<b>a</b>) the scattering coefficient <span class="html-italic">b<sub>p</sub></span>, (<b>b</b>) the backscattering coefficient <span class="html-italic">b<sub>bp</sub></span> and (<b>c</b>) the ratio <span class="html-italic">B<sub>p</sub> = b<sub>bp</sub>/b<sub>p</sub></span> for 22 size fractions of the model inorganic suspension. Graph (<b>d</b>) shows the values of <span class="html-italic">b<sub>turb</sub></span> calculated using the turbidimeter’s weighting function for the same size fractions.</p>
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<p>The volume scattering functions of the model suspension of silica spherical particles for selected size fractions.</p>
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<p>The ratio of the calculated <span class="html-italic">b<sub>turb</sub></span> of the turbidimeter to the scattering coefficient <span class="html-italic">b<sub>p</sub></span> for each of the 22 size fractions of the model particle suspension (<b>a</b>) and linear correlation of <span class="html-italic">b<sub>p</sub></span> to <span class="html-italic">b<sub>turb</sub></span> (<b>b</b>).</p>
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<p>The ratio of the calculated <span class="html-italic">b<sub>turb</sub></span> of the turbidimeter to the backscattering coefficient <span class="html-italic">b<sub>bp</sub></span> for each of the 22 size fractions of the model particle suspension (<b>a</b>) and linear correlation of <span class="html-italic">b<sub>bp</sub></span>-to-<span class="html-italic">b<sub>turb</sub></span> (<b>b</b>). The correlation between <span class="html-italic">b<sub>bp</sub></span> and <span class="html-italic">b<sub>turb</sub></span> for fraction numbers 1–12 (<b>c</b>) as well as for fraction numbers 13–22 (<b>d</b>).</p>
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<p>The ratio of the calculated <span class="html-italic">b<sub>turb</sub></span> of the turbidimeter to the backscattering ratio <span class="html-italic">B<sub>p</sub></span> for each of the 22 size fractions of the model particle suspension (<b>a</b>) and linear correlation of <span class="html-italic">B<sub>p</sub></span> to <span class="html-italic">b<sub>turb</sub></span> (<b>b</b>).</p>
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12 pages, 1731 KiB  
Article
Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months
by Haibo Chu, Zhuoqi Wang and Chong Nie
Water 2024, 16(4), 593; https://doi.org/10.3390/w16040593 - 17 Feb 2024
Cited by 3 | Viewed by 1211
Abstract
Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection [...] Read more.
Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management. Full article
(This article belongs to the Section Hydrology)
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<p>The study area and the map of hydrological and meteorological stations.</p>
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<p>ACF and PACF plots of Jimai, Maqu and Tangnaihai.</p>
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<p>Time series of MLR, RBFNN, RNN, and LSTM predicted results at different stations.</p>
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<p>Taylor diagram obtained by the time series of the simulated values of the eight models in the validation period of the three stations.</p>
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