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Ozonation as Pretreatment of Digested Swine Manure Prior to Microalgae Culture
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Nutrient Recovery via Struvite Precipitation from Wastewater Treatment Plants: Influence of Operating Parameters, Coexisting Ions, and Seeding
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Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis
Journal Description
Water
Water
is a peer-reviewed, open access journal on water science and technology, including the ecology and management of water resources, and is published semimonthly online by MDPI. Water collaborates with the Stockholm International Water Institute (SIWI). In addition, the American Institute of Hydrology (AIH), The Polish Limnological Society (PLS) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Water and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, PubAg, AGRIS, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Water Science and Technology)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.5 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Water include: GeoHazards and Hydrobiology.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.3 (2023)
Latest Articles
A Three–Year Comparison of Fluctuations in the Occurrence of the Giant Jellyfish (Nemopilema nomurai)
Water 2024, 16(16), 2265; https://doi.org/10.3390/w16162265 (registering DOI) - 11 Aug 2024
Abstract
In this study, acoustic, sighting, trawl, and marine environmental surveys were used to determine the vertical distribution and density of giant jellyfish that have been observed in Korean waters over the past 3 years. From 2020 to 2022, annual surveys were conducted in
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In this study, acoustic, sighting, trawl, and marine environmental surveys were used to determine the vertical distribution and density of giant jellyfish that have been observed in Korean waters over the past 3 years. From 2020 to 2022, annual surveys were conducted in May and July in the East China Sea and waters adjacent to South Korea. The acoustic data were processed by identifying and eliminating all signals considered as noise while excluding those suspected to be jellyfish signals. Subsequently, a single target detection method was employed. Giant jellyfish are distributed mostly in the middle and low layers. In May 2021, the average population density of giant jellyfish was recorded as 11.6 (ind./ha), which was the highest density. In July 2022, this value decreased to 1.7 (ind./ha), marking the lowest density. The sighting survey, which allows for the identification of jellyfish distributed in the surface layer, exhibited a difference of approximately 0.13 times compared to the acoustic survey conducted in the middle and low layers in 2020. In 2021 and 2022, this difference was approximately 0.11 times and 0.24 times, respectively. The average of this difference was 0.16 times or greater.
Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
Open AccessArticle
2D-URANS Study on the Impact of Relative Diameter on the Flow and Drag Characteristics of Circular Cylinder Arrays
by
Mengyang Liu, Yisen Wang, Yiqing Gong and Shuxia Wang
Water 2024, 16(16), 2264; https://doi.org/10.3390/w16162264 (registering DOI) - 11 Aug 2024
Abstract
The flow structure around limited-size vegetation patches is crucial for understanding sediment transport and vegetation succession trends. While the influence of vegetation density has been extensively explored, the impact of the relative diameter of vegetation stems remains relatively unclear. After validating the reliability
[...] Read more.
The flow structure around limited-size vegetation patches is crucial for understanding sediment transport and vegetation succession trends. While the influence of vegetation density has been extensively explored, the impact of the relative diameter of vegetation stems remains relatively unclear. After validating the reliability of the numerical model with experimental data, this study conducted 2D-URANS simulations (SST k-ω) to investigate the impact of varying relative diameters d/D under different vegetation densities λ on the hydrodynamic characteristics and drag force of vegetation patches. The results show that increasing d/D and decreasing λ are equivalent, both contributing to increased spacing between cylinder elements, allowing for the formation of element-scale Kármán vortices. Compared to vegetation density λ, the non-dimensional frontal area aD is a better predictor for the presence of array-scale Kármán vortex streets. Within the parameter range covered in this study, array-scale Kármán vortex streets appear when aD ≥ 1.4, which will significantly alter sediment transport patterns. For the same vegetation density, increasing the relative diameter d/D leads to a decrease in the array drag coefficient and an increase in the average element drag coefficient . When parameterizing vegetation resistance using aD, all data points collapse onto a single curve, following the relationships and .
Full article
(This article belongs to the Special Issue The Safety Operations and Intelligent Control of Water Network Engineering Systems)
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Show Figures
![](https://pub.mdpi-res.com/water/water-16-02264/article_deploy/html/images/water-16-02264-g001-550.jpg?1723377312)
Figure 1
Figure 1
<p>Schematic of the computational domain (not to scale).</p> Full article ">Figure 2
<p>Computational grid of the numerical domain: (<b>a</b>) local view; (<b>b</b>) global view.</p> Full article ">Figure 3
<p>Comparison of numerical results with experimental measurements for <span class="html-italic">λ</span> = 0.03 (<span class="html-italic">aD</span> = 1.32): (<b>a</b>) longitudinal time-averaged velocity along the <span class="html-italic">y</span> = 0 line; (<b>b</b>) transverse time-averaged velocity along the <span class="html-italic">y</span> = 0.5<span class="html-italic">D</span> line. The shaded area indicates the location of the vegetation patch.</p> Full article ">Figure 4
<p>Contour plots of non-dimensional time-averaged longitudinal flow velocity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 5
<p>Contour plots of non-dimensional time-averaged transverse flow velocity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 6
<p>Contour plots of near-field non-dimensional turbulent kinetic energy (left) and non-dimensional instantaneous vertical vorticity (right): (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 7
<p>Contour plots of far-field non-dimensional turbulent kinetic energy: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 8
<p>Contour plots of far-field instantaneous non-dimensional vertical vorticity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.036; (<b>b</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>c</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.085; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>f</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>g</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>h</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.041; (<b>i</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>j</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.064.</p> Full article ">Figure 9
<p>Dependence of flow rate through the vegetation patch on (<b>a</b>) vegetation density <span class="html-italic">λ</span> and (<b>b</b>) non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">Figure 10
<p>Dependence of (<b>a</b>) array drag coefficient and (<b>b</b>) average cylinder element drag coefficient on vegetation density.</p> Full article ">Figure 11
<p>Dependence of (<b>a</b>) array drag coefficient and (<b>b</b>) average cylinder element drag coefficient on non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">Figure 12
<p>Longitudinal distribution along the array centerline of (<b>a</b>) time-averaged longitudinal velocity and (<b>b</b>) turbulent kinetic energy. The shaded area indicates the location of the vegetation patch.</p> Full article ">Figure 13
<p>Dependence of (<b>a</b>) bleeding flow velocity, (<b>b</b>) velocity in the steady wake region, and (<b>c</b>) length of the steady wake region on non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">
<p>Schematic of the computational domain (not to scale).</p> Full article ">Figure 2
<p>Computational grid of the numerical domain: (<b>a</b>) local view; (<b>b</b>) global view.</p> Full article ">Figure 3
<p>Comparison of numerical results with experimental measurements for <span class="html-italic">λ</span> = 0.03 (<span class="html-italic">aD</span> = 1.32): (<b>a</b>) longitudinal time-averaged velocity along the <span class="html-italic">y</span> = 0 line; (<b>b</b>) transverse time-averaged velocity along the <span class="html-italic">y</span> = 0.5<span class="html-italic">D</span> line. The shaded area indicates the location of the vegetation patch.</p> Full article ">Figure 4
<p>Contour plots of non-dimensional time-averaged longitudinal flow velocity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 5
<p>Contour plots of non-dimensional time-averaged transverse flow velocity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 6
<p>Contour plots of near-field non-dimensional turbulent kinetic energy (left) and non-dimensional instantaneous vertical vorticity (right): (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 7
<p>Contour plots of far-field non-dimensional turbulent kinetic energy: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>b</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>c</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>f</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05.</p> Full article ">Figure 8
<p>Contour plots of far-field instantaneous non-dimensional vertical vorticity: (<b>a</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.036; (<b>b</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>c</b>) <span class="html-italic">λ</span> = 0.05 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.085; (<b>d</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.039; (<b>e</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>f</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.07; (<b>g</b>) <span class="html-italic">λ</span> = 0.097 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.118; (<b>h</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.041; (<b>i</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.05; (<b>j</b>) <span class="html-italic">λ</span> = 0.16 <span class="html-italic">d</span>/<span class="html-italic">D</span> = 0.064.</p> Full article ">Figure 9
<p>Dependence of flow rate through the vegetation patch on (<b>a</b>) vegetation density <span class="html-italic">λ</span> and (<b>b</b>) non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">Figure 10
<p>Dependence of (<b>a</b>) array drag coefficient and (<b>b</b>) average cylinder element drag coefficient on vegetation density.</p> Full article ">Figure 11
<p>Dependence of (<b>a</b>) array drag coefficient and (<b>b</b>) average cylinder element drag coefficient on non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">Figure 12
<p>Longitudinal distribution along the array centerline of (<b>a</b>) time-averaged longitudinal velocity and (<b>b</b>) turbulent kinetic energy. The shaded area indicates the location of the vegetation patch.</p> Full article ">Figure 13
<p>Dependence of (<b>a</b>) bleeding flow velocity, (<b>b</b>) velocity in the steady wake region, and (<b>c</b>) length of the steady wake region on non-dimensional frontal area <span class="html-italic">aD</span>.</p> Full article ">
Open AccessArticle
Towards Non-Region Specific Large-Scale Inundation Modelling with Machine Learning Methods
by
Lachlan Tychsen-Smith, Mohammad Ali Armin and Fazlul Karim
Water 2024, 16(16), 2263; https://doi.org/10.3390/w16162263 (registering DOI) - 11 Aug 2024
Abstract
►▼
Show Figures
Traditional flood inundation modelling methods are computationally expensive and not suitable for near-real time inundation prediction. In this study we explore a data-driven machine learning method to complement and, in some cases, replace existing methods. Given sufficient training data and model capacity, our
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Traditional flood inundation modelling methods are computationally expensive and not suitable for near-real time inundation prediction. In this study we explore a data-driven machine learning method to complement and, in some cases, replace existing methods. Given sufficient training data and model capacity, our design enables a single neural network instance to approximate the flow characteristics of any input region, opening the possibility of applying the model to regions without available training data. To demonstrate the method we apply it to a very large >8000 km2 region of the Fitzroy river basin in Western Australia with a spatial resolution of 30 m × 30 m, placing an emphasis on efficiency and scalability. In this work we identify and address a range of practical limitations, e.g., we develop a novel water height regression method and cost function to address extreme class imbalances and by carefully constructing the input data, we introduce some natural physical constraints. Furthermore, a compact neural network design and training method was developed to enable the training problem to fit within GPU memory constraints and a novel dataset was constructed from the output of a calibrated two-dimensional hydrodynamic model. A good correlation between the predicted and groundtruth water heights was observed.
Full article
![](https://pub.mdpi-res.com/water/water-16-02263/article_deploy/html/images/water-16-02263-g001-550.jpg?1723367768)
Figure 1
Figure 1
<p>Proposed ML inundation estimation system. Recursion is used to propagate forward through time.</p> Full article ">Figure 2
<p>Driving inflows for Baseline scenario (<b>top left</b>), Dry scenario (<b>top right</b>), Wet scenario (<b>bottom left</b>) and Topographic DEM data (<b>bottom right</b>) for modelling region. DG and MK markers provide location of Dimond Gorge and Mount Krauss inflows. Dashed line indicates when simulation starts after river fill initialization. Two large inflows are present around the 12 and 24 day marks.</p> Full article ">Figure 3
<p>Groundtruth water height and Inflow model prediction for 7 day baseline sequence (sampled daily) from the Mt. Krauss inflow region. Red indicates a water height <math display="inline"><semantics> <mrow> <mo>≥</mo> <mn>15</mn> </mrow> </semantics></math> m, blue is a water height of 0 m. Large flood event occurs around day 2 then gradually declines.</p> Full article ">Figure 4
<p>Time evolution of root mean squared error (<b>left</b>) and mean absolute error (<b>right</b>) for inundation models trained from scratch with 1 Hr, 6 Hr, 12 Hr and 24 Hr time-steps. In general, longer time-steps produced improved modelling performance.</p> Full article ">Figure 5
<p>Groundtruth water height (<b>above</b>) and 24 h Interior model prediction (<b>below</b>) for 7 day Baseline test sequence. Red indicates a water height <math display="inline"><semantics> <mrow> <mo>≥</mo> <mn>5</mn> </mrow> </semantics></math> m, blue is a water height of 0 m. A large flood event occurs around Day 1–2 then gradually declines.</p> Full article ">
<p>Proposed ML inundation estimation system. Recursion is used to propagate forward through time.</p> Full article ">Figure 2
<p>Driving inflows for Baseline scenario (<b>top left</b>), Dry scenario (<b>top right</b>), Wet scenario (<b>bottom left</b>) and Topographic DEM data (<b>bottom right</b>) for modelling region. DG and MK markers provide location of Dimond Gorge and Mount Krauss inflows. Dashed line indicates when simulation starts after river fill initialization. Two large inflows are present around the 12 and 24 day marks.</p> Full article ">Figure 3
<p>Groundtruth water height and Inflow model prediction for 7 day baseline sequence (sampled daily) from the Mt. Krauss inflow region. Red indicates a water height <math display="inline"><semantics> <mrow> <mo>≥</mo> <mn>15</mn> </mrow> </semantics></math> m, blue is a water height of 0 m. Large flood event occurs around day 2 then gradually declines.</p> Full article ">Figure 4
<p>Time evolution of root mean squared error (<b>left</b>) and mean absolute error (<b>right</b>) for inundation models trained from scratch with 1 Hr, 6 Hr, 12 Hr and 24 Hr time-steps. In general, longer time-steps produced improved modelling performance.</p> Full article ">Figure 5
<p>Groundtruth water height (<b>above</b>) and 24 h Interior model prediction (<b>below</b>) for 7 day Baseline test sequence. Red indicates a water height <math display="inline"><semantics> <mrow> <mo>≥</mo> <mn>5</mn> </mrow> </semantics></math> m, blue is a water height of 0 m. A large flood event occurs around Day 1–2 then gradually declines.</p> Full article ">
Open AccessArticle
Pore-Scale Formation Characteristics of Impermeable Frozen Walls for Shallow Groundwater Contamination Remediation
by
Yunfeng Zhang, Zhiqiang Zhao, Guantao Ding, Caiping Hu, Yuan Wang and Shuai Gao
Water 2024, 16(16), 2262; https://doi.org/10.3390/w16162262 (registering DOI) - 11 Aug 2024
Abstract
Impermeability and water blocking are crucial for remediating shallow groundwater contamination. Traditional methods often employ curtain-grouting technology to create impermeable layers. However, cement slurry curing is irreversible, leading to permanent closure of underground aquifers and secondary pollution. This study employs an innovative approach
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Impermeability and water blocking are crucial for remediating shallow groundwater contamination. Traditional methods often employ curtain-grouting technology to create impermeable layers. However, cement slurry curing is irreversible, leading to permanent closure of underground aquifers and secondary pollution. This study employs an innovative approach by fabricating cylindrical models that simulate actual strata and utilizing a high-temperature and high-pressure displacement device. It systematically analyzes the variations in soil pore structure, distribution, porosity, and permeability under different temperatures, pressures, and freezing durations. The microscopic characteristics of the freezing process in water-bearing soils were studied. Results demonstrate that longer freezing time improves the effectiveness of soil freezing, reaching complete freezing at temperatures as low as −4 °C for samples with low water content. For water-saturated samples, freezing below −6 °C results in nearly zero porosity. Increased pressure at a certain freezing temperature significantly reduces permeability. When freezing temperature falls below −4 °C, water permeability in saturated samples after freezing reaches near-zero levels, while unsaturated samples experience complete freezing. These findings provide a theoretical foundation for constructing freezing curtains in remediating shallow groundwater pollution.
Full article
(This article belongs to the Section Hydrogeology)
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![](https://pub.mdpi-res.com/water/water-16-02262/article_deploy/html/images/water-16-02262-g001-550.jpg?1723375557)
Figure 1
Figure 1
<p>Preparation process of the sandy soil specimen.</p> Full article ">Figure 2
<p>Part of the experimental operation process.</p> Full article ">Figure 3
<p>Schematic diagram of nuclear magnetic high-temperature and high-pressure repulsion device.</p> Full article ">Figure 4
<p>Changes in pore size and pore distribution during freezing of saturated samples.</p> Full article ">Figure 4 Cont.
<p>Changes in pore size and pore distribution during freezing of saturated samples.</p> Full article ">Figure 5
<p>Changes in the distribution of different pore sizes during the freezing of water-saturated samples.</p> Full article ">Figure 5 Cont.
<p>Changes in the distribution of different pore sizes during the freezing of water-saturated samples.</p> Full article ">Figure 6
<p>Relationship between freezing time and porosity change at different temperatures.</p> Full article ">Figure 7
<p>The sample permeability under pressure and temperature variations.</p> Full article ">Figure 8
<p>The sample porosity under pressure and temperature variations.</p> Full article ">Figure 9
<p>Changes in pore size and pore distribution during freezing of samples at 10 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 10
<p>Variation in different pore distributions during freezing of unsaturated samples at 10 °C.</p> Full article ">Figure 11
<p>Changes in pore size and pore distribution during freezing of samples at 0 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 12
<p>Variation in different pore distributions during freezing of unsaturated samples at 0 °C.</p> Full article ">Figure 13
<p>Changes in pore size and pore distribution during freezing of samples at −2 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 14
<p>Variation in different pore distributions during freezing of unsaturated samples at −2 °C.</p> Full article ">Figure 15
<p>Changes in pore size and pore distribution during freezing of samples at −4 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 16
<p>Variation in different pore distributions during freezing of unsaturated water samples at −4 °C.</p> Full article ">Figure 17
<p>Relationship between freezing time and change in porosity at different temperatures (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 18
<p>Permeability of samples under pressure and temperature changes (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 19
<p>Porosity of samples under pressure and temperature changes (25%, 50%, 75% water-saturated samples).</p> Full article ">
<p>Preparation process of the sandy soil specimen.</p> Full article ">Figure 2
<p>Part of the experimental operation process.</p> Full article ">Figure 3
<p>Schematic diagram of nuclear magnetic high-temperature and high-pressure repulsion device.</p> Full article ">Figure 4
<p>Changes in pore size and pore distribution during freezing of saturated samples.</p> Full article ">Figure 4 Cont.
<p>Changes in pore size and pore distribution during freezing of saturated samples.</p> Full article ">Figure 5
<p>Changes in the distribution of different pore sizes during the freezing of water-saturated samples.</p> Full article ">Figure 5 Cont.
<p>Changes in the distribution of different pore sizes during the freezing of water-saturated samples.</p> Full article ">Figure 6
<p>Relationship between freezing time and porosity change at different temperatures.</p> Full article ">Figure 7
<p>The sample permeability under pressure and temperature variations.</p> Full article ">Figure 8
<p>The sample porosity under pressure and temperature variations.</p> Full article ">Figure 9
<p>Changes in pore size and pore distribution during freezing of samples at 10 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 10
<p>Variation in different pore distributions during freezing of unsaturated samples at 10 °C.</p> Full article ">Figure 11
<p>Changes in pore size and pore distribution during freezing of samples at 0 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 12
<p>Variation in different pore distributions during freezing of unsaturated samples at 0 °C.</p> Full article ">Figure 13
<p>Changes in pore size and pore distribution during freezing of samples at −2 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 14
<p>Variation in different pore distributions during freezing of unsaturated samples at −2 °C.</p> Full article ">Figure 15
<p>Changes in pore size and pore distribution during freezing of samples at −4 °C (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 16
<p>Variation in different pore distributions during freezing of unsaturated water samples at −4 °C.</p> Full article ">Figure 17
<p>Relationship between freezing time and change in porosity at different temperatures (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 18
<p>Permeability of samples under pressure and temperature changes (25%, 50%, 75% water-saturated samples).</p> Full article ">Figure 19
<p>Porosity of samples under pressure and temperature changes (25%, 50%, 75% water-saturated samples).</p> Full article ">
Open AccessArticle
Effects of Fallen Posidonia Oceanica Seagrass Leaves on Wave Energy at Sandy Beaches
by
Ogan Sevim and Emre N. Otay
Water 2024, 16(16), 2261; https://doi.org/10.3390/w16162261 (registering DOI) - 11 Aug 2024
Abstract
Posidonia Oceanica (PO) is an endemic marine plant in the Mediterranean Sea. In an experimental study conducted in the Eastern Mediterranean, the effects of natural PO leaves on reducing the height of incident waves impacting a beach were measured. The transmission coefficient (
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Posidonia Oceanica (PO) is an endemic marine plant in the Mediterranean Sea. In an experimental study conducted in the Eastern Mediterranean, the effects of natural PO leaves on reducing the height of incident waves impacting a beach were measured. The transmission coefficient (Kt) was found to vary between 0.73 and 0.94, which is equivalent to a wave height decay of 6–27%. The results show that in their natural environment, free-floating dead PO leaves dissipate incoming wave energy and have the capacity to protect beaches against erosion. Further analysis in separate frequency bands showed that waves with periods between 4.5–6.2 s were more sensitive to PO leaves in terms of energy dissipation. The transmission coefficient for medium-period waves, calculated using the medium-frequency part of the wave spectrum, delivered a maximum transmission coefficient of 0.5, corresponding to a 50% decay in wave height due to PO leaves.
Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Ocean Engineering)
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![](https://pub.mdpi-res.com/water/water-16-02261/article_deploy/html/images/water-16-02261-g001-550.jpg?1723360446)
Figure 1
Figure 1
<p>(<b>a</b>) Free-floating dead PO leaves in a wave breaking at Fugla Beach (photo taken in May 2022). (<b>b</b>) Dead PO leaves deposited along Damlatas Beach (photo taken in February 2024).</p> Full article ">Figure 2
<p>Site location and measurement device locations.</p> Full article ">Figure 3
<p>Schematic of test setup.</p> Full article ">Figure 4
<p>(<b>a</b>) ADCP deployment at the sea bottom. (<b>b</b>) Test setup and device locations.</p> Full article ">Figure 5
<p>(<b>a</b>) Bathymetric plan and (<b>b</b>) cross-section of the testing zone.</p> Full article ">Figure 6
<p>Source of PO leaves and testing location.</p> Full article ">Figure 7
<p>Aerial image of the testing site after completion of the tests.</p> Full article ">Figure 8
<p>Measurement of wave periods using video frames of drone imaging. Red and blue lines indicate the crests of two consecutive waves.</p> Full article ">Figure 9
<p>Wave crest distortion due to PO leaves. Blue line indicates undistorted wave crest, whereas the arrow indicates the direction of the wave.</p> Full article ">Figure 10
<p>Comparison of wave spectra measured at three gauges during initial and final stages of PO release: (<b>a</b>) 12:09 PM; (<b>b</b>) 12:31 PM; (<b>c</b>) 15:09 PM; (<b>d</b>) 15:31 PM.</p> Full article ">Figure 11
<p>(<b>a</b>) Significant wave height and mean sea level variations at different times and stations. (<b>b</b>) Peak and mean wave period variations.</p> Full article ">Figure 12
<p>Transmission coefficients between gauges.</p> Full article ">Figure 13
<p>Transmission coefficients with respect to the amount of PO leaves within the test area.</p> Full article ">Figure 14
<p>Transmission coefficients of different wave periods: (<b>a</b>) long-period waves (6.2–9.8 s); (<b>b</b>) medium-period waves (4.5–6.2 s); (<b>c</b>) short-period waves (3.4–4.5 s).</p> Full article ">Figure 15
<p>Transmission coefficients between PG2 and PG1 for different wave periods.</p> Full article ">
<p>(<b>a</b>) Free-floating dead PO leaves in a wave breaking at Fugla Beach (photo taken in May 2022). (<b>b</b>) Dead PO leaves deposited along Damlatas Beach (photo taken in February 2024).</p> Full article ">Figure 2
<p>Site location and measurement device locations.</p> Full article ">Figure 3
<p>Schematic of test setup.</p> Full article ">Figure 4
<p>(<b>a</b>) ADCP deployment at the sea bottom. (<b>b</b>) Test setup and device locations.</p> Full article ">Figure 5
<p>(<b>a</b>) Bathymetric plan and (<b>b</b>) cross-section of the testing zone.</p> Full article ">Figure 6
<p>Source of PO leaves and testing location.</p> Full article ">Figure 7
<p>Aerial image of the testing site after completion of the tests.</p> Full article ">Figure 8
<p>Measurement of wave periods using video frames of drone imaging. Red and blue lines indicate the crests of two consecutive waves.</p> Full article ">Figure 9
<p>Wave crest distortion due to PO leaves. Blue line indicates undistorted wave crest, whereas the arrow indicates the direction of the wave.</p> Full article ">Figure 10
<p>Comparison of wave spectra measured at three gauges during initial and final stages of PO release: (<b>a</b>) 12:09 PM; (<b>b</b>) 12:31 PM; (<b>c</b>) 15:09 PM; (<b>d</b>) 15:31 PM.</p> Full article ">Figure 11
<p>(<b>a</b>) Significant wave height and mean sea level variations at different times and stations. (<b>b</b>) Peak and mean wave period variations.</p> Full article ">Figure 12
<p>Transmission coefficients between gauges.</p> Full article ">Figure 13
<p>Transmission coefficients with respect to the amount of PO leaves within the test area.</p> Full article ">Figure 14
<p>Transmission coefficients of different wave periods: (<b>a</b>) long-period waves (6.2–9.8 s); (<b>b</b>) medium-period waves (4.5–6.2 s); (<b>c</b>) short-period waves (3.4–4.5 s).</p> Full article ">Figure 15
<p>Transmission coefficients between PG2 and PG1 for different wave periods.</p> Full article ">
Open AccessCase Report
Engineering Regulation of the Weird Branches in a Branching Estuary and its Mechanics: Using the North Branch of the Yangtze Estuary as an Example
by
Dechao Hu, Zhanfeng Cui, Xin Zeng, Jianyin Zhou and Yuan Yuan
Water 2024, 16(16), 2260; https://doi.org/10.3390/w16162260 (registering DOI) - 11 Aug 2024
Abstract
Weird horizontal shapes of branches, in large branching estuaries, often cause significant flood risks and environment-related problems. People usually resort to engineering methods to improve the horizontal shape of the weird branches and solve related issues. The responses of the riverbed evolution of
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Weird horizontal shapes of branches, in large branching estuaries, often cause significant flood risks and environment-related problems. People usually resort to engineering methods to improve the horizontal shape of the weird branches and solve related issues. The responses of the riverbed evolution of a branching estuary to anthropogenic activity are complicated because of complex estuarine hydrodynamics and sediment transports, especially when the project locates specially (e.g., at estuary outlets). The North Branch of the Yangtze Estuary has a narrow upper reach which is almost orthogonal to the South Branch and has a trumpet-shaped lower reach with a wide outlet. The weird horizontal shape of the North Branch brings significant flood risks to cities along this branch, the shrinkage of its entrance, and other problems. In this study, a regulation of the North Branch, which is launched at Guyuan Sand (GYS) just outside the exit of the North Branch, is taken as an example. The GYS regulation aims to improve the weird horizontal shape of the North Branch by building new layouts of outlets, by which people decrease the flood risk of the surrounding cities. The GYS regulation is studied using a 2D numerical model. The riverbed evolution of the Yangtze Estuary in a typical hydrological year is simulated, while the water/sediment fluxes at cross-sections of branches in the estuary during a spring/neap tide are quantitatively calculated. It is found that the regulation changes the rotational flows near the shore, and further reshapes the estuarine circulations of mass inside the outlets, especially exchanges of water/sediment between different branches. The regulation directly changes the riverbed evolution at the outlet of the North Branch, and meanwhile has significant indirect influences on the riverbed evolution of the entrance of the North Branch. The varying riverbed evolution at the entrance of the North Branch and the varying water/sediment fluxes, under different designs of regulations, are related and analyzed. An essential improvement for the weird horizontal shape of the North Branch by an engineering method is shown to be possible, while the regulation mechanism of the engineering method and the response of estuarine riverbed evolution to the regulation are clarified. This study provides a new insight for improving estuarine branches with weird horizontal shapes, by reshaping the tidal processes and the accompanying sediment transports in a branching estuary.
Full article
(This article belongs to the Special Issue Coast Sediment Dynamics: Historical Development, Current Situation and Perspectives)
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![](https://pub.mdpi-res.com/water/water-16-02260/article_deploy/html/images/water-16-02260-g001-550.jpg?1723359553)
Figure 1
Figure 1
<p>Description of the open boundaries, three-level bifurcations, and river–coast–sea coupling in the Yangtze Estuary (the computational domain and the grid sample are also given).</p> Full article ">Figure 2
<p>The tidal-level process of the seaward boundaries of the Yangtze Estuary from 0:00 on 14 December to 0:00 on 16 December (e.g., OB 2 represents the open boundary of <a href="#sec2-water-16-02260" class="html-sec">Section 2</a>).</p> Full article ">Figure 3
<p>Partition of zones used for setting the parameters of the 2D model for the Yangtze River.</p> Full article ">Figure 4
<p>Arrangements of hydrology survey locations in the Yangtze Estuary.</p> Full article ">Figure 5
<p>The velocity fields of the Yangtze Estuary at selected times of calibration testing. (<b>a</b>) On 14 December 10:00 (flood-tide period). (<b>b</b>) On 14 December 11:00 (flood-tide period). (<b>c</b>) On 14 December 12:00 (flood-tide period).</p> Full article ">Figure 5 Cont.
<p>The velocity fields of the Yangtze Estuary at selected times of calibration testing. (<b>a</b>) On 14 December 10:00 (flood-tide period). (<b>b</b>) On 14 December 11:00 (flood-tide period). (<b>c</b>) On 14 December 12:00 (flood-tide period).</p> Full article ">Figure 6
<p>Comparisons of the simulated histories of tide levels with field data at gauges of water levels: (<b>a</b>) at Xuliujing (XLJ), (<b>b</b>) at Qinglonggang (QLG), (<b>c</b>) at Lianxingang (LZG), (<b>d</b>) at Nanmen (NM), (<b>e</b>) at Hengsha (HS).</p> Full article ">Figure 7
<p>Comparisons of the simulated histories of depth-averaged velocity with field data at the survey points (a negative value means the direction of the velocity is landward): (<b>a</b>) at B1, (<b>b</b>) at A1, (<b>c</b>) at A3, (<b>d</b>) at A5, (<b>e</b>) at B7.</p> Full article ">Figure 8
<p>The sediment concentration fields of the Yangtze Estuary at selected times of the calibration test. (<b>a</b>) On 14 December 13:00. (<b>b</b>) On 14 December 15:00. (<b>c</b>) On 14 December 17:00. (<b>d</b>) On 14 December 19:00.</p> Full article ">Figure 8 Cont.
<p>The sediment concentration fields of the Yangtze Estuary at selected times of the calibration test. (<b>a</b>) On 14 December 13:00. (<b>b</b>) On 14 December 15:00. (<b>c</b>) On 14 December 17:00. (<b>d</b>) On 14 December 19:00.</p> Full article ">Figure 9
<p>Comparisons of simulated histories of sediment concentration with field data at the survey points: (<b>a</b>) at B1, (<b>b</b>) at A1, (<b>c</b>) at A3, (<b>d</b>) at A5, (<b>e</b>) at B7.</p> Full article ">Figure 10
<p>Comparisons of the simulated riverbed evolutions and field data at cross-sections: (<b>a</b>) at CS2, (<b>b</b>) at CS5, (<b>c</b>) at CS7, (<b>d</b>) at CS9 (the locations of the cross-sections can be found in <a href="#water-16-02260-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 11
<p>Arrangements of dikes: (<b>a</b>) Design 0; (<b>b</b>) Design 1 (using two outlet channels); (<b>c</b>) Design 2 (using a southward outlet channel); (<b>d</b>) Design 3 (using a northward outlet channel).</p> Full article ">Figure 12
<p>Grid refinement around dikes of the GYS regulation.</p> Full article ">Figure 13
<p>Divisions of the domain for recording the riverbed deformation.</p> Full article ">Figure 14
<p>Distribution of riverbed deformation in the Yangtze Estuary after the action of typical-year flow-sediment processes. (+dz: deposition; −dz: erosion; Unit of dz: m). (<b>a</b>) Without the GYS regulation. (<b>b</b>) Under Design 3 of the GYS regulation.</p> Full article ">Figure 15
<p>Local morphological dynamics at the outlet of the N-Branch. (Unit of dz: m). (<b>a</b>) Design 0. (<b>b</b>) Design 1. (<b>c</b>) Design 2. (<b>d</b>) Design 3.</p> Full article ">Figure 16
<p>Local morphological dynamics at the entrance of the N-Branch. (Unit of dz: m.) (<b>a</b>) Design 0. (<b>b</b>) Design 1. (<b>c</b>) Design 2. (<b>d</b>) Design 3.</p> Full article ">Figure 17
<p>Velocity fields for simulations with different designs at the flood-ebb transition time.</p> Full article ">Figure 18
<p>Histories of simulated discharges (<span class="html-italic">Q<sub>W</sub></span>) and sediment transport rate (<span class="html-italic">Q<sub>S</sub></span>) at cross-sections in the Yangtze Estuary (spring tide): (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) <span class="html-italic">Q<sub>W</sub></span> processes; (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) <span class="html-italic">Q<sub>S</sub></span> processes.</p> Full article ">Figure 18 Cont.
<p>Histories of simulated discharges (<span class="html-italic">Q<sub>W</sub></span>) and sediment transport rate (<span class="html-italic">Q<sub>S</sub></span>) at cross-sections in the Yangtze Estuary (spring tide): (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) <span class="html-italic">Q<sub>W</sub></span> processes; (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) <span class="html-italic">Q<sub>S</sub></span> processes.</p> Full article ">
<p>Description of the open boundaries, three-level bifurcations, and river–coast–sea coupling in the Yangtze Estuary (the computational domain and the grid sample are also given).</p> Full article ">Figure 2
<p>The tidal-level process of the seaward boundaries of the Yangtze Estuary from 0:00 on 14 December to 0:00 on 16 December (e.g., OB 2 represents the open boundary of <a href="#sec2-water-16-02260" class="html-sec">Section 2</a>).</p> Full article ">Figure 3
<p>Partition of zones used for setting the parameters of the 2D model for the Yangtze River.</p> Full article ">Figure 4
<p>Arrangements of hydrology survey locations in the Yangtze Estuary.</p> Full article ">Figure 5
<p>The velocity fields of the Yangtze Estuary at selected times of calibration testing. (<b>a</b>) On 14 December 10:00 (flood-tide period). (<b>b</b>) On 14 December 11:00 (flood-tide period). (<b>c</b>) On 14 December 12:00 (flood-tide period).</p> Full article ">Figure 5 Cont.
<p>The velocity fields of the Yangtze Estuary at selected times of calibration testing. (<b>a</b>) On 14 December 10:00 (flood-tide period). (<b>b</b>) On 14 December 11:00 (flood-tide period). (<b>c</b>) On 14 December 12:00 (flood-tide period).</p> Full article ">Figure 6
<p>Comparisons of the simulated histories of tide levels with field data at gauges of water levels: (<b>a</b>) at Xuliujing (XLJ), (<b>b</b>) at Qinglonggang (QLG), (<b>c</b>) at Lianxingang (LZG), (<b>d</b>) at Nanmen (NM), (<b>e</b>) at Hengsha (HS).</p> Full article ">Figure 7
<p>Comparisons of the simulated histories of depth-averaged velocity with field data at the survey points (a negative value means the direction of the velocity is landward): (<b>a</b>) at B1, (<b>b</b>) at A1, (<b>c</b>) at A3, (<b>d</b>) at A5, (<b>e</b>) at B7.</p> Full article ">Figure 8
<p>The sediment concentration fields of the Yangtze Estuary at selected times of the calibration test. (<b>a</b>) On 14 December 13:00. (<b>b</b>) On 14 December 15:00. (<b>c</b>) On 14 December 17:00. (<b>d</b>) On 14 December 19:00.</p> Full article ">Figure 8 Cont.
<p>The sediment concentration fields of the Yangtze Estuary at selected times of the calibration test. (<b>a</b>) On 14 December 13:00. (<b>b</b>) On 14 December 15:00. (<b>c</b>) On 14 December 17:00. (<b>d</b>) On 14 December 19:00.</p> Full article ">Figure 9
<p>Comparisons of simulated histories of sediment concentration with field data at the survey points: (<b>a</b>) at B1, (<b>b</b>) at A1, (<b>c</b>) at A3, (<b>d</b>) at A5, (<b>e</b>) at B7.</p> Full article ">Figure 10
<p>Comparisons of the simulated riverbed evolutions and field data at cross-sections: (<b>a</b>) at CS2, (<b>b</b>) at CS5, (<b>c</b>) at CS7, (<b>d</b>) at CS9 (the locations of the cross-sections can be found in <a href="#water-16-02260-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 11
<p>Arrangements of dikes: (<b>a</b>) Design 0; (<b>b</b>) Design 1 (using two outlet channels); (<b>c</b>) Design 2 (using a southward outlet channel); (<b>d</b>) Design 3 (using a northward outlet channel).</p> Full article ">Figure 12
<p>Grid refinement around dikes of the GYS regulation.</p> Full article ">Figure 13
<p>Divisions of the domain for recording the riverbed deformation.</p> Full article ">Figure 14
<p>Distribution of riverbed deformation in the Yangtze Estuary after the action of typical-year flow-sediment processes. (+dz: deposition; −dz: erosion; Unit of dz: m). (<b>a</b>) Without the GYS regulation. (<b>b</b>) Under Design 3 of the GYS regulation.</p> Full article ">Figure 15
<p>Local morphological dynamics at the outlet of the N-Branch. (Unit of dz: m). (<b>a</b>) Design 0. (<b>b</b>) Design 1. (<b>c</b>) Design 2. (<b>d</b>) Design 3.</p> Full article ">Figure 16
<p>Local morphological dynamics at the entrance of the N-Branch. (Unit of dz: m.) (<b>a</b>) Design 0. (<b>b</b>) Design 1. (<b>c</b>) Design 2. (<b>d</b>) Design 3.</p> Full article ">Figure 17
<p>Velocity fields for simulations with different designs at the flood-ebb transition time.</p> Full article ">Figure 18
<p>Histories of simulated discharges (<span class="html-italic">Q<sub>W</sub></span>) and sediment transport rate (<span class="html-italic">Q<sub>S</sub></span>) at cross-sections in the Yangtze Estuary (spring tide): (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) <span class="html-italic">Q<sub>W</sub></span> processes; (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) <span class="html-italic">Q<sub>S</sub></span> processes.</p> Full article ">Figure 18 Cont.
<p>Histories of simulated discharges (<span class="html-italic">Q<sub>W</sub></span>) and sediment transport rate (<span class="html-italic">Q<sub>S</sub></span>) at cross-sections in the Yangtze Estuary (spring tide): (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) <span class="html-italic">Q<sub>W</sub></span> processes; (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) <span class="html-italic">Q<sub>S</sub></span> processes.</p> Full article ">
Open AccessArticle
Image Influence on Concern about Stormwater Flooding: Exploratory Focus Groups
by
Kristan Cockerill and Tanga Mohr
Water 2024, 16(16), 2259; https://doi.org/10.3390/w16162259 (registering DOI) - 11 Aug 2024
Abstract
Increased urbanization coupled with climate change is increasing the number and intensity of stormwater flooding events. Implementing efforts to successfully manage stormwater flooding depends on understanding how people perceive these events. While images of stormwater flooding abound, how these images influence perceptions about
[...] Read more.
Increased urbanization coupled with climate change is increasing the number and intensity of stormwater flooding events. Implementing efforts to successfully manage stormwater flooding depends on understanding how people perceive these events. While images of stormwater flooding abound, how these images influence perceptions about flooding events or management options remains understudied. Our objective is to contribute to the general understanding of how various types of images depicting stormwater runoff and stormwater related flooding influence individual and group interpretations of causes of events, major impacts of those events, and responsibility for managing stormwater related events. To this end, we convened focus groups, gave participants numerous photos of stormwater flooding, asked them to identify which images were most concerning, and to then discuss the specific aspects of the photos that prompted concern. We also tested whether a priming image implicating climate change or development as a cause of stormwater flooding influenced viewer reactions. Finally, we asked participants about preferences for who should manage stormwater. Our results revealed that photo location, the water’s appearance, and what people were doing in the photo influenced levels of concern. We also found that priming seems to affect opinions regarding urban stormwater management. Finally, there is some evidence that the absence of people in the photo may affect beliefs about who should manage stormwater.
Full article
(This article belongs to the Section Hydrology)
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![](https://pub.mdpi-res.com/water/water-16-02259/article_deploy/html/images/water-16-02259-g001-550.jpg?1723352853)
Figure 1
Figure 1
<p>The general project design.</p> Full article ">Figure 2
<p>Clockwise from upper left, photos N, P, U, S, from Boone, North Carolina represent the kinds of photos shown to focus group participants.</p> Full article ">Figure 3
<p>Photos and captions used to prime the focus groups. Photo sources: US Geological Survey (development) and US National Aeronautics and Space Administration (climate change).</p> Full article ">Figure 4
<p>Codes applied to focus group transcripts and used for analysis with total number of mentions included in parentheses.</p> Full article ">Figure 5
<p>Frequency of mentions in focus group discussions for each photo organized by climate change or development prime. Focus Group 3 mentioned all 12 photos in their scenario and is therefore excluded to provide a comparison.</p> Full article ">Figure 6
<p>(<b>a</b>) Proportion of participants who strongly agreed or agreed with statements about who should be responsible for managing stormwater, organized by the project scenarios 1, 2, and 3. (<b>b</b>) Proportion of participants who strongly agreed or agreed with statements about who should pay for stormwater management, organized by the project scenarios 1, 2, and 3.</p> Full article ">
<p>The general project design.</p> Full article ">Figure 2
<p>Clockwise from upper left, photos N, P, U, S, from Boone, North Carolina represent the kinds of photos shown to focus group participants.</p> Full article ">Figure 3
<p>Photos and captions used to prime the focus groups. Photo sources: US Geological Survey (development) and US National Aeronautics and Space Administration (climate change).</p> Full article ">Figure 4
<p>Codes applied to focus group transcripts and used for analysis with total number of mentions included in parentheses.</p> Full article ">Figure 5
<p>Frequency of mentions in focus group discussions for each photo organized by climate change or development prime. Focus Group 3 mentioned all 12 photos in their scenario and is therefore excluded to provide a comparison.</p> Full article ">Figure 6
<p>(<b>a</b>) Proportion of participants who strongly agreed or agreed with statements about who should be responsible for managing stormwater, organized by the project scenarios 1, 2, and 3. (<b>b</b>) Proportion of participants who strongly agreed or agreed with statements about who should pay for stormwater management, organized by the project scenarios 1, 2, and 3.</p> Full article ">
Open AccessArticle
A Risk Assessment of the Vegetation Ecological Degradation in Hunshandake Sandy Land, China: A Case Study of Dabusennur Watershed
by
Peng Chen, Rong Ma, Letian Si, Lefan Zhao, Ruirui Jiang and Wanggang Dong
Water 2024, 16(16), 2258; https://doi.org/10.3390/w16162258 (registering DOI) - 10 Aug 2024
Abstract
In the context of climate change, it is essential for sustainable development to assess the risks associated with climate change and human-induced vegetation degradation. The Hunshandake Sandy Land provides a variety of ecosystem services and is a substantial ecological security barrier in the
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In the context of climate change, it is essential for sustainable development to assess the risks associated with climate change and human-induced vegetation degradation. The Hunshandake Sandy Land provides a variety of ecosystem services and is a substantial ecological security barrier in the Beijing–Tianjin–Hebei area of China. This study used the Normalized Difference Vegetation Index (NDVI) to analyze the spatiotemporal variation trend in vegetation in the Dabusennur Watershed using linear trend analysis and the GeoDetector model to identify the main drivers of vegetation change in the watershed. Finally, the study assessed the risk of ecological degradation in the vegetation of the watershed. The results show that the NDVI in the study area has had a fluctuating trend in the last 22 years, and the change has been small. Precipitation and groundwater depth are the key factors affecting vegetation change. The NDVI reaches its maximum value when the groundwater depth is at 2.75 m. The vegetation ecology of the basin is relatively fragile, mainly with medium risk and large risk. To cope with the ecological risk of vegetation degradation caused by climate change, appropriate water use strategies should be formulated to ensure ecological water use. The present study’s outcomes provide the basis for developing ecological engineering solutions in the arid and semi-arid parts of northern China.
Full article
(This article belongs to the Special Issue Soil and Groundwater Quality and Resources Assessment)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Workflow of study on assessment of VEDR.</p> Full article ">Figure 3
<p>Spatial distribution of NDVI in DW.</p> Full article ">Figure 4
<p>Percentage of different levels of NDVI in DW from 1998 to 2019.</p> Full article ">Figure 5
<p>NDVI change trend in DW from 1998 to 2019.</p> Full article ">Figure 6
<p>Slope trend analysis results in DW from 1998 to 2019.</p> Full article ">Figure 7
<p>Spatial distribution of driving factors precipitation (<b>a</b>), temperature (<b>b</b>), GD (<b>c</b>), LULC (<b>d</b>), population (<b>e</b>), GDP (<b>f</b>), in DW.</p> Full article ">Figure 8
<p>Interactions detected between different factors on NDVI.</p> Full article ">Figure 9
<p>Response of NDVI to variations of precipitation and GD.</p> Full article ">Figure 10
<p>Spatial distribution of VEDR.</p> Full article ">
<p>Location of the study area.</p> Full article ">Figure 2
<p>Workflow of study on assessment of VEDR.</p> Full article ">Figure 3
<p>Spatial distribution of NDVI in DW.</p> Full article ">Figure 4
<p>Percentage of different levels of NDVI in DW from 1998 to 2019.</p> Full article ">Figure 5
<p>NDVI change trend in DW from 1998 to 2019.</p> Full article ">Figure 6
<p>Slope trend analysis results in DW from 1998 to 2019.</p> Full article ">Figure 7
<p>Spatial distribution of driving factors precipitation (<b>a</b>), temperature (<b>b</b>), GD (<b>c</b>), LULC (<b>d</b>), population (<b>e</b>), GDP (<b>f</b>), in DW.</p> Full article ">Figure 8
<p>Interactions detected between different factors on NDVI.</p> Full article ">Figure 9
<p>Response of NDVI to variations of precipitation and GD.</p> Full article ">Figure 10
<p>Spatial distribution of VEDR.</p> Full article ">
Open AccessArticle
Disinfection Efficacy and Eventual Harmful Effect of Chemical Peracetic Acid (PAA) and Probiotic Phaeobacter inhibens Tested on Isochrisys galbana (var. T-ISO) Cultures
by
Elia Casoni, Gloria Contis, Leonardo Aguiari, Michele Mistri and Cristina Munari
Water 2024, 16(16), 2257; https://doi.org/10.3390/w16162257 (registering DOI) - 10 Aug 2024
Abstract
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One of the main threats to aquaculture is represented by microbial pathogens, causing mass mortality episodes in hatcheries, which result in huge economic losses. Among the many disinfection methods applied to reduce this issue, the use of chemicals and beneficial microorganisms (probiotics) seems
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One of the main threats to aquaculture is represented by microbial pathogens, causing mass mortality episodes in hatcheries, which result in huge economic losses. Among the many disinfection methods applied to reduce this issue, the use of chemicals and beneficial microorganisms (probiotics) seems to be the most efficient. The aim of this study is to test the efficacy of two of them: a chemical, peracetic acid (PAA), and a probiotic, Phaeobacter inhibens. Tests were run on microalgae of the species Isochrysis galbana (var T-ISO). For both remedies, the microalgae survival rate and final cell concentration (cell/mL) were monitored. PAA analysis tested six different concentrations of the chemical: 7.5 µg, 10 µg/L, 20 µg/L, 30 µg/L, 40 µg/L, and 60 µg/L. Meanwhile, P. inhibens was tested with a concentration of 104 CFU/mL. Analysis for both the remedies was conducted on a laboratory scale using glass flasks, and on an industrial scale inside photobioreactors (PBRs). Among all the treatments, the one with PAA dosed with a concentration of 60 µg/L gave the best results, as the culture reached a final density of 8.61 × 106 cell/mL. However, none of the remedies involved in the experiment harmed microalgae or their growth. The results match perfectly with the condition requested for the tested remedies: to obtain an optimal breakdown of pathogens without interfering with culture growth. These features make PAA and P. inhibens good candidates for disinfection methods in aquaculture facilities.
Full article
![](https://pub.mdpi-res.com/water/water-16-02257/article_deploy/html/images/water-16-02257-g001-550.jpg?1723289927)
Figure 1
Figure 1
<p><span class="html-italic">I. galbana</span> growth expressed as 10<sup>6</sup> cell/mL (±SD) (<b>left graphs</b>) and relative pH variation (±SD) (<b>right graphs</b>) for every treatment tested in the laboratory trial involving PAA. Each graph shows the results of two treatments and the control group (CTRL): (<b>a</b>) 7.5 µg/L and 10 µg/L treatments; (<b>b</b>) 20 µg/L and 30 µg/L treatments; (<b>c</b>) 40 µg/L and 60 µg/L treatments. The trial lasted from T0 (0 h) to T2 (48 h).</p> Full article ">Figure 2
<p><span class="html-italic">I. galbana</span> average specific growth rate (µ) related to every treatment tested in each trial, expressed as 10<sup>6</sup> cell/mL/h. (<b>a</b>) PAA laboratory trial: the blue square contains the treatments with µ values grouping under the 0.025 × 10<sup>6</sup> cell/mL/h threshold (bold horizontal line); the red square contains the µ values grouping above the 0.025 × 10<sup>6</sup> cell/mL/h. No statistical differences were detected among treatments. (<b>b</b>) <span class="html-italic">P. inhibens</span> laboratory trial. No statistical differences were detected among treatments. (<b>c</b>) PBR trial: different letters (“x”, “y”) indicate a significant statistical difference, according to Tukey’s HSD test (<span class="html-italic">p</span>-value < 0.05).</p> Full article ">Figure 3
<p><span class="html-italic">I. galbana</span> growth related to the laboratory trial involving <span class="html-italic">P. inhibens</span> strain DSM17395, expressed as 10<sup>6</sup> cell/mL (±SD). The trial lasted from T0 (0 h) to T3 (72 h).</p> Full article ">Figure 4
<p>Growth curve and pH variation related to the PBR trial involving the 60 µg/L PAA treatment, <span class="html-italic">P. inhibens</span> treatment, and a control group. The results for cell growth are expressed as 10<sup>6</sup> cell/mL (±SD). The trial lasted from T0 (0 h) to T2 (48 h).</p> Full article ">Figure 5
<p>Rapid efficacy evaluation test results, to assess the presence/absence of pathogen bacteria belonging to genus <span class="html-italic">Vibrio</span> in cultures analyzed during the PBR trial: (<b>a</b>) PBR control group; (<b>b</b>) PBR treated with PAA; (<b>c</b>) PBR treated with probiotic <span class="html-italic">P. inhibens</span>.</p> Full article ">
<p><span class="html-italic">I. galbana</span> growth expressed as 10<sup>6</sup> cell/mL (±SD) (<b>left graphs</b>) and relative pH variation (±SD) (<b>right graphs</b>) for every treatment tested in the laboratory trial involving PAA. Each graph shows the results of two treatments and the control group (CTRL): (<b>a</b>) 7.5 µg/L and 10 µg/L treatments; (<b>b</b>) 20 µg/L and 30 µg/L treatments; (<b>c</b>) 40 µg/L and 60 µg/L treatments. The trial lasted from T0 (0 h) to T2 (48 h).</p> Full article ">Figure 2
<p><span class="html-italic">I. galbana</span> average specific growth rate (µ) related to every treatment tested in each trial, expressed as 10<sup>6</sup> cell/mL/h. (<b>a</b>) PAA laboratory trial: the blue square contains the treatments with µ values grouping under the 0.025 × 10<sup>6</sup> cell/mL/h threshold (bold horizontal line); the red square contains the µ values grouping above the 0.025 × 10<sup>6</sup> cell/mL/h. No statistical differences were detected among treatments. (<b>b</b>) <span class="html-italic">P. inhibens</span> laboratory trial. No statistical differences were detected among treatments. (<b>c</b>) PBR trial: different letters (“x”, “y”) indicate a significant statistical difference, according to Tukey’s HSD test (<span class="html-italic">p</span>-value < 0.05).</p> Full article ">Figure 3
<p><span class="html-italic">I. galbana</span> growth related to the laboratory trial involving <span class="html-italic">P. inhibens</span> strain DSM17395, expressed as 10<sup>6</sup> cell/mL (±SD). The trial lasted from T0 (0 h) to T3 (72 h).</p> Full article ">Figure 4
<p>Growth curve and pH variation related to the PBR trial involving the 60 µg/L PAA treatment, <span class="html-italic">P. inhibens</span> treatment, and a control group. The results for cell growth are expressed as 10<sup>6</sup> cell/mL (±SD). The trial lasted from T0 (0 h) to T2 (48 h).</p> Full article ">Figure 5
<p>Rapid efficacy evaluation test results, to assess the presence/absence of pathogen bacteria belonging to genus <span class="html-italic">Vibrio</span> in cultures analyzed during the PBR trial: (<b>a</b>) PBR control group; (<b>b</b>) PBR treated with PAA; (<b>c</b>) PBR treated with probiotic <span class="html-italic">P. inhibens</span>.</p> Full article ">
Open AccessArticle
Optimal Coordinated Operation for Hydro–Wind Power System
by
Huanhuan Li, Huiyang Jia, Zhiwang Zhang and Tian Lan
Water 2024, 16(16), 2256; https://doi.org/10.3390/w16162256 (registering DOI) - 10 Aug 2024
Abstract
The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study
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The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility.
Full article
(This article belongs to the Special Issue Advances in Water Conservancy and Hydropower Engineering: Modelling, Performances, Optimization Application and Environmental Effects)
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![](https://pub.mdpi-res.com/water/water-16-02256/article_deploy/html/images/water-16-02256-g001-550.jpg?1723280479)
Figure 1
Figure 1
<p>The huge development potential for hydropower and wind power generation: an upward tendency over the world to 2030. The capacity of hydropower generation will increase by half, to about 6000 TWh relative to the 2020 level. The capacity of wind power generation will quadruple to 14,000 TWh compared with the 2000 level. Such growth trends are much higher than other renewable energy sources like solar power and bioenergy.</p> Full article ">Figure 2
<p>Integration of hydropower and wind power systems.</p> Full article ">Figure 3
<p>The curve of wind power coefficient <span class="html-italic">C<sub>p</sub></span>.</p> Full article ">Figure 4
<p>Optimization framework of this paper.</p> Full article ">Figure 5
<p>Wind speed, power output, and power structure in Scenario 1. (<b>a</b>) Scenario 1: complementary characteristic of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 1: power structure of hybrid hydro–wind system. <a href="#water-16-02256-f005" class="html-fig">Figure 5</a>b is a larger version of the red dashed area in <a href="#water-16-02256-f005" class="html-fig">Figure 5</a>a.</p> Full article ">Figure 6
<p>End-use power, mix-produced power, and power loss in Scenario 1. (<b>a</b>) Scenario 1: end-use power of grid and mix-produced power of hybrid system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. <b>(b</b>) Scenario 1: transmission power loss under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds.</p> Full article ">Figure 7
<p>Hydraulic tracing effect and power loss in Scenario 2. (<b>a</b>) Scenario 2: hydraulic tracing effect of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 2: power loss under different wind speeds.</p> Full article ">Figure 7 Cont.
<p>Hydraulic tracing effect and power loss in Scenario 2. (<b>a</b>) Scenario 2: hydraulic tracing effect of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 2: power loss under different wind speeds.</p> Full article ">Figure 8
<p>Effect of WTs’ number on hydraulic suppression effect under Type 2 wind. (<b>a</b>) Power complementarity and fluctuation in hydraulic frequency. (<b>b</b>) Out-of-control hydraulic frequency.</p> Full article ">Figure 9
<p>Effect of capacity allocation on power loss under Type 2 wind.</p> Full article ">Figure A1
<p>Block diagram of the full-size converter.</p> Full article ">Figure A2
<p>Block diagram of the hydro-turbine and frequency regulation system.</p> Full article ">Figure A3
<p>Model of the hybrid hydro–wind power system. The symbols A/a, B/b, and C/c represents three phases.</p> Full article ">Figure A4
<p>Responses of the hydraulic frequency. The dotted blue lines represent the acceptable frequency range of ±0.5 Hz.</p> Full article ">Figure A5
<p>Comparative model: (<b>a</b>) the complementarity of output power and, (<b>b</b>) the fluctuation in hydraulic frequency.</p> Full article ">Figure A6
<p>Model in this work: (<b>a</b>) the complementarity of output power and, (<b>b</b>) the fluctuation in hydraulic frequency.</p> Full article ">
<p>The huge development potential for hydropower and wind power generation: an upward tendency over the world to 2030. The capacity of hydropower generation will increase by half, to about 6000 TWh relative to the 2020 level. The capacity of wind power generation will quadruple to 14,000 TWh compared with the 2000 level. Such growth trends are much higher than other renewable energy sources like solar power and bioenergy.</p> Full article ">Figure 2
<p>Integration of hydropower and wind power systems.</p> Full article ">Figure 3
<p>The curve of wind power coefficient <span class="html-italic">C<sub>p</sub></span>.</p> Full article ">Figure 4
<p>Optimization framework of this paper.</p> Full article ">Figure 5
<p>Wind speed, power output, and power structure in Scenario 1. (<b>a</b>) Scenario 1: complementary characteristic of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 1: power structure of hybrid hydro–wind system. <a href="#water-16-02256-f005" class="html-fig">Figure 5</a>b is a larger version of the red dashed area in <a href="#water-16-02256-f005" class="html-fig">Figure 5</a>a.</p> Full article ">Figure 6
<p>End-use power, mix-produced power, and power loss in Scenario 1. (<b>a</b>) Scenario 1: end-use power of grid and mix-produced power of hybrid system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. <b>(b</b>) Scenario 1: transmission power loss under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds.</p> Full article ">Figure 7
<p>Hydraulic tracing effect and power loss in Scenario 2. (<b>a</b>) Scenario 2: hydraulic tracing effect of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 2: power loss under different wind speeds.</p> Full article ">Figure 7 Cont.
<p>Hydraulic tracing effect and power loss in Scenario 2. (<b>a</b>) Scenario 2: hydraulic tracing effect of the hydro–wind power system under the constant (Type 1), mutational (Type 2), and random (Type 3) wind speeds. (<b>b</b>) Scenario 2: power loss under different wind speeds.</p> Full article ">Figure 8
<p>Effect of WTs’ number on hydraulic suppression effect under Type 2 wind. (<b>a</b>) Power complementarity and fluctuation in hydraulic frequency. (<b>b</b>) Out-of-control hydraulic frequency.</p> Full article ">Figure 9
<p>Effect of capacity allocation on power loss under Type 2 wind.</p> Full article ">Figure A1
<p>Block diagram of the full-size converter.</p> Full article ">Figure A2
<p>Block diagram of the hydro-turbine and frequency regulation system.</p> Full article ">Figure A3
<p>Model of the hybrid hydro–wind power system. The symbols A/a, B/b, and C/c represents three phases.</p> Full article ">Figure A4
<p>Responses of the hydraulic frequency. The dotted blue lines represent the acceptable frequency range of ±0.5 Hz.</p> Full article ">Figure A5
<p>Comparative model: (<b>a</b>) the complementarity of output power and, (<b>b</b>) the fluctuation in hydraulic frequency.</p> Full article ">Figure A6
<p>Model in this work: (<b>a</b>) the complementarity of output power and, (<b>b</b>) the fluctuation in hydraulic frequency.</p> Full article ">
Open AccessArticle
The Role and Significance of Operational Flood Defense Plans on the Waters Second-Order in Republic of Serbia
by
Aleksandar Drobnjak, Ratko Ristić and Nada Dragović
Water 2024, 16(16), 2255; https://doi.org/10.3390/w16162255 (registering DOI) - 10 Aug 2024
Abstract
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The aim of this research is to present the role and importance of planning documents for flood defense during the development of the Flood Risk Management Plan (FRMP) in the Republic of Serbia. The scope of the work is the Operational Plans for
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The aim of this research is to present the role and importance of planning documents for flood defense during the development of the Flood Risk Management Plan (FRMP) in the Republic of Serbia. The scope of the work is the Operational Plans for Flood Defense on Second-Order Waters (OPFDSWs), which are the responsibility of local governments units (LGU). The paper contains an overview analysis of the implementation of the Flood Risk Management Directive (FRMD) in the legal framework of the Republic of Serbia, as well as an analysis of the legislative framework in the field of flood defense. The method of multi-criteria analysis was used for a qualitative assessment of the elements that are part of the OPFDSW. Through the results and discussion of the work, the similarities between the OPFDSW and FRMP were highlighted and explained, which can serve to better understand the importance of quality production of the OPFDSW. In order to harmonize all activities on the territory of LGU, care should be taken that planning documentation for flood protection occupies one of the priority activities in the management of planning acts. The conclusion is that it is necessary to clearly define the rulebook on the methodology for the preparation of the OPFDSW, all in the function of the preparation of the FRMP.
Full article
![](https://pub.mdpi-res.com/water/water-16-02255/article_deploy/html/images/water-16-02255-g001-550.jpg?1723258651)
Figure 1
Figure 1
<p>Map showing APSFRs from 2012 to 2019 (author).</p> Full article ">Figure 2
<p>APSFR maps: <b>upper left</b>—hazard map, <b>upper right</b>—hazard map updated, <b>down left</b>—risk map, <b>down right</b>—updated risk map (author).</p> Full article ">Figure 3
<p><b>Left</b>—view of local self-governments created by OPFDSW, <b>right</b>—view of OPFDSW by year of preparation (author).</p> Full article ">
<p>Map showing APSFRs from 2012 to 2019 (author).</p> Full article ">Figure 2
<p>APSFR maps: <b>upper left</b>—hazard map, <b>upper right</b>—hazard map updated, <b>down left</b>—risk map, <b>down right</b>—updated risk map (author).</p> Full article ">Figure 3
<p><b>Left</b>—view of local self-governments created by OPFDSW, <b>right</b>—view of OPFDSW by year of preparation (author).</p> Full article ">
Open AccessArticle
Enhancing Hydraulic Efficiency of Side Intakes Using Spur Dikes: A Case Study of Hemmat Water Intake, Iran
by
Saman Abbasi Chenari, Hossein Azizi Nadian, Javad Ahadiyan, Mohammad Valipour, Giuseppe Oliveto and Seyed Mohsen Sajjadi
Water 2024, 16(16), 2254; https://doi.org/10.3390/w16162254 (registering DOI) - 9 Aug 2024
Abstract
This study investigates the problem of low efficiency and the lack of a water supply at the Hemmat Water Intake, in Iran, where severe sediment accumulation was observed at the intake mouth. The Flow-3D software was used to simulate the flow patterns under
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This study investigates the problem of low efficiency and the lack of a water supply at the Hemmat Water Intake, in Iran, where severe sediment accumulation was observed at the intake mouth. The Flow-3D software was used to simulate the flow patterns under various scenarios of hydraulic regimentation works. The considered parameters include: (i) three alternative locations of the spur dike (i.e., a spur dike placed on the opposite side of the intake inlet and aligned with the upstream edge of the intake, to be regarded as a witness spur dike; a spur dike at a distance DS of 7 m downstream of the witness spur dike, which implies a dimensionless distance DS/bi1 of 1/3, with bi1 being the intake opening width; and a spur dike at a distance of 7 m upstream of the witness spur dike with a dimensionless distance, still, of 1/3); (ii) four spur dike lengths, LS/Br, with LS being the effective spur dike length and Br the approach river width; and (iii) five spur dike deviation angles of 75, 90, 105, 120, and 135 degrees (the deviation angle is the angle between the spur dike axis and the original river-bank line from which the spur dike extends). The results showed that, with the increase in the relative spur dike length (LS/Br), the velocity of the flow entering the water intake increases by 11%. A spur deviation angle of 135 degrees increases the flow depth at the intake inlet by 9% compared to a smaller deviation angle of 75 degrees. In addition, the spur dike increases the flow shear stresses at the intake inlet by up to 50%. Overall, the main flow of the river with the highest velocity and depth, and best directed towards the water intake, occurs for the placement of the longest spur dike (i.e., LS/Br = 0.46) in front of the inlet (i.e., witness spur dike) and for a spur dike deviation angle of 135 degrees. The spur dike increases the shear stress at the intake entrance by more than five times with respect to the case of its absence. In general, the presence of a spur dike on the opposite bank and with a deviation angle in the direction of the intake inlet well directs the main flow towards the canal intake. Moreover, it reduces the possibility of sedimentation in the canal inlet by increasing the flow velocity. Therefore, the results of this study could also be useful in increasing the hydraulic efficiency of lateral intakes by reducing the sedimentation phenomena.
Full article
(This article belongs to the Topic Research on River Engineering)
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![](https://pub.mdpi-res.com/water/water-16-02254/article_deploy/html/images/water-16-02254-g001a-550.jpg?1723214981)
Figure 1
Figure 1
<p>Photographs of the lateral intake structure of the Shahid Hemmat Dam. (<b>a</b>) Aerial view of the Hemmat Dam, lateral intake, and pumping station; (<b>b</b>) intake inlet from the top of the pumping station; (<b>c</b>) sedimentation at the intake inlet; (<b>d</b>) pumping station.</p> Full article ">Figure 1 Cont.
<p>Photographs of the lateral intake structure of the Shahid Hemmat Dam. (<b>a</b>) Aerial view of the Hemmat Dam, lateral intake, and pumping station; (<b>b</b>) intake inlet from the top of the pumping station; (<b>c</b>) sedimentation at the intake inlet; (<b>d</b>) pumping station.</p> Full article ">Figure 2
<p>3D view of the monitored area (and structures) and target parameters used in simulations. <span class="html-italic">B<sub>r</sub></span>: main river width; <span class="html-italic">b<sub>i</sub></span>1: intake inlet width; <span class="html-italic">b<sub>i</sub></span>2: intake channel width; <span class="html-italic">L<sub>S</sub></span>: spur dike length, <span class="html-italic">D<sub>S</sub></span>: distance of the spur dike base from the witness spur dike; G1, G2, and G3: dam gates.</p> Full article ">Figure 3
<p>Meshing around the intake structure and spur dike. (<b>a</b>) General view of the two mesh blocks; (<b>b</b>) magnification of the two mesh blocks around the intake inlet and the spur dike.</p> Full article ">Figure 4
<p>Overview on the upstream (Q), downstream (O), and lateral (W) boundary conditions.</p> Full article ">Figure 5
<p>Flow velocity distribution around the dam, intake structure, and spur dike for the following positions of the latter: (<b>a</b>) in front of the upstream edge of the intake structure (witness spur dike); (<b>b</b>) 7 m upstream of the position of the witness spur dike; (<b>c</b>) 7 m downstream of the position of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 6
<p>Position of the five control points for local velocity comparisons.</p> Full article ">Figure 7
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different positions of the spur dike.</p> Full article ">Figure 8
<p>Simulation of the vortices at and around the intake structure in the case of a spur dike placed downstream of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 9
<p>Simulation of the vortices at and around the intake structure in the case of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 10
<p>Simulation of the vortices at and around the intake structure in the case of a spur dike placed upstream of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 11
<p>Simulation of the kinematic field around the intake inlet in the case of a spur dike with: (<b>a</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.24; (<b>b</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.32; (<b>c</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.40; and (<b>d</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.46. The approach discharge <span class="html-italic">Q</span> is equal to 12 m<sup>3</sup>/s and velocities are given in m/s. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 12
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different lengths of the spur dike.</p> Full article ">Figure 13
<p>Simulation of the kinematic field around the intake inlet in the case of a spur dike with deflection angle of: (<b>a</b>) 75; (<b>b</b>) 90; (<b>c</b>) 105; (<b>d</b>) 120; and (<b>e</b>) 135 degrees. The approach discharge <span class="html-italic">Q</span> is equal to 12 m<sup>3</sup>/s. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 14
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different deflection angles of the spur dike.</p> Full article ">Figure 15
<p>Position of the 12 control points for local flow depth comparisons.</p> Full article ">Figure 16
<p>Flow depths <span class="html-italic">Z</span> along the transect shown in <a href="#water-16-02254-f015" class="html-fig">Figure 15</a> for different deflection angles of the spur dike. Only gate No. 3 is open and the witness spur dike is placed.</p> Full article ">Figure 17
<p>Indication of the three transects with the related control point positions for the comparison of shear stresses for different scenarios.</p> Full article ">Figure 18
<p>Shear stresses <span class="html-italic">τ<sub>b</sub></span> along the central transect shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">Figure 19
<p>Shear stresses along the transect downstream of the central one shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">Figure 20
<p>Shear stresses along the transect upstream of the central one shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">
<p>Photographs of the lateral intake structure of the Shahid Hemmat Dam. (<b>a</b>) Aerial view of the Hemmat Dam, lateral intake, and pumping station; (<b>b</b>) intake inlet from the top of the pumping station; (<b>c</b>) sedimentation at the intake inlet; (<b>d</b>) pumping station.</p> Full article ">Figure 1 Cont.
<p>Photographs of the lateral intake structure of the Shahid Hemmat Dam. (<b>a</b>) Aerial view of the Hemmat Dam, lateral intake, and pumping station; (<b>b</b>) intake inlet from the top of the pumping station; (<b>c</b>) sedimentation at the intake inlet; (<b>d</b>) pumping station.</p> Full article ">Figure 2
<p>3D view of the monitored area (and structures) and target parameters used in simulations. <span class="html-italic">B<sub>r</sub></span>: main river width; <span class="html-italic">b<sub>i</sub></span>1: intake inlet width; <span class="html-italic">b<sub>i</sub></span>2: intake channel width; <span class="html-italic">L<sub>S</sub></span>: spur dike length, <span class="html-italic">D<sub>S</sub></span>: distance of the spur dike base from the witness spur dike; G1, G2, and G3: dam gates.</p> Full article ">Figure 3
<p>Meshing around the intake structure and spur dike. (<b>a</b>) General view of the two mesh blocks; (<b>b</b>) magnification of the two mesh blocks around the intake inlet and the spur dike.</p> Full article ">Figure 4
<p>Overview on the upstream (Q), downstream (O), and lateral (W) boundary conditions.</p> Full article ">Figure 5
<p>Flow velocity distribution around the dam, intake structure, and spur dike for the following positions of the latter: (<b>a</b>) in front of the upstream edge of the intake structure (witness spur dike); (<b>b</b>) 7 m upstream of the position of the witness spur dike; (<b>c</b>) 7 m downstream of the position of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 6
<p>Position of the five control points for local velocity comparisons.</p> Full article ">Figure 7
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different positions of the spur dike.</p> Full article ">Figure 8
<p>Simulation of the vortices at and around the intake structure in the case of a spur dike placed downstream of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 9
<p>Simulation of the vortices at and around the intake structure in the case of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 10
<p>Simulation of the vortices at and around the intake structure in the case of a spur dike placed upstream of the witness spur dike. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 1.0 m/s.</p> Full article ">Figure 11
<p>Simulation of the kinematic field around the intake inlet in the case of a spur dike with: (<b>a</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.24; (<b>b</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.32; (<b>c</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.40; and (<b>d</b>) <span class="html-italic">L<sub>S</sub></span>/<span class="html-italic">B<sub>r</sub></span> = 0.46. The approach discharge <span class="html-italic">Q</span> is equal to 12 m<sup>3</sup>/s and velocities are given in m/s. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 12
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different lengths of the spur dike.</p> Full article ">Figure 13
<p>Simulation of the kinematic field around the intake inlet in the case of a spur dike with deflection angle of: (<b>a</b>) 75; (<b>b</b>) 90; (<b>c</b>) 105; (<b>d</b>) 120; and (<b>e</b>) 135 degrees. The approach discharge <span class="html-italic">Q</span> is equal to 12 m<sup>3</sup>/s. In the legend, flow velocities are in m/s and range from 0.0 to 4.0 m/s with steps of 0.667 m/s.</p> Full article ">Figure 14
<p>Flow velocities along the transect shown in <a href="#water-16-02254-f006" class="html-fig">Figure 6</a> for different deflection angles of the spur dike.</p> Full article ">Figure 15
<p>Position of the 12 control points for local flow depth comparisons.</p> Full article ">Figure 16
<p>Flow depths <span class="html-italic">Z</span> along the transect shown in <a href="#water-16-02254-f015" class="html-fig">Figure 15</a> for different deflection angles of the spur dike. Only gate No. 3 is open and the witness spur dike is placed.</p> Full article ">Figure 17
<p>Indication of the three transects with the related control point positions for the comparison of shear stresses for different scenarios.</p> Full article ">Figure 18
<p>Shear stresses <span class="html-italic">τ<sub>b</sub></span> along the central transect shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">Figure 19
<p>Shear stresses along the transect downstream of the central one shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">Figure 20
<p>Shear stresses along the transect upstream of the central one shown in <a href="#water-16-02254-f017" class="html-fig">Figure 17</a>. The values are in kPa.</p> Full article ">
Open AccessArticle
Predicting Coastal Water Quality with Machine Learning, A Case Study of Beibu Gulf, China
by
Yucai Bai, Zhefeng Xu, Wenlu Lan, Xiaoyan Peng, Yan Deng, Zhibiao Chen, Hao Xu, Zhijian Wang, Hui Xu, Xinglong Chen and Jinping Cheng
Water 2024, 16(16), 2253; https://doi.org/10.3390/w16162253 (registering DOI) - 9 Aug 2024
Abstract
Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In this study, we characterized the spatiotemporal change
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Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In this study, we characterized the spatiotemporal change in the water quality in Beibu Gulf and proposed a machine learning approach to predict the water pollution level in Beibu Gulf on the basis of 5-year (2018–2022) observation data of ten water quality parameters from ten selected sites. Random forest (rf) and linear algorithms were utilized. Results show that a high frequency of exceedance of water quality parameters was observed particularly in summer and autumn, e.g., the exceeding rate of Dissolved Inorganic Nitrogen (DIN) at GX01, GX03, GX06, and GX07 station were 28.2~78.1% (average is 52.0%), 6.0~21.7% (average is 52.0%), 23.0~44.7% (average is 31.9%), and 5.2~33.4% (average is 21.2%), respectively. With regard to the spatial distribution, the pH, Water Salinity (WS), and Dissolved Oxygen (DO) values of stations inside the bay were overall lower than those of corresponding stations at the mouth of the bay and stations outside the bay. The concentrations of Chlorophyll-a concentration (except QZB) and nutrient salts showed a clearly opposite trend compared with the above concerned three parameters. For instance, the average Chl-a value of station GX09 was 22.5% higher than that of GX08 and GX10 between 2018 and 2022. Correlation analysis among water quality factors shows a significant positive correlation (r > 0.85) between Dissolved Inorganic Nitrogen (DIN) and NO3-N, followed by NO2-N and NH4-N, indicating that the main component of DIN is NO3-N. The forecasting results with machine learning also demonstrate the possibility to estimate the water quality parameters, such as chl-a concentration, DIN, and NH4-N in a cost-effective manner with prediction accuracy of approximately 60%, and thereby could provide near-real-time information to monitor the water quality of the Beibu Gulf. Predicting models initiated in this study could be of great interest for local authorities and the tourism and fishing industries.
Full article
(This article belongs to the Section Oceans and Coastal Zones)
Open AccessArticle
An Assessment of the Embedding of Francis Turbines for Pumped Hydraulic Energy Storage
by
Georgi Todorov, Ivan Kralov, Konstantin Kamberov, Evtim Zahariev, Yavor Sofronov and Blagovest Zlatev
Water 2024, 16(16), 2252; https://doi.org/10.3390/w16162252 (registering DOI) - 9 Aug 2024
Abstract
In this paper, analyses of Francis turbine failures for powerful Pumped Hydraulic Energy Storage (PHES) are conducted. The structure is part of PHES Chaira, Bulgaria (HA4—Hydro-Aggregate 4). The aim of the study is to assess the structure-to-concrete embedding to determine the possible causes
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In this paper, analyses of Francis turbine failures for powerful Pumped Hydraulic Energy Storage (PHES) are conducted. The structure is part of PHES Chaira, Bulgaria (HA4—Hydro-Aggregate 4). The aim of the study is to assess the structure-to-concrete embedding to determine the possible causes of damage and destruction of the HA4 Francis spiral casing units. The embedding methods that have been applied in practice for decades are discussed and compared to those used for HA4. A virtual prototype is built based on the finite-element method to clarify the influence of workloads under the generator mode. The stages of the simulation include structural analysis of the spiral casing and concrete under load in generator mode, as well as structural analysis of the spiral casing under loads in generator mode without concrete. Both simulations are of major importance. Since the failure of the surface between the turbine, the spiral casing, and the concrete is observed, the effect of the growing contact gap (no contact) is analyzed. The stresses, strains, and displacements of the turbine units are simulated, followed by an analysis for reliability. The conclusions reveal the possible reasons for cracks and destruction in the main elements of the structure.
Full article
(This article belongs to the Special Issue Hydraulic Engineering and Numerical Simulation of Two-Phase Flows)
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![](https://pub.mdpi-res.com/water/water-16-02252/article_deploy/html/images/water-16-02252-g001-550.jpg?1723206526)
Figure 1
Figure 1
<p>Simple design scheme of a Francis turbine.</p> Full article ">Figure 2
<p>Embedding of the spiral casing in concrete [<a href="#B26-water-16-02252" class="html-bibr">26</a>].</p> Full article ">Figure 3
<p>Erosion and destruction of the basement concrete [<a href="#B28-water-16-02252" class="html-bibr">28</a>].</p> Full article ">Figure 4
<p>HA4 Francis turbine (the units in the drawings are mm): (<b>a</b>) 3D model of the spiral casing, cover ring, and stay vanes; (<b>b</b>) examined static components of the hydraulic units.</p> Full article ">Figure 5
<p>The embedding of the spiral casing: (<b>a</b>) concrete layers; (<b>b</b>) enforcement.</p> Full article ">Figure 6
<p>Finite-element mesh for the model: (<b>a</b>) layers of concrete embedding; (<b>b</b>) mesh of the concrete; (<b>c</b>) mesh of the turbine; (<b>d</b>) mesh zoom of a stay vane.</p> Full article ">Figure 7
<p>Flow chart of the stages of the investigations, including the study of current destruction and construction data, records of previous incidents, and repairs.</p> Full article ">Figure 8
<p>A1: Equivalent (von Mises) stress distribution MPa; (<b>a</b>) Segment cross-sectional view (see <a href="#water-16-02252-f005" class="html-fig">Figure 5</a>b); (<b>b</b>) Socal section of a stay vane.</p> Full article ">Figure 9
<p>A1: Displacement and strain distributions; (<b>a</b>) Total deformation, mm; (<b>b</b>) Plastic strains mm/mm.</p> Full article ">Figure 10
<p>A2: Equivalent (von Mises) stress distribution, MPa; (<b>a</b>) Segment cross-sectional view; (<b>b</b>) Local section of a stay vane.</p> Full article ">Figure 11
<p>A2: Displacement and strain distributions; (<b>a</b>) Total deformation, mm; (<b>b</b>) Plastic strains mm/mm.</p> Full article ">Figure 12
<p>Equivalent (von Mises) stresses distribution, MPa.</p> Full article ">Figure 13
<p>Plastic strains mm/mm.</p> Full article ">Figure 14
<p>Total deformations, mm.</p> Full article ">
<p>Simple design scheme of a Francis turbine.</p> Full article ">Figure 2
<p>Embedding of the spiral casing in concrete [<a href="#B26-water-16-02252" class="html-bibr">26</a>].</p> Full article ">Figure 3
<p>Erosion and destruction of the basement concrete [<a href="#B28-water-16-02252" class="html-bibr">28</a>].</p> Full article ">Figure 4
<p>HA4 Francis turbine (the units in the drawings are mm): (<b>a</b>) 3D model of the spiral casing, cover ring, and stay vanes; (<b>b</b>) examined static components of the hydraulic units.</p> Full article ">Figure 5
<p>The embedding of the spiral casing: (<b>a</b>) concrete layers; (<b>b</b>) enforcement.</p> Full article ">Figure 6
<p>Finite-element mesh for the model: (<b>a</b>) layers of concrete embedding; (<b>b</b>) mesh of the concrete; (<b>c</b>) mesh of the turbine; (<b>d</b>) mesh zoom of a stay vane.</p> Full article ">Figure 7
<p>Flow chart of the stages of the investigations, including the study of current destruction and construction data, records of previous incidents, and repairs.</p> Full article ">Figure 8
<p>A1: Equivalent (von Mises) stress distribution MPa; (<b>a</b>) Segment cross-sectional view (see <a href="#water-16-02252-f005" class="html-fig">Figure 5</a>b); (<b>b</b>) Socal section of a stay vane.</p> Full article ">Figure 9
<p>A1: Displacement and strain distributions; (<b>a</b>) Total deformation, mm; (<b>b</b>) Plastic strains mm/mm.</p> Full article ">Figure 10
<p>A2: Equivalent (von Mises) stress distribution, MPa; (<b>a</b>) Segment cross-sectional view; (<b>b</b>) Local section of a stay vane.</p> Full article ">Figure 11
<p>A2: Displacement and strain distributions; (<b>a</b>) Total deformation, mm; (<b>b</b>) Plastic strains mm/mm.</p> Full article ">Figure 12
<p>Equivalent (von Mises) stresses distribution, MPa.</p> Full article ">Figure 13
<p>Plastic strains mm/mm.</p> Full article ">Figure 14
<p>Total deformations, mm.</p> Full article ">
Open AccessArticle
Interpreting Controls of Stomatal Conductance across Different Vegetation Types via Machine Learning
by
Runjia Xue, Wenjun Zuo, Zhaowen Zheng, Qin Han, Jingyan Shi, Yao Zhang, Jianxiu Qiu, Sheng Wang, Yan Zhu, Weixing Cao and Xiaohu Zhang
Water 2024, 16(16), 2251; https://doi.org/10.3390/w16162251 - 9 Aug 2024
Abstract
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Plant stomata regulate transpiration (T) and CO2 assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance
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Plant stomata regulate transpiration (T) and CO2 assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance and hydro-metrological observations from FLUXNET sites with four plant functional types and using three widely applied methods to estimate ecosystem T from eddy covariance data, namely uWUE, Perez-Priego, and TEA, we quantified the regulation effect of environmental factors on canopy stomatal conductance (Gs). The environmental factors considered here include radiation (net radiation and solar radiation), water (soil moisture, relative air humidity, and vapor pressure deficit), temperature (air temperature), and atmospheric conditions (CO2 concentration and wind speed). Our findings reveal variation in the influence of these factors on Gs across biomes, with air temperature, relative humidity, soil water content, and net radiation being consistently significant. Wind speed had the least influence. Incorporating the leaf area index into a Random Forest model to account for vegetation phenology significantly improved model accuracy (R2 increased from 0.663 to 0.799). These insights enhance our understanding of the primary factors influencing stomatal conductance, contributing to a broader knowledge of vegetation physiology and ecosystem functioning.
Full article
![](https://pub.mdpi-res.com/water/water-16-02251/article_deploy/html/images/water-16-02251-g001-550.jpg?1723197526)
Figure 1
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<p>Locations of the study sites.</p> Full article ">Figure 2
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site. The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure 2 Cont.
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site. The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure 3
<p>Model accuracy of before (Benchmark) and after adding LAI (LAI). The darker colors in the panels correspond to higher values. (<b>a</b>) presents R<sup>2</sup> results for the training set, while (<b>b</b>) displays R<sup>2</sup> results for the test set. (<b>c</b>) shows RMSE outcomes for the training set, and (<b>d</b>) outlines RMSE results for the test set.</p> Full article ">Figure A1
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site (adding LAI). The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure A1 Cont.
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site (adding LAI). The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">
<p>Locations of the study sites.</p> Full article ">Figure 2
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site. The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure 2 Cont.
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site. The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure 3
<p>Model accuracy of before (Benchmark) and after adding LAI (LAI). The darker colors in the panels correspond to higher values. (<b>a</b>) presents R<sup>2</sup> results for the training set, while (<b>b</b>) displays R<sup>2</sup> results for the test set. (<b>c</b>) shows RMSE outcomes for the training set, and (<b>d</b>) outlines RMSE results for the test set.</p> Full article ">Figure A1
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site (adding LAI). The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">Figure A1 Cont.
<p>Histogram of the importance of <span class="html-italic">G</span><sub>s</sub> characteristics at each site (adding LAI). The y-axis starts at −0.5 because the importance and error bars exceed 0 for some sites, and the negative y indicates that the feature hurts the RF prediction results.</p> Full article ">
Open AccessReview
Water Poverty Index over the Past Two Decades: A Comprehensive Review and Future Prospects—The Middle East as a Case Study
by
Ashraf Isayed, Juan M. Menendez-Aguado, Hatem Jemmali and Nidal Mahmoud
Water 2024, 16(16), 2250; https://doi.org/10.3390/w16162250 - 9 Aug 2024
Abstract
This paper summarises the evolution of the Water Poverty Index (WPI) application at different scales since its emergence. The review captures the main milestones and remarkable developments around the world. It sets the foundation for identifying the most appropriate version of the WPI,
[...] Read more.
This paper summarises the evolution of the Water Poverty Index (WPI) application at different scales since its emergence. The review captures the main milestones and remarkable developments around the world. It sets the foundation for identifying the most appropriate version of the WPI, building on learning from previous versions. In addition, the paper sheds light on the linkages between the WPI and sustainable development goals and applications to fragile contexts. Therefore, it provides a synthesis of knowledge researchers and practitioners’ need in sustainable water resources management that helps boost human development in unstable/fragile arid and semi-arid contexts. The methodology included (i) WPI literature shortlisting and reviewing, (ii) review literature links WPI with sustainable human development and fragility, and (iii) data analysis, identification of gaps and future trends. Intensive research was found to address the limitations of the WPI. However, further research is needed to shortlist the multiple versions of the WPI and match them to their respective scale, purpose and context (including fragile contexts). In addition, a time-based WPI was rarely touched to forecast the impact of decisions on community welfare.
Full article
(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)
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![](https://pub.mdpi-res.com/water/water-16-02250/article_deploy/html/images/water-16-02250-g001-550.jpg?1723197235)
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<p>Summary of the main milestones in the development of WPI.</p> Full article ">Figure 2
<p>Water poverty in Middle East and North Africa (MENA) region (Source: [<a href="#B63-water-16-02250" class="html-bibr">63</a>]). The concerned countries are Middle Eastern listed in <a href="#water-16-02250-t002" class="html-table">Table 2</a>. The use and environment components are not included because, empirically, they have not shown any significance after conducting the PCA analysis. (<b>a</b>): Resources component; (<b>b</b>): Capacity component; (<b>c</b>): Access component; (<b>d</b>): Water poverty index.</p> Full article ">Figure 2 Cont.
<p>Water poverty in Middle East and North Africa (MENA) region (Source: [<a href="#B63-water-16-02250" class="html-bibr">63</a>]). The concerned countries are Middle Eastern listed in <a href="#water-16-02250-t002" class="html-table">Table 2</a>. The use and environment components are not included because, empirically, they have not shown any significance after conducting the PCA analysis. (<b>a</b>): Resources component; (<b>b</b>): Capacity component; (<b>c</b>): Access component; (<b>d</b>): Water poverty index.</p> Full article ">
<p>Summary of the main milestones in the development of WPI.</p> Full article ">Figure 2
<p>Water poverty in Middle East and North Africa (MENA) region (Source: [<a href="#B63-water-16-02250" class="html-bibr">63</a>]). The concerned countries are Middle Eastern listed in <a href="#water-16-02250-t002" class="html-table">Table 2</a>. The use and environment components are not included because, empirically, they have not shown any significance after conducting the PCA analysis. (<b>a</b>): Resources component; (<b>b</b>): Capacity component; (<b>c</b>): Access component; (<b>d</b>): Water poverty index.</p> Full article ">Figure 2 Cont.
<p>Water poverty in Middle East and North Africa (MENA) region (Source: [<a href="#B63-water-16-02250" class="html-bibr">63</a>]). The concerned countries are Middle Eastern listed in <a href="#water-16-02250-t002" class="html-table">Table 2</a>. The use and environment components are not included because, empirically, they have not shown any significance after conducting the PCA analysis. (<b>a</b>): Resources component; (<b>b</b>): Capacity component; (<b>c</b>): Access component; (<b>d</b>): Water poverty index.</p> Full article ">
Open AccessArticle
New Insights into Changes in DOM Fractions in a Crab Farming Park and Key Factors in the Removal Process Using Fluorescence Spectra with MW-2DCOS and SEM
by
Ruijuan Zhou, Yan Hao, Benxin Yu, Junwen Hou, Kuotian Lu, Fang Yang and Qingqian Li
Water 2024, 16(16), 2249; https://doi.org/10.3390/w16162249 - 9 Aug 2024
Abstract
With the explosion of crab farming in China, the urgent need to treat crab wastewater can never be overemphasized. Hence, in this study, excitation–emission matrix (EEM) fluorescence spectroscopy with parallel factor analysis (PARAFAC), moving window two-dimensional correlation spectroscopy (MW-2DCOS) and structural equation modeling
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With the explosion of crab farming in China, the urgent need to treat crab wastewater can never be overemphasized. Hence, in this study, excitation–emission matrix (EEM) fluorescence spectroscopy with parallel factor analysis (PARAFAC), moving window two-dimensional correlation spectroscopy (MW-2DCOS) and structural equation modeling (SEM) were employed to identify changes in the dissolved organic matter (DOM) fractions in a crab farming park and reveal latent factors associated with removal processes. Seven components (C1–C7) were extracted from DOMs by EEM-PARAFAC as follows: C1: microbial byproduct-like substances, C2: visible-tryptophan-like substances, C3: fulvic-like substances, C4: phenolic-like substances, C5: ultraviolet tyrosine-like substances, C6: D-tryptophan-like substances and C7: L-tryptophan-like substances. Interestingly, C7 (39.20%), a representative component of DOM in the crab farming pond, was deeply degraded in the aeration pond by aerobic microbes, whereas C6 was absent in the crab pond. According to 2DCOS, the changing order of the components was C7 → C4 → C6 → C5 → C2 → C1 → C3, and the changing order of the functional groups was carboxylic → phenolic → aromatic. As assessed by MW-2DCOS, the Fmax of the components, especially components C2, C5 and C6 (and with the exception of C4 and C7) exponentially increased in the aeration pond, where an accumulative effect occurred. C2, C5 and C7 were removed by 24.26%, 39.42% and 98.25% in the crab farming system, and were deeply degraded in the paddy-field, purification pond and aeration pond, respectively. As assessed by SEM, the latent factors of organic matter removal were C1, C2, C4, C5, SUVA254, CODMn and DO. This study could be conducive to comprehensively characterizing the removal of components and functional groups of DOMs in crab farming parks.
Full article
(This article belongs to the Special Issue Water Environment Pollution and Control, Volume III)
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![](https://pub.mdpi-res.com/water/water-16-02249/article_deploy/html/images/water-16-02249-g001-550.jpg?1723196934)
Figure 1
Figure 1
<p>Generalized diagram of the crab farming industry park and locations of sampling sites.</p> Full article ">Figure 2
<p>Variations in water quality parameters in different treatment process sections of the crab farming park. (<b>a</b>) pH, (<b>b</b>) EC, (<b>c</b>) DO, (<b>d</b>) NTU, (<b>e</b>) TOC, (<b>f</b>) COD<sub>Cr</sub>, (<b>g</b>) COD<sub>Mn</sub>, (<b>h</b>) NH<sub>3</sub>-N, (<b>i</b>) TN, and (<b>j</b>) TP.</p> Full article ">Figure 3
<p>EEM spectroscopies of DOMs from the crab farming wastewater at sampling site.</p> Full article ">Figure 4
<p>UV-visible absorbing spectra at 200–700 nm (<b>a</b>) and 230–500 nm (<b>b</b>).</p> Full article ">Figure 5
<p>PARAFAC components identified from EEM spectroscopies of DOMs in the crab farming industry park.</p> Full article ">Figure 5 Cont.
<p>PARAFAC components identified from EEM spectroscopies of DOMs in the crab farming industry park.</p> Full article ">Figure 6
<p>Fmax (<b>a</b>) and proportions (<b>b</b>) of DOM fractions in the crab farming industry park.</p> Full article ">Figure 7
<p>Synchronous and asynchronous maps as described by 2DCOS of DOMs from the crab farming park between C1 and C2 (<b>a</b>,<b>b</b>), C1 and C3 (<b>c</b>,<b>d</b>), C2 and C5 (<b>e</b>,<b>f</b>), C5 and C6 (<b>g</b>,<b>h</b>), C4 and C6 (<b>i</b>,<b>j</b>), C4 and C7 (<b>k</b>,<b>l</b>).</p> Full article ">Figure 8
<p>MW-2DCOS map of a given unit in the crab farming park of C1 (<b>a</b>), C2 (<b>b</b>), C3 (<b>c</b>), C4 (<b>d</b>), C5 (<b>e</b>), C6 (<b>f</b>), and C7 (<b>g</b>).</p> Full article ">Figure 9
<p>Synchronous map (<b>a</b>) and asynchronous map (<b>b</b>) of 2DCOS using UV-visible absorbing spectra at 230–450 nm.</p> Full article ">Figure 10
<p>Plots based on the RDA of the interactions between response variables and environmental explanatory variables (solid arrows with red fonts are the response variables and hollow arrows with black fonts are the environmental explanatory variables).</p> Full article ">Figure 11
<p>SEM modeling for the relationship between fluorescent components (C1, C2, C4, C5), water quality parameters (CODMn, DO), and spectroscopic indices (SUVA254), and contributions to removal efficiencies of FDOMs and TOC (R-FDOM, R-TOC). Significance levels of standardized path coefficient are: *** <span class="html-italic">p</span> < 0.001, ** <span class="html-italic">p</span> < 0.01, * <span class="html-italic">p</span> < 0.05.</p> Full article ">
<p>Generalized diagram of the crab farming industry park and locations of sampling sites.</p> Full article ">Figure 2
<p>Variations in water quality parameters in different treatment process sections of the crab farming park. (<b>a</b>) pH, (<b>b</b>) EC, (<b>c</b>) DO, (<b>d</b>) NTU, (<b>e</b>) TOC, (<b>f</b>) COD<sub>Cr</sub>, (<b>g</b>) COD<sub>Mn</sub>, (<b>h</b>) NH<sub>3</sub>-N, (<b>i</b>) TN, and (<b>j</b>) TP.</p> Full article ">Figure 3
<p>EEM spectroscopies of DOMs from the crab farming wastewater at sampling site.</p> Full article ">Figure 4
<p>UV-visible absorbing spectra at 200–700 nm (<b>a</b>) and 230–500 nm (<b>b</b>).</p> Full article ">Figure 5
<p>PARAFAC components identified from EEM spectroscopies of DOMs in the crab farming industry park.</p> Full article ">Figure 5 Cont.
<p>PARAFAC components identified from EEM spectroscopies of DOMs in the crab farming industry park.</p> Full article ">Figure 6
<p>Fmax (<b>a</b>) and proportions (<b>b</b>) of DOM fractions in the crab farming industry park.</p> Full article ">Figure 7
<p>Synchronous and asynchronous maps as described by 2DCOS of DOMs from the crab farming park between C1 and C2 (<b>a</b>,<b>b</b>), C1 and C3 (<b>c</b>,<b>d</b>), C2 and C5 (<b>e</b>,<b>f</b>), C5 and C6 (<b>g</b>,<b>h</b>), C4 and C6 (<b>i</b>,<b>j</b>), C4 and C7 (<b>k</b>,<b>l</b>).</p> Full article ">Figure 8
<p>MW-2DCOS map of a given unit in the crab farming park of C1 (<b>a</b>), C2 (<b>b</b>), C3 (<b>c</b>), C4 (<b>d</b>), C5 (<b>e</b>), C6 (<b>f</b>), and C7 (<b>g</b>).</p> Full article ">Figure 9
<p>Synchronous map (<b>a</b>) and asynchronous map (<b>b</b>) of 2DCOS using UV-visible absorbing spectra at 230–450 nm.</p> Full article ">Figure 10
<p>Plots based on the RDA of the interactions between response variables and environmental explanatory variables (solid arrows with red fonts are the response variables and hollow arrows with black fonts are the environmental explanatory variables).</p> Full article ">Figure 11
<p>SEM modeling for the relationship between fluorescent components (C1, C2, C4, C5), water quality parameters (CODMn, DO), and spectroscopic indices (SUVA254), and contributions to removal efficiencies of FDOMs and TOC (R-FDOM, R-TOC). Significance levels of standardized path coefficient are: *** <span class="html-italic">p</span> < 0.001, ** <span class="html-italic">p</span> < 0.01, * <span class="html-italic">p</span> < 0.05.</p> Full article ">
Open AccessArticle
Hydrological Monitoring System of the Navío-Quebrado Coastal Lagoon (Colombia): A Very Low-Cost, High-Value, Replicable, Semi-Participatory Solution with Preliminary Results
by
Andrea Gianni Cristoforo Nardini, Jairo R. Escobar Villanueva and Jhonny I. Pérez-Montiel
Water 2024, 16(16), 2248; https://doi.org/10.3390/w16162248 - 9 Aug 2024
Abstract
Like many coastal lagoons in several countries, the “Navío Quebrado” lagoon (La Guajira, Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, including climate change, are threatening its existence because of changes
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Like many coastal lagoons in several countries, the “Navío Quebrado” lagoon (La Guajira, Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, including climate change, are threatening its existence because of changes in the governing hydro-morphological and biological processes. Certainly, the first step to addressing this problem is to understand its hydrological behavior and to be able to replicate, via simulation, its recent history before inferring likely futures. These potential futures will be marked by changes in the water input by its tributary, the Camarones River, and by modified water exchange with the sea, according to a foreseen sea level rise pattern, as well as by a different evaporation rate from the free surface, according to temperature changes. In order to achieve the required ability to simulate future scenarios, data on the actual behavior have to be gathered, i.e., a monitoring system has to be set up, which to date is non-existent. Conceptually, designing a suitable monitoring system is not a complex issue and seems easy to implement. However, the environmental, socio-cultural, and socio-economic context makes every little step a hard climb. An extremely simple—almost “primitive”—monitoring system has been set up in this case, which is based on very basic measurements of river flow velocity and water levels (river, lagoon, and sea) and the direct participation of local stakeholders, the most important of which is the National Park unit of the Sanctuary. All this may clash with the latest groovy advances of science, such as in situ automatized sensors, remote sensing, machine learning, and digital twins, and several improvements are certainly possible and desirable. However, it has a strong positive point: it provides surprisingly reasonable data and operates at almost zero additional cost. Several technical difficulties made this exercise interesting and worthy of being shared. Its novelty lies in showing how old, simple methods may offer a working solution to new challenges. This humble experience may be of help in several other similar situations across the world.
Full article
(This article belongs to the Special Issue Climate Change and Hydrological Processes)
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![](https://pub.mdpi-res.com/water/water-16-02248/article_deploy/html/images/water-16-02248-g001-550.jpg?1723197399)
Figure 1
Figure 1
<p>Scheme of the typical hydrological and ecological cycle of a coastal lagoon in La Guajira: (<b>a</b>) dry season; (<b>b</b>) flood season with opening of la boca and outflow of semi-fresh water; (<b>c</b>) sea–lagoon exchange according to the tide.</p> Full article ">Figure 2
<p>Navío-Quebrado (Camarones) lagoon: (<b>a</b>) wet season; (<b>b</b>) opening of the mouth (“la boca”); (<b>c</b>) the bar between the sea and lagoon (closed mouth).</p> Full article ">Figure 3
<p>Study area: (<b>a</b>) general location; (<b>b</b>) location of specific points of interest.</p> Full article ">Figure 4
<p>Location of hydrometers: (<b>a</b>) view from downstream at Puente Troncal; (<b>b</b>) view from the observation point at Puente Viejo; (<b>c</b>) rule at Puente Troncal; (<b>d</b>) rule at the same site during a flood (this is located on the opposite side of the pillar).</p> Full article ">Figure 5
<p>Cross-section at Puente Troncal. It can be noted that the 0 of the hydrometer (on the right) was placed where the water was on the day of installation; however, the water level can be lower (the depth was estimated by directly wading into the section). This means that negative values of the water height h are also possible. The wetted topography was manually surveyed by measuring depth with respect to the water surface every 100 cm, as represented in the figure.</p> Full article ">Figure 6
<p>Difficult Access to the measurement sites: (<b>a</b>) Puente Troncal; (<b>b</b>) Puente Viejo.</p> Full article ">Figure 7
<p>Stage–discharge relationship (polynomial regression) of the Tomarrazon-Camarones River in Puente Troncal with gauging data from 23 April 2022 until 23 October 2023 (y denotes elevation m asl).</p> Full article ">Figure 8
<p>Analytic relationships (approximated) for the cross-section of the river at P.Troncal: (<b>a</b>) wetted area A = A(h); (<b>b</b>) wetted perimeter p = p(h).</p> Full article ">Figure 9
<p>Matching between measured Q and Q estimated via the Chezy–Manning equation (R<sup>2</sup> = 0.9738), with data from 23 April 2022–23 October 2023.</p> Full article ">Figure 10
<p>Surprise from new data on the Tomarrazon-Camarones River in Puente Troncal: (<b>a</b>) Stage–discharge relationship (power law regression, R<sup>2</sup> = 0.9172) with gauging data from 23 April 2022 until 23 November 2023; (<b>b</b>) matching between measured and estimated values (red line: perfect matching, dotted line: linear regression with R<sup>2</sup> = 0.9119).</p> Full article ">Figure 11
<p>Deviation Q measured vs. Q estimated by the found stage–discharge relationship (m<sup>3</sup>/<sub>s</sub>) as a function of the water elevation y<sub>Lagoon</sub> (in cm above sea level). The blue dotted line interpolates the points linearly.</p> Full article ">Figure 12
<p>Improvement of the matching between measured Q and Q estimated by the Q = Q(y<sub>river</sub>, y<sub>lagoon</sub>) relationship (light blue dots are the same as in <a href="#water-16-02248-f009" class="html-fig">Figure 9</a> for ease of comparison). Data until 23 November 2023.</p> Full article ">Figure 13
<p>Extract of the time series of recorded data (at hourly time steps) showing the inconsistency between lagoon data and sea data, which are always higher than 0 and higher than the lagoon levels (top: sea elevation data kindly provided by DIMAR: daily moving average indicated by the darker line; bottom: lagoon water elevation data collected by our project).</p> Full article ">Figure 14
<p>General view of the lagoon water level measurement system.</p> Full article ">Figure 15
<p>Construction details of the water surface measurement system: (<b>a</b>) sealed inlet of the hydrometer; (<b>b</b>) filtering lateral surface of the piezometer covered by a plastic grid and inserted into gravel-filled holes; (<b>c</b>) fully installed system.</p> Full article ">Figure 16
<p>Scheme of the construction details of the measuring systems: (<b>a</b>) hydrometer and (<b>b</b>) piezometer.</p> Full article ">Figure 17
<p>Alteration of the measurement of the water level h because of the volume of the inserted rule.</p> Full article ">Figure 18
<p>“Instantaneous altimetry” criterion: Elevation pattern of lagoon perimeter according to satellite images taken in 2017 (basis of the adopted DEM). The local peaks (“outliers”) are attributed to DEM imperfections, possibly due to imprecision in the definition of the water surface polygon which may create incorrect height values. What counts here, anyway, is the prevailing behavior. The mean elevation is denoted by the brown bar.</p> Full article ">Figure 19
<p>Horizontality check: synchronic monitoring criterion: (<b>a</b>) original data obtained; (<b>b</b>) the three sets of curves refer to three different survey days (in May, no exchange with the sea or river inflow and negligible evaporation effect during daytime, so constant values; in June, outgoing flow is emptying the lagoon, although a moderate river inflow was present; in November a significant river inflow is filling the lagoon, in spite of a moderate open mouth); the top curves refer to the lagoon, the bottom ones to the river at the same time: a synchronic behavior is apparent, as well as the existence of an elevation difference of about 12–20 cm.</p> Full article ">Figure 20
<p>Instantaneous altimetry test based on DEM analysis: Shore affected by lower (<b>a</b>) and higher (<b>b</b>) elevations; location of anomalous points: the most depressed point (y= −1 m.a.s.l) corresponds to the boca and was most probably captured near the surface of the sea; the highest one, on the other hand, lies in the middle of nowhere and seems to be a local imperfection.</p> Full article ">Figure 21
<p>Details of the mouth and velocity measurements: (<b>a</b>) lagoon during an “open period”; (<b>b</b>) Our vehicle for surveying the cross-section; (<b>c</b>) Manual measurement of depth and velocity.</p> Full article ">Figure 22
<p>Spatial pattern of 111 GNSS-RTK points (red). The background image is a Landsat 8 of 20 September 2022 when the lagoon was at maximum filling. The false color image identifies water (dark blue tone) under a combination of bands: NIR, SWIR1, and Red.</p> Full article ">Figure 23
<p>Hypsometric curves: Surface area S = S(y) (m<sup>2</sup>); Storage volume V= V(y) (m<sup>3</sup>) related to lagoon elevation y<sub>L</sub> [masl]. Polynomial curves: S(y) = 8010914.35 y<sup>3</sup> − 8335673.88 y<sup>2</sup> + 7166288.16 y + 16056191.36 (R<sup>2</sup> = 1.00); V(y) = 4983114.71 y<sup>2</sup> + 15272088.13 y + 8414661.47 (R<sup>2</sup> = 1.00).</p> Full article ">Figure 24
<p>Climatological variables of the study area (from IDEAM data: Rain from Camarones station ID 15050010. All others from Riohacha station ID 15065180).</p> Full article ">Figure 25
<p>Output of the monitoring system for the period of 10 December 2021 to 14 January 2023 (hourly time step; one square is 500 h), with no correction for the sea level data. At the bottom is the status of the lagoon mouth: C: closed; O: Open; S: Semi-open.</p> Full article ">Figure 26
<p>Summary of the whole exercise conducted to set up the hydrological monitoring system.</p> Full article ">
<p>Scheme of the typical hydrological and ecological cycle of a coastal lagoon in La Guajira: (<b>a</b>) dry season; (<b>b</b>) flood season with opening of la boca and outflow of semi-fresh water; (<b>c</b>) sea–lagoon exchange according to the tide.</p> Full article ">Figure 2
<p>Navío-Quebrado (Camarones) lagoon: (<b>a</b>) wet season; (<b>b</b>) opening of the mouth (“la boca”); (<b>c</b>) the bar between the sea and lagoon (closed mouth).</p> Full article ">Figure 3
<p>Study area: (<b>a</b>) general location; (<b>b</b>) location of specific points of interest.</p> Full article ">Figure 4
<p>Location of hydrometers: (<b>a</b>) view from downstream at Puente Troncal; (<b>b</b>) view from the observation point at Puente Viejo; (<b>c</b>) rule at Puente Troncal; (<b>d</b>) rule at the same site during a flood (this is located on the opposite side of the pillar).</p> Full article ">Figure 5
<p>Cross-section at Puente Troncal. It can be noted that the 0 of the hydrometer (on the right) was placed where the water was on the day of installation; however, the water level can be lower (the depth was estimated by directly wading into the section). This means that negative values of the water height h are also possible. The wetted topography was manually surveyed by measuring depth with respect to the water surface every 100 cm, as represented in the figure.</p> Full article ">Figure 6
<p>Difficult Access to the measurement sites: (<b>a</b>) Puente Troncal; (<b>b</b>) Puente Viejo.</p> Full article ">Figure 7
<p>Stage–discharge relationship (polynomial regression) of the Tomarrazon-Camarones River in Puente Troncal with gauging data from 23 April 2022 until 23 October 2023 (y denotes elevation m asl).</p> Full article ">Figure 8
<p>Analytic relationships (approximated) for the cross-section of the river at P.Troncal: (<b>a</b>) wetted area A = A(h); (<b>b</b>) wetted perimeter p = p(h).</p> Full article ">Figure 9
<p>Matching between measured Q and Q estimated via the Chezy–Manning equation (R<sup>2</sup> = 0.9738), with data from 23 April 2022–23 October 2023.</p> Full article ">Figure 10
<p>Surprise from new data on the Tomarrazon-Camarones River in Puente Troncal: (<b>a</b>) Stage–discharge relationship (power law regression, R<sup>2</sup> = 0.9172) with gauging data from 23 April 2022 until 23 November 2023; (<b>b</b>) matching between measured and estimated values (red line: perfect matching, dotted line: linear regression with R<sup>2</sup> = 0.9119).</p> Full article ">Figure 11
<p>Deviation Q measured vs. Q estimated by the found stage–discharge relationship (m<sup>3</sup>/<sub>s</sub>) as a function of the water elevation y<sub>Lagoon</sub> (in cm above sea level). The blue dotted line interpolates the points linearly.</p> Full article ">Figure 12
<p>Improvement of the matching between measured Q and Q estimated by the Q = Q(y<sub>river</sub>, y<sub>lagoon</sub>) relationship (light blue dots are the same as in <a href="#water-16-02248-f009" class="html-fig">Figure 9</a> for ease of comparison). Data until 23 November 2023.</p> Full article ">Figure 13
<p>Extract of the time series of recorded data (at hourly time steps) showing the inconsistency between lagoon data and sea data, which are always higher than 0 and higher than the lagoon levels (top: sea elevation data kindly provided by DIMAR: daily moving average indicated by the darker line; bottom: lagoon water elevation data collected by our project).</p> Full article ">Figure 14
<p>General view of the lagoon water level measurement system.</p> Full article ">Figure 15
<p>Construction details of the water surface measurement system: (<b>a</b>) sealed inlet of the hydrometer; (<b>b</b>) filtering lateral surface of the piezometer covered by a plastic grid and inserted into gravel-filled holes; (<b>c</b>) fully installed system.</p> Full article ">Figure 16
<p>Scheme of the construction details of the measuring systems: (<b>a</b>) hydrometer and (<b>b</b>) piezometer.</p> Full article ">Figure 17
<p>Alteration of the measurement of the water level h because of the volume of the inserted rule.</p> Full article ">Figure 18
<p>“Instantaneous altimetry” criterion: Elevation pattern of lagoon perimeter according to satellite images taken in 2017 (basis of the adopted DEM). The local peaks (“outliers”) are attributed to DEM imperfections, possibly due to imprecision in the definition of the water surface polygon which may create incorrect height values. What counts here, anyway, is the prevailing behavior. The mean elevation is denoted by the brown bar.</p> Full article ">Figure 19
<p>Horizontality check: synchronic monitoring criterion: (<b>a</b>) original data obtained; (<b>b</b>) the three sets of curves refer to three different survey days (in May, no exchange with the sea or river inflow and negligible evaporation effect during daytime, so constant values; in June, outgoing flow is emptying the lagoon, although a moderate river inflow was present; in November a significant river inflow is filling the lagoon, in spite of a moderate open mouth); the top curves refer to the lagoon, the bottom ones to the river at the same time: a synchronic behavior is apparent, as well as the existence of an elevation difference of about 12–20 cm.</p> Full article ">Figure 20
<p>Instantaneous altimetry test based on DEM analysis: Shore affected by lower (<b>a</b>) and higher (<b>b</b>) elevations; location of anomalous points: the most depressed point (y= −1 m.a.s.l) corresponds to the boca and was most probably captured near the surface of the sea; the highest one, on the other hand, lies in the middle of nowhere and seems to be a local imperfection.</p> Full article ">Figure 21
<p>Details of the mouth and velocity measurements: (<b>a</b>) lagoon during an “open period”; (<b>b</b>) Our vehicle for surveying the cross-section; (<b>c</b>) Manual measurement of depth and velocity.</p> Full article ">Figure 22
<p>Spatial pattern of 111 GNSS-RTK points (red). The background image is a Landsat 8 of 20 September 2022 when the lagoon was at maximum filling. The false color image identifies water (dark blue tone) under a combination of bands: NIR, SWIR1, and Red.</p> Full article ">Figure 23
<p>Hypsometric curves: Surface area S = S(y) (m<sup>2</sup>); Storage volume V= V(y) (m<sup>3</sup>) related to lagoon elevation y<sub>L</sub> [masl]. Polynomial curves: S(y) = 8010914.35 y<sup>3</sup> − 8335673.88 y<sup>2</sup> + 7166288.16 y + 16056191.36 (R<sup>2</sup> = 1.00); V(y) = 4983114.71 y<sup>2</sup> + 15272088.13 y + 8414661.47 (R<sup>2</sup> = 1.00).</p> Full article ">Figure 24
<p>Climatological variables of the study area (from IDEAM data: Rain from Camarones station ID 15050010. All others from Riohacha station ID 15065180).</p> Full article ">Figure 25
<p>Output of the monitoring system for the period of 10 December 2021 to 14 January 2023 (hourly time step; one square is 500 h), with no correction for the sea level data. At the bottom is the status of the lagoon mouth: C: closed; O: Open; S: Semi-open.</p> Full article ">Figure 26
<p>Summary of the whole exercise conducted to set up the hydrological monitoring system.</p> Full article ">
Open AccessArticle
Numerical Simulation Study on Three-Dimensional Flow Characteristics and Probability Density Distribution of Water-Permeable Gabion Backflow Zone in Different Curvature Bends
by
Peng Xie, Suiju Lv, Zelin Li, Ying Zhang and Jianping Lv
Water 2024, 16(16), 2247; https://doi.org/10.3390/w16162247 - 9 Aug 2024
Abstract
This study explored the three-dimensional flow characteristics in a recirculation zone near a permeable buttress in curved channels with varying curvatures. Understanding these characteristics is crucial for managing natural river bends, as rivers often meander, with backwater zones formed behind obstructions, such as
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This study explored the three-dimensional flow characteristics in a recirculation zone near a permeable buttress in curved channels with varying curvatures. Understanding these characteristics is crucial for managing natural river bends, as rivers often meander, with backwater zones formed behind obstructions, such as mountains in the riverbed. The direct comparison of the recirculation zones across different bend types revealed the correlation between the flow characteristics and bend curvature. However, previous studies have focused on flow velocities and turbulent kinetic energy without a probability density analysis. This analysis provided a more comprehensive understanding of the flow characteristics. Gaussian kernel density estimation was applied in this study to observe the distribution of the flow velocities, turbulent kinetic energy, and turbulent kinetic energy dissipation rate. The results indicated that the longitudinal time-averaged flow velocity in the recirculation zone typically ranged from −0.2 m/s to −0.8 m/s, with all the skewness coefficients exceeding 0. The horizontal time-averaged flow velocity in the recirculation zone fell between −0.175 m/s and −0.1 m/s. The skewness coefficients were negative at water depths of 16%, 33%, and 50% within the 90° and 180° bends, indicating a non-normal distribution. The probability density distribution of turbulent kinetic energy in the recirculation zone was skewed, ranging from 0 to 0.02 m2·s−2, with the skewness coefficient almost always greater than 0. The plot demonstrated multiple peaks, indicating a broad distribution of turbulent kinetic energy rather than a concentration within a specific interval. This distribution included both the high and low regions of turbulent kinetic energy. Although the overall rate of turbulent kinetic energy dissipation in the recirculation zone was relatively low, there were multiple peaks, suggesting the localized areas with higher dissipation rates alongside the regions with lower rates. These findings were significant for managing the meandering river channels, restoring the subaqueous ecosystems, understanding the pollutant diffusion mechanisms in backwater areas, the sedimentation of nutrient-laden sediments, and optimizing the parameters for spur dike design.
Full article
(This article belongs to the Special Issue Mathematical Models of Fluid Dynamics)
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![](https://pub.mdpi-res.com/water/water-16-02247/article_deploy/html/images/water-16-02247-g001-550.jpg?1723183915)
Figure 1
Figure 1
<p>U-curve slope flume.</p> Full article ">Figure 2
<p>High-speed particle image velocimetry (PIV).</p> Full article ">Figure 3
<p>Model of permeable spur dike.</p> Full article ">Figure 4
<p>Boundary Conditions Setup.</p> Full article ">Figure 5
<p>Computational Domain.</p> Full article ">Figure 6
<p>Location plan of measurement points and lines.</p> Full article ">Figure 7
<p>Correlation analysis between the experimental and numerical simulation values of the average flow velocity in the three turbulence models.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>e</b>) Variation of average flow velocity along the longitudinal direction.</p> Full article ">Figure 9
<p>(<b>a</b>–<b>e</b>) Variation of mean flow velocity along the transverse time.</p> Full article ">Figure 10
<p>Longitudinal time-averaged velocity field at different bathymetric planes.</p> Full article ">Figure 11
<p>Transverse time-averaged velocity field at different water depth planes.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>e</b>) Probability density of longitudinal mean flow velocity at different bathymetric planes.</p> Full article ">Figure 13
<p>(<b>a</b>–<b>e</b>) Probability density of average flow velocity transversely at different water depth planes.</p> Full article ">Figure 14
<p>Turbulent kinetic energy contours of different water depth planes in the reflux zone.</p> Full article ">Figure 15
<p>Turbulent energy dispersion rate diagram of different water depth planes in the reflux area.</p> Full article ">Figure 16
<p>(<b>a</b>–<b>e</b>) Probability density of turbulent kinetic energy at different water depth planes in the reflux zone.</p> Full article ">Figure 17
<p>(<b>a</b>–<b>e</b>) Probability density of turbulent energy dispersion rate at different water depth planes in the reflux zone.</p> Full article ">Figure 18
<p>Time-averaged streamlines at different water depth planes in the 45°, 90°, and 180° curve return areas.</p> Full article ">
<p>U-curve slope flume.</p> Full article ">Figure 2
<p>High-speed particle image velocimetry (PIV).</p> Full article ">Figure 3
<p>Model of permeable spur dike.</p> Full article ">Figure 4
<p>Boundary Conditions Setup.</p> Full article ">Figure 5
<p>Computational Domain.</p> Full article ">Figure 6
<p>Location plan of measurement points and lines.</p> Full article ">Figure 7
<p>Correlation analysis between the experimental and numerical simulation values of the average flow velocity in the three turbulence models.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>e</b>) Variation of average flow velocity along the longitudinal direction.</p> Full article ">Figure 9
<p>(<b>a</b>–<b>e</b>) Variation of mean flow velocity along the transverse time.</p> Full article ">Figure 10
<p>Longitudinal time-averaged velocity field at different bathymetric planes.</p> Full article ">Figure 11
<p>Transverse time-averaged velocity field at different water depth planes.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>e</b>) Probability density of longitudinal mean flow velocity at different bathymetric planes.</p> Full article ">Figure 13
<p>(<b>a</b>–<b>e</b>) Probability density of average flow velocity transversely at different water depth planes.</p> Full article ">Figure 14
<p>Turbulent kinetic energy contours of different water depth planes in the reflux zone.</p> Full article ">Figure 15
<p>Turbulent energy dispersion rate diagram of different water depth planes in the reflux area.</p> Full article ">Figure 16
<p>(<b>a</b>–<b>e</b>) Probability density of turbulent kinetic energy at different water depth planes in the reflux zone.</p> Full article ">Figure 17
<p>(<b>a</b>–<b>e</b>) Probability density of turbulent energy dispersion rate at different water depth planes in the reflux zone.</p> Full article ">Figure 18
<p>Time-averaged streamlines at different water depth planes in the 45°, 90°, and 180° curve return areas.</p> Full article ">
Open AccessArticle
The Relationship between Ribosomal RNA Operon Copy Number and Ecological Characteristics of Activated Sludge Microbial Communities across China
by
Jiaying Li, Yunwei Zhao, Ruisi Ye, Jingyue Zhang, Qianhui Chen, Ting Yang, Tan Chen and Bing Zhang
Water 2024, 16(16), 2246; https://doi.org/10.3390/w16162246 - 9 Aug 2024
Abstract
It is well accepted that the high performance of wastewater treatment plants (WWTPs) relies on the microbial community in activated sludge (AS). Hence, it is crucial to illuminate the geographic distributions and influencing factors of the ecological strategies employed by the AS microbial
[...] Read more.
It is well accepted that the high performance of wastewater treatment plants (WWTPs) relies on the microbial community in activated sludge (AS). Hence, it is crucial to illuminate the geographic distributions and influencing factors of the ecological strategies employed by the AS microbial community. Here, we investigated how the ecological strategies of AS microbial communities influenced their ecological characteristics in 60 WWTPs across 15 cities in China. Our study showed that the average rrn copy number of the whole AS microbial community across China was 2.25 ± 0.12. The highest average rrn copy number of the core community indicated that core members tend to be r-strategists with an advantage in rapid pollutant removal and recovery of the community after environmental disturbances. High nutrient availability promoted microorganisms with higher average rrn copy numbers, while long sludge retention time (SRT) was preferred to the microorganisms with lower average rrn copy numbers. Homogenous selection and dispersal limitation were the predominant assembling processes at the city level, with a shift from deterministic to stochastic processes with increasing average rrn copy numbers. Furthermore, more r-strategists participated in chemoheterotrophic functions, while more K-strategists were related to the nitrification processes. Overall, our findings enrich the knowledge of AS microbial ecology and lay the theoretical foundation for the precise regulation of WWTPs.
Full article
(This article belongs to the Special Issue Wastewater Pollution and Control)
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![](https://pub.mdpi-res.com/water/water-16-02246/article_deploy/html/images/water-16-02246-g001-550.jpg?1723180008)
Figure 1
Figure 1
<p>Geographic distributions of sampled cities across China.</p> Full article ">Figure 2
<p>Average <span class="html-italic">rrn</span> copy numbers of the whole, core, rare, and abundant communities (** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001; ◆ indicates outliers).</p> Full article ">Figure 3
<p>Comparison of the whole, core, rare, and abundant average <span class="html-italic">rrn</span> copy numbers and intra-group differences among 15 cities (* indicates <span class="html-italic">P</span> < 0.05; ** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001).</p> Full article ">Figure 4
<p>Spearman correlation analysis of copy number and environmental factors in four groups. NH<sub>3</sub>, F/M, TN, and COD loading describe the nutrient level of AS, while Inf.COD, Inf.BOD, and Inf.TN characterize the nutrient level of the influent wastewater (* indicates <span class="html-italic">P</span> < 0.05; ** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001; black lines indicate cluster analysis).</p> Full article ">Figure 5
<p>Correlation analysis (Pearson correlation) between the average <span class="html-italic">rrn</span> copy number of the whole microbial community and (<b>a</b>–<b>c</b>) taxonomic and (<b>d</b>–<b>f</b>) phylogenetic α diversity. q<sub>0</sub> represents microbial richness, q<sub>1</sub> represents Shannon–Wiener Index, and q<sub>2</sub> represents inverse Simpson index (Red line indicates linear fit).</p> Full article ">Figure 6
<p>Relative contributions of (<b>a</b>) each assembling process and (<b>b</b>) the deterministic and stochastic processes of the whole AS microbial community at the city level.</p> Full article ">Figure 7
<p>Relationship between the (<b>a</b>) deterministic process and (<b>b</b>) stochastic process and the average <span class="html-italic">rrn</span> copy number of the three groups (Blue line indicates linear fit).</p> Full article ">Figure 8
<p>Relationships (Spearman correlation) between average <span class="html-italic">rrn</span> copy numbers: (<b>a</b>) the whole microbial communities, (<b>b</b>) the core community, (<b>c</b>) the rare community, and (<b>d</b>). the abundant community and their corresponding predicted functions based on FAPROTAX.</p> Full article ">
<p>Geographic distributions of sampled cities across China.</p> Full article ">Figure 2
<p>Average <span class="html-italic">rrn</span> copy numbers of the whole, core, rare, and abundant communities (** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001; ◆ indicates outliers).</p> Full article ">Figure 3
<p>Comparison of the whole, core, rare, and abundant average <span class="html-italic">rrn</span> copy numbers and intra-group differences among 15 cities (* indicates <span class="html-italic">P</span> < 0.05; ** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001).</p> Full article ">Figure 4
<p>Spearman correlation analysis of copy number and environmental factors in four groups. NH<sub>3</sub>, F/M, TN, and COD loading describe the nutrient level of AS, while Inf.COD, Inf.BOD, and Inf.TN characterize the nutrient level of the influent wastewater (* indicates <span class="html-italic">P</span> < 0.05; ** indicates <span class="html-italic">P</span> < 0.01; *** indicates <span class="html-italic">P</span> < 0.001; black lines indicate cluster analysis).</p> Full article ">Figure 5
<p>Correlation analysis (Pearson correlation) between the average <span class="html-italic">rrn</span> copy number of the whole microbial community and (<b>a</b>–<b>c</b>) taxonomic and (<b>d</b>–<b>f</b>) phylogenetic α diversity. q<sub>0</sub> represents microbial richness, q<sub>1</sub> represents Shannon–Wiener Index, and q<sub>2</sub> represents inverse Simpson index (Red line indicates linear fit).</p> Full article ">Figure 6
<p>Relative contributions of (<b>a</b>) each assembling process and (<b>b</b>) the deterministic and stochastic processes of the whole AS microbial community at the city level.</p> Full article ">Figure 7
<p>Relationship between the (<b>a</b>) deterministic process and (<b>b</b>) stochastic process and the average <span class="html-italic">rrn</span> copy number of the three groups (Blue line indicates linear fit).</p> Full article ">Figure 8
<p>Relationships (Spearman correlation) between average <span class="html-italic">rrn</span> copy numbers: (<b>a</b>) the whole microbial communities, (<b>b</b>) the core community, (<b>c</b>) the rare community, and (<b>d</b>). the abundant community and their corresponding predicted functions based on FAPROTAX.</p> Full article ">
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