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17 pages, 17273 KiB  
Article
Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia
by Zeineb Kassouk, Emna Ayari, Benoit Deffontaines and Mohamed Ouaja
Remote Sens. 2024, 16(20), 3895; https://doi.org/10.3390/rs16203895 - 19 Oct 2024
Viewed by 1264
Abstract
The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, [...] Read more.
The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article
(This article belongs to the Special Issue Advances in Remote Sensing in Coastal Geomorphology (Third Edition))
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<p>Location of the studied northern Tunisian coastal area (southern Mediterranean seashore) between Cape Serrat and Ragoubet El Golea, showing (blue rectangle) the three main dams and rivers, including Ziatine, Gamgoum, and El Harka. The study area was divided into six zones, according to their morphologies: Three zones are characterized by rocky coasts, a sandy coastal area with a tombolo, and fixed and (semi-)fixed dune zones. The background is the MapTiler Satellite map.</p>
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<p>Flowchart related to Landsat data analysis for the years 1985–2019. It involves the following: (<b>a</b>) the pre-processes steps: radiometric calibration, geometric, and atmospheric corrections; (<b>b</b>) the multi-time coastline extraction based on the Tasseled map transformation (greenness/wetness data extraction); and (<b>c</b>) coastline evolution.</p>
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<p>Net shoreline movement (NSM) in the period from 1985 to 2019 between Cape Serrat and Ragoubet el Golea points. Shoreline retreat is indicated by red lines, while green lines represent relatively unchanged areas. Shoreline advance is indicated by blue lines. The background is the MapTiler Topo map.</p>
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<p>Erosion forms are mainly identified around the tombolo areas, indicated by red lines.</p>
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<p>(<b>a</b>) Seasonal sedimentary balance of shoreline movement based on near-shore movement values (the distance between the oldest and youngest shorelines), net shoreline movement and (<b>b</b>) seasonal NSM variation showing the balance between erosion (blue color) and accretion (red color) and the total NSM value.</p>
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<p>Spatial distribution of dunes as examples of different stages, from stable and vegetated dunes to system instability and the development of a mobile transgressive dune system. High sand dune (Level 1 or L1); incipient dune, L2, and foredune (LN) (background map is a GoogleEarth<sup>®</sup> image of 2019).</p>
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<p>The disappearance of the incipient dune between 1994 and 2018, based on two satellite GoogleEarth<sup>®</sup> views. The phenomena highlight the important erosion process around the tombolo.</p>
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<p>Example of wind action on the dunes based on GoogleEarth<sup>®</sup> time-series imagery (a view of zone 2 (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>)). Green arrows highlight the perpendicular direction of the wind reactivation by a secondary wind from the north–east (NE) of the old dunes under the dominant north–west (NW) wind.</p>
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<p>An example of dune zonation in the study area in relation to wind direction. Five categories of dunes were identified, including near-shore zones, high sand dunes, incipient dunes, foredunes, and transgressive dunes.</p>
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<p>Examples of two dune systems in the study area: (<b>a</b>) the long dunes form caused by the interaction of multiple coastal currents or wind directions; (<b>b</b>) the short dunes form under the influence of a single, dominant current and wind direction. The white line in the sea (<b>a</b>,<b>b</b>) represents the coastal current direction.</p>
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<p>The retreat and removal of dunes (<b>a</b>). A series of parallel dune ridges, with the oldest dune ridges located furthest inland. (<b>b</b>) Formation and growth of flat dune deposits in zone 5 (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>), characterized by semi-fixed dunes.</p>
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<p>The coastal landscape in Cape Serrat (<a href="#remotesensing-16-03895-f001" class="html-fig">Figure 1</a>, zone 1) (<b>a</b>), showing the reshaping of the rocky shoreline from 1994 to 2019. Sedimentary rock layers with visible ripple marks, highlighting geomorphological features caused by weathering and erosion in the cliff, and the abrasion phenomena in the cliff (<b>b</b>).</p>
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<p>The dynamics of coastal erosion and dune dynamics processes, particularly in relation to waves action, dune formations, and stability. (<b>a</b>) shows the coastal features (dunes, inshore sand deposits); (<b>b</b>) the relationship between water level and micro-cliff formation caused by erosion; and (<b>c</b>) illustrates the effects of storm wave attacks on dunes.</p>
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13 pages, 2023 KiB  
Article
Dam Impact on Fish Assemblages Associated with Macrophytes in Natural and Regulated Floodplains of Pandeiros River Basin
by Ivo Gavião Prado, Marcela Alves de Souza, Flávia Freitas Coelho and Paulo Santos Pompeu
Limnol. Rev. 2024, 24(4), 437-449; https://doi.org/10.3390/limnolrev24040025 - 14 Oct 2024
Viewed by 629
Abstract
The impacts of hydropower plants and their reservoirs on floodplains can potentially create new environmental filters and reduce the exchange of organisms and access to habitats. In this study, we aimed to compare the fish assemblage associated with aquatic macrophytes between floodplain lakes [...] Read more.
The impacts of hydropower plants and their reservoirs on floodplains can potentially create new environmental filters and reduce the exchange of organisms and access to habitats. In this study, we aimed to compare the fish assemblage associated with aquatic macrophytes between floodplain lakes under natural conditions and a regulated floodplain lake in the Environmental Protection Area of Rio Pandeiros, Brazil. We tested the hypothesis that in the regulated floodplain lake, there would be a lower richness and a greater of abundance of macrophytes and fish than is natural. We also verified the influence of the seasons, macrophyte bank richness, and biomass on the fish assemblage abundance. The fish assemblages differed between the regulated and natural floodplains due to the higher richness and abundance of fish in the natural floodplains. The presence of non-native and generalist species in the regulated floodplain influenced the dissimilarity between the floodplains. Migratory species have been found only in natural floodplains. Fish abundance was negatively related to macrophyte richness on the regulated lake. There was a lower fish abundance and macrophyte richness in the regulated lake. There was no evidence that macrophyte biomass affected the abundance and richness of fishes. Our results confirm that the Pandeiros small hydroelectric dam affects the fishes’ assemblage and the macrophyte community, since the regulated floodplain lake has a lower richness and abundance of fish. The regulated floodplain lake is connected to a reservoir created by a small hydroelectric dam, which will be removed in the coming years. The removal of this dam might change these dynamics, and this must be evaluated when the change is implemented. Full article
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<p>Locations of the natural and regulated floodplain lakes sampled.</p>
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<p>Species accumulation curves for the sampled floodplains at Pandeiros River, Minas Gerais.</p>
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<p>Non-metric multidimensional scaling analysis (NMDS) of fish assemblage at the natural and regulated floodplains during dry and rainy seasons. N = natural floodplains, R = regulated lake (Stress = 0.19).</p>
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<p>Non-metric multidimensional scaling analysis (NMDS) of the fish assemblage at the natural and regulated floodplains.</p>
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<p>Relationship among fish richness (<b>a</b>) and fish abundance (<b>b</b>) and macrophyte richness for both regulated (dotted blue line) and natural floodplain lakes (red line).</p>
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16 pages, 7270 KiB  
Article
The Drawdown of a Reservoir: Its Effect on Seepage Conditions and Stability of Earth Dams
by Nikolay Aniskin, Andrey Stupivtsev, Stanislav Sergeev and Ilia Bokov
Water 2024, 16(18), 2660; https://doi.org/10.3390/w16182660 - 18 Sep 2024
Viewed by 1043
Abstract
This article addresses the reliability and safety of an earth dam in the case of a change in the reservoir water level. The water level must often be reduced to remove water or as a response to an emergency situation in the process [...] Read more.
This article addresses the reliability and safety of an earth dam in the case of a change in the reservoir water level. The water level must often be reduced to remove water or as a response to an emergency situation in the process of operation of a hydraulic structure. Lower water levels change seepage conditions, such as the surface of depression, values and directions of seepage gradients, seepage rates, and volumetric hydrodynamic loading. Practical hydraulic engineering shows that these changes can have a number of negative consequences. Higher seepage gradients can lead to seepage-triggered deformations in the vicinity of the upstream slope of a structure. Hydrodynamic loads, arising during drawdown, reduce the stability of an upstream slope of a dam and cause its failure. Potential consequences of a drawdown can be evaluated by solving the problem of drawdown seepage for the dam body and base. A numerical solution to this problem is based on the finite element method applied using the PLAXIS 2D software package. Results thus obtained are compared with those obtained using the finite element method in the locally variational formulation. A numerical experiment was conducted to analyze factors affecting the value of the maximum seepage gradient and stability of the earth dam slope. Recommendations were formulated to limit the drawdown parameters and to ensure the safe operation of a structure. Full article
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<p>Schematic diagram of a homogeneous soil dam.</p>
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<p>The results of determining the position of the depression curve at <span class="html-italic">k</span> = 1.92 m/day in the PC Plaxis. The isofields show seepage head distribution in a dam: (<b>a</b>) at the initial moment; (<b>b</b>–<b>d</b>) at times equal to 4, 12, and 18 h from the beginning of drawdown.</p>
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<p>Schematic diagram of a homogeneous earth dam.</p>
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<p>Position of the depression curve for different values of the filtration coefficient of the dam body material on the 18th day after the onset of drawdown.</p>
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<p>Changes in values of the maximum filtration gradient at the upstream slope during drawdown for soils with different permeability: (<b>a</b>) at different drawdown rates for 1:3 upstream slope; (<b>b</b>) at different upstream slopes for the drawdown rate of 1 m/day.</p>
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<p>Breakdown of the filtration gradient during drawdown for the option with the soil filtration coefficient <span class="html-italic">k</span> = 0.001 m/day at the moment of emergence of maximum gradients: (<b>a</b>) at the drawdown rate; (<b>b</b>) at the filtration rate of 1.0 m/day.</p>
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<p>Graph illustrating a change in the maximum filtration gradient in the course of drawdown for soils of different permeability at the following rates (<span class="html-italic">V<sub>drawdown</sub></span>) of the water drawdown: (<b>a</b>) 0.25 m/day; (<b>b</b>) 1.0 m/day, (<b>c</b>) 2.0 m/day.</p>
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<p>Results of computation of the stability factor for different soil options and drawdown rates.</p>
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<p>Failure surface for cases of reservoir drawdown at the rate of 1 m/day and the filtration coefficient of 0.1 m/day: (<b>a</b>) when the reservoir is completely filled; (<b>b</b>) 9.0 m drawdown; (<b>c</b>) 18.0 m drawdown.</p>
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<p>Dependence between the stability factor and the drawdown at the following drawdown rates: (<b>a</b>) 0.25 m/day; (<b>b</b>) 1.0 m/day; (<b>c</b>) 2.0 m/day.</p>
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20 pages, 1236 KiB  
Article
Photo-Fenton Treatment under UV and Vis Light Reduces Pollution and Toxicity in Water from Madín Dam, Mexico
by Deysi Amado-Piña, Rubi Romero, Emmanuel Salazar Carmona, Armando Ramírez-Serrano, Leobardo Manuel Gómez-Oliván, Gustavo Elizalde-Velázquez and Reyna Natividad
Catalysts 2024, 14(9), 620; https://doi.org/10.3390/catal14090620 - 14 Sep 2024
Viewed by 925
Abstract
Water from Madín Dam in Mexico has been shown to contain a wide variety of pollutants such as drugs, pesticides, personal care products and compounds that are released into the environment as waste from production processes. In this work, the effect of the [...] Read more.
Water from Madín Dam in Mexico has been shown to contain a wide variety of pollutants such as drugs, pesticides, personal care products and compounds that are released into the environment as waste from production processes. In this work, the effect of the main process variables on the percentage of total organic carbon (TOC) removal in water samples from the Madín reservoir was studied by applying a photo-Fenton treatment catalyzed with iron-pillared clays. The catalyst was characterized by XRD, N2 physisorption, DRS and XPS. The sampling and characterization of the water from the Madín reservoir was carried out according to Mexican standards. The system for treatment tests was 0.1 L of reaction volume and a controlled temperature of 23–25 °C, and the reaction system was kept under constant stirring. After 4 h of treatment time under UV light, the TOC removal was 90%, and it was 60% under Vis light. The main ROS involved in the photo-Fenton process driven by UVC light were hydroxyl radicals, while hydroperoxyl radicals predominate in the Vis-light-driven process. Evidence of superoxide anion participation was not found. The toxicity of untreated and treated water was assessed on Danio rerio specimens, and it was observed to be reduced after the photo-Fenton treatment. Full article
(This article belongs to the Section Photocatalysis)
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<p>Diffractograms of Fe-PILC and bentonite clay.</p>
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<p>Total organic carbon removal efficiency in water samples from Madín reservoir through different treatment processes. Reaction volume: 0.1 L, T: 25 C, pH<sub>o</sub>: 6.02, treatment time: 60 min, UV light: 254 nm, 166 W/m<sup>2</sup>, Vis light: (3 lamps, 100 W/m<sup>2</sup> each).</p>
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<p>Effect of initial oxygen presence (photo-Fenton-N<sub>2</sub>) and addition of radical scavengers (isopropanol, IPA; benzoquinone, BQ) to the photo-Fenton system under (<b>a</b>) UVC light (254 nm, 166 W/m<sup>2</sup>) and (<b>b</b>) Vis light (3 lamps, 100 W/m<sup>2</sup> each). Reaction conditions: volume: 0.1 L, T: 25 °C, pH<sub>o</sub>: 6.02, catalyst loading (W<sub>cat</sub>): 0.500 g/L, reaction time: 60 min for processes in (<b>a</b>) and 240 min for processes in (<b>b</b>).</p>
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<p>Location of the water sampling stations inside the Madín Dam: New Madín (1), Old Madín (2), entrance of the Tlalnepantla River (3), entrance of the San Juan River (4), dam curtain (5).</p>
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11 pages, 281 KiB  
Article
The Potential of Helichsryum splendidum (Thunb.) Less. for the Restoration of Sites Polluted with Coal Fly Ash
by Alexis Munyengabe, Ledwaba Samuel Kamogelo, Titus Yeliku-ang Ngmenzuma and Maria Fezile Banda
Plants 2024, 13(18), 2551; https://doi.org/10.3390/plants13182551 - 11 Sep 2024
Viewed by 817
Abstract
The disposal of coal fly ash (CFA) generated from coal-fired power stations has serious impact on the ecosystem, by converting large pieces of land to barren ash dams with the potential to contaminate groundwater, surface water, air and soil. The aim of this [...] Read more.
The disposal of coal fly ash (CFA) generated from coal-fired power stations has serious impact on the ecosystem, by converting large pieces of land to barren ash dams with the potential to contaminate groundwater, surface water, air and soil. The aim of this study was to clarify the potential of phytoremediation using Helichrysum splendidum (Thunb.) Less. in areas polluted by CFA through conduction of pot trial experiments for 14 weeks. Plants of the same age were cultivated in CFA to assess their growth, photosynthetic rate and tolerance towards metal toxicity. This study revealed that the CFA was moderately polluted with heavy metals, and a lower photosynthetic rate was recorded for the CFA plants in comparison to the controls (plants grown in soil). Although the CO2 assimilation rate was lower for the CFA plants, increased growth was recorded for all the plants tested. Inductively coupled plasma mass spectrometry (ICP-MS) was used to quantify the amount of trace elements in samples and parameters including translocation factor (TF) and bioconcentration factor (BCF) were used to evaluate the phytoremediation potential of H. splendidum (Thunb.) Less. The results revealed that higher concentrations of Cd, Co, Cr, Cu, Mn and Pb were accumulated in the roots, while As, Ni and Zn were found in the shoots. Elements including As, Cr and Zn reported TF values above 1, indicating the plants’ phytoextraction potential. The BCF values for As, Cu and Zn were 1.22, 1.19 and 1.03, indicating effectiveness in the phytostabilization processes. A removal rate efficiency ranging from 18.0 to 56.7% was recorded confirming that, H. splendidum (Thunb.) Less. can be employed for restoration of CFA dams. Full article
(This article belongs to the Topic Effect of Heavy Metals on Plants, 2nd Volume)
13 pages, 2566 KiB  
Article
Changes in Soil Total and Microbial Biomass Nitrogen in Deforested and Eroded Areas in the Western Black Sea Region of Turkey
by İlyas Bolat and Huseyin Sensoy
Forests 2024, 15(8), 1468; https://doi.org/10.3390/f15081468 - 21 Aug 2024
Viewed by 663
Abstract
The microbial biomass in soil is an active and living constituent of organic matter. It is both a storage pool and a source of plant nutrients that can be used as required. In addition, each microbial indicator evaluates soil quality and health from [...] Read more.
The microbial biomass in soil is an active and living constituent of organic matter. It is both a storage pool and a source of plant nutrients that can be used as required. In addition, each microbial indicator evaluates soil quality and health from different perspectives, which are not necessarily very different. This study was conducted to compare some physical, chemical, and biochemical characteristics of the soils of forest (SF) and deforested (SDE) areas located on the slopes of the Kirazlıköprü area, which was previously deforested due to dam construction in Bartın province in northwestern Turkey. Soil samples were taken from the topsoil surface (0–5 cm) to determine the microbial soil characteristics of the SF and SDE sites. The soil microbial biomass N (Nmic) was determined by chloroform fumigation extraction, and the Cmic/Nmic ratio and Nmic/Ntotal percentage were calculated using the original values. Total N, Nmic and Cmic/Nmic values are higher in the forest area. The lowest and highest total N (Ntotal) contents in the SF and SDE soils varied between 1.50 and 3.47 g kg−1 and 0.91 and 1.46 g kg−1, respectively. Similarly, the Nmic contents of the SF and SDE soils varied between 75.56 and 143.42 μg g−1 and 10.40 and 75.96 μg g−1, respectively. A statistical analysis revealed that the mean Ntotal and mean Nmic values differed (p < 0.05) in the SF and SDE soils. The mean Cmic/Nmic values in the SF and SDE soils were 8.79 (±1.65) and 5.64 (±1.09), respectively, and a statistical difference was found between the fields (p < 0.05). Our findings indicate that the soil microbial community structure varies according to the site. As a result, it can be concluded that deforestation and erosion due to dam construction in the area led to the removal of plant nutrients from the soil; deterioration in the amount and activity of microbial biomass; and, consequently, soil losses and degradation of soil quality. Full article
(This article belongs to the Section Forest Soil)
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<p>Kirazlıköprü Dam and sampling points in forest and deforested sites.</p>
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<p>Images of grooves (<b>A</b>) and gullies (<b>B</b>) in the deforested site.</p>
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<p>Mean total N (<b>A</b>) and mean microbial biomass N (<b>B</b>) values in S<sub>F</sub> and S<sub>DE</sub> soils. The letters in parentheses represent a difference (<span class="html-italic">p</span> &lt; 0.05) between fields.</p>
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<p>Mean N<sub>mic</sub>/N<sub>total</sub> (%) values in S<sub>F</sub> and S<sub>DE</sub> soils. The letters in parentheses represent no difference (<span class="html-italic">p</span> &gt; 0.05) between fields.</p>
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<p>Mean C<sub>mic</sub>/N<sub>mic</sub> values in S<sub>F</sub> and S<sub>DE</sub> soils. The letters in parentheses represent the difference (<span class="html-italic">p</span> &lt; 0.05) between fields.</p>
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26 pages, 4621 KiB  
Article
Recasting Klamath Dam Removal as Eco-Cultural Revitalization and Restorative Justice through Karuk Tribal Leadership
by Sibyl Diver, John R. Oberholzer Dent, Daniel Sarna-Wojcicki, Ron Reed and Cole Dill-De Sa
Water 2024, 16(16), 2295; https://doi.org/10.3390/w16162295 - 14 Aug 2024
Viewed by 2144
Abstract
Moving from an era of dam building to dam removal brings additional perspectives to indigenous water governance and hydrosocial relations in the Klamath River Basin (US). This collaborative research initiative with the Karuk Tribe builds greater understanding of the sociocultural impacts of Klamath [...] Read more.
Moving from an era of dam building to dam removal brings additional perspectives to indigenous water governance and hydrosocial relations in the Klamath River Basin (US). This collaborative research initiative with the Karuk Tribe builds greater understanding of the sociocultural impacts of Klamath dam removal and river restoration through Karuk knowledge. Addressing a knowledge gap around the social dimensions of dam removal, we held focus groups and interviews with Karuk cultural practitioners, tribal leaders, and tribal youth in the six-month period leading up to demolition. Extending beyond a focus on infrastructure removal or single-species restoration, we consider how Indigenous environmental relations and cosmologies are embedded in dam removal and river restoration. Specifically, Karuk knowledge shifts the significance of dam removal by elucidating deeply interconnected ecological, cultural, and ceremonial relations that are co-constituted with the Klamath watershed, thereby recasting dam removal as a holistic eco-cultural revitalization initiative. This reconfigures dam removal goals to include improving community health and well-being, enhancing spiritual elements of river restoration, responding to colonial legacies, and engaging tribal youth. In the Klamath case, restorative justice becomes possible through Karuk participation in river restoration to facilitate the revitalization of reciprocal relations held between Karuk people and the Klamath River—including Karuk eco-cultural and ceremonial practices for restoring balance in the world. Full article
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<p>Former Iron Gate reservoir after drawdown, 2024 (Photo: John R. Oberholzer Dent).</p>
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<p>Karuk aboriginal territory (purple area outlined in bold) and additional tribal lands are located in the mid-Klamath region, downriver from dam removal sites. They include areas around the towns of Yreka, Happy Camp, and Orleans (California and Oregon, US). (Map: Klamath River Renewal Corporation, Berkeley, CA, USA and the Karuk Tribe Department of Natural Resources, Happy Camp, CA).</p>
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<p>Tribal leader and co-author Ron Reed dipnet fishing at Ishi Pishi Falls (Photo: Wingspan Media).</p>
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<p>Based on focus groups and interviews, this diagram shows a simplified model of selected biophysical factors associated with a free-flowing river that affect Karuk cultural resources (not comprehensive). While useful as a schematic, we note that the unidirectional flow of benefits does not represent how Karuk cultural practices, such as coppicing, cultural burning, and ceremony, also shape the system.</p>
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<p>Sunset over the Klamath River in Orleans, CA (Photo: Sibyl Diver).</p>
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<p>Cutting fish with youth for cooking on sticks at Ti Creek traditional foods workshop with Kenneth “Binks” Brink, Jason Reed, and Nate Pennington (Photo: Konrad Fisher).</p>
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27 pages, 3951 KiB  
Article
Seeding Density Alters the Assembly of a Restored Plant Community after the Removal of a Dam in Southern Wisconsin, USA
by Ana J. Wells, John Harrington and Nick J. Balster
Environments 2024, 11(6), 115; https://doi.org/10.3390/environments11060115 - 29 May 2024
Viewed by 909
Abstract
Recently exposed reservoir sediments, prone to colonization by invasive species, provide novel settings to test hypotheses related to soil conditions and propagule supply as potential drivers of plant assembly in disturbed ecosystems. We used a dam removal site in southwestern Wisconsin to examine [...] Read more.
Recently exposed reservoir sediments, prone to colonization by invasive species, provide novel settings to test hypotheses related to soil conditions and propagule supply as potential drivers of plant assembly in disturbed ecosystems. We used a dam removal site in southwestern Wisconsin to examine the relationship between the physiochemical properties of dewatered sediments, seeding density, and plant community assembly. The plant communities from five seed densities (1000, 500, 250, 125, and 0 seed m−2) were annually assessed over four years. We hypothesized (1) that the native aboveground biomass and the proportion of native to invasive (non-seeded species) aboveground biomass would increase with the seeding density and (2) that the diversity of seeded native species would increase with a higher seeding density. We found evidence that sowing at least 500 seeds m−2 of prairie species increased their abundance, establishment, and plot diversity compared to non-seeded plants that persisted four years after seeding (p < 0.05). The seeding density treatments led to the assembly of two distinct communities: “native” and “invasive”. The “native” community, assembled in plots seeded with at least 500 seeds m−2, had a greater aboveground biomass and diversity (i.e., richness) of seeded plants compared to plots with lower seed densities, and its productivity was positively related to this richness. In the “weedy” community, the diversity of invasive species had no relationship to their aboveground biomass, likely because these species share similar traits (i.e., redundancy) and may have performed similar functions within the plant community. These findings suggest that the seeding density interacted with the disturbed soil resources to increase the diversity and productivity of seeded native species and may serve as a positive feedback mechanism for the establishment of native communities in dewatered sediments. Full article
(This article belongs to the Special Issue Environments: 10 Years of Science Together)
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<p>Study site map of a drained reservoir in Green County, Wisconsin (<b>a</b>). The research site was located in the approximate center of the dewatered basin, where the stream naturally flows (<b>b</b>). The 133 by 41.5 m research area was divided into four blocks along the stream, and randomly assigned within each block was one of four (7 by 41.5 m) treatments plots. The treatments represented seeding mixes at four densities (A = 1000, B = 500, C = 250, and D = 125 seed m<sup>−2</sup>). Three control plots (E = control) without a seed application were placed at the beginning, middle, and end of the area (<b>c</b>). Maps are not drawn to scale.</p>
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<p>Differences in mean (<b>a</b>) seeded species and (<b>b</b>) non-seeded aboveground biomass (g m<sup>−2</sup>) among seeding density treatments. Standard errors are plotted with aboveground biomass means over four years (2006–2009) after seeding; there was no significant interaction between year and treatment. Different letters denote significant differences among seeding density treatments at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Differences in mean (<b>a</b>) seeded and (<b>b</b>) non-seeded aboveground biomass (g m<sup>−2</sup>) among four years of seeding. Standard errors are plotted with annual aboveground biomass means independent of seeding density; there was no significant interaction between year and treatment. Different letters denote significant differences among the years after seeding at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Annual differences in the percentage of the seeded species aboveground biomass (%, seeded species g g<sup>−1</sup>) among the different seeding densities. Standard errors are plotted around the aboveground biomass means.</p>
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<p>Annual biplots of the two first principal component scores from the PCA of the aboveground biomass of all the species surveyed in this study. Each point represents the mean score of each of the different seeding density treatments (A = 1000, B = 500, C = 250, D = 125, and E = 0 seeds m<sup>−2</sup>, respectively). Standard errors are plotted around the means. Orthogonal contrast analyses of the PC1 scores revealed significant differences at <span class="html-italic">p</span> &lt; 0.05, denoted by circles, among plant communities assembled under the different seeding treatments in the years 2007 (<b>b</b>) and 2009 (<b>d</b>); no significant differences were found in years 2006 (<b>a</b>) or 2008 (<b>c</b>).</p>
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<p>Effects of seeding density treatments on (<b>a</b>) the mean Shannon–Wiener diversity index (<span class="html-italic">H</span>′ m<sup>−2</sup>), (<b>b</b>) the mean richness (number of species per plot), and (<b>c</b>) the mean evenness index (<span class="html-italic">E</span> m<sup>−2</sup>) of the seeded species community, regardless of year after seeding. Standard errors are plotted with the indices’ means averaged from 2006 to 2009. Different letters denote significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Annual changes in (<b>a</b>) the mean Shannon–Wiener diversity index (<span class="html-italic">H</span>′ m<sup>−2</sup>), (<b>b</b>) the mean richness (number of species per plot), and (<b>c</b>) the mean evenness index (<span class="html-italic">E</span> m<sup>−2</sup>) of the seeded species community regardless of seeding treatment. Standard errors are plotted with the indices’ means averaged across all treatments. Different letters denote significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Annual changes in (<b>a</b>) the mean Shannon–Wiener diversity index (<span class="html-italic">H</span>′ m<sup>−2</sup>) and (<b>c</b>) the mean evenness index (<span class="html-italic">E</span> m<sup>−2</sup>) of the non-seeded species community regardless of seeding treatment. Standard errors are plotted with the indices’ means averaged across all treatments. The effect of the seeding density treatments on (<b>b</b>) the mean Shannon–Wiener diversity index (<span class="html-italic">H</span>′ m<sup>−2</sup>) and (<b>d</b>) the mean evenness index (<span class="html-italic">E</span> m<sup>−2</sup>) of the non-seeded plant community regardless of the year after seeding. Standard errors are plotted, with the indices’ means averaged from 2006 to 2009.</p>
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<p>The annual effect of the seeding density treatment on the mean richness (number of species per plot) of the community of non-seeded species. Standard errors are plotted around the species richness means averaged by year and by seeding density treatment.</p>
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<p>Changes to aboveground biomass (g m<sup>−2</sup>) of seeded (solid circles) and non-seeded (open circles) species with respect to species richness. The regression line describes the relationship between the natural logarithm of the species richness and the aboveground biomass (g m<sup>−2</sup>) of the seeded species. The regression equation is ln (seeded species aboveground biomass) = 0.47 + 2.86 ln (seeded species richness); <span class="html-italic">r</span><sup>2</sup> = 0.58 (<span class="html-italic">p</span> &lt; 0.01). No relationship existed between the number of non-seeded species and their aboveground biomass (<span class="html-italic">r</span><sup>2</sup> = −0.01 <span class="html-italic">p</span> = 0.95).</p>
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<p>Biplot with results from the canonical correspondence analysis for the plant community and soil conditions in 2006. Seeded species are represented with dark circles, non-seeded species with white circles, and significant environmental variables are represented by arrows. The length of the lines associated with the environmental variables indicates their importance in explaining the species assemblage, and the direction indicates how well species composition and environmental variables correlate. The angle of each arrow indicates the correlation between environmental variables.</p>
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<p>Biplot with results from the canonical correspondence analysis for the plant community and soil conditions in 2009. Seeded species are represented with dark circles, non-seeded species with white circles, and environmental variables are represented by arrows. The length of the arrows associated with the environmental variables indicates their importance in explaining the species assemblage, their direction indicates how well species composition and environmental variables correlate, and their angle indicates the correlation between environmental variables.</p>
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16 pages, 1751 KiB  
Article
Prioritisation of Barriers According to Their Impact on Migratory Fish in the Lowland River Basin District
by Tomas Virbickas and Vytautas Kesminas
Fishes 2024, 9(4), 113; https://doi.org/10.3390/fishes9040113 - 22 Mar 2024
Cited by 1 | Viewed by 1383
Abstract
Artificial barriers are one of the most damaging anthropogenic factors, but are also socio-economic constructions, so the decision between removing a barrier and installing a fishway must be justified. The aim was to develop a system to quantify the amount of suitable habitat [...] Read more.
Artificial barriers are one of the most damaging anthropogenic factors, but are also socio-economic constructions, so the decision between removing a barrier and installing a fishway must be justified. The aim was to develop a system to quantify the amount of suitable habitat for migratory fish above barriers, to assess barriers’ passability, to prioritise them in current and historical terms and to take into account the impact of the reservoir in the selection of barrier management alternatives. For this purpose, the proportion of suitable area and the potential number of spawners were calculated separately for the flooded and free-flowing river sections above the barrier. The effect of the flooded area and fishway efficiency on the potential number of individuals was assessed and the historical importance of the areas above each barrier was evaluated. The results show that the proportion of suitable area in the total area above the barriers varies between 5 and 31%. Short river stretches of high habitat quality have a higher reproductive potential than much longer stretches of lower habitat quality. Dams with fishways can still be among the most negatively impacting barriers if they are located in the migration route of fish into a large part of the basin. Full article
(This article belongs to the Section Biology and Ecology)
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Figure 1

Figure 1
<p>Map of study area.</p>
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<p>Barriers affecting anadromous fish migration in the study area (see <a href="#fishes-09-00113-t001" class="html-table">Table 1</a> for barriers indicated by a circled dot and barrier code).</p>
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<p>The position of all 155 dams according to the Rbarr<sub>i</sub> and HRbarr<sub>i</sub> scores and the calculated Asuit in the river network sections above each dam. The primary vertical axis denotes the Rbarr<sub>i</sub> and HRbarr<sub>i</sub> values; the secondary axis denotes Asuit in km<sup>2</sup>.</p>
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<p>Relationship between the channel slope and Asuit/Atot ratio.</p>
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<p>Relationship between the channel slope and density of salmonid parr.</p>
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28 pages, 9615 KiB  
Article
Landscape-Scale Mining and Water Management in a Hyper-Arid Catchment: The Cuajone Mine, Moquegua, Southern Peru
by Morag Hunter, D. H. Nimalika Perera, Eustace P. G. Barnes, Hugo V. Lepage, Elias Escobedo-Pacheco, Noorhayati Idros, David Arvidsson-Shukur, Peter J. Newton, Luis de los Santos Valladares, Patrick A. Byrne and Crispin H. W. Barnes
Water 2024, 16(5), 769; https://doi.org/10.3390/w16050769 - 4 Mar 2024
Cited by 1 | Viewed by 2560
Abstract
The expansion of copper mining on the hyper-arid pacific slope of Southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern [...] Read more.
The expansion of copper mining on the hyper-arid pacific slope of Southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern Copper, provides a unique opportunity in a little-studied region to examine the relative impact of the landscape-scale mining on water resources in the region. Principal component and cluster analyses of the water chemistry data from 16 sites, collected over three seasons during 2017 and 2018, show distinct statistical groupings indicating that, above the settlement of Torata, water geochemistry is a function of chemical weathering processes acting upon underlying geological units, and confirming that the Cuajone mine does not significantly affect water quality in the Torata river. Impact mitigation strategies that firstly divert channel flow around the mine and secondly divert mine waste to the Toquepala river and tailings dam at Quebrada Honda remove the direct effects on the water quality in the Torata river for the foreseeable future. In the study area, our results further suggest that water quality has been more significantly impacted by urban effluents and agricultural runoff than the Cuajone mine. The increase in total dissolved solids in the waters of the lower catchment reflects the cumulative addition of dissolved ions through chemical weathering of the underlying geological units, supplemented by rapid recharge of surface waters contaminated by residues associated with agricultural and urban runoff through the porous alluvial aquifer. Concentrations in some of the major ions exceeded internationally recommended maxima for agricultural use, especially in the coastal region. Occasionally, arsenic and manganese contamination also reached unsafe levels for domestic consumption. In the lower catchment, below the Cuajone mine, data and multivariate analyses point to urban effluents and agricultural runoff rather than weathering of exposed rock units, natural or otherwise, as the main cause of contamination. Full article
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Graphical abstract

Graphical abstract
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<p>Map of selected study site locations in the foothill and headwaters (site 0A, 40 km downstream from site 1 near Ilo, is not shown). Inset shows underlying geological units adapted from Decou et al. [<a href="#B39-water-16-00769" class="html-bibr">39</a>]. Geological lithologies coded by color: pink—Coastal Batholith (intrusive 145–155 Ma); buff—Moquegua Group (sedimentary 50–54 Ma); green—Cretaceous volcanics and Eocene intrusives; grey—Miocene to recent pyroclastic deposits. Study area shown in dashed box.</p>
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<p>Spatial and temporal variation in cations (<math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Na</mi> </mrow> <mo>+</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Ca</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Mg</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mo>+</mo> </msup> </semantics></math>), anions (<math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">SO</mi> </mrow> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Cl</mi> </mrow> <mo>−</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mo>−</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">NO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </semantics></math>) and total trace metals (Cd, Cu, Co, Cr, Li, Pb, Mo, Ni, Se, U, V, and Zn) in water samples from Moquegua river sites plotted against distance upstream from site 1 in km. (<b>a</b>) January 2017 (17-R), (<b>b</b>) July 2017 (17-D) and (<b>c</b>) January 2018 (18-R). Site numbers are given in the labels.</p>
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<p>(<b>a</b>) Na/Cl equivalent molar ratio and (<b>b</b>) SO<sub>4</sub>/Cl equivalent molar ratio for Moquegua river system.</p>
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<p>Al, Fe, and Mn concentrations and pH, showing elevated wet season metal concentrations in the Torata river below the Cuajone mine. Distance is measured upstream from site 1. Red shaded regions show the concentration levels above the safe limits for aquatic life. The Cuajone mine is located between site 5B and site 16 (vertical shaded region). Error bars represent standard deviation of measurement fluctuation. Labels represent site numbers.</p>
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<p>(<b>a</b>) Gibbs plot for the Moquegua river showing changes in water chemistry along the river and (<b>b</b>) compared with other major rivers, Hauang He river, China and Amazon river, rio Grande, AB, USA, and Amazon river, South America. Data from [<a href="#B70-water-16-00769" class="html-bibr">70</a>,<a href="#B71-water-16-00769" class="html-bibr">71</a>].</p>
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<p>Arsenic concentrations in samples from Torata and Moquegua river site. Error bars show the standard deviations of measurement. Red-shaded regions show the concentration levels above the safe limits for potable use. The Cuajone mine is located between site 5B and site 16 (vertical shaded region).</p>
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<p>(<b>a</b>) Dendrogram showing the Euclidean dissimilarity between the 33 measured water parameters taking 17 Torata–Moquegua sample sites and seasons 17-R, 17-D, and 18-R into account. (<b>b</b>) Dendrogram showing the largest dissimilarity between the 17 Torata–Moquegua sample sites in different seasons (identified by the code [SITE]/[YEAR]–[SEASON]), taking 33 measured water parameters into account. (<b>c</b>) Beck map showing the spatial distribution of site clusters. The color map is the same as in (<b>b</b>). The color of the outer ring of each circle identifies the season using the key on the right.</p>
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<p>PCA biplots using 33 water parameters measured at 17 Torata–Moquegua sample sites in the three seasons 17-R, 17-D and 18-R. The colors of the parameter vectors are taken from <a href="#water-16-00769-f007" class="html-fig">Figure 7</a>a. The colors for site points are taken from <a href="#water-16-00769-f007" class="html-fig">Figure 7</a>b,c. (<b>a</b>) PC1 vs. PC2, (<b>b</b>) PC1 vs. PC3. Dashed lines at ±0.2 indicate the qualitative boundary used to indicate where components become significant at a site.</p>
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<p>Combined data sources from field work, ANA data and INGEMMET groundwater reports [<a href="#B5-water-16-00769" class="html-bibr">5</a>,<a href="#B88-water-16-00769" class="html-bibr">88</a>,<a href="#B89-water-16-00769" class="html-bibr">89</a>,<a href="#B90-water-16-00769" class="html-bibr">90</a>]. Concentration of major ions (<b>a</b>) calcium, (<b>b</b>) sodium, and (<b>c</b>) sulphate in the Torata river system.</p>
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<p>Combined data sources from field work, ANA data and INGEMMET groundwater report [<a href="#B5-water-16-00769" class="html-bibr">5</a>,<a href="#B88-water-16-00769" class="html-bibr">88</a>,<a href="#B89-water-16-00769" class="html-bibr">89</a>,<a href="#B90-water-16-00769" class="html-bibr">90</a>]. Concentration of trace elements in the Torata river system. The red dashed line in panel (<b>a</b>) indicates the safe limit for arsenic. The safety limits for cadmium and copper concentrations do not appear in panels (<b>b</b>,<b>c</b>), as they are above every data point. The green dotted lines in panel (<b>a</b>) delimit the area of intensive agricultural land ranging from above Torata to below Moquegua. The red circle in panel (<b>a</b>) identifies data taken at Site O. The blue box in panel (<b>c</b>) outlines data that were taken after a flash flood event.</p>
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<p>Black polygon: area of direct mining and mine related infrastructural impact. Red polygons: upper red polygon = Southern Copper Cuajone mine, middle red polygon = Anglo American Quellaveco mine, lower red polygon = Southern Copper Toquepala mine. Yellow lines: mine waste channels. Blue polygons: Cortaderas 2 and Quebrada Honda tailings dams. Purple line: railway line to Ilo smelter. Pink polygon: Ilo smelter. White lines: Pasto Grande project irrigation canals (tunnels, concrete-lined channels, and existing river channels). Green polygon: AH Pampa Sitana irrigation project.</p>
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28 pages, 7482 KiB  
Article
Coupled Microstructural EBSD and LA-ICP-MS Trace Element Mapping of Pyrite Constrains the Deformation History of Breccia-Hosted IOCG Ore Systems
by Samuel Anthony King, Nigel John Cook, Cristiana Liana Ciobanu, Kathy Ehrig, Yuri Tatiana Campo Rodriguez, Animesh Basak and Sarah Gilbert
Minerals 2024, 14(2), 198; https://doi.org/10.3390/min14020198 - 15 Feb 2024
Cited by 1 | Viewed by 2041
Abstract
Electron backscatter diffraction (EBSD) methods are used to investigate the presence of microstructures in pyrite from the giant breccia-hosted Olympic Dam iron–oxide copper gold (IOCG) deposit, South Australia. Results include the first evidence for ductile deformation in pyrite from a brecciated deposit. Two [...] Read more.
Electron backscatter diffraction (EBSD) methods are used to investigate the presence of microstructures in pyrite from the giant breccia-hosted Olympic Dam iron–oxide copper gold (IOCG) deposit, South Australia. Results include the first evidence for ductile deformation in pyrite from a brecciated deposit. Two stages of ductile behavior are observed, although extensive replacement and recrystallization driven by coupled dissolution–reprecipitation reaction have prevented widespread preservation of the earlier event. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) element maps of pyrite confirm that many pyrite grains display compositional zoning with respect to As, Co, and Ni, but that the zoning is often irregular, patchy, or otherwise disrupted and are readily correlated with observed microstructures. The formation of ductile microstructures in pyrite requires temperatures above ~260 °C, which could potentially be related to heat from radioactive decay and fault displacements during tectonothermal events. Coupling EBSD methods with LA-ICP-MS element mapping allows a comprehensive characterization of pyrite textures and microstructures that are otherwise invisible to conventional reflected light or BSE imaging. Beyond providing new insights into ore genesis and superimposed events, the two techniques enable a detailed understanding of the grain-scale distribution of minor elements. Such information is pivotal for efforts intended to develop new ways to recover value components (precious and critical metals), as well as remove deleterious components of the ore using low-energy, low-waste ore processing methods. Full article
(This article belongs to the Special Issue Microanalysis Applied to Mineral Deposits)
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Figure 1

Figure 1
<p>(<b>A</b>) Generalized stratigraphic column that depicts relationships between basement and cover rocks in the Olympic Dam district and major tectonothermal events, BIF–banded iron formation. (<b>B</b>) Geological map of the district showing major faults and IOCG systems superimposed onto basement geology. Modified after Courtney-Davies et al. [<a href="#B30-minerals-14-00198" class="html-bibr">30</a>].</p>
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<p>Geological sketch map of the Olympic Dam deposit modified after Ehrig et al. [<a href="#B28-minerals-14-00198" class="html-bibr">28</a>] showing zoning with respect to Cu-Fe-sulfide mineralogy: pyrite–chalcopyrite (Py-Ccp), chalcopyrite–bornite (Ccp-Bn) and bornite–chalcocite (Bn-Cc) within the Olympic Dam Breccia Complex (ODBC). Annotations: RDG—Roxby Downs Granite, HEMQ—hematite quartz ± barite breccia.</p>
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<p>Overview reflected light maps (<b>A</b>,<b>B</b>) and scanned images (<b>C</b>,<b>D</b>) of analyzed polished blocks with assemblages and pyrite textures described in <a href="#minerals-14-00198-t001" class="html-table">Table 1</a>. The assemblage in CLC23 is closely analogous to that of CLC6. White boxes shown on the insets mark EBSD maps of pyrite. Abbreviations: Ab—albite, Chl—chlorite, Ccp—chalcopyrite, Hm—hematite, Kfs—K-feldspar, Py—pyrite, Qz—quartz, Sd—siderite, Ser—sericite.</p>
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<p>Reflected light and BSE images of pyrite grains selected for EBSD analysis. (<b>A</b>) Grain impingement in pyrite from sample MV65 with fracturing and pulverization to rock flour with (<b>B</b>,<b>C</b>) BSE images displaying rounded multiphase inclusions within pyrite. (<b>D</b>) Grain rich in healed fractures and inclusions of surrounding gangue from sample MV18B with white box depicting E. (<b>E</b>) Detail of grain from D, showing compositional zoning with respect to Co (white arrows) and presence of fine trails of gangue minerals (black arrows) that seem to offset pyrite zonation patterns. (<b>F</b>) Intact pyrite grain adjacent to a pulverized grain along a rupture in quartz from sample MV37. (<b>G</b>) Sub-idiomorphic highly pulverized pyrite grain from sample CLC6. Abbreviations: Au—native-gold, Cat—cattierite, Ccp—chalcopyrite, Chl—chlorite, Hm—hematite, Py—pyrite, Pyh—pyrrhotite, Qz—quartz, Sd—siderite, Ser—sericite.</p>
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<p>Orientation contrast EBSD maps and data from pyrite in sample MV65 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>A) (<b>A</b>) BC map with a BSE image inset displaying galena trails of sub-micron thickness at the grain margin. (<b>B</b>) Y-IPF map and corresponding (<b>C</b>,<b>D</b>) {100} pole figures with matching colors displaying pyrite orientations from (<b>C</b>) the outlined pyrite in B and (<b>D</b>) pyrite external to ‘outlined pyrite’ larger than 1000 pixels. Pole figures display two recognizable deformations, rotation about a &lt;100&gt; axis labelled D1, and a shift of all {100} axes labelled D2. Abbreviations: Gn—galena, Py—pyrite.</p>
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<p>Orientation contrast maps and data from pyrite in sample MV18B (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>D). (<b>A</b>) BC map and (<b>B</b>) GROD angle map that displays low- (yellow) and medium-angle (cyan) boundaries and progressive misorientation in pyrite. (<b>C</b>) A closeup of the medium-angle boundary domain that displays a lenticular morphology with corresponding {100} and {110} pole figures from the area. (<b>D</b>) Reflected light micrograph depicting the relationship between Co-As-Ni zonation (white arrows), the lenticular domain and microstructure 1 (yellow dotted line). (<b>E</b>) {100} pole figures taken across microstructures labelled 1, 2, and 3 shown on (<b>B</b>). (<b>F</b>) Transects (shown in (<b>B</b>)) taken across subtle misorientation (orange) and low-angle boundaries (red) show cumulative misorientation across the grain.</p>
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<p>Orientation contrast maps and data from pyrite in sample MV37 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>F). (<b>A</b>) BC map displaying microstructures, (<b>B</b>) shown at higher magnification in BSE. (<b>C</b>) Y-IPF map displaying microfractures across the right grain with low- (yellow) and medium-angle (cyan) boundaries in the left grain. (<b>D</b>) {100} pole figure displaying rotation about a &lt;100&gt; axis with colors derived from (<b>E</b>) the TC map that shows two unique orientations. The red pyrite apparently impinges upon the blue, with a (<b>F</b>) closeup BSE image displaying quartz and galena trails at this margin. Ccp—chalcopyrite, Gn—galena, Qz—quartz.</p>
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<p>Orientation contrast EBSD maps and data from pyrite in sample CLC6 (<a href="#minerals-14-00198-f004" class="html-fig">Figure 4</a>G). (<b>A</b>) BC, (<b>B</b>) Z-IPF and (<b>C</b>) GROD angle maps depicting spatial misorientation. (<b>D</b>) {100}/{110}/{111} pole figures with colors derived from (<b>B</b>). Two orientations of rotation are displayed in ‘Py1′ (yellow and red on (<b>B</b>)) and shown (<b>E</b>) along transects. The pink axis rotation annotations on (<b>D</b>) correspond to the pink transect on (<b>B</b>,<b>E</b>), whereas the cyan annotations depicting a shift of all axes on (<b>D</b>) reflects the cyan transect on (<b>B</b>,<b>E</b>). ‘Py2′, shown on (<b>B</b>), exhibits rotation about two axis marked a and b with black annotations on (<b>D</b>).</p>
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<p>(<b>A</b>) BC and (<b>B</b>) GROD angle maps with associated (<b>C</b>) {100}, {110} and {111} pole figures of pyrite from sample CLC23. (<b>D</b>) A transect, with its trajectory shown in (<b>B</b>) by a white line, depicts subtle and progressive cumulative misorientation across the grain.</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grain from sample MV18B annotated with yellow dashed lines corresponding to microstructures 1, 2, 3, and the lenticular {110} domain shown by EBSD (<a href="#minerals-14-00198-f006" class="html-fig">Figure 6</a>). Pyrite contains patchy zonation with respect to As, and antithetic Ni-Co zonation enclosed by a reaction rim relatively enriched in Co and As marked by a white dashed line. Crosscutting microstructures display elevated Co, As, Te, Au, Bi, Pb, and Ag. Scales in counts-per-second.</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grains in sample MV37 annotated with sub angle boundaries shown by EBSD (<a href="#minerals-14-00198-f007" class="html-fig">Figure 7</a>). Pyrite shows patchy zoning with respect to Ni, Co, As, and Au annotated by white dashed lines, with yellow or black dashed lines over microstructures with relative enrichment in Bi and Pb. Scales in counts-per-second; <sup>197</sup>Au in linear scale (n × 10<sup>0</sup>) and <sup>125</sup>Te in linear scale (n × 10<sup>1</sup>).</p>
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<p>LA-ICP-MS maps and corresponding reflected light micrograph of pyrite grain from sample CLC6. Chemical data depicts a relict Ni-rich core, surrounding an oscillatory zonation with respect to Ni, Co, As, Te, and Se and an As-Se-enriched reaction rim. Scales in counts-per-second; <sup>59</sup>Co in linear scale (n × 10<sup>4</sup>) and <sup>77</sup>Se in linear scale (n × 10<sup>2</sup>).</p>
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14 pages, 6473 KiB  
Article
Correlation between the Density of Acipenser sinensis and Its Environmental DNA
by Xiaojing Wang, Guangpeng Feng, Jiazhi Zhu and Wei Jiang
Biology 2024, 13(1), 19; https://doi.org/10.3390/biology13010019 - 28 Dec 2023
Cited by 1 | Viewed by 1672
Abstract
Since the construction of the Gezhouba Dam, Chinese sturgeon (Acipenser sinensis) numbers have gradually declined, rendering this species critically endangered according to the International Union for the Conservation of Nature. Environmental DNA (eDNA) technology plays an important role in monitoring the [...] Read more.
Since the construction of the Gezhouba Dam, Chinese sturgeon (Acipenser sinensis) numbers have gradually declined, rendering this species critically endangered according to the International Union for the Conservation of Nature. Environmental DNA (eDNA) technology plays an important role in monitoring the abundance of aquatic organisms. Species density and biomass have been proven to be estimable by researchers, but the level of accuracy depends on the specific species and ecosystem. In this study, juvenile A. sinensis, an endangered fish, were selected as the research target. Under controlled laboratory conditions in an aquarium, one, two, four, six, and eight juvenile A. sinensis were cultured in five fish tanks, respectively. Water samples were filtered at eight different time points for eDNA content analysis. Additionally, eDNA yield was tested at six different time points after a 0.114 ind./L density of A. sinensis was removed, and the employed degradation model was screened using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The results showed that eDNA content remained stable after 3 days and exhibited a significant positive linear correlation with the density of A. sinensis (R2 = 0.768~0.986). Furthermore, eDNA content was negatively correlated with the 3-day period after the removal of A. sinensis. The power function had the smallest AIC and BIC values, indicating better fitting performance. This study lays a momentous foundation for the application of eDNA for monitoring juvenile A. sinensis in the Yangtze Estuary and reveals the applicability of eDNA as a useful tool for assessing fish density/biomass in natural environments. Full article
(This article belongs to the Section Biochemistry and Molecular Biology)
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<p>The amplification curve of <span class="html-italic">A. sinensis</span> specific primers. (<b>a</b>) Positive amplification. (<b>b</b>) Negative amplification. The red line represents the threshold line, while the green line depicts the amplification curve of the mixed sample. The CT value is determined via the intersection of these two lines.</p>
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<p>The amplification curve of <span class="html-italic">A. sinensis</span> specific primers. (<b>a</b>) Positive amplification. (<b>b</b>) Negative amplification. The red line represents the threshold line, while the green line depicts the amplification curve of the mixed sample. The CT value is determined via the intersection of these two lines.</p>
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<p>The standard qPCR curve of the mtDNA <span class="html-italic">D-loop</span> gene of <span class="html-italic">A. sinensis</span>.</p>
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<p>Variation trend of eDNA yield of <span class="html-italic">A. sinensis</span> cultured in lab tanks.</p>
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<p>Correlation between the density of <span class="html-italic">A. sinensis</span> and the eDNA content from 0 h to 7 days.</p>
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<p>Correlation between the density of <span class="html-italic">A. sinensis</span> and the eDNA content from 3 days to 7 days.</p>
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<p>The degradation relationship between eDNA content and time based on three models.</p>
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24 pages, 5469 KiB  
Article
Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods
by Chuan Lin, Yun Zou, Xiaohe Lai, Xiangyu Wang and Yan Su
Appl. Sci. 2023, 13(19), 10827; https://doi.org/10.3390/app131910827 - 29 Sep 2023
Cited by 4 | Viewed by 1034
Abstract
The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction [...] Read more.
The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)
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<p>Prediction of dam displacement under different scenarios.</p>
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<p>Flowchart of the proposed CSSKEE method.</p>
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<p>Project example chart.</p>
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<p>The decomposition results of the E04 test set deformation via CEEMDAN.</p>
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<p>Comparison of the prototype dam displacement data before and after denoising.</p>
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<p>PACF charts of dam displacement data before and after denoising.</p>
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<p>Performance comparison of different algorithms before and after dam displacement denoising on the test set.</p>
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<p>Performance comparison of the first day predicted values for each model on the test set ((<b>a</b>) EMELM, (<b>b</b>) LSTM, (<b>c</b>) CNN).</p>
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<p>Performance comparison of the second day predicted values for each model on the test set ((<b>a</b>) EMELM, (<b>b</b>) LSTM, (<b>c</b>) CNN).</p>
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<p>Performance comparison of the third day predicted values for each model on the test set ((<b>a</b>) EMELM, (<b>b</b>) LSTM, (<b>c</b>) CNN).</p>
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<p>Clustering results at K = 3.</p>
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<p>Clustering results at K = 6.</p>
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<p>Performance comparison of different number of clusters after denoising on the test set.</p>
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15 pages, 4362 KiB  
Article
A 2D Hydraulic Simulation Model Including Dynamic Piping and Overtopping Dambreach
by Javier Fernández-Pato, Sergio Martínez-Aranda and Pilar García-Navarro
Water 2023, 15(18), 3268; https://doi.org/10.3390/w15183268 - 14 Sep 2023
Viewed by 1422
Abstract
Numerical simulation of unsteady free surface flows using depth averaged equations that consider the presence of initial discontinuities has been often reported for situations dealing with dam break flow. The usual approach is to assume a sudden removal of the gate at the [...] Read more.
Numerical simulation of unsteady free surface flows using depth averaged equations that consider the presence of initial discontinuities has been often reported for situations dealing with dam break flow. The usual approach is to assume a sudden removal of the gate at the dam location. Additionally, in order to prevent any kind of dam risk in earthen dams, it is very interesting to include the possibility of a progressive dam breach leading to dam overtopping or dam piping so that predictive hydraulic models benefit the global analysis of the water flow. On the other hand, when considering a realistic large domain with complex topography, a fine spatial discretization is mandatory. Hence, the number of grid cells is usually very large and, therefore, it is necessary to use parallelization techniques for the calculation, with the use of Graphic Processing Units (GPU) being one of the most efficient, due to the leveraging of thousands of processors within a single device. The aim of the present work is to describe an efficient GPU-based 2D shallow water flow solver (RiverFlow2D-GPU) supplied with the formulation of internal boundary conditions to represent dynamic dam failure processes. The results obtained indicate that it is able to develop a transient flow analysis taking into account several scenarios. The efficiency of the model is proven in two complex domains, leading to >76× faster simulations compared with the traditional CPU computation. Full article
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<p>Cross-section of the expansion due to piping process before the dam collapse (<b>left</b>) and trapezoidal breach evolution after the dam collapse (<b>right</b>).</p>
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<p>Schematic diagram of the piping situation.</p>
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<p>Diagram of the cells in a two-dimensional case with triangular cells.</p>
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<p>Internal boundary cells.</p>
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<p>Bed level (<b>a</b>) and detail of the dam geometry together with the triangular computational mesh (<b>b</b>).</p>
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<p>Temporal evolution of the surface water depth <span class="html-italic">h</span> after the dam breach at the initial state, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.32</mn> </mrow> </semantics></math> h, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.36</mn> </mrow> </semantics></math> h, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.60</mn> </mrow> </semantics></math> h, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> h and <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> h, respectively.</p>
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<p>Pipe/breach discharge (<span class="html-italic">Q</span>) temporal evolution for a piping starting time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> h. Full simulation time (<b>a</b>) and detail at the collapse time (<b>b</b>).</p>
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<p>Temporal evolution of the top (<span class="html-italic">B</span>) and bottom (<span class="html-italic">b</span>) width of the breach for a piping process starting at time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> h. Full simulation time (<b>a</b>) and detail at the collapse time (<b>b</b>).</p>
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<p>Temporal evolution of the top (<math display="inline"><semantics> <msub> <mi>z</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>p</mi> </mrow> </msub> </semantics></math>) and bottom level (<math display="inline"><semantics> <msub> <mi>z</mi> <mi>b</mi> </msub> </semantics></math>) of the breach for a piping process starting at time time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> h.</p>
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<p>Three-dimensional view of the Test Case 2 geometry.</p>
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<p>Details of the computational mesh and dam breach line for Test Case 2.</p>
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<p>Peak breach discharge as a function of dam height for synthetic sets (continuous line) and comparison with the results provided by WinDam C (dashed line).</p>
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15 pages, 2759 KiB  
Article
The Impact of Weir Construction in Korea’s Nakdong River on the Population Genetic Variability of the Endangered Fish Species, Rapid Small Gudgeon (Microphysogobio rapidus)
by Yang-Ki Hong, Kang-Rae Kim, Keun-Sik Kim and In-Chul Bang
Genes 2023, 14(8), 1611; https://doi.org/10.3390/genes14081611 - 11 Aug 2023
Cited by 3 | Viewed by 1512
Abstract
Microphysogobio rapidus, an endemic cyprinid fish species found exclusively in Korea, has been identified in only two tributaries of the Nakdong River. The species predominantly occupies the near-gravel bottom waters within shallow sections of the middle and lower reaches of the river, [...] Read more.
Microphysogobio rapidus, an endemic cyprinid fish species found exclusively in Korea, has been identified in only two tributaries of the Nakdong River. The species predominantly occupies the near-gravel bottom waters within shallow sections of the middle and lower reaches of the river, characterized by swift currents. M. rapidus is currently recognized as a critically endangered species due to its distinct habitat preference, as well as the negative impacts of stream dam development and water environment pollution. In this study, we used 10 microsatellite markers to examine the genetic diversity of M. rapidus in the upper Nam (UN), lower Nam (LN), and Deokcheon Rivers (DC) in Korea, with a specific focus on assessment of the impact of dam development. Fish sampled from the UN and LN showed a greater average number of alleles and allelic richness (A = 18.3–18.4, AR = 13.8) compared to those from DC (A = 11.8, AR = 11.5). The observed heterozygosity among the fish examined ranged from HO = 0.748 (LN) to 0.766 (DC). All three fish groups exhibited a significant departure from Hardy–Weinberg equilibrium (HWE) (p < 0.05). Despite having the largest effective population size (Ne = 175 and 157, respectively), the fish sampled from UN and LN showed the highest inbreeding coefficients (FIS = 0.056–0.053, respectively), which were highly significant (p < 0.01). In contrast, the fish sampled from DC exhibited the smallest effective population size (Ne = 61) and showed an inbreeding coefficient close to zero (p > 0.05). BOTTLENECK analysis and estimated M-ratio values (0.341–0.372) revealed indications of past population size reduction in all fish groups examined. No significant genetic differentiation (FST < 0.05) was detected using the DAPC, STRUCTURE, and AMOVA among the fish studied. However, pairwise comparisons of FST between fish sampled from the Nam and Deokcheon Rivers revealed significant values (p < 0.001) ranging from 0.013 to 0.014. In addition, the closest genetic distance (0.026) was observed between UN and LN, while the greatest distance (0.087) was found between UN and DC. Analysis of gene flow rates among the fish examined indicated asymmetrical gene exchange within the Nam River, which was 31.51% in the downstream direction (from UN to LN), with a minimal gene flow rate (0.41%) in the upstream (from LN to UN) direction. The opposite trend was recorded between DC and LN, with a higher gene flow rate (29.74%) in the upstream direction compared to the downstream direction (0.12%). Our study highlighted the importance of implementing long-term conservation efforts focused on maintaining river integrity by removing water barriers such as weirs that impede fish migration and implementing active protection measures, such as aquaculture breeding and reasonable stocking practices, to preserve M. rapidus in the study area. Full article
(This article belongs to the Special Issue Genetic Studies of Fish)
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<p>(<b>a</b>): Male (<b>top</b>) and female (<b>bottom</b>) <span class="html-italic">Microphysogobio rapidus</span>. (<b>b</b>): <span class="html-italic">M</span>. <span class="html-italic">rapidus</span> and its habitat. (<b>c</b>): <span class="html-italic">M</span>. <span class="html-italic">rapidus</span> from the Nam and Deokcheon river tributaries in Republic of Korea.</p>
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<p>Genetic structure plot of the UN, LN, and DC groups of <span class="html-italic">M. rapidus</span> for putative <span class="html-italic">K</span> = 1–3. Each vertical bar denotes one individual.</p>
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<p>Genetic population plot based on the results of a discriminant analysis of principal components (DAPC) of <span class="html-italic">M</span>. <span class="html-italic">rapidus</span>. 1, Saengcho population (UN); 2, Danseong population (LN); 3, Deokcheon population (DC).</p>
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<p>Genotype distribution according to the DAPC results for <span class="html-italic">M</span>. <span class="html-italic">rapidus</span> populations. 1, Saengcho population (UN); 2, Danseong population (LN); 3, Deokcheon population (DC).</p>
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<p>Migration rates between the UN, LN, and DC groups of <span class="html-italic">M</span>. <span class="html-italic">rapidus</span> in the Nam River basin at Gyeongsangnam-do, Korea. Arrows, smooth migration rates; black bars vertically crossing the river, weirs; black parallelograms, dams; circles, sampling stations; DC, Deokcheon River (Sugok); LN, lower Nam River (Danseong); UN, upper Nam River (Saengcho).</p>
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