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Water, Volume 12, Issue 11 (November 2020) – 343 articles

Cover Story (view full-size image): Remarkable 3D flow structures occur at river confluences with small density differences due to differences in sediment concentration or temperature. We explain these by comparing numerical simulations for an idealized confluence with aerial photographs of several river confluences where color differences express the pattern of density differences at the surface. We analyzed numerical simulations of the Rio Negro–Solimões confluence near Manaus, Brazil, in more detail. View this paper
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12 pages, 2049 KiB  
Technical Note
Calibrations of Suspended Sediment Concentrations in High-Turbidity Waters Using Different In Situ Optical Instruments
by Yunwei Wang, Yun Peng, Zhiyun Du, Hangjie Lin and Qian Yu
Water 2020, 12(11), 3296; https://doi.org/10.3390/w12113296 - 23 Nov 2020
Cited by 12 | Viewed by 2978
Abstract
In environments of high suspended sediment concentration (SSC > 1 kg/m3), efficient measurements of SSC through accurate calibration relationships between turbidity and SSC are necessary for studies on marine sediment dynamics. Here, we investigated the performance of three types of optical [...] Read more.
In environments of high suspended sediment concentration (SSC > 1 kg/m3), efficient measurements of SSC through accurate calibration relationships between turbidity and SSC are necessary for studies on marine sediment dynamics. Here, we investigated the performance of three types of optical instrument (OBS-3A, AQUAlogger 310TY, and RBRsolo3Tu with Seapoint sensor) in observations carried out at the middle of the Jiangsu coast, China. These instruments were calibrated in the lab using the water and suspended sediment samples collected from the observation site. It was found that both the calibration curves of OBS-3A and RBRsolo3Tu have an inflection point (at SSC of ca. 15 kg/m3 for OBS-3A and ca. 2 kg/m3 for RBRsolo3Tu), on either side of which turbidity increases (the left side) or decreases (the right side) with the increasing SSC. Only under SSCs smaller than the inflection point can OBS-3A and RBRsolo3Tu be applied to continuous SSC measurements at a fixed point. However, the turbidity output of AQUAlogger 310TY has always a positive correlation with SSC, which applies for SSC up to 40 kg/m3; thus, three fluid-mud events are quantified during this observation. AQUAlogger 310TY has important prospects for field applications in high-SSC environments. Full article
(This article belongs to the Special Issue Sedimentary Evolution of Estuaries and Coastal Plains: Subsidence, Sediment Loss and Aquifer Hazards)
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<p>Maps of the study area: (<b>a</b>) regional overview. The red rectangle denotes the study area; (<b>b</b>) observation sites. The triangle and circle represent the tripod and the anchored floater, respectively; (<b>c</b>) scheme of the vertical deployment of the instruments.</p> Full article ">Figure 2
<p>Grain-size distributions of the samples of suspended sediment (curves in green, blue, and orange) and bed surface sediment (red curve). The dotted lines mark the grain-size range of silt (4~62.5 μm).</p> Full article ">Figure 3
<p>Laboratory calibration for OBS-3A on the tripod showing the response of <span class="html-italic">Tu</span> to SSC of the lab samples: (<b>a</b>) 0.4 m asb; (<b>b</b>) 1.0 m asb. The dashed horizontal lines mark the maximum values of the measured turbidity.</p> Full article ">Figure 4
<p>Laboratory calibration for 310TY on the tripod showing the response of <span class="html-italic">Tu</span> to SSC of the lab samples: (<b>a</b>) 0.1m asb; (<b>b</b>) 0.2 m asb.</p> Full article ">Figure 5
<p>Laboratory calibration for RBR<span class="html-italic">solo</span><sup>3</sup>Tu bonded to the anchored floater showing the response of <span class="html-italic">Tu</span> to SSC of the lab samples: (<b>a</b>) 0.5<span class="html-italic">H</span> asb; (<b>b</b>) 0.9<span class="html-italic">H</span> asb. <span class="html-italic">H</span> is the water depth.</p> Full article ">Figure 6
<p>Diagram of <span class="html-italic">Tu</span>~SSC conversions: (<b>a</b>) Schematized <span class="html-italic">Tu</span>~SSC calibration curve and the full range of <span class="html-italic">Tu</span> measurement is marked; (<b>b</b>) Tu-1 and Tu-2: two types of temporal variations of <span class="html-italic">Tu</span> both having only one peak (≤full range); (<b>c</b>) SSC-1 and SSC-2: temporal variations of SSC converted from Tu-1 and Tu-2 by the left (monotonic increasing) side of the calibration curve in subplot (<b>a</b>), respectively; (<b>d</b>) Tu-3: the third type temporal variations of <span class="html-italic">Tu</span>, showing two peaks at the full range of <span class="html-italic">Tu</span> measurement; (<b>e</b>) SSC-3-1 and SSC-3-2: possible SSC time series corresponding to Tu-3, in which SSC-3-1 indicates that SSC firstly increases and then decreases between the two peaks of Tu-3 while SSC-3-2 is the opposite. Note that the values in the coordinates only denote relative magnitudes.</p> Full article ">Figure 7
<p>Time series of the in situ SSC measured at the middle of the Jiangsu coast, China, by the three types of turbidimeters (310TY, OBS-3A, and RBR<span class="html-italic">solo</span><sup>3</sup>Tu), starting from 09:30 November 12, 2019. The mean water depth of the observation site is around 7 m.</p> Full article ">Figure 8
<p>Outputs from the six turbidimeters in response to an identical SSC (0.04, 0.51, 0.69, and 1.48 kg/m<sup>3</sup>).</p> Full article ">
16 pages, 3950 KiB  
Article
Short-Term Impact of Tillage on Soil and the Hydrological Response within a Fig (Ficus Carica) Orchard in Croatia
by Leon Josip Telak, Paulo Pereira, Carla S. S. Ferreira, Vilim Filipovic, Lana Filipovic and Igor Bogunovic
Water 2020, 12(11), 3295; https://doi.org/10.3390/w12113295 - 23 Nov 2020
Cited by 16 | Viewed by 3862
Abstract
Tillage is well known to have impacts on soil properties and hydrological responses. This work aims to study the short-term impacts of tillage (0–3 months) on soil and hydrological responses in fig orchards located in Croatia. Understanding the soil hydrological response in the [...] Read more.
Tillage is well known to have impacts on soil properties and hydrological responses. This work aims to study the short-term impacts of tillage (0–3 months) on soil and hydrological responses in fig orchards located in Croatia. Understanding the soil hydrological response in the study area is crucial for soil management due to frequent autumn floods. The hydrological response was investigated using rainfall simulation experiments (58 mm h−1, for 30 min, over 0.785 m2 plots). The results show that the bulk density was significantly higher 3 months after tillage than at 0 and 1 months. The water holding capacity and amount of soil organic matter decreased with time. The water runoff and phosphorous loss (P loss) increased over time. The sediment concentration (SC) was significantly higher 3 months after tillage than in the previous monitoring periods, while sediment loss (SL) and carbon loss (C loss) were significantly lower 0 months after tillage than 3 months after tillage. Overall, there was an increase in soil erodibility with time (high SC, SL, C loss, and P loss), attributed to the precipitation patterns that increase the soil water content and therefore the hydrological response. Therefore, sustainable agricultural practices are needed to avoid sediment translocation and to mitigate floods and land degradation. Full article
(This article belongs to the Special Issue Impact of Land-Use Changes on Surface Hydrology and Water Quality)
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<p>Location of the Peračko Blato study area, the eight experimental plots, and temporal changes in the soil surface within the fig orchard.</p> Full article ">Figure 2
<p>Monthly average precipitation and temperature between 1998 and 2018 and during the study year (2018). Data from the Ploče city meteorological station (43°2′ N, 17°26′ E, 2 m a.s.l.). The meteorological station is located 2.9 km from the studied fig orchard.</p> Full article ">Figure 3
<p>Differences in soil physical properties within the fig orchard study site over the study period: (<b>A</b>) soil water content, (<b>B</b>) water holding capacity, (<b>C</b>) bulk density, (<b>D</b>) mean weight diameter, and (<b>E</b>) water-stable aggregate distribution. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median) and lower box line (quartile 1). Different lower-case letters represent significant differences between monitoring periods (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 3 Cont.
<p>Differences in soil physical properties within the fig orchard study site over the study period: (<b>A</b>) soil water content, (<b>B</b>) water holding capacity, (<b>C</b>) bulk density, (<b>D</b>) mean weight diameter, and (<b>E</b>) water-stable aggregate distribution. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median) and lower box line (quartile 1). Different lower-case letters represent significant differences between monitoring periods (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 4
<p>Differences in soil chemical properties within the fig orchard study site over the study period: (<b>A</b>) soil organic matter and (<b>B</b>) available phosphorus according to the time after tillage. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median), and lower box line (quartile 1). Different lower-case letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 5
<p>Hydrological response over the study period: (<b>A</b>) time to ponding, (<b>B</b>) time to runoff, (<b>C</b>) water runoff, (<b>D</b>) sediment concentration, and (<b>E</b>) sediment loss distribution according to the time after tillage. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median), and lower box line (quartile 1). Different lower-case letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 5 Cont.
<p>Hydrological response over the study period: (<b>A</b>) time to ponding, (<b>B</b>) time to runoff, (<b>C</b>) water runoff, (<b>D</b>) sediment concentration, and (<b>E</b>) sediment loss distribution according to the time after tillage. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median), and lower box line (quartile 1). Different lower-case letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 6
<p>Nutrient loss during the rainfall simulation experiments: (<b>A</b>) carbon loss and (<b>B</b>) phosphorus loss distribution according to the time after tillage. Upper hanging bar (maximum), lower hanging bar (minimum), upper box line (quartile 3), line (median), and lower box line (quartile 1). Different lower-case letters represent significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). Dots next to a boxplot represent measured values.</p> Full article ">Figure 7
<p>Results of the principal component analyses. Relation between Factors 1 and 2, (<b>A</b>) variables and (<b>B</b>) cases. Bulk density (BD); water holding capacity (WHC); soil water content (SWC); mean weight diameter (MWD); water-stable aggregate (WSA) content; soil organic matter (SOM); available phosphorous (P<sub>2</sub>O<sub>5</sub>); time to ponding (TP); time to runoff (TR); water runoff (WR), sediment concentration (SC); sediment loss (SL); carbon loss (C loss) and available phosphorous loss (P loss).</p> Full article ">
19 pages, 4588 KiB  
Article
Assessing Land-Cover Effects on Stream Water Quality in Metropolitan Areas Using the Water Quality Index
by TaeHo Kim, YoungWoo Kim, Jihoon Shin, ByeongGeon Go and YoonKyung Cha
Water 2020, 12(11), 3294; https://doi.org/10.3390/w12113294 - 23 Nov 2020
Cited by 11 | Viewed by 6892
Abstract
This study evaluated the influence of different land-cover types on the overall water quality of streams in urban areas. To ensure national applicability of the results, this study encompassed ten major metropolitan areas in South Korea. Using cluster analysis, watersheds were classified into [...] Read more.
This study evaluated the influence of different land-cover types on the overall water quality of streams in urban areas. To ensure national applicability of the results, this study encompassed ten major metropolitan areas in South Korea. Using cluster analysis, watersheds were classified into three land-cover types: Urban-dominated (URB), agriculture-dominated (AGR), and forest-dominated (FOR). For each land-cover type, factor analysis (FA) was used to ensure simple and feasible parameter selection for developing the minimum water quality index (WQImin). The chemical oxygen demand, fecal coliform (total coliform for FOR), and total nitrogen (nitrate-nitrogen for URB) were selected as key parameters for all land-cover types. Our results suggest that WQImin can minimize bias in water quality assessment by reducing redundancy among correlated parameters, resulting in better differentiation of pollution levels. Furthermore, the dominant land-cover type of watersheds, not only affects the level and causes of pollution, but also influences temporal patterns, including the long-term trends and seasonality, of stream water quality in urban areas in South Korea. Full article
(This article belongs to the Special Issue Assessing Water Quality by Statistical Methods)
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<p>Location of monitoring sites in ten major metropolitan areas of South Korea.</p> Full article ">Figure 2
<p>Clustering results of 35 watersheds, named metropolitan area with numbering, based on six land-cover categories. (<b>a</b>) Dendrogram exhibiting three clusters generated from hierarchical agglomerative cluster analysis. The horizontal dashed gray line represents the height for dendrogram partitioning, (D<sub>link</sub>/D<sub>max</sub>)∙100 &gt; 60. (<b>b</b>) Percentage (%) of the dominant land-cover type for each of the three clusters. The red circle, yellow triangle, and green square denote watersheds that are urban-dominated, agriculture-dominated, and forest-dominated, respectively.</p> Full article ">Figure 3
<p>Long-term (2007–2018) trends of objective water quality index (WQI<sub>obj</sub>) for watersheds with (<b>a</b>) urban-dominated, (<b>b</b>) agriculture-dominated, and (<b>c</b>) forest-dominated land-cover. Blue and red circles denote the mean monthly WQI<sub>obj</sub> for dry and wet seasons, respectively, and vertical lines denote one standard deviation of monthly WQI<sub>obj</sub>. The gray area represents a period exhibiting no significant increase or decrease in WQI<sub>obj</sub> based on the results of seasonal Mann-Kendall tests.</p> Full article ">Figure 4
<p>Relationships between objective water quality index (WQI<sub>obj</sub>) and minimum WQI (WQI<sub>min</sub>) for watersheds with, (<b>a</b>) urban-dominated, (<b>b</b>) agriculture-dominated, and (<b>c</b>) forest-dominated land use. Black circles denote WQI values calculated using the testing data set (2017–2018). Black dotted and blue dashed lines represent one-to-one, and regression lines, respectively. Red square represents the point of intersection between the one-to-one line and regression line.</p> Full article ">Figure 5
<p>Spatial distribution of the water quality index (WQI) in ten major metropolitan areas of South Korea. (<b>a</b>) Mean objective WQI (WQI<sub>obj</sub>) and minimum WQI (WQI<sub>min</sub>) values and grades from 2015 to 2018 for each of the 58 monitoring sites. (<b>b</b>) Relationship between mean WQI<sub>obj</sub> and WQI<sub>min</sub> values.</p> Full article ">Figure 6
<p>Monthly distribution (%) of minimum water quality index (WQI<sub>min</sub>) grades from 2015 to 2018 for watersheds with, (<b>a</b>) urban-dominated, (<b>b</b>) agriculture-dominated, and (<b>c</b>) forest-dominated land-cover. Month names for dry and wet seasons are colored blue and red, respectively.</p> Full article ">
25 pages, 6077 KiB  
Article
Innovative Trend Analysis of Air Temperature and Precipitation in the Jinsha River Basin, China
by Zengchuan Dong, Wenhao Jia, Ranjan Sarukkalige, Guobin Fu, Qing Meng and Qin Wang
Water 2020, 12(11), 3293; https://doi.org/10.3390/w12113293 - 23 Nov 2020
Cited by 19 | Viewed by 4477
Abstract
Trend detection based on hydroclimatological time series is crucial for understanding climate change. In this study, the innovative trend analysis (ITA) method was applied to investigate trends in air temperature and precipitation over the Jinsha River Basin (JRB), China, from 1961 to 2016 [...] Read more.
Trend detection based on hydroclimatological time series is crucial for understanding climate change. In this study, the innovative trend analysis (ITA) method was applied to investigate trends in air temperature and precipitation over the Jinsha River Basin (JRB), China, from 1961 to 2016 based on 40 meteorological stations. Climatic factors series were divided into three categories according to percentile, and the hidden trends were evaluated separately. The ITA results show that annual and seasonal temperatures have significantly increased whereas the variation range of annual temperature tended to narrow. Spatial pattern analysis of the temperature indicates that high elevation areas show more increasing trends than flat areas. Furthermore, according to ITA, significant increase trends are observed in annual precipitation and “high” category of spring precipitation. The sub-basins results show a significant decreasing trend in elevation zones of ≤2000 m and an increasing trend where elevation is >2000 m. Moreover, linkage between temperature and precipitation was analyzed and the potential impact of the combined changes was demonstrated. The results of this study provide a reference for future water resources planning in the JRB and will help advance the understanding of climate change in similar areas. Full article
(This article belongs to the Section Hydrology)
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<p>Location of the Jinsha River Basin (JRB), China, along with meteorological stations, topographical gradients, and sub-basin boundaries.</p> Full article ">Figure 2
<p>Spatial and temporal distributions of annual (<b>a</b>,<b>b</b>) T<sub>min</sub>, (<b>c</b>,<b>d</b>) T<sub>max</sub>, and (<b>e</b>,<b>f</b>) precipitation in the Jinsha River Basin (JRB).</p> Full article ">Figure 3
<p>Illustration of the innovative trend analysis (ITA) method. The solid line is the no-trend (1:1) line and the dashed line is the data line. Yellow, orange, and red points denote the mean central points of the low, medium, and high categories, respectively.</p> Full article ">Figure 4
<p>Median value of S (indicator of ITA) based on annual and seasonal (<b>a</b>) Tmin, (<b>c</b>) Tmax, and (<b>e</b>) precipitation over the whole Jinsha River Basin (JRB). Median value of S (indicator of ITA) was calculated by the annual (<b>b</b>) Tmin, (<b>d</b>) Tmax, and (<b>f</b>) precipitation in four sub-basins of the JRB.</p> Full article ">Figure 5
<p>Results of innovative trend analysis (ITA) for the minimum temperature over the whole JRB: (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual minimum temperature in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB, and (<b>i</b>) MLJRB. Yellow, orange, and red points denote the mean central points of the low, medium, and high categories, respectively.</p> Full article ">Figure 6
<p>Results of innovative trend analysis (ITA) for the maximum temperature over the whole JRB: (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual maximum temperature in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB, and (<b>i</b>) MLJRB. Yellow, orange, and red points denote the mean central points of the low, medium, and high categories, respectively.</p> Full article ">Figure 7
<p>Median value of S (indicator of ITA) for the minimum temperature over the whole JRB: (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual minimum temperature in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB, and (<b>i</b>) MLJRB. Blue, orange, and gray box denote the low, medium, and high categories, respectively.</p> Full article ">Figure 8
<p>Median value of S (indicator of ITA) for maximum temperature over the whole Jinsha River Basin (JRB): (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual maximum temperature in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB, and (<b>i</b>) MLJRB. Blue, orange, and gray box denote the low, medium, and high categories, respectively.</p> Full article ">Figure 9
<p>Correlation between elevation and temperature time series for (<b>a</b>) annual minimum temperature, (<b>b</b>) low category annual minimum temperature, (<b>c</b>) annual maximum temperature, and (<b>d</b>) high category annual maximum temperature during 1961–2016 in the Jinsha River Basin (JRB). Black dashed lines show the average values for all sub-basins.</p> Full article ">Figure 10
<p>Results of innovative trend analysis (ITA) for precipitation over the whole JRB: (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual precipitation in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB, and (<b>i</b>) MLJRB. Yellow, orange, and red points denote the mean central points of the low, medium, and high categories, respectively.</p> Full article ">Figure 11
<p>Median value of S (indicator of ITA) for precipitation over the whole JRB: (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter. Annual precipitation in the sub-basins (<b>f</b>) SRJRB, (<b>g</b>) UPJRB, (<b>h</b>) YLRB and (<b>i</b>) MLJRB. Blue, orange, and gray box denote the low, medium, and high categories, respectively.</p> Full article ">Figure 12
<p>Correlation between elevation and temperature time series of (<b>a</b>) annual precipitation, (<b>b</b>) low category precipitation, (<b>c</b>) medium category precipitation, and (<b>d</b>) high category precipitation during 1961–2016 in the JRB. Black dashed lines show the average values for all sub-basins.</p> Full article ">Figure 13
<p>Spatial distribution of the correlation between seasonal minimum temperature and precipitation during (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p> Full article ">Figure 14
<p>Spatial distribution of the correlation between seasonal maximum temperature and precipitation in the (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p> Full article ">
21 pages, 5100 KiB  
Article
Sources, Influencing Factors, and Pollution Process of Inorganic Nitrogen in Shallow Groundwater of a Typical Agricultural Area in Northeast China
by Xinqiang Du, Jing Feng, Min Fang and Xueyan Ye
Water 2020, 12(11), 3292; https://doi.org/10.3390/w12113292 - 23 Nov 2020
Cited by 10 | Viewed by 2911
Abstract
As one of the largest agricultural areas, the Sanjiang Plain of Northeast China has faced serious inorganic nitrogen pollution of groundwater, but the sources and the formation mechanism of pollution in the regional shallow groundwater remain unclear, which constrains the progress of pollution [...] Read more.
As one of the largest agricultural areas, the Sanjiang Plain of Northeast China has faced serious inorganic nitrogen pollution of groundwater, but the sources and the formation mechanism of pollution in the regional shallow groundwater remain unclear, which constrains the progress of pollution control and agricultural development planning. An investigation on potential nitrogen sources, groundwater inorganic nitrogen compounds (NH4+, NO3−, NO2−), and topsoil total nitrogen concentration (TN) was conducted in a typical paddy irrigation area of Sanjiang Plain. Multivariate statistical analysis combined with geospatial-based assessment was applied to identify the sources, determine the governing influencing factors, and analyze the formation process of inorganic nitrogen compounds in shallow groundwater. The results show that the land use type, oxidation-reduction potential (Eh), groundwater depth, NO2− concentration, and electrical conductivity (EC) are highly correlated with the NO3− pollution in groundwater, while DO and Eh affected the distribution of NH4+ most; the high concentrations of NO3− in sampling wells are most likely to be found in the residential land and are distributed mainly in densely populated areas, whereas the NH4+ compounds are most likely to accumulate in the paddy field or the lands surrounded by paddy field and reach the highest level in the northwest of the area, where the fields were cultivated intensively with higher fertilization rates and highest values of topsoil TN. From the results, it can be concluded that that the NO3− compounds in groundwater originated from manure and domestic waste and accumulated in the oxidizing environment, while the NH4+ compounds were derived from N fertilization and remained steady in the reducing environment. NO2− compounds in groundwater were the immediate products of nitrification as a result of microorganism activities. Full article
(This article belongs to the Special Issue Assessing Water Quality by Statistical Methods)
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<p>Location (<b>a</b>), water richness (<b>b</b>), and landform pattern (<b>c</b>) of Puyang irrigation area.</p> Full article ">Figure 2
<p>Location of groundwater and topsoil samples and distribution of potential nitrogen sources.</p> Full article ">Figure 3
<p>Box plot of NH<sub>4</sub><sup>+</sup> (<b>a</b>), NO<sub>3</sub><sup>−</sup> (<b>b</b>) and NO<sub>2</sub><sup>−</sup> (<b>c</b>) concentration in groundwater from different land use types.</p> Full article ">Figure 4
<p>Box plot of NH<sub>4</sub><sup>+</sup> (<b>a</b>), NO<sub>3</sub><sup>−</sup> (<b>b</b>) and NO<sub>2</sub><sup>−</sup> (<b>c</b>) concentration in groundwater from different well depths.</p> Full article ">Figure 5
<p>Correlation heat map of variables in factor analysis.</p> Full article ">Figure 6
<p>Loading diagram of the variables for principle factors. (<b>a</b>) Loading of the variables for PC1 and PC2. (<b>b</b>) Loading of the variables for PC1 and PC3. (<b>c</b>) Loading of the variables for PC3 and PC2.</p> Full article ">Figure 7
<p>Primary factorial plane of correspondence analysis (CA) based on the variables of NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>−</sup>, Eh, well depth, and land use type.</p> Full article ">Figure 8
<p>Spatial distribution of NH<sub>4</sub><sup>+</sup>-N concentration in groundwater using ordinary kriging: (<b>a</b>) WO<sub>20</sub>, (<b>b</b>) WU<sub>20</sub>.</p> Full article ">Figure 9
<p>Spatial distribution of NO<sub>3</sub><sup>−</sup>-N concentration in groundwater using ordinary kriging: (<b>a</b>) WO<sub>20</sub>, (<b>b</b>) WU<sub>20</sub>.</p> Full article ">Figure 10
<p>Descriptive statistics of total nitrogen (TN) concentration in topsoil of various land use types.</p> Full article ">Figure 11
<p>Scatter plot of topsoil TN and the NH<sub>4</sub><sup>+</sup>-N (<b>a</b>) and NO<sub>3</sub><sup>−</sup>-N (<b>b</b>) concentrations of groundwater in nearby sampling wells.</p> Full article ">
13 pages, 3949 KiB  
Article
Analysis of the Flow Performance of the Complex Cross-Section Module to Reduce the Sedimentation in a Combined Sewer Pipe
by Hyon Wook Ji, Sung Soo Yoo, Dan Daehyun Koo and Jeong-Hee Kang
Water 2020, 12(11), 3291; https://doi.org/10.3390/w12113291 - 23 Nov 2020
Cited by 4 | Viewed by 2740
Abstract
The difference in the amount of stormwater and sewage in a combined sewer system is significantly large in areas where heavy rainfall is concentrated. This leads to a low water level and slow flow velocity inside the pipes, which causes sedimentation and odor [...] Read more.
The difference in the amount of stormwater and sewage in a combined sewer system is significantly large in areas where heavy rainfall is concentrated. This leads to a low water level and slow flow velocity inside the pipes, which causes sedimentation and odor on non-rainy days. A complex cross-section module increases the flow velocity by creating a small waterway inside the pipe for sewage to flow on non-rainy days. While considering Manning’s equation, we applied the principle where the flow velocity is proportional to two-thirds of the power of the hydraulic radius. The flow velocity of a circular pipe with a diameter of 450 mm and the corresponding complex cross-section module was analyzed by applying Manning’s equation and numerical modeling to show the effects of the complex cross-section module. The tractive force was compared based on a lab-scale experiment. When all conditions were identical except for the cross-sectional shape, the average flow velocity of the complex cross-section module was 14% higher while the size of the transported sand grains was up to 0.5 mm larger. This increase in flow velocity can be even higher if the roughness coefficient of aging pipes can be decreased. Full article
(This article belongs to the Section Urban Water Management)
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<p>Schematics of the installation of complex cross-section modules in a sewer pipe.</p> Full article ">Figure 2
<p>A complex cross-section module for the D450 mm sewer pipe.</p> Full article ">Figure 3
<p>Box plot for the rate of area between the pipe and sewage flow by the diameter.</p> Full article ">Figure 4
<p>Schematic drawing of the experimental sewer pipelines for particle transportation.</p> Full article ">Figure 5
<p>Cumulative distribution of the particle size for the tractive force experiment.</p> Full article ">Figure 6
<p>Velocity distribution by numerical modeling of circular pipe (<b>a</b>) and complex cross-section module (<b>b</b>) for <span class="html-italic">Q</span>: 0.001668 m<sup>3</sup>/s, <span class="html-italic">S</span>: 0.005.</p> Full article ">Figure 7
<p>Centerline velocity profile (<b>a</b>) and their differences (<b>b</b>) (Ci: circular pipe, Mo: complex cross-section module, the numbers beside Ci and Mo are slope, S: slope).</p> Full article ">Figure 8
<p>Transportation of the sediment on the experimental sewer pipe lines.</p> Full article ">Figure 9
<p>Largest sand grains being transported from circular pipe (<b>a</b>) and complex cross-section module (<b>b</b>).</p> Full article ">
13 pages, 4581 KiB  
Article
An Augmented Reality Facility to Run Hybrid Physical-Numerical Flood Models
by Jerónimo Puertas, Luis Hernández-Ibáñez, Luis Cea, Manuel Regueiro-Picallo, Viviana Barneche-Naya and Francisco-Alberto Varela-García
Water 2020, 12(11), 3290; https://doi.org/10.3390/w12113290 - 23 Nov 2020
Cited by 7 | Viewed by 3832
Abstract
This article presents a novel installation for the development of hybrid physical-numerical flood models in an augmented reality environment. This installation extends the concept introduced by the well-known Augmented Reality-SandBox (AR-Sandbox) module, which presents a more educational, and less research-based and professional application. [...] Read more.
This article presents a novel installation for the development of hybrid physical-numerical flood models in an augmented reality environment. This installation extends the concept introduced by the well-known Augmented Reality-SandBox (AR-Sandbox) module, which presents a more educational, and less research-based and professional application. It consists of a physical scale topography built in a sandbox into which other elements (such as buildings, roads or dikes) can be incorporated. A scanner generates, in real time, a Digital Terrain Model (DTM) from the sandbox topography, which serves as a basis for the simulation of overland flow using professional hydraulic software (Iber+). The hydraulic and hydrological parameters (surface roughness, inlet discharges, boundary conditions) are entered with a simple Graphical User Interface (GUI) developed specifically for this project, as indeed was the entire system that allows the visualization of the simulation results. This allows us to obtain quantitative results of flood extension and magnitude, which are represented directly over the physical topography, yielding a realistic visual effect. This installation is conceived for both educational and professional uses. An example of its use is presented, through which its accuracy can be appreciated, and which also illustrates its potential. Full article
(This article belongs to the Special Issue Physical Modelling in Hydraulics Engineering)
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<p>(<b>a</b>) General view of the Augmented Reality SandBox (AR-SandBox) module located in the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) facilities, and (<b>b</b>) detail of the topography projection on the sand.</p> Full article ">Figure 2
<p>Graphic interface of the TopoSandBox software: (<b>a</b>) hypsometric, (<b>b</b>) depth map and contours, and (<b>c</b>) light/shadows modules. (<b>d</b>) Module combination and result visualization.</p> Full article ">Figure 3
<p>General view of the facility with the description of its main elements.</p> Full article ">Figure 4
<p>Representation of different mesh sizes. Cell size of (<b>a</b>) 10 cm, and (<b>b</b>) 1 cm.</p> Full article ">Figure 5
<p>(<b>a</b>) View of the river basin in the sand model; (<b>b</b>-up and <b>c</b>-down) differences between the two sand topographies analyzed.</p> Full article ">Figure 6
<p>Elevation and water depth maps after 1 h from the beginning of the hydrograph under (<b>a</b>) flood and (<b>b</b>) anti-flood system conditions. (<b>c</b>,<b>d</b>) Detail of the projection of both results on the sand.</p> Full article ">
20 pages, 3578 KiB  
Article
Response and Modeling of Hybrid Maize Seed Vigor to Water Deficit at Different Growth Stages
by Rongchao Shi, Ling Tong, Taisheng Du and Manoj K. Shukla
Water 2020, 12(11), 3289; https://doi.org/10.3390/w12113289 - 23 Nov 2020
Cited by 18 | Viewed by 2969
Abstract
Research is imperative to predict seed vigor of hybrid maize production under water deficit in arid areas. Field experiments were conducted in 2018 and 2019 in arid areas of northwestern China to investigate the effects of different irrigation strategies at various growth stages [...] Read more.
Research is imperative to predict seed vigor of hybrid maize production under water deficit in arid areas. Field experiments were conducted in 2018 and 2019 in arid areas of northwestern China to investigate the effects of different irrigation strategies at various growth stages with drip irrigation under film mulching on grain yield, kernel weight, seed protein content, and seed vigor of hybrid maize (Zea mays L.). Water deficit at vegetative, flowering, and grain-filling stages was considered and a total of 16 irrigation treatments was applied. A total of 12 indices of germination percentage, germination index (GI), shoot length (SL), and root length (RL) under different germination conditions (standard germination and accelerated aging); electrical conductivity (EC) of the leachate; and activities of peroxidase, catalase, and superoxide dismutase in seeds were measured and analyzed using the combinational evaluation method (CEM). Furthermore, five water production functions (Blank, Stewart, Rao, Jensen, and Minhas) were used to predict seed vigor evaluated by CEM under water deficit. The results showed that leachate EC was higher under water deficit than that under sufficient irrigation. The SL, RL, and GI of different germination conditions increased under water deficit at the flowering stage. The Rao model was considered the best fitted model to predict the vigor of hybrid maize seeds under water deficit, and an appropriate water deficit at the flowering stage is recommended to ensure high seed vigor of hybrid maize production with drip irrigation under film mulching. Our findings would be useful for reducing crop water use while ensuring seed vigor for hybrid maize production in arid areas. Full article
(This article belongs to the Special Issue Evapotranspiration and Plant Irrigation Strategies)
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<p>Trends of volumetric soil water content during hybrid maize seed production under different treatments in 2018 (<b>a</b>–<b>d</b>) and 2019 (<b>e</b>–<b>h</b>). V, vegetative; F, flowering; G, grain-filling; 2, sufficient irrigation; 1, 50% sufficient irrigation; 0, no irrigation; DAP, days after planting; <span class="html-fig-inline" id="water-12-03289-i001"> <img alt="Water 12 03289 i001" src="/water/water-12-03289/article_deploy/html/images/water-12-03289-i001.png"/></span> Irrigation of V2F2G2; <span class="html-fig-inline" id="water-12-03289-i002"> <img alt="Water 12 03289 i002" src="/water/water-12-03289/article_deploy/html/images/water-12-03289-i002.png"/></span> rainfall; <span class="html-fig-inline" id="water-12-03289-i003"> <img alt="Water 12 03289 i003" src="/water/water-12-03289/article_deploy/html/images/water-12-03289-i003.png"/></span> field water capacity; <span class="html-fig-inline" id="water-12-03289-i004"> <img alt="Water 12 03289 i004" src="/water/water-12-03289/article_deploy/html/images/water-12-03289-i004.png"/></span> wilting point; <span class="html-fig-inline" id="water-12-03289-i005"> <img alt="Water 12 03289 i005" src="/water/water-12-03289/article_deploy/html/images/water-12-03289-i005.png"/></span> lower limit of readily available water, and here considered as 0.20 cm<sup>−3</sup> cm<sup>−3</sup> [<a href="#B40-water-12-03289" class="html-bibr">40</a>].</p> Full article ">Figure 2
<p>Leachate electrical conductivity (EC) and weight of EC determined using the entropy method (W<sub>EC</sub>) in 2018 (<b>a</b>) and 2019 (<b>b</b>). V, vegetative; F, flowering; G, grain-filling; 2, sufficient irrigation; 1, 50% sufficient irrigation; 0, no irrigation. Inserted error bars denote standard error of the mean. Different letters above error bars indicate significant differences among treatments within a year at a 5% probability level using Duncan multiple range test.</p> Full article ">Figure 3
<p>Linear relationships between relative yield (<span class="html-italic">Y<sub>i</sub></span>/<span class="html-italic">Y</span><sub>CK</sub>) and relative seasonal evapotranspiration (ET<span class="html-italic"><sub>i</sub></span>/ET<sub>CK</sub>) (<b>a</b>), as well as relative seed vigor evaluated by a combinational evaluation method (<span class="html-italic">V<sub>i</sub></span>/<span class="html-italic">V</span><sub>CK</sub>) and ET<span class="html-italic"><sub>i</sub></span>/ET<sub>CK</sub> (<b>b</b>).</p> Full article ">Figure 4
<p>The validation results of the Blank (<b>a</b>), Stewart (<b>b</b>), Rao (<b>c</b>), Jensen (<b>d</b>), and Minhas (<b>e</b>) models. Model parameters were validated using data of 20 irrigation treatments in 2018 and 2019. The dotted line indicates the 1:1 line. <span class="html-italic">V<sub>i</sub></span>, seed vigor evaluated by combinational evaluation method; <span class="html-italic">R</span><sup>2</sup>, determination coefficients; RRMSE, relative root-mean-square error; ARE, average relative error; EF, modeling efficiency; <span class="html-italic">d</span><sub>IA</sub>, agreement index.</p> Full article ">
16 pages, 2741 KiB  
Article
Comparison of NCEP-CFSR and CMADS for Hydrological Modelling Using SWAT in the Muda River Basin, Malaysia
by Dandan Zhang, Mou Leong Tan, Sharifah Rohayah Sheikh Dawood, Narimah Samat, Chun Kiat Chang, Ranjan Roy, Yi Lin Tew and Mohd Amirul Mahamud
Water 2020, 12(11), 3288; https://doi.org/10.3390/w12113288 - 23 Nov 2020
Cited by 14 | Viewed by 3756
Abstract
Identification of reliable alternative climate input data for hydrological modelling is important to manage water resources and reduce water-related hazards in ungauged or poorly gauged basins. This study aims to evaluate the capability of the National Centers for Environmental Prediction Climate Forecast System [...] Read more.
Identification of reliable alternative climate input data for hydrological modelling is important to manage water resources and reduce water-related hazards in ungauged or poorly gauged basins. This study aims to evaluate the capability of the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) and China Meteorological Assimilation Driving Dataset for the Soil and Water Assessment Tool (SWAT) model (CMADS) for simulating streamflow in the Muda River Basin (MRB), Malaysia. The capability was evaluated in two perspectives: (1) the climate aspect—validation of precipitation, maximum and minimum temperatures from 2008 to 2014; and (2) the hydrology aspect—comparison of the accuracy of SWAT modelling by the gauge station, NCEP-CFSR and CMADS products. The results show that CMADS had a better performance than NCEP-CFSR in the climate aspect, especially for the temperature data and daily precipitation detection capability. For the hydrological aspect, the gauge station had a “very good” performance in a monthly streamflow simulation, followed by CMADS and NCEP-CFSR. In detail, CMADS showed an acceptable performance in SWAT modelling, but some improvements such as bias correction and further SWAT calibration are needed. In contrast, NCEP-CFRS had an unacceptable performance in validation as it dramatically overestimated the low flows of MRB and contains time lag in peak flows estimation. Full article
(This article belongs to the Section Hydrology)
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<p>Muda River basin.</p> Full article ">Figure 2
<p>Scatter plots of gauge stations with (<b>a</b>–<b>c</b>) NCEP-CFSR and (<b>d</b>–<b>f</b>) CMADS in daily precipitation (Pcp), daily maximum temperature (Tmax) and daily minimum temperature (Tmin) estimations.</p> Full article ">Figure 3
<p>Scatter plots of gauge stations with (<b>a</b>–<b>c</b>) NCEP-CFSR and (<b>d</b>–<b>f</b>) CMADS in monthly precipitation (Pcp), monthly maximum temperature (Tmax) and monthly minimum temperature (Tmin) estimations.</p> Full article ">Figure 4
<p>Observed monthly runoff and SWAT model simulations at the hydrological stations during the calibration period (2009–2011) and validation period (2012–2014).</p> Full article ">
15 pages, 1518 KiB  
Article
The Adsorption Selectivity of Short and Long Per- and Polyfluoroalkyl Substances (PFASs) from Surface Water Using Powder-Activated Carbon
by Heejong Son, Taehoon Kim, Hoon-Sik Yoom, Dongye Zhao and Byungryul An
Water 2020, 12(11), 3287; https://doi.org/10.3390/w12113287 - 23 Nov 2020
Cited by 55 | Viewed by 5751
Abstract
Nine per- and polyfluoroalkyl substances (PFASs), including six perfluoroalkyl carboxylic acids (PFCAs) and three perfluoroalkyl sulfonic acids (PFSAs), were tested to find their adsorption selectivity from surface water and the feasibility of the powder activated carbon (PAC) process between the perchlorination and coagulation [...] Read more.
Nine per- and polyfluoroalkyl substances (PFASs), including six perfluoroalkyl carboxylic acids (PFCAs) and three perfluoroalkyl sulfonic acids (PFSAs), were tested to find their adsorption selectivity from surface water and the feasibility of the powder activated carbon (PAC) process between the perchlorination and coagulation processes by operating parameters such as mixing intensity, dosage, contact time, initial pH, and concentration of perchlorination. The removal efficiency of four types of PAC revealed that the coal-based activated carbon was clearly advanced for all of the PFASs, and the thermal regenerated PAC did not exhibit a significant reduction in adsorption capacity. The longer carbon chain or the higher molecular weight (MW) obtained a higher adsorption capacity and the MW exhibited a more proportional relationship with the removal efficiency than the carbon chain number, regardless of the PFCA and PFSA species. Approximately 80% and 90% equilibria were accomplished within 60 and 120 min for the long chain carbon PFAS, respectively, while for the short chain PFAS, 240 min was required to reach 85% equilibrium. The effect of mixing intensity (rpm) was not considered for the removal of the PFAS, although it was relatively influenced in the short PFAS species. Due to the surface charge of the PAC and the properties of protonation of the PFASs, the acid condition increased the PFASs’ adsorption capacity. The prechlorination decreased the removal efficiency, and the reduction rate was more significantly influenced for the short chain PFAS than for the long chain PFAS. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Removal efficiency (%) of the per- and polyfluoroalkyl substances (PFASs) with different PAC types. The number in the legend denotes the carbon chain length.</p> Full article ">Figure 2
<p>Comparison of removal efficiency (%) as a function of carbon chain length (<b>a</b>) and molecular weight (<b>b</b>) using PCO-0 (solid line) and PCC-0 (dash line).</p> Full article ">Figure 3
<p>Removal efficiency (%) of PFASs at different dosages of PCO-0 with a condition of 30 min of contact time.</p> Full article ">Figure 4
<p>Removal efficiency (<b>a</b>) and equilibrium rate (C<sub>e</sub>/C<sub>t</sub>) (<b>b</b>) as a function of time with a condition of 10 mg/L of PCO-0 and initial concentration of 100 ng/L (C<sub>e</sub> and C<sub>t</sub> are concentration at equilibrium and a desired time, respectively).</p> Full article ">Figure 5
<p>Removal efficiency of nine PFASs at an intensity of mixing (<b>a</b>) and the change of removal efficiency for five PFASs (<b>b</b>).</p> Full article ">Figure 6
<p>Removal efficiency of pH at 5.5, 7.0, 8.5, and 10.0.</p> Full article ">Figure 7
<p>The effect of prechlorination at different concentrations (<b>a</b>) and reduction rates of removal efficiency (<b>b</b>).</p> Full article ">
21 pages, 6397 KiB  
Review
The Water Footprint of the United States
by Megan Konar and Landon Marston
Water 2020, 12(11), 3286; https://doi.org/10.3390/w12113286 - 23 Nov 2020
Cited by 25 | Viewed by 7554
Abstract
This paper commemorates the influence of Arjen Y. Hoekstra on water footprint research of the United States. It is part of the Special Issue “In Memory of Prof. Arjen Y. Hoekstra”. Arjen Y. Hoekstra both inspired and enabled a community of scholars to [...] Read more.
This paper commemorates the influence of Arjen Y. Hoekstra on water footprint research of the United States. It is part of the Special Issue “In Memory of Prof. Arjen Y. Hoekstra”. Arjen Y. Hoekstra both inspired and enabled a community of scholars to work on understanding the water footprint of the United States. He did this by comprehensively establishing the terminology and methodology that serves as the foundation for water footprint research. His work on the water footprint of humanity at the global scale highlighted the key role of a few nations in the global water footprint of production, consumption, and virtual water trade. This research inspired water scholars to focus on the United States by highlighting its key role amongst world nations. Importantly, he enabled the research of many others by making water footprint estimates freely available. We review the state of the literature on water footprints of the United States, including its water footprint of production, consumption, and virtual water flows. Additionally, we highlight metrics that have been developed to assess the vulnerability, resiliency, sustainability, and equity of sub-national water footprints and domestic virtual water flows. We highlight opportunities for future research. Full article
(This article belongs to the Special Issue In Memory of Prof. Arjen Y. Hoekstra)
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<p>Map of the sector with the largest blue water footprint in each US county. Agriculture is the largest water user in 2164 of the 3143 counties. In other counties, service industries (354), thermoelectric power generation (289), manufacturing (234), and mining (102) are the dominant water users. Note that hydropower, aquaculture, and nonrevenue water uses are not included in the ranking since county-level data are not available for these water uses. This figure is taken from Marston et al. [<a href="#B4-water-12-03286" class="html-bibr">4</a>] and covers the period 2010–2012.</p> Full article ">Figure 2
<p>Agricultural virtual water flows between states in the United States. States are ranked according to the total virtual water flow volume and plotted clockwise in descending order. The size of the outer bar indicates the total virtual water flow volume of each state as a percentage of total US virtual water flows. Links emanating from the outer bar of the same color show outflows. Links with a white area separating the outer bar from links of a different color illustrate inflows. The volume of virtual water flows captured in this graph is 317 billion m<sup>3</sup> year<sup>−1</sup>. This figure is taken from Dang et al. [<a href="#B56-water-12-03286" class="html-bibr">56</a>] and is circa 2007.</p> Full article ">Figure 3
<p>Percent change (%) in virtual water transfers from the California Central Valley to other areas of the United States and the world from 2011 to 2014. Green, surface, and groundwater virtual water transfers are shown. Note that green and surface virtual water transfers predominantly decrease, while groundwater transfers mostly increase, due to increased reliance on irrigation from the Central Valley aquifer during drought. This figure is adapted from Marston et al. [<a href="#B34-water-12-03286" class="html-bibr">34</a>] and is for the period 2011–2014.</p> Full article ">Figure 4
<p>Summer streamflow depletion in the Western US due to water footprints of production, specifically those related to irrigated agriculture. Predictive ecological models estimate that some basins will lose over 25% of native fish species due to streamflow depletion caused by large water withdrawals and consumption. This figure is taken from Richter et al. [<a href="#B20-water-12-03286" class="html-bibr">20</a>] and is for the period 2010–2012.</p> Full article ">Figure 5
<p>Significant reductions in streamflow depletion in the Snake River watershed can be achieved by moving unproductive water users to their industry-specific water footprint benchmark. The ‘BM’ levels on the graph represent three, Äòtarget benchmark, Äô levels: BM50 = 50th percentile or median performance; BM25 = 25th percentile or high performance; and BM10 = 10th percentile or outstanding performance. Increased river flows in the upper basin due to reduced water footprints would bolster reservoir storage, which is important to farmers and hydroelectric power producers. Increased flows in the lower portion of the watershed would benefit imperiled salmon populations. This figure is taken from Marston et al. [<a href="#B53-water-12-03286" class="html-bibr">53</a>] and is for the period 2007–2017.</p> Full article ">
18 pages, 4819 KiB  
Article
Distribution of Carbon and Nitrogen as Indictors of Environmental Significance in Coastal Sediments of Weizhou Island, Beibu Gulf
by Zhiyi Tang, Chao Cao, Kunxian Tang, Hongshuai Qi, Yuanmin Sun and Jiangbo Yang
Water 2020, 12(11), 3285; https://doi.org/10.3390/w12113285 - 23 Nov 2020
Cited by 3 | Viewed by 2782
Abstract
Carbon and nitrogen contents and their isotopic components, and AMS (Accelerator Mass Spectrometry) radiocarbon dating ages, were measured for 57 coastal sediments from Weizhou Island to analyze the distribution of total inorganic carbon (TIC) and its carbon and oxygen isotopic components (δ [...] Read more.
Carbon and nitrogen contents and their isotopic components, and AMS (Accelerator Mass Spectrometry) radiocarbon dating ages, were measured for 57 coastal sediments from Weizhou Island to analyze the distribution of total inorganic carbon (TIC) and its carbon and oxygen isotopic components (δ13Ccarb and δ18Ocarb), total organic carbon (TOC) and total nitrogen (TN) contents and their stable isotopic components (δ13CTOC and δ15NTN), and their environmental significance. The results showed that the oldest age of coastal sediments on Weizhou Island was 2750 cal. a BP (before present), and the average TIC contents of cores A1, A2, B1, C1, and D1 in the intertidal zone were all greater than 5%, where δ13Ccarb and δ18Ocarb were enriched, whereas the TIC contents in cores A3, C2, and D2 of the supra-tidal zone were low, where δ13Ccarb and δ18Ocarb were depleted. Moreover, TIC decreased sharply, 4.95% on average, to close to zero from the estuary to the upstream region in the C1-C2 section. The average C/N ratio was 7.02, and δ13CTOC and δ15NTN were between −14.96‰ and −27.26‰ and −14.38‰ and 4.12‰, respectively. These measurements indicate that the TIC in coastal sediments mainly came from seawater. Cores A1, A2, and B1 in the northern intertidal zone exhibited organic terrestrial signals because of C3 and C4 plant inputs, which indicates that the important source on the northern coast of Weizhou Island came from island land but followed the decrease in C3 plants. The lacustrine facies deposits were mainly distributed in the upper reaches of the river, the northern coastline was advancing toward the sea, and part of the southwestern coastal sediments rapidly accumulated on the shore under the influence of a storm surge. The relative sea level of the Weizhou Island area has continuously declined at a rate of approximately 2.07 mm/a, using beach rock as a marker, since the Holocene. Full article
(This article belongs to the Special Issue Sedimentary Evolution of Estuaries and Coastal Plains: Subsidence, Sediment Loss and Aquifer Hazards)
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<p>Map of study area showing the core location on Weizhou Island. Country and place names are expressed by red and white color, respectively, and core name and depth in yellow with orange columns. In addition, supra-tidal and intertidal cores are displayed with purple and blue circles, and place locations with red triangles.</p> Full article ">Figure 2
<p>Distribution of TIC of the core sediments</p> Full article ">Figure 3
<p>Distribution of <span class="html-italic">δ</span>13Ccarb of the core sediments.</p> Full article ">Figure 4
<p>Distribution of <span class="html-italic">δ</span>18O<sub>carb</sub> of the core sediments.</p> Full article ">Figure 5
<p>Distribution of TOC of the core sediments.</p> Full article ">Figure 6
<p>Distribution of TN of the core sediments.</p> Full article ">Figure 7
<p>Distribution of <span class="html-italic">δ</span>13CTOC of the core sediments.</p> Full article ">Figure 8
<p>Distribution of <span class="html-italic">δ</span>15NTN of the core sediments.</p> Full article ">Figure 9
<p>The relationship between total organic carbon (TOC) and total nitrogen (TN) of the core sediments on Weizhou Island. The color that represents the core is shown in the lower right corner of the figure.</p> Full article ">Figure 10
<p>Distribution of <span class="html-italic">δ</span>13CTOC and C/N of the core sediments in the correlation plots of different organic sources [<a href="#B11-water-12-03285" class="html-bibr">11</a>,<a href="#B38-water-12-03285" class="html-bibr">38</a>,<a href="#B39-water-12-03285" class="html-bibr">39</a>].</p> Full article ">Figure 11
<p>Distribution of <span class="html-italic">δ</span>13CTOC and <span class="html-italic">δ</span>15NTN of core sediments.</p> Full article ">Figure 12
<p><sup>14</sup>C dating age of the coral sediments with their average deposition rate.</p> Full article ">Figure 13
<p>Schematic diagram of the source of the core sediments on Weizhou Island. Different material sources in cores are shown in different colors. The stratum information is also color-coded in the figure. In addition, the coastline and 0-m waterline are marked.</p> Full article ">
16 pages, 3896 KiB  
Article
Carabus Population Response to Drought in Lowland Oak Hornbeam Forest
by Bernard Šiška, Mariana Eliašová and Ján Kollár
Water 2020, 12(11), 3284; https://doi.org/10.3390/w12113284 - 23 Nov 2020
Cited by 8 | Viewed by 2584
Abstract
Forest management practices and droughts affect the assemblages of carabid species, and these are the most important factors in terms of influencing short- and long-term population changes. During 2017 and 2018, the occurrences and seasonal dynamics of five carabid species (Carabus coriaceus, [...] Read more.
Forest management practices and droughts affect the assemblages of carabid species, and these are the most important factors in terms of influencing short- and long-term population changes. During 2017 and 2018, the occurrences and seasonal dynamics of five carabid species (Carabus coriaceus, C. ulrichii, C. violaceus, C. nemoralis and C. scheidleri) in four oak hornbeam forest stands were evaluated using the method of pitfall trapping. The climate water balance values were cumulatively calculated here as cumulative water balance in monthly steps. The cumulative water balance was used to identify the onset and duration of drought. The number of Carabus species individuals was more than three times higher in 2018 than in 2017. Spring activity was influenced by temperature. The extremely warm April in 2018 accelerated spring population dynamics; however, low night temperatures in April in 2017 slowed the spring activity of nocturnal species. Drought negatively influenced population abundance, and the effect of a drought is likely to be expressed with a two-year delay. In our investigation, a drought in 2015 started in May and lasted eight months; however, the drought was not recorded in 2016, and 2016 was evaluated as a humid year. The meteorological conditions in the year influenced seasonal activity patterns and the timings of peaks of abundance for both spring breeding and autumn breeding Carabus species. Full article
(This article belongs to the Special Issue Impact of Natural Hazards on Forest Ecosystems and Their Surrounding Landscape under Climate Change)
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<p>The localization of the Báb research area (RA) and meteorological station (MS) where the meteorological data were derived. The coordinates for the RA are 17.886, 48.3033, with an average elevation of 190 m a.s.l. (minimum of 170 m a.s.l, maximum of 210 m a.s.l.). The coordinates for the MS are 17.87881, 48.30331, with an elevation of 207 m a.s.l. The data were obtained from the DEIMS-SDR (Dynamic Ecological Information Management System-Site and dataset registry) database [<a href="#B25-water-12-03284" class="html-bibr">25</a>].</p> Full article ">Figure 2
<p>The seasonal activity of Carabus species in the oak hornbeam forest at the locality of Báb (southwestern Slovakia) during 2017 and 2018.</p> Full article ">Figure 3
<p>The monthly activity of five carabid species at Báb forest stands with four different study plots in 2017. Two forest stands: L1—close-to-nature forest plot; L2—managed forest plot. Two stands after logging operations 10 years ago: R1—recovering indigenous tree species plot; R2—alien <span class="html-italic">Ailanthus altissima</span> dominance.</p> Full article ">Figure 4
<p>The monthly activity of five carabid species at Báb forest stands with four different study plots in 2018. Two forest stands: L1—close-to-nature forest plot; L2—managed forest plot. Two stands after logging operations 10 years ago: R1—recovering indigenous tree species plot; R2—alien <span class="html-italic">Ailanthus altissima</span> dominance.</p> Full article ">Figure 5
<p>The cumulative water balance for the locality of Báb (southwestern Slovakia) in 2015, 2016, 2017 and 2018.</p> Full article ">Figure A1
<p>The forest research area in Báb with the localizations of the research plots. (R1, R2, L1, L2). The picture is a screenshot of an aerial map from the mapy.cz web application. The tool for adding the marks on the map was that used [<a href="#B48-water-12-03284" class="html-bibr">48</a>].</p> Full article ">Figure A2
<p>The night temperatures (20:00–05:00) through April and May in 2017 and 2018 at the locality of Báb (southwestern Slovakia).</p> Full article ">
18 pages, 6356 KiB  
Article
Integrated Taxonomy for Halistemma Species from the Northwest Pacific Ocean
by Nayeon Park, Andrey A. Prudkovsky and Wonchoel Lee
Water 2020, 12(11), 3283; https://doi.org/10.3390/w12113283 - 22 Nov 2020
Cited by 2 | Viewed by 3160
Abstract
During a survey of the siphonophore community in the Kuroshio Extension, Northwest Pacific Ocean, a new Halistemma Huxley, 1859 was described using integrated molecular and morphological approaches. The Halistemma isabu sp. nov. nectophore is most closely related morphologically to H. striata Totton, [...] Read more.
During a survey of the siphonophore community in the Kuroshio Extension, Northwest Pacific Ocean, a new Halistemma Huxley, 1859 was described using integrated molecular and morphological approaches. The Halistemma isabu sp. nov. nectophore is most closely related morphologically to H. striata Totton, 1965 and H. maculatum Pugh and Baxter, 2014. These species can be differentiated by their nectosac shape, thrust block size, ectodermal cell patches and ridge patterns. The new species’ bracts are divided into two distinct types according to the number of teeth. Type A bracts are more closely related to ventral bracts in H. foliacea (Quoy and Gaimard, 1833) while Type B bracts are more similar to H. rubrum (Vogt, 1852). Each type differs, however, from the proximal end shape, distal process and bracteal canal. Both of the new species’ morphological type and phylogenetic position within the genus Halistemma are supported by phylogenetic analysis of concatenated DNA dataset (mtCOI, 16S rRNA and 18S rRNA). Integrated morphological and molecular approaches to the taxonomy of siphonophores showed a clear delimitation of the new species from the congeners. Halistemma isabu sp. nov. is distributed with the congeners H. rubrum, H. cupulifera, H. foliacea and H. striata in the northwestern Pacific Ocean. Full article
(This article belongs to the Special Issue Species Richness and Diversity of Aquatic Ecosystems)
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<p>Map of sampling stations. (QGIS, Version: 3.6.0).</p> Full article ">Figure 2
<p>Digital images of <span class="html-italic">Halistemma isabu</span> sp. nov. (Nectophore). (<b>a</b>) Upper view; 1: Axial wings; 2: Ostium; 3: Lateral radial canal; (<b>b</b>) Lower view; 1: Thrust block; 2: Mouth plate; 3: Nectosac; (<b>c</b>) 1: Upper lateral ridges; 2: Hook shape of the upper lateral ridges; 3: Branched end of lateral ridges; (<b>d</b>) 1: Y-shape of the upper lateral ridges; 2: Lateral ridges; 3: Incomplete oblique ridges between the upper lateral ridges and the lateral ridges; (<b>e</b>) 1: Ascending branch of the mantle canal; 2: Descending branch of the mantle canal; 3: Internal pedicular canal, 4: Stem attachment point; Scale bar: (<b>a</b>,<b>b</b>) 5 mm; (<b>c</b>–<b>e</b>) 1 mm.</p> Full article ">Figure 3
<p>Illustration of <span class="html-italic">Halistemma isabu</span> sp. nov. (Nectophore). (<b>a</b>) Upper; (<b>b</b>) Lower; (<b>c</b>) Lateral views. tb: thrust block, aw: axial wing, pec: ectodermal cell patch, rul: upper lateral ridges, rvl: vertical lateral ridges, rl: lateral ridges, rll: lower lateral ridges, ro: obliquely ridges, po: oval plane, n: nectosac, sp: stem attachment point, mc: mantle canal, pc: pedicular canal, clr: lateral canal, cl: lower canal, cu: upper canal, o: ostium, mp: mouth plate; Scale bar: 5 mm.</p> Full article ">Figure 4
<p>Digital images of <span class="html-italic">Halistemma isabu</span> sp. nov. (Bracts). (<b>a</b>) Type A_1 upper views: Distal process; 2: Lateral teeth; 3: Bracteal canal; 4: Bracteal attachment lamellas; (<b>b</b>–<b>f</b>) Type B upper views; Scale bar: 5 mm.</p> Full article ">Figure 5
<p>Illustration of <span class="html-italic">Halistemma isabu</span> sp. nov. (Bracts). (<b>a</b>) Type A upper views; (<b>b</b>–<b>f</b>) Type B upper views. bc: Bracteal canal, t: Lateral teeth, p: Distal process, ep: Ectodermal cell patches; Scale bar: 5 mm.</p> Full article ">Figure 6
<p>Molecular phylogenetic tree of family Agalmatidae based on concatenated data. Analysis involved 12 nucleotide sequences. All positions containing gaps and missing data were eliminated. The first number along the branches represents BI, the second number represents ML bootstrap values, and “-” indicates topologies not identical for BI and ML.</p> Full article ">
18 pages, 1838 KiB  
Article
Livelihood Vulnerability and Adaptation Capacity of Rice Farmers under Climate Change and Environmental Pressure on the Vietnam Mekong Delta Floodplains
by Dung Duc Tran, Chau Nguyen Xuan Quang, Pham Duy Tien, Pham Gia Tran, Pham Kim Long, Ho Van Hoa, Ngo Ngoc Hoang Giang and Le Thi Thu Ha
Water 2020, 12(11), 3282; https://doi.org/10.3390/w12113282 - 22 Nov 2020
Cited by 27 | Viewed by 6291
Abstract
Agricultural production is the primary source of income and food security for rural households in many deltas of the world. However, the sustainability of farm livelihoods is under threat, due to the impacts of climate change and environmental pressure, including shifting hydrological regimes, [...] Read more.
Agricultural production is the primary source of income and food security for rural households in many deltas of the world. However, the sustainability of farm livelihoods is under threat, due to the impacts of climate change and environmental pressure, including shifting hydrological regimes, droughts, water pollution, land subsidence and riverbank erosion. This study evaluated the livelihood sustainability and vulnerability of triple rice farmers on the floodplains of the Vietnam Mekong Delta (VMD). We focused on the perceptions of rice farmers, based on a survey of 300 farmers. Increasing temperatures, drought, water pollution and sediment shortages were the four factors considered by farmers to have the most impact on their agricultural livelihoods. We analyzed farmers’ capacity to sustain their livelihoods and adapt to the changing environment. Results show relatively low vulnerability of rice farmers overall, though many of those surveyed reported very low incomes from rice production. Factors of most concern to farmers were rising temperatures and more frequent droughts. Farmers were already taking steps to adapt, for example, increasing production inputs and investing more labor time, as well as switching production methods. Yet, our findings suggest that policymakers and scientists have a role to play in developing more sustainable adaptation paths. The research clarifies the livelihood vulnerability of triple rice farmers on the VMD floodplains, while more generally contributing to the body of literature on farming and climate change and environmental pressure. Full article
(This article belongs to the Special Issue Water Resources Vulnerability and Resilience in a Changing Climate)
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<p>Map showing An Giang province and surroundings, with the study districts shaded and locations of the 10 survey communes.</p> Full article ">Figure 2
<p>Vulnerability spider diagram of livelihood vulnerability index (<span class="html-italic">LVI</span>) key components for the three study districts of An Giang province.</p> Full article ">Figure 3
<p>Vulnerability triangle of LVI-Intergovernmental Panel on Climate Change (IPCC) contributing factors for the three study districts in An Giang province.</p> Full article ">Figure 4
<p>Farmers’ perceptions regarding the past five years (<b>a</b>) and future (<b>b</b>) impacts of climate change and environmental pressure.</p> Full article ">Figure 5
<p>Farmers’ perceptions of impacts of climate change and environmental pressure on their livelihoods in the past five years.</p> Full article ">Figure 6
<p>Adaption measures implemented by farmers to deal with climate change and environmental pressure.</p> Full article ">
19 pages, 5574 KiB  
Article
Using the Turnover Time Index to Identify Potential Strategic Groundwater Resources to Manage Droughts within Continental Spain
by David Pulido-Velazquez, Javier Romero, Antonio-Juan Collados-Lara, Francisco J. Alcalá, Francisca Fernández-Chacón and Leticia Baena-Ruiz
Water 2020, 12(11), 3281; https://doi.org/10.3390/w12113281 - 22 Nov 2020
Cited by 12 | Viewed by 3131
Abstract
The management of droughts is a challenging issue, especially in water scarcity areas, where this problem will be exacerbated in the future. The aim of this paper is to identify potential groundwater (GW) bodies with reduced vulnerability to pumping, which can be used [...] Read more.
The management of droughts is a challenging issue, especially in water scarcity areas, where this problem will be exacerbated in the future. The aim of this paper is to identify potential groundwater (GW) bodies with reduced vulnerability to pumping, which can be used as buffer values to define sustainable conjunctive use management during droughts. Assuming that the long term natural mean reserves are maintained, a preliminary assessment of GW vulnerability can be obtained by using the natural turnover time (T) index, defined in each GW body as the storage capacity (S) divided by the recharge (R). Aquifers where R is close to S are extremely vulnerable to exploitation. This approach will be applied in the 146 Spanish GW bodies at risk of not achieving the Water Framework Directive (WFD objectives, to maintain a good quantitative status. The analyses will be focused on the impacts of the climate drivers on the mean T value for Historical and potential future scenarios, assuming that the Land Use and Land Cover (LULC) changes and the management strategies will allow maintenance of the long term mean natural GW body reserves. Around 26.9% of these GW bodies show low vulnerability to pumping, when viewing historical T values over 100 years, this percentage growing to 33.1% in near future horizon values (until 2045). The results show a significant heterogeneity. The range of variability for the historical T values is around 3700 years, which also increases in the near future to 4200 years. These T indices will change in future horizons, and, therefore, the potential of GW resources to undergo sustainable strategies to adapt to climate change will also change accordingly, making it necessary to apply adaptive management strategies. Full article
(This article belongs to the Special Issue Impacts of Climate on Renewable Groundwater Resources and/or Stream-Aquifer Interactions)
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<p>Flowchart of the methodology developed to assess groundwater (GW) bodies’ vulnerability to pumping. Notation and units for variables used: P, E, R, and Q are respectively precipitation, actual evapotranspiration, net GW recharge from P, and net GW discharge in mm year<sup>−1</sup>; Ta is temperature in °C; C and S are respectively a dimensionless effective recharge coefficient (−) and a GW storage (Mm<sup>3</sup>); and T is the natural turnover time index in years.</p> Full article ">Figure 2
<p>Map of continental Spain, showing the 146 Spanish GW bodies at quantitative risk of not fulfilling the Water Framework Directive (WFD) [<a href="#B14-water-12-03281" class="html-bibr">14</a>] (2000) objectives (red shadowed areas), the main mountain ranges and hydrographic basins, and the hydrogeological behavior of geological materials forming the GW bodies according to permeability type [<a href="#B37-water-12-03281" class="html-bibr">37</a>], modified from [<a href="#B31-water-12-03281" class="html-bibr">31</a>] as: (<b>a</b>) low to moderate permeability pre-Triassic metamorphic rocks, granitic outcrops, and Triassic to Miocene marly sedimentary formations; (<b>b</b>) moderate to high permeability Paleozoic to Tertiary; (<b>c</b>) moderate to high permeability Pleo-Quaternary detritic; and (<b>d</b>) Triassic to Miocene evaporitic outcrops.</p> Full article ">Figure 3
<p>Map of historical mean (<b>a</b>) precipitation (mm year<sup>−1</sup>) and (<b>b</b>) temperature (°C) across continental Spain during the reference period (1976–2005), (<b>c</b>) temporal series of mean precipitation (mm year<sup>−1</sup>) Modified from [<a href="#B29-water-12-03281" class="html-bibr">29</a>].</p> Full article ">Figure 4
<p>Potential future mean precipitation (mm year<sup>−1</sup>) and temperature (°C) obtained with the equi-feasible delta and bias ensembles scenarios (E<sub>D</sub>, E<sub>B</sub>). Modified from [<a href="#B29-water-12-03281" class="html-bibr">29</a>].</p> Full article ">Figure 5
<p>Potential storage capacity (Mm<sup>3</sup>) under the surface connection for the 146 Spanish GW bodies at quantitative risk of not achieving the WFD [<a href="#B1-water-12-03281" class="html-bibr">1</a>] objectives.</p> Full article ">Figure 6
<p>Historical (1976–2005) and future (2011–2045) potential R (mm year<sup>−1</sup>) for the 2 defined equi-feasible ensemble scenarios. Modified from [<a href="#B29-water-12-03281" class="html-bibr">29</a>].</p> Full article ">Figure 7
<p>Box-whiskers (<b>a</b>) and maps of the T index in the 146 Spanish GW bodies at risk [<a href="#B1-water-12-03281" class="html-bibr">1</a>]. Historical (<b>b</b>) values and future potential scenarios (EB (<b>c</b>) and ED (<b>d</b>) in the horizon 2011–2045. The differences between the future scenarios (E<sub>B</sub> and E<sub>D</sub>) in terms of impacts on the T index are small, due to the differences between the impacts on mean R also being small (see maps of <a href="#water-12-03281-f006" class="html-fig">Figure 6</a>). The mean values of R for both scenarios are very similar, although the monthly series are different (see temporal series of <a href="#water-12-03281-f006" class="html-fig">Figure 6</a>).</p> Full article ">Figure 8
<p>Box-Whiskers (<b>a</b>) and maps (<b>b</b>,<b>c</b>) of the distances between historical natural T and future potential values in horizon 2011–2045.</p> Full article ">Figure 9
<p>(<b>a</b>) GW body volume (Mm<sup>3</sup>) vs. natural mean T index (year), (<b>b</b>) R (mm year<sup>–1</sup>) vs. natural mean T index (year), T and (<b>c</b>) box-whiskers of R, S and T for the three considered GW bodies lithological categories: carbonated, detrital, and mixed.</p> Full article ">Figure 10
<p>Box-Whisker (<b>a</b>) and maps of the of the “recharge coefficients” (RC) (<b>b</b>) and the “effective recharge coefficient (C)” (<b>c</b>) in the GW bodies at risk [<a href="#B1-water-12-03281" class="html-bibr">1</a>].</p> Full article ">Figure 11
<p>Absolute distance (years) of future T values (<b>a</b>) EB scenario and (<b>b</b>) ED scenario, with respect to the historical T vs. difference between effective recharge coefficients and recharge coefficients.</p> Full article ">
26 pages, 6328 KiB  
Article
Is It Optimal to Use the Entirety of the Available Flow Records in the Range of Variability Approach?
by Yuanyuan Sun, Cailing Liu, Yanwei Zhao, Xianqiang Mao, Jun Zhang and Hongrui Liu
Water 2020, 12(11), 3280; https://doi.org/10.3390/w12113280 - 22 Nov 2020
Cited by 1 | Viewed by 2003
Abstract
Reducing the degree of flow regime alteration is a basic principle for biodiversity conservation in rivers. The range of variability approach (RVA) is the most widely used method to assess flow regime alteration. Generally, researchers tend to put all of the available pre-impact [...] Read more.
Reducing the degree of flow regime alteration is a basic principle for biodiversity conservation in rivers. The range of variability approach (RVA) is the most widely used method to assess flow regime alteration. Generally, researchers tend to put all of the available pre-impact and post-impact flow records into the RVA. However, no research has tested whether it is optimal to use the entirety of the available flow records from the perspective of calculation accuracy for the degree of flow regime alteration. In this research, a series of numerical simulations is conducted, demonstrating that the greatest accuracy for flow regime alteration degree assessed by the RVA is achieved when the length of both the pre- and post-impact flow time series is set equal to multiples of periodicity length, and that, when attempting to put the whole available flow record into the RVA, calculation accuracy may be reduced. On the basis of these findings, we further propose revising the traditional RVA procedure by assessing the periodicity of the pre- and post-impact flow time series in advance. If the periodicity of the pre- or post-impact flows is detected, the length of the time series should be set equal to its periodicity. Full article
(This article belongs to the Section Hydrology)
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<p>Variation of flow regime alteration degree (D) measured by the range of variation approach (RVA) with changes in length of the pre-impact flow time series (LPR) under a periodicity of 30 years. (<b>a</b>–<b>j</b>) correspond to a post-impact flow time series of length = 21, 26, 30, 34, 39, 43, 47, 51, 56, and 60 years, respectively. The abscissa value for the vertical dotted line is 30. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure 2
<p>Variation of flow regime alteration degree (D) measured by the RVA with the changes in length of post-impact flow time series (LPR) under a periodicity of 30 years. (<b>a</b>–<b>j</b>) correspond to a pre-impact flow time series length = 21, 26, 30, 34, 39, 43, 47, 51, 56, and 60 years, respectively. The abscissa value for the vertical dotted line is 30. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A1
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 25 years. (<b>a</b>–<b>j</b>) correspond to post-impact flow time series length = 18, 21, 25, 29, 32, 36, 39, 43, 46, and 50 years, respectively. The abscissa value for the vertical dotted line is 25. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A2
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 25 years. Figure (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 18, 21, 25, 29, 32, 36, 39, 43, 46, and 50 years, respectively. The abscissa value for the vertical dotted line is 25. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A3
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 26 years. (<b>a</b>–<b>j</b>) correspond to post-impact flow time series length = 19, 22, 26, 30, 33, 37, 41, 45, 48, and 52 years, respectively. The abscissa value for the vertical dotted line is 26. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A4
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 26 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 19, 22, 26, 30, 33, 37, 41, 45, 48, 52 years, respectively. The abscissa value for the vertical dotted line is 26. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A5
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 27 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 19, 23, 27, 31, 35, 39, 42, 46, 50, and 54 years, respectively. The abscissa value for the vertical dotted line is 27. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A6
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 27 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 19, 23, 27, 31, 35, 39, 42, 46, 50, and 54 years, respectively. The abscissa value for the vertical dotted line is 27. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A7
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 28 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 20, 24, 28, 32, 36, 40, 44, 48, 52, and 56 years, respectively. The abscissa value for the vertical dotted line is 28. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A8
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 28 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 20, 24, 28, 32, 36, 40, 44, 48, 52, and 56 years, respectively. The abscissa value for the vertical dotted line is 28. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A9
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 29 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 21, 25, 29, 33, 37, 41, 46, 50, 54, and 58 years, respectively. The abscissa value for the vertical dotted line is 29. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A10
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 29 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 21, 25, 29, 33, 37, 41, 46, 50, 54, and 58 years, respectively. The abscissa value for the vertical dotted line is 29. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A11
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 30 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 21, 26, 30, 34, 39, 43, 47, 51, 56, and 60 years, respectively. The abscissa value for the vertical dotted line is 30. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A12
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 30 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 21, 26, 30, 34, 39, 43, 47, 51, 56, and 60 years, respectively. The abscissa value for the vertical dotted line is 30. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A13
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 31 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 22, 27, 31, 35, 40, 44, 49, 53, 58, and 62 years, respectively. The abscissa value for the vertical dotted line is 31. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A14
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 31 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 22, 27, 31, 35, 40, 44, 49, 53, 58, and 62 years, respectively. The abscissa value for the vertical dotted line is 31. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A15
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 32 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 23, 27, 32, 37, 41, 46, 50, 55, 59, and 64 years, respectively. The abscissa value for the vertical dotted line is 32. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A16
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 32 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 23, 27, 32, 37, 41, 46, 50, 55, 59, and 64 years, respectively. The abscissa value for the vertical dotted line is 32. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A17
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 33 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 24, 28, 33, 38, 42, 47, 52, 57, 61, and 66 years, respectively. The abscissa value for the vertical dotted line is 33. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A18
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 33 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 24, 28, 33, 38, 42, 47, 52, 57, 61, and 66 years, respectively. The abscissa value for the vertical dotted line is 33. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A19
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 34 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 24, 29, 34, 39, 44, 49, 53, 58, 63, and 68 years, respectively. The abscissa value for the vertical dotted line is 34. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A20
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 34 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 24, 29, 34, 39, 44, 49, 53, 58, 63, and 68 years, respectively. The abscissa value for the vertical dotted line is 34. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A21
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 35 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70 years, respectively. The abscissa value for the vertical dotted line is 35. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A22
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 35 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70 years, respectively. The abscissa value for the vertical dotted line is 35. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A23
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under the periodicity of 36 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 26, 31, 36, 41, 46, 51, 57, 62, 67, and 72 years, respectively. The abscissa value for the vertical dotted line is 36. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A24
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 36 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 26, 31, 36, 41, 46, 51, 57, 62, 67, and 72 years, respectively. The abscissa value for the vertical dotted line is 37. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A25
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 37 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 26, 32, 37, 42, 48, 53, 58, 63, 69, and 74 years, respectively. The abscissa value for the vertical dotted line is 37. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A26
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 37 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 26, 32, 37, 42, 48, 53, 58, 63, 69, and 74 years, respectively. The abscissa value for the vertical dotted line is 37. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A27
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 38 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 27, 33, 38, 43, 49, 54, 60, 65, 71, and 76 years, respectively. The abscissa value for the vertical dotted line is 38. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A28
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 38 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 27, 33, 38, 43, 49, 54, 60, 65, 71, and 76 years, respectively. The abscissa value for the vertical dotted line is 39. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A29
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 39 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 28, 33, 39, 45, 50, 56, 61, 67, 72, and 78 years, respectively. The abscissa value for the vertical dotted line is 39. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A30
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 39 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 28, 33, 39, 45, 50, 56, 61, 67, 72, and 78 years, respectively. The abscissa value for the vertical dotted line is 39. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A31
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of pre-impact flow time series (LPR) under a periodicity of 40 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 29, 34, 40, 46, 51, 57, 63, 69, 74, and 80 years, respectively. The abscissa value for the vertical dotted line is 40. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">Figure A32
<p>Variation of flow regime alteration degree (D) measured by the RVA with change in the length of post-impact flow time series (LPR) under a periodicity of 40 years. (<b>a</b>–<b>j</b>) correspond to pre-impact flow time series length = 29, 34, 40, 46, 51, 57, 63, 69, 74, and 80 years, respectively. The abscissa value for the vertical dotted line is 40. The ordinate value for the horizontal dotted line is the mean degree of flow regime alteration in each subfigure.</p> Full article ">
10 pages, 1698 KiB  
Article
Net Primary Production Predicted by the Proportion of C:N:P Stoichiometric Ratio in the Leaf-Stem and Root of Cynodon Dactylon (Linn.) in the Riparian Zone of the Three Gorges Reservoir
by Dan Liu, Liping He, Zhiguo Yu, Zhengxue Liu and Junjie Lin
Water 2020, 12(11), 3279; https://doi.org/10.3390/w12113279 - 22 Nov 2020
Cited by 4 | Viewed by 2602
Abstract
Net primary production (NPP) is closely related to the proportion of carbon (C), nitrogen (N) and phosphorus (P) in the leaf-stem and root of perennial herbs. However, the relationship of NPP with the C:N:P stoichiometric ratio in above- and below-ground plant tissues remains [...] Read more.
Net primary production (NPP) is closely related to the proportion of carbon (C), nitrogen (N) and phosphorus (P) in the leaf-stem and root of perennial herbs. However, the relationship of NPP with the C:N:P stoichiometric ratio in above- and below-ground plant tissues remains unknown under the periodic flooding stresses in the riparian zone ecosystem. In this study, the leaf-stem and root C, N, P content and biomass of Cynodon dactylon (Linn.) Pers. (C. dactylon) were investigated at the riparian zone altitudes of 145–155, 155–165, and 165–175 m above sea level (masl) of in a Three Gorges Reservoir (TGR) tributary–Pengxi River. The results showed that the NPP and biomass of C. dactylon had a similar decreasing trend with a riparian zone altitudes decrease. The root of C. dactylon showed relatively lower N and P content, but much higher N and P use efficiency with higher C:N and C:P ratio than that of a leaf-stem under N limitation conditions. NPP was positively correlated to C:N in the stem-leaf to root ratio (C:Nstem-leaf/root) and C:P ratio in the root (C:Proot ratio). Hydrological and C:N:P stoichiometric variables could predict 68% of the NPP variance, and thus could be regarded as the main predictor of NPP in the riparian zone of the TGR. Full article
(This article belongs to the Special Issue Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)
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<p>Sampling sites in the Pengxi River (QK: Qukou; SJ: Shuangjiang).</p> Full article ">Figure 2
<p>Water level fluctuation (<b>a</b>) and submerging time (<b>b</b>) from 2013 to 2018. The sampling was conducted in July 2017.</p> Full article ">Figure 3
<p>Net primary production (NPP) (<b>a</b>) and biomass (<b>b</b>) of <span class="html-italic">C. dactylon</span> among the riparian zone altitudes.</p> Full article ">Figure 4
<p>C:N:P stoichiometry in the leaf-stem to root ratio among the riparian zone altitudes. Different lowercase letters of a, b, c indicate significant differences among riparian zone altitudes at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>C:N:P stoichiometry with NPP. Leaf-stem: (<b>a1</b>–<b>f1</b>), Root: (<b>a2</b>–<b>f2</b>), leaf-stem/root: (<b>a3</b>–<b>f3</b>).</p> Full article ">Figure 6
<p>Structure equation modeling (SEM) with variables (boxes) and potential causal relationships (arrows) for NPP (<b>a</b>) and standardized total effects (direct effect plus indirect effect) on NPP derived from SEM (<b>b</b>). The black-headed arrows indicate that the hypothesized direction of causation is a positive relationship; on the contrary, the red-headed arrows represent a negative relationship. Arrow width is proportional to the strength of path coefficients. Standardized path coefficients (numbers) can reflect the importance of the variables within the model [<a href="#B24-water-12-03279" class="html-bibr">24</a>]. The model for NPP had χ<sup>2</sup> = 2.660, df = 3, <span class="html-italic">p</span> = 0.447, RMSEA = 0.000, AIC = 50.66.</p> Full article ">
15 pages, 7459 KiB  
Editorial
Application of Innovative Technologies for Active Control and Energy Efficiency in Water Supply Systems
by Armando Carravetta, Maurizio Giugni and Stefano Malavasi
Water 2020, 12(11), 3278; https://doi.org/10.3390/w12113278 - 22 Nov 2020
Cited by 2 | Viewed by 3263
Abstract
The larger anthropic pressure on the Water Supply Systems (WSS) and the increasing concern for the sustainability of the large energy use for water supply, transportation, distribution, drainage and treatment are determining a new perspective in the management of water systems [...] Full article
(This article belongs to the Special Issue Application of Innovative Technologies for Active Control and Energy Efficiency in Water Supply Systems)
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<p>Decision tree of a Water Supply System (WSS).</p> Full article ">Figure 2
<p>Water distribution losses in EC countries in the 2012–2015 period [<a href="#B4-water-12-03278" class="html-bibr">4</a>].</p> Full article ">Figure 3
<p>Reference proactive architecture for a network modeling and control based on historical data.</p> Full article ">Figure 4
<p>Interventions per kilometer of wastewater pipelines vs. inhabitants [<a href="#B15-water-12-03278" class="html-bibr">15</a>].</p> Full article ">Figure 5
<p>Patterns of aggregated measured hourly demand for 31 days for Case study 1 (<b>a</b>) and Case study 2 (<b>b</b>), respectively [<a href="#B16-water-12-03278" class="html-bibr">16</a>].</p> Full article ">Figure 6
<p>Daily temporal patterns of both mean μ (continuous lines) and intervals μ ± 0.5σ (dotted lines) for measured (black lines) and generated (grey lines) aggregated demands: the generated demands were obtained applying the top-down approach (<b>a</b>) and the bottom-up approach (<b>b</b>), respectively [<a href="#B16-water-12-03278" class="html-bibr">16</a>].</p> Full article ">Figure 7
<p>Velocity field for longitudinal orifice at <span class="html-italic">P</span> = 5.0 bar and Qin = (<b>a</b>) 10 L/s, (<b>b</b>) 20 L/s, (<b>c</b>) 30 L/s [<a href="#B17-water-12-03278" class="html-bibr">17</a>].</p> Full article ">Figure 8
<p>Real data on the dissipated power in Pressure Reducing Valves (PRVs) [<a href="#B18-water-12-03278" class="html-bibr">18</a>] showing (<b>a</b>) the numbers of valve for class of dissipated power, and (<b>b</b>) the Annual Net Income (ANI) for ten years investment in case of specific installation cost of 3700€/kW and 7400€/kW.</p> Full article ">Figure 9
<p>Green Valve System [<a href="#B18-water-12-03278" class="html-bibr">18</a>].</p> Full article ">Figure 10
<p>Experimental and theoretical performance curves for MSV pumps used in inverse mode.</p> Full article ">Figure 11
<p>Test setup: (<b>a</b>) main elements of the supply system; and (<b>b</b>) vortex generation at the Pumps As Turbines (PAT) outlet [<a href="#B20-water-12-03278" class="html-bibr">20</a>].</p> Full article ">Figure 12
<p>Measured PAT curves and daily distribution of the head drop for variable impeller speed [<a href="#B20-water-12-03278" class="html-bibr">20</a>].</p> Full article ">Figure 13
<p>Water resources and main demand zones in Saudi Arabia [<a href="#B21-water-12-03278" class="html-bibr">21</a>].</p> Full article ">Figure 14
<p>Hydraulic scheme of a freshwater turbine–wastewater pump system [<a href="#B22-water-12-03278" class="html-bibr">22</a>].</p> Full article ">Figure 15
<p>Difference of Net Present Value (NPV) vs the runoff coefficient (φ), for different time periods (5, 10, and 20 years) [<a href="#B22-water-12-03278" class="html-bibr">22</a>].</p> Full article ">Figure 16
<p>Comparison of characteristic and efficiency curves for different stages of wear [<a href="#B23-water-12-03278" class="html-bibr">23</a>].</p> Full article ">Figure 17
<p>(<b>a</b>) Simulated plant head curve; (<b>b</b>) global plant efficiency for different stages of wear.</p> Full article ">Figure 18
<p>Layout of the studied canal system [<a href="#B24-water-12-03278" class="html-bibr">24</a>].</p> Full article ">Figure 19
<p>Middle Route Project (MRP) for South-to-North Water Transfer [<a href="#B25-water-12-03278" class="html-bibr">25</a>].</p> Full article ">
17 pages, 2815 KiB  
Article
A Study on Heavy Metals in the Surface Soil of the Region around the Qinghai Lake in Tibet Plateau: Pollution Risk Evaluation and Pollution Source Analysis
by Peiru Wei, Tianjie Shao, Ruojin Wang, Zongyan Chen, Zhongdi Zhang, Zhiping Xu, Yadi Zhu, Dongze Li, Lijuan Fu and Feier Wang
Water 2020, 12(11), 3277; https://doi.org/10.3390/w12113277 - 22 Nov 2020
Cited by 21 | Viewed by 3660
Abstract
In order to reveal the pollution characteristics and sources of heavy metals in surface soil of the region around the Qinghai Lake in Tibet Plateau, improve the prevention awareness and measures of local residents and urge the local government to implement necessary prevention [...] Read more.
In order to reveal the pollution characteristics and sources of heavy metals in surface soil of the region around the Qinghai Lake in Tibet Plateau, improve the prevention awareness and measures of local residents and urge the local government to implement necessary prevention and control measures, nine heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) in the surface soil samples of the region around the Qinghai Lake have been collected and analyzed. The methods such as statistic method, geo-accumulation index method, Nemerow index method, potential ecological risk index method, human health risk evaluation method and positive matrix factor analysis model (PMF) have been used to evaluate pollution characteristics and potential risks and analyze the sources of heavy metals. The results are shown below. First, the average contents of heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) in soil are 11.73 ± 3.78, 0.62 ± 1.40, 12.38 ± 3.68, 41.35 ± 13.01, 19.33 ± 8.92, 546.96 ± 159.28, 21.18 ± 7.04, 21.86 ± 6.61 and 63.51 ± 19.71 mg·kg−1, respectively. Compared with the background values of the soil environment in Qinghai Province, it can be seen that there is an accumulation of these heavy metals to varying degrees, which is the most serious in Cd, Co and Pb. Second, the analysis of the geo-accumulation index and Nemerow index indicates that the heavy metals in the surface soil of the region around the Qinghai Lake have reached the level of heavy pollution, mainly polluted by Cd, and the accumulation of heavy metal pollution in the north, south, southwest and southeast of the study area is more serious. Third, the results of potential ecological risk evaluation show that the study area as a whole is classified as an area with high ecological risk, and Cd contributes the most to the overall risk. In fact, the heavy metals in the soil of the study area produce no noncarcinogenic and carcinogenic health risks to human health, and children and adults may be exposed to these risks by the mouth. Finally, the PMF results reveal that the sources of heavy metals in the study area include the sources of agricultural production, the nature, coal burning and transportation, with a contribution rate of 43.10%, 25.34%, 19.67% and 11.89%, respectively. Full article
(This article belongs to the Special Issue Environmental Biogeochemistry of Trace Elements in Contaminated Soils and Water)
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<p>Distribution of the soil sampling sites around Qinghai Lake.</p> Full article ">Figure 2
<p>Spatial distribution of the proportions of heavy metals in the surface soil around Qinghai Lake.</p> Full article ">Figure 3
<p>The results of the geo-accumulation index (I<sub>geo</sub>) index of heavy metals in the surface soil around Qinghai Lake.</p> Full article ">Figure 4
<p>Spatial distribution of the Nemerow index (P<sub>n</sub>) of heavy metals in the surface soil around Qinghai Lake.</p> Full article ">Figure 5
<p>The results of the potential ecological risk index of heavy metals in the surface soil around Qinghai Lake.</p> Full article ">Figure 6
<p>Factors profiles and source contributions of heavy metals from the positive matrix factor analysis (PMF) model.</p> Full article ">Figure 7
<p>Contributions rate of different sources by PMF.</p> Full article ">
20 pages, 3649 KiB  
Article
Variability in Environmental Conditions Strongly Impacts Ostracod Assemblages of Lowland Springs in a Heavily Anthropized Area
by Giampaolo Rossetti, Valentina Pieri, Rossano Bolpagni, Daniele Nizzoli and Pierluigi Viaroli
Water 2020, 12(11), 3276; https://doi.org/10.3390/w12113276 - 21 Nov 2020
Cited by 5 | Viewed by 3255
Abstract
The Po river plain (Northern Italy) hosts artificial, lowland springs locally known as fontanili, which provide important ecosystem services in an area dominated by intensive agricultural activities. Here we present a study carried out in 50 springs. Each spring was visited once from [...] Read more.
The Po river plain (Northern Italy) hosts artificial, lowland springs locally known as fontanili, which provide important ecosystem services in an area dominated by intensive agricultural activities. Here we present a study carried out in 50 springs. Each spring was visited once from October 2015 to January 2016. The sampled sites were selected to include springs studied in 2001 and 2004, to evaluate changes in water quality and ostracod assemblages that possibly occurred over a period of 10–15 years, and explore the relationships between ostracod community composition and water physical and chemical variables. Our results showed a decrease in the chemical water quality especially, in springs south of the Po river, evidenced by high nitrate levels. Most of the studied springs showed a relevant decrease in dissolved reactive silica, probably related to recent transformations of either agricultural practices or crop typology. Ostracods were mostly represented by common and tolerant species, and communities were characterized by low alpha diversity and high species turnover. Water temperature and mineralization level were the most influential variables in structuring the ostracod communities. We stress the need to implement conservation and restoration measures for these threatened ecosystems, to regain their role as ecosystem services providers. Full article
(This article belongs to the Special Issue Biodiversity and Functionality in Freshwater and Transitional Ecosystems)
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<p>Upper left panel: map of Italy in which the Lombardy and Emilia-Romagna regions are indicated, as well as the grey area corresponding to the main panel. Main panel: location of sampling sites (black dots) in the provinces of Parma and Piacenza (Emilia-Romagna) and Lodi, Cremona and Milano (Lombardy).</p> Full article ">Figure 2
<p>Box-plots showing comparison between water temperature (T), conductivity (EC), dissolved oxygen (DO) and pH values measured in the present study and in previous research [<a href="#B10-water-12-03276" class="html-bibr">10</a>,<a href="#B15-water-12-03276" class="html-bibr">15</a>]. The boxes show the 25th and 75th percentile (interquartile) ranges. Median values are shown as a horizontal black bar in each box. The whiskers extend up from the top of the box to the largest value that is ≤1.5 times the interquartile range, and down from the bottom of the box to the smallest value that is &gt;1.5 times the interquartile range. Values outside this range are considered as outliers and are represented by dots. SUM: summer; AUT: autumn. No DO data available for springs of Lombardy in 2004.</p> Full article ">Figure 3
<p>Box-plots showing comparison between TA, SRP, DIN, and DRSi values measured in the present study and in previous research [<a href="#B10-water-12-03276" class="html-bibr">10</a>,<a href="#B15-water-12-03276" class="html-bibr">15</a>]. Symbols of box-plots are as in <a href="#water-12-03276-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Principal component analysis (PCA) diagram representing the ordination of springs in relation to environmental variables. T: temperature; EC: electric conductivity; TA: total alkalinity; DO: dissolved oxygen saturation; SRP: soluble reactive phosphorus; DRSi: dissolved reactive silica; DIN: dissolved inorganic nitrogen. Point symbols refer to sub-catchments (Chiavenna: ●; Arda-Ongina: +; Staffora-Luria-Versa-Coppa: ☐; Taro: ■; Parma: X; Enza: ○; Lambro-Olona meridionale: ◇; Adda: ✳; Po: △).</p> Full article ">Figure 5
<p>Comparison between DIN and DRSi concentrations, and DRSi to DIN molar ratio (except for PR39, which scores lie outside the graph) for springs of Emilia-Romagna and Lombardy in 2015– 2016 and in previous studies. Note that axis scales are different for each graph.</p> Full article ">Figure 6
<p>Canonical correspondence ordination of ostracods and environmental variables on the space defined by the first two canonical axes. The only significant variables (<span class="html-italic">p</span> &lt; 0.05) in explaining species occurrence are displayed. Can_ca: <span class="html-italic">Candona candida</span>; Cyc_la: <span class="html-italic">Cyclocypris laevis</span>; Cyc_ov: <span class="html-italic">Cyclocypris ovum</span>; Cyp_op: <span class="html-italic">Cypria ophthalmica</span>; Cyp_vi: <span class="html-italic">Cypridopsis vidua</span>; Her_br: <span class="html-italic">Herpetocypris brevicaudata</span>; Her_re: <span class="html-italic">Herpetocypris reptans</span>; Her_sp: <span class="html-italic">Herpetocypris</span> sp.; Het_re: <span class="html-italic">Heterocypris reptans</span>; Het_sa: <span class="html-italic">Heterocypris salina</span>; Ily_br: <span class="html-italic">Ilyocypris bradyi</span>; Ily_gi: <span class="html-italic">Ilyocypris gibba</span>; Ily_in: <span class="html-italic">Ilyocypris inermis</span>; Neg_ne: <span class="html-italic">Neglecandona</span> gr. <span class="html-italic">neglecta</span>; Not_pe: <span class="html-italic">Notodromas persica</span>; Pot_sm: <span class="html-italic">Potamocypris smaragdina</span>; Pri_ze: <span class="html-italic">Prionocypris zenkeri</span>; Pse_lo: <span class="html-italic">Pseudocandona lobipes</span>; Pse_pr: <span class="html-italic">Pseudocandona pratensis</span>; Sco_ps: <span class="html-italic">Scottia pseudobrowniana.</span></p> Full article ">
19 pages, 4687 KiB  
Article
Oxidation of Selected Trace Organic Compounds through the Combination of Inline Electro-Chlorination with UV Radiation (UV/ECl2) as Alternative AOP for Decentralized Drinking Water Treatment
by Philipp Otter, Katharina Mette, Robert Wesch, Tobias Gerhardt, Frank-Marc Krüger, Alexander Goldmaier, Florian Benz, Pradyut Malakar and Thomas Grischek
Water 2020, 12(11), 3275; https://doi.org/10.3390/w12113275 - 21 Nov 2020
Cited by 10 | Viewed by 3773
Abstract
A large variety of Advanced Oxidation Processes (AOPs) to degrade trace organic compounds during water treatment have been studied on a lab scale in the past. This paper presents the combination of inline electrolytic chlorine generation (ECl2) with low pressure UV [...] Read more.
A large variety of Advanced Oxidation Processes (AOPs) to degrade trace organic compounds during water treatment have been studied on a lab scale in the past. This paper presents the combination of inline electrolytic chlorine generation (ECl2) with low pressure UV reactors (UV/ECl2) in order to allow the operation of a chlorine-based AOP without the need for any chlorine dosing. Lab studies showed that from a Free Available Chlorine (FAC) concentration range between 1 and 18 mg/L produced by ECl2 up to 84% can be photolyzed to form, among others, hydroxyl radicals (OH) with an UV energy input of 0.48 kWh/m3. This ratio could be increased to 97% by doubling the UV energy input to 0.96 kWh/m3 and was constant throughout the tested FAC range. Also the achieved radical yield of 64% did not change along the given FAC concentration range and no dependence between pH 6 and pH 8 could be found, largely simplifying the operation of a pilot scale system in drinking water treatment. Whereas with ECl2 alone only 5% of benzotriazoles could be degraded, the combination with UV improved the degradation to 89%. Similar results were achieved for 4-methylbenzotriazole, 5-methylbenzotriazole and iomeprol. Oxipurinol and gabapentin were readily degraded by ECl2 alone. The trihalomethanes values were maintained below the Germany drinking water standard of 50 µg/L, provided residual chlorine concentrations are kept within the permissible limits. The here presented treatment approach is promising for decentralized treatment application but requires further optimization in order to reduce its energy requirements. Full article
(This article belongs to the Special Issue Advanced Oxidation Processes for Water and Wastewater Treatment)
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<p>Pathway of chloride ions used for radical production (excerpt) in UV/ECl<sub>2</sub> process.</p> Full article ">Figure 2
<p>Lab test setting for performance evaluation of chlorine and radical formation.</p> Full article ">Figure 3
<p>Pilot system tested with Elbe river water.</p> Full article ">Figure 4
<p>Relation between current and FAC concentration (<span class="html-italic">n</span> = 9) (Q = 75 L/h, EC = 400 µS/cm, T = 19 ± 1 °C).</p> Full article ">Figure 5
<p>Relation between chloride concentration and FAC concentrations (<span class="html-italic">n</span> = 9) (Q = 75 L/h, EC = 400 µS/cm, T = 19 ± 1 °C).</p> Full article ">Figure 6
<p>Relation of chloride concentration (with charge specific FAC production rate and current efficiency (<span class="html-italic">n</span> = 9) (Q = 75 L/h, EC = 400 µS/cm, T = 19 ± 1 °C).</p> Full article ">Figure 7
<p>Chlorine consumption by UV treatment in dependence of FAC (Q = 75 L/h, EC = 400 µS/cm, T = 19 ± 1 °C).</p> Full article ">Figure 8
<p>Radical formation in dependence of FAC demand at different pH (Q = 75 L/h, EC = 400 µS/L, T = 19 ± 1 °C) (<span class="html-italic">n</span> = 26).</p> Full article ">Figure 9
<p>FAC and total chlorine concentrations measured in three different settings during field tests with Elbe river water.</p> Full article ">Figure 10
<p>Concentrations of tested TOrCs (<b>a</b>,<b>b</b>) and degradation percentages (<b>c</b>,<b>d</b>) concentrations measured in three different settings during field tests with Elbe river water.</p> Full article ">Figure 11
<p>DOC concentrations measured in three different settings during field tests with Elbe river water.</p> Full article ">Figure 12
<p>THM concentrations measured in three different settings during field tests with Elbe river water.</p> Full article ">Figure 13
<p>E<sub>EOs</sub> for gabapentin and oxipurinol (<b>a</b>) and 4-methylbenzotriazole, 5-methylbenzotriazole iomeprol and benzotriazole (<b>b</b>) calculated from three different settings during field tests with Elbe river water.</p> Full article ">
39 pages, 30926 KiB  
Article
The Characteristics of Coastal Highway Wave Attack and Nearshore Morphology: Provincial Highway No. 9, Taiwan
by Wei-Shiun Lu, Han-Lun Wu, Kai-Cheng Hu, Yen-Lung Chen, Wei-Bo Chen, Shih-Chun Hsiao, Yu Hsiao, Chun-Yen Chen and Li-Hung Tsai
Water 2020, 12(11), 3274; https://doi.org/10.3390/w12113274 - 21 Nov 2020
Viewed by 2698
Abstract
This study explores coastal hazard characteristics along Provincial Highway No. 9 (hereafter the Provincial Highway) in Taiwan. Numerical simulation was conducted to analyze wave attacks and medium-to-long-term coastal morphological change along the Provincial Highway and identify areas of high hazard potential. Hydrodynamic and [...] Read more.
This study explores coastal hazard characteristics along Provincial Highway No. 9 (hereafter the Provincial Highway) in Taiwan. Numerical simulation was conducted to analyze wave attacks and medium-to-long-term coastal morphological change along the Provincial Highway and identify areas of high hazard potential. Hydrodynamic and morphological change numerical models were used to simulate various meteorological scenarios in the research site; specifically, far-field, medium-field, and near-field simulations were performed. Subsequently, the simulated results were employed to analyze hazard characteristics and determine the potential for hazard along the Provincial Highway. According to the analysis of hazard characteristics, the high potential of wave attacks was revealed in the following sections of the highway: 440K+000-441K+000, areas near 424K+500, and 396K+000-396K+500, and the highest potential for erosion was shown in the areas near 418K+000 and 397K+500. Finally, these areas with a high potential for wave attacks and erosion were marked to create a map of hazard potential for the provincial highway, and thus provide insights into future construction works or hazard-prevention operations. Full article
(This article belongs to the Special Issue Wave and Tide Modelling in Coastal and Ocean Hydrodynamics)
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<p>Location of Taitung Provincial Highway No. 9 in eastern Taiwan.</p> Full article ">Figure 2
<p>Schematic of the nested numerical modelling process and relevant operations.</p> Full article ">Figure 3
<p>Bathymetry and unstructured grid for Far-field simulation.</p> Full article ">Figure 4
<p>Tracks of Typhoon Meranti (2016) and Typhoon Nesat (2017) (CWB).</p> Full article ">Figure 5
<p>Locations of tide stations and wave buoys.</p> Full article ">Figure 6
<p>Comparisons of storm surges between model hindcasts and observations (Typhoon Meranti September 2017). Refer to <a href="#water-12-03274-f005" class="html-fig">Figure 5</a> for observation site locations.</p> Full article ">Figure 7
<p>Comparisons of significant wave heights between model hindcasts and observations (Typhoon Meranti September 2017). Refer to <a href="#water-12-03274-f005" class="html-fig">Figure 5</a> for observation site locations.</p> Full article ">Figure 8
<p>Comparisons of storm surges between model hindcasts and observations (Typhoon Nesat July 2017). Refer to <a href="#water-12-03274-f005" class="html-fig">Figure 5</a> for observation site locations.</p> Full article ">Figure 9
<p>Comparisons of significant wave heights between model hindcasts and observations (Typhoon Nesat July 2017). Refer to <a href="#water-12-03274-f005" class="html-fig">Figure 5</a> for observation site locations.</p> Full article ">Figure 10
<p>Unstructured grid for Medium-field simulation.</p> Full article ">Figure 11
<p>Bathymetry for near-field simulation.</p> Full article ">Figure 12
<p>Locations of TMW1 station and the sediment size measured.</p> Full article ">Figure 13
<p>Comparisons of water level and velocity between numerical model and observations at TMW1 station (refer to <a href="#water-12-03274-f012" class="html-fig">Figure 12</a> for location map).</p> Full article ">Figure 13 Cont.
<p>Comparisons of water level and velocity between numerical model and observations at TMW1 station (refer to <a href="#water-12-03274-f012" class="html-fig">Figure 12</a> for location map).</p> Full article ">Figure 14
<p>Comparison of morphological change between measured data and numerical result in the Taimali River mouth. (<b>a</b>) measured data (WRA, 2018); (<b>b</b>) numerical result; (<b>c</b>) quantitative comparison between measured data and simulated result.</p> Full article ">Figure 15
<p>Frequency analysis of the surge heights and wave heights.</p> Full article ">Figure 16
<p>Wave observational data of Taitung Open-Ocean Buoy.</p> Full article ">Figure 17
<p>Wave rose diagrams for the entire year, the summer and winter of Taitung Open-Ocean Buoy.</p> Full article ">Figure 18
<p>Provincial Highway elevation, distribution of wave height, and distribution of nearshore wave height.</p> Full article ">Figure 19
<p>Wave field and water level distribution in Region A.</p> Full article ">Figure 20
<p>Wave field and water level distribution in Region B.</p> Full article ">Figure 21
<p>Wave field and water level distribution in Region C.</p> Full article ">Figure 22
<p>Simulated coastal current and morphological change in Region A. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 22 Cont.
<p>Simulated coastal current and morphological change in Region A. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 22 Cont.
<p>Simulated coastal current and morphological change in Region A. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 23
<p>Simulated coastal current and morphological change in Region B. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 23 Cont.
<p>Simulated coastal current and morphological change in Region B. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 23 Cont.
<p>Simulated coastal current and morphological change in Region B. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 24
<p>Simulated coastal current and morphological change in Region C. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 24 Cont.
<p>Simulated coastal current and morphological change in Region C. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 24 Cont.
<p>Simulated coastal current and morphological change in Region C. (<b>a</b>): coastal current; (<b>b</b>): morphological change.</p> Full article ">Figure 25
<p>Map of the potential for wave attacks along the Provincial Highway in Region A.</p> Full article ">Figure 26
<p>Map of the potential for wave attacks along the Provincial Highway in Region B.</p> Full article ">Figure 27
<p>Map of the potential for wave attacks along the Provincial Highway in Region C.</p> Full article ">Figure 28
<p>Map of the potential for erosion in Region A.</p> Full article ">Figure 29
<p>Map of the potential for erosion in Region B.</p> Full article ">Figure 30
<p>Map of the potential for erosion in Region C.</p> Full article ">
20 pages, 5385 KiB  
Article
Floating Wetland Islands Implementation and Biodiversity Assessment in a Port Marina
by Cristina S. C. Calheiros, João Carecho, Maria P. Tomasino, C. Marisa R. Almeida and Ana P. Mucha
Water 2020, 12(11), 3273; https://doi.org/10.3390/w12113273 - 21 Nov 2020
Cited by 11 | Viewed by 6877
Abstract
Floating wetland islands (FWI) are considered nature-based solutions with great potential to promote several ecosystem services, such as biodiversity and water quality enhancement through phytoremediation processes. To our knowledge, the present work is the first to scientifically document the in-situ establishment of an [...] Read more.
Floating wetland islands (FWI) are considered nature-based solutions with great potential to promote several ecosystem services, such as biodiversity and water quality enhancement through phytoremediation processes. To our knowledge, the present work is the first to scientifically document the in-situ establishment of an FWI in a seawater port marina. The establishment and performance of a cork floating platform with a polyculture (Sarcocornia perennis, Juncus maritimus, Phragmites australis, Halimione portulacoides, Spartina maritima, Limonium vulgare) was evaluated. The diversity of organisms present in the FWI was undertaken based on the macrofauna assessment, taking into consideration marine water characterization, with a focus on hydrocarbons. Microbial communities were assessed based on metabarcoding approach to study 16S rRNA gene from environmental DNA retrieved from biofilm (from the planting media), marine biofouling (from the submerged platform) and surface marina water. S. perennis was the species with the highest survival rate and growth. The structure of the microbial community showed clear differences between those established in the FWI and those in the surrounding water, showing the presence of some bacterial groups that can be relevant for bioremediation processes (e.g., Saprospiraceae family). Concerning the macrofauna analysis, Mytilus sp. was the predominant taxa. To be of relevance, total petroleum hydrocarbons were detected at the marina up to ca. 6 mg/L. This study gives new insights into broadening FWI application to the saline environments of port marinas and to supporting a management strategy to promote several ecosystem services such biodiversity, species habitat, water quality enhancement and added aesthetic value to the marina landscape. Full article
(This article belongs to the Special Issue Water Management: New Paradigms for Water Treatment and Reuse)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>A</b>) Schematic representation of the study area, (<b>B</b>) photo of the marina of the Porto Cruise Terminal. <span style="color:#00BFFF">★</span> Floating wetland island location.</p> Full article ">Figure 2
<p>Floating wetland island design: (<b>A</b>) Floating platform modules; (<b>B</b>) detail of the cable responsible for the anchoring system; (<b>C</b>) joints between modules; (<b>D</b>) plants setup.</p> Full article ">Figure 3
<p>Floating wetland island in the marina of the Porto Cruise Terminal.</p> Full article ">Figure 4
<p>Macrofauna found on or surrounding the floating wetland island: (<b>A</b>) Polychaeta; (<b>B</b>) <span class="html-italic">Chthamalus</span> sp.; (<b>C</b>) <span class="html-italic">Mytilus</span> sp.; (<b>D</b>) <span class="html-italic">Ulva</span> sp.; (<b>E</b>) <span class="html-italic">Ceramium</span> sp.; (<b>F</b>) <span class="html-italic">Patella</span> sp.; (<b>G</b>) <span class="html-italic">Palaemon</span> sp.; (<b>H</b>) <span class="html-italic">Laminaria</span> sp. (left elypse) and <span class="html-italic">Mugil</span> sp. (right elypse); (<b>I</b>) <span class="html-italic">Mytilus</span> sp.</p> Full article ">Figure 5
<p>(<b>A</b>) Maximum electron transport rate (ETRm); (<b>B</b>) the Fv/Fm ratio and (<b>C</b>) the quantum efficiency of photosynthesis (α) registered for <span class="html-italic">Sarcocornia perennis</span> (mean ± SD, n = 4) for four different months of 2018 and 2019. The different letters placed on the bars show the statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between months.</p> Full article ">Figure 6
<p>Alpha-diversity metrics (Shannon diversity index and richness–observed number of genera): (<b>A</b>) across all the individual samples; (<b>B</b>) comparison between type of samples. Brown bars represent the biofouling samples from the floating platform (H = non-planted holes and U = Under surface), green bars represent the biofilm samples from the planting media (SP = <span class="html-italic">Spartina maritima</span>; SA = <span class="html-italic">Sarcocornia perennis</span>; and P = <span class="html-italic">Phragmites australis</span>) and blue bars represent the water samples (W); S18 = Summer of 2018; W19 = Winter of 2019 and SP19 = Spring of 2019. The different letters placed on the bars show the statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between the type of samples.</p> Full article ">Figure 7
<p>Taxonomic profile of top 20 prokaryotic taxa at lower level (genus) in each type of samples (biofouling from the floating platform, biofilm from the planting media and water). Top 20 represents 33% of the total sequences dataset. Codes: biofouling samples from the floating platform (H = non-planted holes and U = under surface); biofilm samples from the planting media (SP = <span class="html-italic">Spartina maritima</span>; SA = <span class="html-italic">Sarcocornia perennis</span>; and P = <span class="html-italic">Phragmites australis</span>) and water samples (W); S18 = Summer of 2018; W19 = Winter of 2019 and SP19 = Spring of 2019.</p> Full article ">Figure 8
<p>axonomic profile of top 10 prokaryotic taxa at higher level (phylum) across all samples. Top 10 represents 91% of the total sequences dataset. Samples considered: biofouling samples from the floating platform; biofilm samples from the planting media and water samples.</p> Full article ">Figure 9
<p>Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity of all the samples analyzed: brown points represent biofouling samples collected from the floating platform; green points represent the biofilm samples from the planting media and blue points represent samples from the water. Codes: biofouling samples from the surface of the floating platform (H = non-planted holes and U = Under surface); biofilm samples from the planting media (SP = <span class="html-italic">Spartina maritima</span>; SA = <span class="html-italic">Sarcocornia perennis</span>; and P = <span class="html-italic">Phragmites australis</span>) and water samples (W); S18 = Summer of 2018; W19 = Winter of 2019 and SP19 = Spring of 2019.</p> Full article ">Figure A1
<p>Taxonomic profile of top 10 prokaryotic taxa at higher level (phylum) across all samples. Top 10 represents 91% of the total sequences dataset. Codes: biofouling samples from the floating platform (H = non-planted holes and U = Under surface); biofilm samples from the planting media (SP = <span class="html-italic">Spartina maritima</span>; SA = <span class="html-italic">Sarcocornia perennis</span>; and P = <span class="html-italic">Phragmites australis</span>) and water samples (W); S18 = Summer of 2018; W19 = Winter of 2019 and SP19 = Spring of 2019.</p> Full article ">
31 pages, 5407 KiB  
Article
The Impacts of Climate Change on Wastewater Treatment Costs: Evidence from the Wastewater Sector in China
by Ami Reznik, Yu Jiang and Ariel Dinar
Water 2020, 12(11), 3272; https://doi.org/10.3390/w12113272 - 21 Nov 2020
Cited by 8 | Viewed by 4556
Abstract
Treatment of wastewater is expected to become a major development issue in the years to come. We investigate the relationship between climate and costs of wastewater treatment with the objective of examining if changes in climate might have an impact on the costs [...] Read more.
Treatment of wastewater is expected to become a major development issue in the years to come. We investigate the relationship between climate and costs of wastewater treatment with the objective of examining if changes in climate might have an impact on the costs of wastewater treatment. For that purpose, we use a cross-section sample of 163 treatment plants from China to estimate the industry’s cost function. The methodology used comprises an econometric estimation procedure of treatment costs of the wastewater sector, and a simulation of changes in these costs predicted with future climate conditions, policy implementation scenarios, population growth and development trends. Our results find evidence of climate change impact on treatment costs. We also simulate potential impact of future policy and climate scenarios on costs of treatment, and we measure the cost impact of all other cost determinants but climate—as these are indirectly affected by accounting for climate in the estimation procedure. This indirect impact predicts total cost changes of different magnitudes across the range of future scenarios investigated. Full article
(This article belongs to the Special Issue Water Management: New Paradigms for Water Treatment and Reuse)
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<p>Relationship between historical climate variables in the dataset. (<b>a</b>) Annual average temperature and precipitation levels for each wastewater treatment plant in the sample; (<b>b</b>) Annual average temperature levels and intra-annual variation in temperature for each wastewater treatment plant in the sample; (<b>c</b>) Annual average precipitation levels and intra-annual variation in precipitation for each wastewater treatment plant in the sample.</p> Full article ">Figure 2
<p>Temporal variation in climate (temperature) variables in the sample. (<b>a</b>) Ratio between observed and historical annual average temperature levels for each wastewater treatment plant with respect to historical annual average temperature levels; (<b>b</b>) Ratio between observed and historical intra-annual variation in temperature for each wastewater treatment plant with respect to historical intra-annual variation in temperature.</p> Full article ">Figure 3
<p>Temporal variation in climate (precipitation) variables in the sample. (<b>a</b>) Ratio between observed and historical annual average precipitation levels for each wastewater treatment plant with respect to historical annual average precipitation levels; (<b>b</b>) Ratio between observed and historical intra-annual variation in precipitation for each wastewater treatment plant with respect to historical intra-annual variation in precipitation.</p> Full article ">Figure 4
<p>Future trajectories of wastewater flows (index, 2020 = 100). Labels V1 through V6 refer to different trends of predicted wastewater flows.</p> Full article ">Figure A1
<p>Annual average temperature levels in sample year (2006), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A2
<p>Annual average temperature levels in short-term future (2020–2046), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A3
<p>Annual average temperature levels in mid-term future (2047–2073), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A4
<p>Annual average temperature levels in long-term future (2074–2100), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A5
<p>Annual average precipitation levels in sample year (2006), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A6
<p>Annual average precipitation levels in short-term future (2020–2046), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A7
<p>Annual average precipitation levels in mid-term future (2047–2073), and locations of wastewater treatment plants included in the sample.</p> Full article ">Figure A8
<p>Annual average precipitation levels in long-term future (2074–2100), and locations of wastewater treatment plants included in the sample.</p> Full article ">
23 pages, 8143 KiB  
Article
Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau
by Alexandra Hamm, Anselm Arndt, Christine Kolbe, Xun Wang, Boris Thies, Oleksiy Boyko, Paolo Reggiani, Dieter Scherer, Jörg Bendix and Christoph Schneider
Water 2020, 12(11), 3271; https://doi.org/10.3390/w12113271 - 21 Nov 2020
Cited by 27 | Viewed by 4784
Abstract
Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide [...] Read more.
Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km. Full article
(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)
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<p>Overview of the study area and the 3 rain gauge stations located within the boundaries of the area.</p> Full article ">Figure 2
<p>Schematic overview of the method applied to derive terrain complexity. Black lines represent the grid of the lowest resolved precipitation product (GPCC), red lines represent the grid of the ALOS digital elevation model (DEM). The topography in the background is an example topography. In the equation to calculate the DEM standard deviation (SD) in each GPCC grid cell, <math display="inline"><semantics> <msub> <mi>x</mi> <mi>i</mi> </msub> </semantics></math> stands for the values within the ALOS DEM cell, <math display="inline"><semantics> <mi>μ</mi> </semantics></math> for the overall mean and <span class="html-italic">N</span> for the number of ALOS DEM grid cells within each GPCC grid cell.</p> Full article ">Figure 3
<p>Spatial mean cumulative sum of precipitation throughout the study period.</p> Full article ">Figure 4
<p>Spatial average monthly sum of precipitation during the study period. The gray dashed line represents the mean precipitation in each month over all datasets.</p> Full article ">Figure 5
<p>Spatial log-scaled per-grid-cell sum over the study period for each of the precipitation products. Sums were only calculated for valid values, which excluded the south-western corner in the PRETIP product (hatched area) and individual grid cells lower than 2500 m.a.s.l.</p> Full article ">Figure 6
<p>Cumulative sum of daily precipitation throughout the study period for the station data (black line) and the gridded precipitation products (colored lines).</p> Full article ">Figure 7
<p>Absolute precipitation difference (mm day<sup>−1</sup>) based on terrain complexity aligned with the coarsest grid (GPCC). Complexity is described as high (SD &gt; Q3) or low (SD ≤ Q3) standard deviation of ALOS-DEM elevation within a single grid cell of the common grid. Blue rectangles represent low terrain complexity, red dots indicate high terrain complexity and the yellow diamonds depict the mean difference.</p> Full article ">Figure 8
<p>Visualization of the selected climdex indices R1, R10, R20, Rx1, Rx5 and PTOT as boxplots (for descriptions, see <a href="#water-12-03271-t003" class="html-table">Table 3</a>). Each box contains all grid cell values within the precipitation product. Boxes range from the 1st to 3rd quartile; the yellow line denotes the median; and whiskers indicate 1.5 fold interquartile ranges from the upper to lower boundaries. Values outside this range are displayed as black dots. Please note that the different products have different spatial resolutions.</p> Full article ">Figure 9
<p>Visualization of precipitation differences between each two precipitation products based on the relationship between mean difference (yellow diamonds in <a href="#water-12-03271-f007" class="html-fig">Figure 7</a>) and the difference between high (red dots in <a href="#water-12-03271-f007" class="html-fig">Figure 7</a>) and low (blue squares in <a href="#water-12-03271-f007" class="html-fig">Figure 7</a>) complexity precipitation. The groups describe: (I) low mean difference and low difference between high and low terrain complexity, (II) high mean difference but low difference with respect to terrain complexity and (III) medium overall difference but large variation depending on terrain complexity. Only some labels of all pairs as listed in <a href="#water-12-03271-f007" class="html-fig">Figure 7</a> are displayed.</p> Full article ">Figure A1
<p>Amount of available PRETIP scenes per day. The maximum value is 48 (2 scenes per hour) and marked with the black dotted line. On average, 32.6 scenes per day are available.</p> Full article ">Figure A2
<p>Visualization of the selected climdex indices R1, R10, R20, Rx1, Rx5 and PTOT as boxplot charts equivalent to <a href="#water-12-03271-f008" class="html-fig">Figure 8</a> (for description see <a href="#water-12-03271-t003" class="html-table">Table 3</a>). (<b>a</b>) depicts resulting values after resampling every product to the grid resolution of the lowest resolved product. (<b>b</b>) shows the same boxplot charts as <a href="#water-12-03271-f008" class="html-fig">Figure 8</a>, but with the y-axis limits adjusted to the range in (a) to allow for direct comparison between both versions.</p> Full article ">
15 pages, 3983 KiB  
Article
Analysis of the Uncertainty in Estimates of Manning’s Roughness Coefficient and Bed Slope Using GLUE and DREAM
by Guilherme da Cruz dos Reis, Tatiane Souza Rodrigues Pereira, Geovanne Silva Faria and Klebber Teodomiro Martins Formiga
Water 2020, 12(11), 3270; https://doi.org/10.3390/w12113270 - 21 Nov 2020
Cited by 2 | Viewed by 2460
Abstract
River discharge data are critical to elaborating on engineering projects and water resources management. Discharge data must be precise and collected with good temporal resolution. To elaborate on a more accurate database, this paper aims to quantify the uncertainty generated while applying Bayesian [...] Read more.
River discharge data are critical to elaborating on engineering projects and water resources management. Discharge data must be precise and collected with good temporal resolution. To elaborate on a more accurate database, this paper aims to quantify the uncertainty generated while applying Bayesian inference through the GLUE and DREAM methods. Both methods were used to estimate hydraulic parameters and compare between them with Manning’s equation. Throughout the statistical analysis, the uncertainties in the application of the models are used to determine the parameters of Manning’s roughness coefficient and bed slope. The validation was made via a comparison of the calculated maximum and minimum discharges, and the observed flow available at HidroWeb. In conclusion, both methods estimated the hydraulic parameters well, but a higher relative deviation was seen in the intervals with smaller calculated discharges; DREAM appears to be more accurate than GLUE, once the relative deviation in GLUE became greater. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>Meia Ponte River’s and study area.</p> Full article ">Figure 2
<p>Representation of <span class="html-italic">n</span> along the cross-section.</p> Full article ">Figure 3
<p>Scheme showing the cross-section area before (<b>a</b>) and after (<b>b</b>) the trapezoidal interpolation.</p> Full article ">Figure 4
<p>Boxplot of absolute deviation, point the outlier results—GLUE.</p> Full article ">Figure 5
<p>Boxplot of relative deviation (logarithmical scale)—GLUE.</p> Full article ">Figure 6
<p>Interval of calculated and observed discharges—GLUE.</p> Full article ">Figure 7
<p>Matrix of graphics relating <span class="html-italic">n</span><sub>1</sub>, <span class="html-italic">n</span><sub>2</sub>, <span class="html-italic">n</span><sub>3</sub>, and <span class="html-italic">S</span><sub>0</sub>—GLUE. The main diagonal shows the histogram of each parameter demonstrating the frequency with each value of <span class="html-italic">n</span><sub>1</sub>, <span class="html-italic">n</span><sub>2</sub>, <span class="html-italic">n</span><sub>3</sub>, and <span class="html-italic">S</span><sub>0</sub> in the distribution. Below the main diagonal, we see the scatter plots for each pair of parameters. The line is drawn to guide the eye.</p> Full article ">Figure 8
<p>Annual discharge of 2016—GLUE.</p> Full article ">Figure 9
<p>Relative deviation of 2007 to 2016—GLUE.</p> Full article ">Figure 10
<p>Boxplot of absolute deviation—DREAM.</p> Full article ">Figure 11
<p>Boxplot of relative deviation (logarithmical scale) and points on the outlier results—GLUE.</p> Full article ">Figure 12
<p>Interval of calculated and observed discharges—DREAM.</p> Full article ">Figure 13
<p>Matrix of graphics relating <span class="html-italic">n</span><sub>1</sub>, <span class="html-italic">n</span><sub>2</sub>, <span class="html-italic">n</span><sub>3</sub> and <span class="html-italic">S</span><sub>0</sub>—DREAM.</p> Full article ">Figure 14
<p>Annual discharge of 2014—DREAM.</p> Full article ">Figure 15
<p>Relative deviation trough 2007 to 2016—GLUE.</p> Full article ">
23 pages, 2726 KiB  
Article
Conflict Resilience of Water and Energy Supply Infrastructure: Insights from Yemen
by Mohammad Al-Saidi, Emma Lauren Roach and Bilal Ahmed Hassen Al-Saeedi
Water 2020, 12(11), 3269; https://doi.org/10.3390/w12113269 - 21 Nov 2020
Cited by 12 | Viewed by 7378
Abstract
Political instability and conflicts are contemporary problems across the Middle East. They threaten not only basic security, but also infrastructure performance. Supply infrastructure, providing basic services such as water and electricity, has been subjected to damage, capacity deterioration, and the bankruptcy of public [...] Read more.
Political instability and conflicts are contemporary problems across the Middle East. They threaten not only basic security, but also infrastructure performance. Supply infrastructure, providing basic services such as water and electricity, has been subjected to damage, capacity deterioration, and the bankruptcy of public providers. Often, in conflict countries such as Yemen, the continuity of basic supply is only possible thanks to adaptation efforts on the community and household levels. This paper examines the conflict resilience of water and energy supply infrastructure in Yemen during the armed conflict 2015–today. It contributes to resilience studies by linking knowledge on state fragility and conflicts, humanitarian aid, and infrastructure resilience. The paper presents adaptation responses of communities and public entities in the water and energy sectors in Yemen and critically evaluates these responses from the perspective of conflict resilience of infrastructure. The gained insights reaffirm the notion about the remarkable adaptive capacities of communities during conflicts and the importance of incorporating community-level adaptation responses into larger efforts to enhance the conflict resilience of infrastructure systems. Full article
(This article belongs to the Special Issue Sustainable and Resilient Water Management in Regional, Vulnerable or Island Communities)
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<p>Conceptual framework and study outline.</p> Full article ">Figure 2
<p>Map of Yemen (source: Public domain provided by United States Central Intelligence Agency’s World Factbook).</p> Full article ">Figure 3
<p>(<b>a</b>) Photovoltaic for water pumping and utilities operations: A pumping station of a private water well run on solar energy in the capital city of Sana’a. (<b>b</b>) Solar panels on the roof of a building of the Sana’a Water and Sanitation Corporation for operating a public water well nearby (pictures by the authors).</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Photovoltaic for water pumping and utilities operations: A pumping station of a private water well run on solar energy in the capital city of Sana’a. (<b>b</b>) Solar panels on the roof of a building of the Sana’a Water and Sanitation Corporation for operating a public water well nearby (pictures by the authors).</p> Full article ">Figure 4
<p>(<b>a</b>) Photovoltaic for households’ energy needs: Small rooftop photovoltaic systems widely used by households in the capital city Sana’a. (<b>b</b>) Diesel-powered electricity generators at a facility of a private vendor in the capital city of Sana’a (pictures by the authors).</p> Full article ">
23 pages, 4004 KiB  
Article
Identifying Optimal Security Management Policy for Water–Energy–Food Nexus System under Stochastic and Fuzzy Conditions
by Jing Liu, Yongping Li and Xiao Li
Water 2020, 12(11), 3268; https://doi.org/10.3390/w12113268 - 21 Nov 2020
Cited by 7 | Viewed by 2487
Abstract
An interval-stochastic-fuzzy policy analysis model is proposed to generate optimal security management policy for a water–energy–food nexus system of the urban agglomeration under multiple uncertainties. A number of planning policies under interval-stochastic surface water and groundwater conditions are obtained. Ranking scores of all [...] Read more.
An interval-stochastic-fuzzy policy analysis model is proposed to generate optimal security management policy for a water–energy–food nexus system of the urban agglomeration under multiple uncertainties. A number of planning policies under interval-stochastic surface water and groundwater conditions are obtained. Ranking scores of all policies in descending order, policy with the highest score is the best choice. Results disclose that (a) interval-stochastic available water resources lead to changing system benefits. (b) The shares of cropland area targets are 2.7% (Xiamen), 42.6% (Zhangzhou), and 54.7% (Quanzhou). (c) Different available water scenarios result in varied irrigation patterns. (d) Surface water takes a high fraction of the total water supply (about [71.34, 73.68]%), diesel agricultural machinery service more than 60% of the total cropland. (e) Zhangzhou contributes about 50.01% of total TN and TP emissions, while Quanzhou contributes about 50.61% of total carbon emission. (f) Security level of policies would change with the varied σ and α values, due to the risk attitudes of policy makers. (h) Sweet potato and others are the crops with the highest safety performance; (i) Zhangzhou is the city with highest comprehensive safety performance. Full article
(This article belongs to the Special Issue Management of Water-Energy-Food Security Nexus)
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Figure 1
<p>The study area.</p> Full article ">Figure 2
<p>Framework of the interval-stochastic-fuzzy policy analysis (ISF-PA) model.</p> Full article ">Figure 3
<p>Revenues, costs, and benefits: (<b>a</b>) Lower bound, (<b>b</b>) Upper bound.</p> Full article ">Figure 4
<p>Optimal cropland area target: (<b>a</b>) Early rice, (<b>b</b>) Middle rice, (<b>c</b>) Late rice, (<b>d</b>) Sweet potato, (<b>e</b>) Potato, (<b>f</b>) Soybean, (<b>g</b>) Others.</p> Full article ">Figure 5
<p>Cropland irrigated by surface water: (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3, (<b>d</b>) S4, (<b>e</b>) S5, (<b>f</b>) S6.</p> Full article ">Figure 5 Cont.
<p>Cropland irrigated by surface water: (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3, (<b>d</b>) S4, (<b>e</b>) S5, (<b>f</b>) S6.</p> Full article ">Figure 6
<p>Cropland irrigated by groundwater: (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3, (<b>d</b>) S4, (<b>e</b>) S5, (<b>f</b>) S6.</p> Full article ">Figure 7
<p>Water resources allocation: (<b>a</b>) Lower bound, (<b>b</b>) Upper bound.</p> Full article ">Figure 8
<p>Energy consumption: (<b>a</b>) Diesel, (<b>b</b>) Gasoline, (<b>c</b>) Electricity.</p> Full article ">Figure 9
<p>Carbon and effluent emissions: (<b>a1</b>) TN emission in Xiamen, (<b>a2</b>) TN emission in Zhangzhou, (<b>a3</b>) TN emission in Quanzhou, (<b>b1</b>) TP emission in Xiamen, (<b>b2</b>) TP emission in Zhangzhou, (<b>b3</b>) TP emission in Quanzhou, (<b>c1</b>) CO<sub>2</sub> emission in Xiamen, (<b>c2</b>) CO<sub>2</sub> emission in Zhangzhou, (<b>c3</b>) CO<sub>2</sub> emission in Quanzhou.</p> Full article ">Figure 10
<p>Integrated scoring for the planning policy: (<b>a</b>) α = 0.1, (<b>b</b>) α = 0.3, (<b>c</b>) α = 0.5, (<b>d</b>) α = 0.7, (<b>e</b>) α = 0.9.</p> Full article ">
22 pages, 3081 KiB  
Article
Variable Seepage Meter Efficiency in High-Permeability Settings
by Donald O. Rosenberry, José Manuel Nieto López, Richard M. T. Webb and Sascha Müller
Water 2020, 12(11), 3267; https://doi.org/10.3390/w12113267 - 21 Nov 2020
Cited by 6 | Viewed by 3161
Abstract
The efficiency of seepage meters, long considered a fixed property associated with the meter design, is not constant in highly permeable sediments. Instead, efficiency varies substantially with seepage bag fullness, duration of bag attachment, depth of meter insertion into the sediments, and seepage [...] Read more.
The efficiency of seepage meters, long considered a fixed property associated with the meter design, is not constant in highly permeable sediments. Instead, efficiency varies substantially with seepage bag fullness, duration of bag attachment, depth of meter insertion into the sediments, and seepage velocity. Tests conducted in a seepage test tank filled with isotropic sand with a hydraulic conductivity of about 60 m/d indicate that seepage meter efficiency varies widely and decreases unpredictably when the volume of the seepage bag is greater than about 65 to 70 percent full or less than about 15 to 20 percent full. Seepage generally decreases with duration of bag attachment even when operated in the mid-range of bag fullness. Stopping flow through the seepage meter during bag attachment or removal also results in a decrease in meter efficiency. Numerical modeling indicates efficiency is inversely related to hydraulic conductivity in highly permeable sediments. An efficiency close to 1 for a meter installed in sediment with a hydraulic conductivity of 1 m/d decreases to about 60 and then 10 percent when hydraulic conductivity is increased to 10 and 100 m/d, respectively. These large efficiency reductions apply only to high-permeability settings, such as wave- or tidally washed coarse sand or gravel, or fluvial settings with an actively mobile sand or gravel bed, where low resistance to flow through the porous media allows bypass flow around the seepage cylinder to readily occur. In more typical settings, much greater resistance to bypass flow suppresses small changes in meter resistance during inflation or deflation of seepage bags. Full article
(This article belongs to the Special Issue Advances in the Study and Understanding of Groundwater Discharge to Surface Water)
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<p>Increase in the literature-based seepage meter efficiency determined by relating measured versus known seepage rates in a calibration tank [<a href="#B3-water-12-03267" class="html-bibr">3</a>,<a href="#B5-water-12-03267" class="html-bibr">5</a>,<a href="#B6-water-12-03267" class="html-bibr">6</a>,<a href="#B7-water-12-03267" class="html-bibr">7</a>,<a href="#B12-water-12-03267" class="html-bibr">12</a>,<a href="#B15-water-12-03267" class="html-bibr">15</a>,<a href="#B19-water-12-03267" class="html-bibr">19</a>,<a href="#B24-water-12-03267" class="html-bibr">24</a>,<a href="#B25-water-12-03267" class="html-bibr">25</a>,<a href="#B26-water-12-03267" class="html-bibr">26</a>,<a href="#B27-water-12-03267" class="html-bibr">27</a>,<a href="#B28-water-12-03267" class="html-bibr">28</a>]. Orange triangle symbol is from Solder et al. [<a href="#B29-water-12-03267" class="html-bibr">29</a>].</p> Full article ">Figure 2
<p>Electromagnetic seepage meter (ESM) installed in a 1.5 m diameter seepage calibration tank. A seepage bag is attached at the exhaust end of an electromagnetic flowmeter (gray cylinder with data cable attached). The entire system is submerged. Note that the Y-valve connector with the valve in line with the bag open and the other valve closed.</p> Full article ">Figure 3
<p>ESM output in ml/min with average values provided every 15 s. Blue rectangles are periods during seepage bag attachment efficiency measurements. Horizontal blue line indicates the average ESM value when no bag is attached. ESM average, standard deviation, and n are for periods with no bag attachment when output was stabilized.</p> Full article ">Figure 4
<p>Effect of bag fullness on bag efficiency during upward seepage. Bag efficiency is plotted for each minute the bag was attached to the ESM. Bag-attachment plots are labeled based on the percent fullness of the bag at the beginning and ending of each attachment (i.e., 0–48 indicates the bag was 0 percent full at the beginning of the attachment and 48 percent full at the end of the attachment). Panels (<b>A</b>–<b>F</b>) represent: tests conducted with seepage rates ranging from 2.1 to 38.7 cm/d, respectively. Values in bold in each chart are average seepage rates through the ESM (cm/d) when no bag was attached. Dotted or dashed lines indicate tests when bag fullness was largely outside of the recommended 25 to 75 percent.</p> Full article ">Figure 5
<p>Seepage bag efficiency for long-duration bag connection times. Panels (<b>A</b>,<b>C</b>) show upward seepage (empty-to-full bag connections) and (<b>B</b>,<b>D</b>) downward seepage (full-to-empty bag connections). Panel (<b>E</b>) is a large-volume seepage bag. Legend entries indicate beginning and ending percent bag fullness. Values in bold in each chart are seepage rates through the ESM (cm/d) in between bag attachments.</p> Full article ">Figure 6
<p>Seepage bag efficiency for upward versus downward seepage. Panels (<b>A</b>,<b>C</b>,<b>E</b>) show upward seepage at slow, moderate, and fast seepage rates, respectively. Panels (<b>B</b>,<b>D</b>,<b>F</b>) show downward seepage at slow, moderate, and fast seepage rates, respectively. Legend entries indicate beginning and ending percent bag fullness. Values in bold in each chart are seepage rates through the ESM (cm/d) in between bag attachments. Percentages indicated in red are average bag efficiencies for bag tests when bag fullness was within the recommended operating range.</p> Full article ">Figure 7
<p>Two bag efficiency measurements with a seepage rate of 4.7 cm/day, the first made with the seepage cylinder inserted 4 cm into the sand bed and the second made with the seepage cylinder inserted 8 cm into the sand.</p> Full article ">Figure 8
<p>Simulated seepage meter efficiency for meter insertions at 5, 10, and 15 cm depths over a range of <span class="html-italic">K</span> from 0.01 to 100 m/d.</p> Full article ">Figure 9
<p>Deflection of flow around a simulated seepage meter inserted 0.1 m into the flow domain with a 95 percent meter efficiency at <span class="html-italic">K</span><sub>bulk</sub> = 1 m/d. Simulations shown for <span class="html-italic">K</span><sub>bulk</sub> of 0.1, 1, 10, and 100 m/d. Red lines indicate flow paths of 80 “particles” released at the bottom of the domain. Blue shading indicates bypass flow of water that would otherwise have discharged within the simulated confines of the seepage cylinder. Green shading indicates area where otherwise vertical flow is visibly diverted laterally by the bypass flow.</p> Full article ">Figure 10
<p>Overnight flow through the seepage tank and the ESM in between two sets of seepage bag tests. A zero-flow period was initiated at 5:45. Large reduction in ESM output at 3:39 was likely due to external electromagnetic noise in the building.</p> Full article ">
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