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Water, Volume 12, Issue 2 (February 2020) – 307 articles

Cover Story (view full-size image): Phosphorus (P) removal structures are an edge-of-field practice for removing dissolved P from flowing water that comes from soils built-up with excessive P concentrations (i.e. legacy soils). These landscape-scale filters contain P sorption materials (PSMs) that sorb P by various reactions. Already proven effective, current research is dedicated to increasing efficiency and decreasing cost of structures. Cover photo shows a 35 Mg slag subsurface P removal structure for treating agricultural tile drainage with flow from the bottom-up. The structure handled 12 L s-1 flow and removed 19 kg (55%) of the cumulative dissolved P load. View this paper.
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24 pages, 11505 KiB  
Article
Topographic Effects on Three-Dimensional Slope Stability for Fluctuating Water Conditions Using Numerical Analysis
by Yue Zhou, Shun-Chao Qi, Gang Fan, Ming-Liang Chen and Jia-Wen Zhou
Water 2020, 12(2), 615; https://doi.org/10.3390/w12020615 - 24 Feb 2020
Cited by 8 | Viewed by 5405
Abstract
With recent advances in calculation methods, the external factors that affect slope stability, such as water content fluctuations and self-configuration, can be more easily assessed. In this study, a three-dimensional finite element strength reduction method was used to analyze the stability of three-dimensional [...] Read more.
With recent advances in calculation methods, the external factors that affect slope stability, such as water content fluctuations and self-configuration, can be more easily assessed. In this study, a three-dimensional finite element strength reduction method was used to analyze the stability of three-dimensional slopes under fluctuating water conditions. Based on soil parameter variations in engineering practice, the calculation models were established using heterogeneous layers, including a cover layer with inferior properties. An analysis of seepage, deformation and slope stability was carried out with 27 different models, including three different slope gradients and nine different corner angles under five different hydraulic conditions. The failure mechanism has been shown to be closely related to the change in matric suction of unsaturated soils and the geometric slope configuration. Finally, the effect of geometry (surface shape, turning corner and slope gradient) and water (fluctuations) on slope stability are discussed in detail. Emphasis is given to comparing safety factors obtained considering or ignoring matric suction. Full article
(This article belongs to the Special Issue Water-Induced Landslides: Prediction and Control)
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<p>The complex river flows in the upper reaches of the reservoir: the Dagangshan Hydropower Station.</p> Full article ">Figure 2
<p>The average water level of the Daganshan reservoir from 30 June 2017 to 11 January 2018.</p> Full article ">Figure 3
<p>The 2D extending model for the studied slope.</p> Full article ">Figure 4
<p>Calculating models with different corner angles and slope gradients: (<b>a</b>) different corner angles and (<b>b</b>) different slope gradients.</p> Full article ">Figure 5
<p>Element distribution in the 3D slope mass.</p> Full article ">Figure 6
<p>Variation of safety factor with turning the corner under the initial condition.</p> Full article ">Figure 7
<p>The failure mechanism of convex-shaped slopes with different corner angles: (<b>a</b>) gentle slope gradient of 1:2; (<b>b</b>) steep slope gradient of 1:1 and (<b>c</b>) very steep slope gradient of 2:1.</p> Full article ">Figure 7 Cont.
<p>The failure mechanism of convex-shaped slopes with different corner angles: (<b>a</b>) gentle slope gradient of 1:2; (<b>b</b>) steep slope gradient of 1:1 and (<b>c</b>) very steep slope gradient of 2:1.</p> Full article ">Figure 8
<p>The failure mechanism of concave-shaped slopes with different corner angles: (<b>a</b>) gentle slope gradient of 1:2; (<b>b</b>) steep slope gradient of 1:1 and (<b>c</b>) very steep slope gradient of 2:1.</p> Full article ">Figure 8 Cont.
<p>The failure mechanism of concave-shaped slopes with different corner angles: (<b>a</b>) gentle slope gradient of 1:2; (<b>b</b>) steep slope gradient of 1:1 and (<b>c</b>) very steep slope gradient of 2:1.</p> Full article ">Figure 9
<p>The difference in safety factor between slopes with three gradients: (<b>a</b>) turning corner = 90°; (<b>b</b>) turning corner = 120°; (<b>c</b>) turning corner = 135°; (<b>d</b>) turning corner = 150° and (<b>e</b>) turning corner = 180°.</p> Full article ">Figure 10
<p>The difference in failure mechanism between slopes with three gradients: turning corner equal to 90°.</p> Full article ">Figure 11
<p>Variation of safety factor with water fluctuations: (<b>a</b>) gentle slope gradient of 1:2; (<b>b</b>) steep slope gradient of 1:1 and (<b>c</b>) very steep slope gradient of 2:1.</p> Full article ">Figure 12
<p>Variation of pore water pressure with water fluctuations: using an example slope with a turning corner of 90° and a gradient of 1:2: (<b>a</b>) initial condition; (<b>b</b>) water rising condition; (<b>c</b>) water infiltration condition; (<b>d</b>) water-dropping condition and (<b>e</b>) water drainage condition.</p> Full article ">Figure 13
<p>Distribution of nodes and stress points in the calculating model: an example using a slope with a turning corner of 90° and gradient of 1:2.</p> Full article ">Figure 14
<p>Monitoring node deformations for different corner angles with a gentle gradient of 1:2: (<b>a</b>) turning corner = 90°; (<b>b</b>) turning corner = 135° and (<b>c</b>) turning corner = 180°.</p> Full article ">Figure 15
<p>Deformations of monitoring nodes for different slope gradients with a small turning corner of 90°: (<b>a</b>) slope gradient = 1:2; (<b>b</b>) slope gradient = 1:1 and (<b>c</b>) slope gradient = 2:1.</p> Full article ">
21 pages, 9536 KiB  
Article
Importance of Precipitation on the Upper Ocean Salinity Response to Typhoon Kalmaegi (2014)
by Fu Liu, Han Zhang, Jie Ming, Jiayu Zheng, Di Tian and Dake Chen
Water 2020, 12(2), 614; https://doi.org/10.3390/w12020614 - 24 Feb 2020
Cited by 22 | Viewed by 4743
Abstract
Using multiple-satellite datasets, in situ observations, and numerical simulations, the influence of typhoon-induced precipitation on the oceanic response to Typhoon Kalmaegi has been discussed. It is found that the convective system and precipitation distribution of Kalmaegi was asymmetric, which leaded to the asymmetric [...] Read more.
Using multiple-satellite datasets, in situ observations, and numerical simulations, the influence of typhoon-induced precipitation on the oceanic response to Typhoon Kalmaegi has been discussed. It is found that the convective system and precipitation distribution of Kalmaegi was asymmetric, which leaded to the asymmetric rainfall at observational stations. The sea surface salinity (SSS) of the buoy to the right of storm track increased with a 0.176 practical salinity units (psu) maximal positive anomaly, while the two buoys on the left side underwent several desalination processes, with a maximum decreases of 0.145 psu and 0.278 psu. Numerical simulations with and without precipitation forcing were also performed. Model results showed that typhoon-induced precipitation can weaken sea surface cooling by approximately 0.03–0.40 °C and suppress the SSS increase by approximately 0.074–0.152 psu. The effect of precipitation can be divided into the direct effect and indirect effect. On one hand, freshwater from precipitation directly dilutes the salinity. On the other hand, when salinity decreases, the ocean stratification will be enhanced, the vertical mixing will be restrained, and then the temperature and salinity can be further affected by weakened vertical mixing. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>(<b>a</b>) Track of Kalmaegi (2014) from the CMA. Blue dots with white numbers (1–5) showed the buoy positions. The color bar shows the value of MSW (m/s), which indicates the intensity. (<b>b</b>) MSW (m/s, blue line) and MSLP (hPa, black line) of Kalmaegi (2014) from the CMA best track dataset. The magenta vertical line and red vertical line represent the time when the typhoon center reaches those stations, which are UTC 03:00 on 15 September (Stations 4 and 5) and UTC 08:00 on 15 September (Station 2), respectively.</p> Full article ">Figure 2
<p>MTSAT-2 satellite cloud-top TBB (K) images at 6 different times: (<b>a</b>) 03:00 UTC on 14 September; (<b>b</b>) 15:00 UTC on 14 September; (<b>c</b>) 00:00 UTC on 15 September; (<b>d</b>) 06:00 UTC on 15 September; (<b>e</b>) 18:00 UTC on 15 September; (<b>f</b>) and 00:00 UTC on 16 September. The pink solid lines denote the track of Kalmaegi, and the white rings denote the location of the TC center. Black plus signs with numbers indicate the positions of Stations 2, 4, and 5. The time is marked at the top right of each picture.</p> Full article ">Figure 3
<p>CMORPH precipitation (mm/3 h) distribution at 6 different times: (<b>a</b>) 03:00 UTC on 14 September; (<b>b</b>) 15:00 UTC on 14 September; (<b>c</b>) 00:00 UTC on 15 September; (<b>d</b>) 06:00 UTC on 15 September; (<b>e</b>) 18:00 UTC on 15 September; (<b>f</b>) and 00:00 UTC on 16 September. The pink solid lines denote the storm track, and the black rings denote the location of the TC center. Black plus signs with numbers indicate the positions of Stations 2, 4, and 5. The time is marked at the top right of each picture.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>c</b>) Hourly precipitation from the observation array at three stations during 14–17 September. (<b>d</b>–<b>f</b>) Three-hourly precipitation from the observational array after an average of 3 h (black dots) and three-hourly CMORPH rainfall (red dots) at three stations during 14–17 September. The three rows from top to bottom represent the data at Stations 2, 4 and 5, respectively. The R and <span class="html-italic">P</span> values in (d-f) represent the correlation coefficients and the corresponding significance level between the CMORPH data and measured precipitation. After excluding the CMORPH data at the time when there was no available data of observed precipitation, the two datasets used to calculate the correlation have the same sample number, which were 21 (Station 2), 19 (Station 4), and 24 (Station 5), respectively. Note that these samples correspond one-to-one in time.</p> Full article ">Figure 5
<p>Time series of (<b>a</b>–<b>c</b>) air temperature (°C, red line) and relative humidity (%, black line), (<b>d</b>–<b>f</b>) 2-min sustained wind speed (m/s, blue line), 2-min gust wind speed (m/s, red line) and central pressure (hPa, black line), and (<b>g</b>–<b>i</b>) wind (blue) and wind gust (red) vectors observed at Stations 2, 4, and 5. Upward (rightward) arrows indicate northward (eastward). The three rows from top to bottom represent the data at Stations 2, 4, and 5, respectively.</p> Full article ">Figure 6
<p>Salinity anomalies obtained from observations, Nopre and Pre simulations at Stations 2 (<b>a</b>–<b>c</b>), 4 (<b>d</b>–<b>f</b>), and 5 (<b>g</b>–<b>i</b>). The 0 on the x-axis represents the arrival time of Kalmaegi. The top X-axis in (<b>a</b>,<b>d</b>,<b>g</b>) show the actual date (month/day). The three rows from top to bottom represent the data obtained from observations, Nopre and Pre simulations, respectively.</p> Full article ">Figure 7
<p>The discrepancies in (<b>a</b>–<b>c</b>) temperature (ΔT, °C) and (<b>e</b>–<b>g</b>) salinity (ΔS, psu) above 200 m between the Pre and Nopre cases at three stations. The values were calculated using Pre minus Nopre. (<b>d</b>) The ratio of the SST difference between Pre and Nopre to SST anomaly in Nopre, and (<b>h</b>) same as in (<b>d</b>) but for SSS. The 0 on the x-axis represents Kalmaegi’s arrival time. The first three rows from top to bottom represent the data at Stations 2, 4, and 5, respectively.</p> Full article ">Figure 8
<p>Average profiles of (<b>a</b>–<b>c</b>) salinity and (<b>d</b>–<b>f</b>) temperature above 100 m before and after Kalmaegi at Stations 2, 4, and 5. The solid lines represent the pre-storm value calculated by the average during 10 September to 14 September. The dotted lines represent the post-storm value, which is the average of the second to fifth inertial periods after the storm. Note that two model simulations share the same initial conditions, i.e., pre-storm vertical profiles, which are represented by a yellow solid line. Black lines, blue lines, and red lines represent the values in the observations and Pre and Nopre cases, respectively.</p> Full article ">Figure 9
<p>Post-storm average temperature induced by horizontal advection, vertical advection, and vertical mixing in experiment (<b>a</b>–<b>c</b>) Pre, (<b>d</b>–<b>f</b>) Nopre, and (<b>g</b>–<b>i</b>) the corresponding differences between Pre and Nopre. The average is taken for the second to fifth inertial periods after the storm. Note that there are two dotted lines to the left of the track, representing the positions of Stations 2 and 5. The dotted line at the right side represent the locations of Station 4.</p> Full article ">Figure 10
<p>Same as in <a href="#water-12-00614-f009" class="html-fig">Figure 9</a> but for salinity.</p> Full article ">Figure 11
<p>Post-storm average temperature anomaly and salinity anomaly in experiment (<b>a</b>,<b>e</b>) Pre, (<b>b</b>,<b>f</b>) Nopre, and (<b>c</b>,<b>g</b>) the corresponding differences between Pre and Nopre. (<b>d</b>,<b>h</b>) The value of difference calculated by the whole salinity (temperature) anomaly minus the sum of three components induced by three dynamic processes. The average is taken for the second to fifth inertial periods after the storm. The two dotted lines to the left of the track represent the positions of Stations 2 and 5. The dotted line at the right side represent the locations of Station 4.</p> Full article ">
19 pages, 5226 KiB  
Article
Ubiquitous Fractal Scaling and Filtering Behavior of Hydrologic Fluxes and Storages from A Mountain Headwater Catchment
by Ravindra Dwivedi, John F. Knowles, Christopher Eastoe, Rebecca Minor, Nathan Abramson, Bhaskar Mitra, William E. Wright, Jennifer McIntosh, Thomas Meixner, Paul A. “Ty” Ferre, Christopher Castro, Guo-Yue Niu, Greg A. Barron-Gafford, Michael Stanley and Jon Chorover
Water 2020, 12(2), 613; https://doi.org/10.3390/w12020613 - 24 Feb 2020
Cited by 3 | Viewed by 3661
Abstract
We used the weighted wavelet method to perform spectral analysis of observed long-term precipitation, streamflow, actual evapotranspiration, and soil water storage at a sub-humid mountain catchment near Tucson, Arizona, USA. Fractal scaling in precipitation and the daily change in soil water storage occurred [...] Read more.
We used the weighted wavelet method to perform spectral analysis of observed long-term precipitation, streamflow, actual evapotranspiration, and soil water storage at a sub-humid mountain catchment near Tucson, Arizona, USA. Fractal scaling in precipitation and the daily change in soil water storage occurred up to a period of 14 days and corresponded to the typical duration of relatively wet and dry intervals. In contrast, fractal scaling could be observed up to a period of 0.5 years in streamflow and actual evapotranspiration. By considering long-term observations of hydrologic fluxes and storages, we show that, in contrast to previous findings, the phase relationships between water balance components changed with component period and were not perfectly in or out of phase at all periods. Self-averaging behavior was apparent, but the temporal scales over which this behavior was applicable differed among the various water balance components. Conservative tracer analysis showed that this catchment acted as a fractal filter by transforming white noise in the precipitation input signal to a 1/f flicker in the streamflow output signal by means of both spatial and temporal subsurface advection and dispersion processes and soil wetting properties. This study provides an improved understanding of hydrological filtering behavior in mountain critical zones that are critical sources of water and ecosystem services throughout the world. Full article
(This article belongs to the Special Issue Catchments as Observatories of the Hydrological and Biogeochemical Functioning of the Critical Zone)
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<p>(<b>A</b>) The Marshall Gulch Catchment (MGC) field site (the catchment boundary is shown in green) showing existing instrumentation. Inset: Map of Arizona showing the location of the study site. Digital elevation model (DEM) is from [<a href="#B33-water-12-00613" class="html-bibr">33</a>]. (<b>B</b>) Schematic representation of three-dimensional flowpath structure at MGC with various water balance components. The symbol “?” in (B) suggests unknown total fractured bedrock storage at MGC.</p> Full article ">Figure 2
<p>Catchment-scale hydrological fluxes and storages through time. (<b>A</b>) Daily precipitation (bars) and precipitation δ<sup>18</sup>O (points), (<b>B</b>) daily streamflow (bars) and stream water δ<sup>18</sup>O (points), (<b>C</b>) daily actual evapotranspiration (AET), and (<b>D</b>) daily soil water storage from water year 2008 through 2017.</p> Full article ">Figure 3
<p>Transformed time series plots of catchment-scale daily precipitation (<b>A</b>), daily streamflow (<b>B</b>), daily actual evapotranspiration (<b>C</b>), and daily change in soil water storage (<b>D</b>) from water year 2008 through 2017. Note that the y-axis range is different in each plot to clearly show time series variability in hydrologic fluxes and soil water storage at MGC. Each time series is transformed using the arcsine hyperbolic transformation function or Equation (2).</p> Full article ">Figure 4
<p>Observed power spectra for daily precipitation (<b>A</b>), daily streamflow (<b>B</b>), daily actual evapotranspiration (<b>C</b>), and daily change in soil water storage (<b>D</b>) between water years 2008 and 2017.</p> Full article ">Figure 5
<p>Power spectra of δ<sup>18</sup>O in precipitation (P, aqua circles) and streamflow (Q, purple circles) between water years 2008 and 2012. The best fit lines are shown in black. Note: the triplets for any fitted lines in legend correspond to (R<sup>2</sup>, p-value, and slope of the best fit line).</p> Full article ">Figure 6
<p>The period-dependent Hydrologic Phase Index (HPI) between precipitation (P) and (<b>A</b>) streamflow (Q), (<b>B</b>) AET, and (<b>C</b>) daily change in soil water storage (ΔS<sub>soil</sub>).</p> Full article ">Figure 7
<p>Root mean square (RMS) differences between successive local averages of precipitation (<b>A</b>), streamflow (<b>B</b>), actual evapotranspiration (<b>C)</b>, and the change in soil water storage (<b>D</b>) as a function of the averaging time scale.</p> Full article ">Figure 8
<p>Ubiquitous fractal filtering behavior of the various water balance components at MGC.</p> Full article ">
12 pages, 9517 KiB  
Article
Marine Litter in Transitional Water Ecosystems: State of The Art Review Based on a Bibliometric Analysis
by Monia Renzi, Valentina H. Pauna, Francesca Provenza, Cristina Munari and Michele Mistri
Water 2020, 12(2), 612; https://doi.org/10.3390/w12020612 - 24 Feb 2020
Cited by 17 | Viewed by 4608
Abstract
Transitional water ecosystems (TWEs), despite their ecological and economic importance, are largely affected by human pressures that could be responsible for significant inputs of litter in the marine environment. Plastic input in coastal ponds, lagoons, river deltas and estuaries, could be driven by [...] Read more.
Transitional water ecosystems (TWEs), despite their ecological and economic importance, are largely affected by human pressures that could be responsible for significant inputs of litter in the marine environment. Plastic input in coastal ponds, lagoons, river deltas and estuaries, could be driven by a wide range of human activities such as agriculture, waste disposal, municipal and industrial wastewater effluents, aquaculture, fishing and touristic activities and urban impacts. However, it remains unknown what the impact of plastic input in these TWEs could have on natural capital and, therefore, the ability for an ecosystem to provide goods and services to human beings. Given the large interest with regards to the conservation of transitional water ecosystems and the clear exposure risk to plastic and microplastic pollution, this study aims to perform: (i) a bibliometric analyses on existing literature regarding the levels of marine litter in such environments; (ii) a selection among the available literature of homogeneous data; and (iii) statistical analyses to explore data variability. Results suggest that: (i) research on microplastics in these ecosystems did not begin to be published until 2013 for lagoons, 2014 for river mouths and 2019 for coastal ponds. The majority of articles published on studies of microplastics in lagoons did not occur until 2019; (ii) sediments represent the matrix on which sampling and extraction variability allow the statistical analyses on data reported by the literature; (iii) the Analysis of Similarities (ANOSIM) test two-way evidenced that the level of protection of marine and terrestrial areas produced similar values while the habitat type showed low significance in terms of its effect on microplastic levels, shape and size in sediments. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>VOSviewer network map of keywords from Web of Science Search. Sizes of nodes are based off of Total Link Strength.</p> Full article ">Figure 2
<p>Highlighted sections of the co-occurrence keyword network map: (<b>a</b>) the blue curved line which starts at "Sediments" and gradually turns to red when it meets "Lagoon" shows the direct connection between the terms, the exact opposite for "Lagoon" and "Sediments” is shown in (<b>b</b>).</p> Full article ">Figure 3
<p>VOSviewer network map of authors based on Total Link Strength.</p> Full article ">Figure 4
<p>Principal Component Analysis performed to evaluate similarities according to literature data. Data used to plot PCA are related to loadings (percentages) of data reported by the literature. Sampling stations are labelled according three different habitat types (HT): Sea (sampling station highly influenced by sea dynamics); TWE (sampling station properly affected by transitional water dynamics); Beach (sampling station closed or on sandbars). Geographical locations are, also, highlighted (Location).</p> Full article ">
18 pages, 32267 KiB  
Article
Impact of Sea Ice on the Hydrodynamics and Suspended Sediment Concentration in the Coastal Waters of Qinhuangdao, China
by Man Jiang, Chongguang Pang, Zhiliang Liu and Jingbo Jiang
Water 2020, 12(2), 611; https://doi.org/10.3390/w12020611 - 24 Feb 2020
Cited by 1 | Viewed by 3377
Abstract
The influence of sea ice on the hydrodynamics, sediment resuspension, and suspended sediment concentration (SSC) in the coastal area of Qinhuangdao was systematically investigated using 45-day in situ measurements at two stations (with ice at station M1 and without ice at station M2) [...] Read more.
The influence of sea ice on the hydrodynamics, sediment resuspension, and suspended sediment concentration (SSC) in the coastal area of Qinhuangdao was systematically investigated using 45-day in situ measurements at two stations (with ice at station M1 and without ice at station M2) in the Bohai Sea in the winter of 2018. It was found that the daily fluctuations of temperature and salinity at M1 are more significant than those at M2. During a typical seawater icing event on January 28, the temperature and salinity of the bottom water at M1 were decreased by 1.77 °C and increased by 0.4 psu, respectively. Moreover, due to the shielding effect of the sea ice, the residual current was much less affected by the wind at M1 than at M2. For the vertical distribution of current velocity, it changed from a traditional logarithmic type under ice-free conditions to parabolic type under ice-covered conditions due to the larger drag coefficient of the water body on the solid ice surface. For the SSC and turbidity at the bottom layer, the average values were 4.9 μL/L and 8.6 NTU at M1, respectively, approximately half of those at M2. The smaller SSC and turbidity at M1 can be attributed to the lower near-bottom turbulent kinetic energy (TKE). At M2, however, the larger SSC is closely related to the strong wind forcing, which could induce higher TKE without sea ice cover, and hence stronger turbulent resuspension. The seabed sediment analysis results showed that in the study area, fine sand is most likely to resuspend, while cohesive particles would resuspend only under strong hydrodynamic conditions. Full article
(This article belongs to the Special Issue Observation and Numerical Modeling of Sediment Transport in Coastal Areas)
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<p>The position of Bohai Sea and its distribution of average surface suspended sediment concentration in winter, as well as sea ice information. (<b>a</b>) Distribution of average surface suspended sediment concentration in winter in the Bohai Sea in 1998–2004 retrieved from ocean color remote sensing inversion [<a href="#B31-water-12-00611" class="html-bibr">31</a>]. The red circles are the in situ observation stations for flow and sediment, which are stations M1 and M2, respectively. The blue circles are the offshore buoys used to measure sea surface wind speed. (<b>b</b>) The position of the Bohai Sea [<a href="#B32-water-12-00611" class="html-bibr">32</a>]. (<b>c</b>,<b>d</b>) Sea ice images of the study area on 29 January 2018 and 5 February 2018, respectively, contain the ice cover area and ice thickness information, which were downloaded from the National Marine Environmental Forecasting Center [<a href="#B33-water-12-00611" class="html-bibr">33</a>].</p> Full article ">Figure 2
<p>Seabed observation platform. Note: ADV, acoustic Doppler velocimeter, ADCP, acoustic Doppler current profiler; PC-ADP, pulse-coherent acoustic Doppler profiler; LISST, laser in situ scattering and transmissometry; OBS, optical backscatter sensor; CTD, conductivity temperature depth sensor.</p> Full article ">Figure 3
<p>Changes in wind speed (<b>a,b</b>), temperature (<b>c,d</b>), salinity (<b>e,f</b>), and water depth (<b>g,h</b>) at stations M1 and M2.</p> Full article ">Figure 4
<p>Original velocity profile at full water depth measured by upward-looking ADCP and downward-looking PCADP. (<b>a</b>) Vertical profile of current velocity in the middle and upper layers of the water body at M1 station, measured by upward-looking ADCP. (<b>b</b>) Vertical profile of current velocity in the near bottom layers of the water body at M1 station, measured by downward-looking PCADP. (<b>c</b>) Vertical profile of current velocity in the middle and upper layers of the water body at M2 station, measured by upward-looking ADCP. (<b>d</b>) Vertical profile of current velocity in the near bottom layers of the water body at M2 station, measured by downward-looking PCADP.</p> Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Demonstration of MATLAB t_tide toolbox analysis for M1 (16.6 mab) and M2 (14.6 mab), respectively. (<b>c</b>,<b>d</b>) Tidal currents from t_tide analysis. (<b>e</b>,<b>f</b>) Analyzed lines with 95% significance levels for M1 and M2, respectively. (<b>g</b>,<b>h</b>) Spectral estimates before and after the removal of tidal energy for M1 and M2, respectively.</p> Full article ">Figure 6
<p>Variation in wind speed and upper residual current at M1 and M2 stations. (<b>a</b>,<b>b</b>) Wind speeds at M1 and M2 stations, respectively. (<b>c</b>,<b>d</b>) Residual currents at 2.4 m at M1 and M2 stations, respectively. (<b>e</b>,<b>f</b>) Residual currents at 3.4 m at M1 and M2 stations, respectively. (<b>g</b>,<b>h</b>) Residual currents at 4.4 m at M1 and M2 stations, respectively.</p> Full article ">Figure 7
<p>Vertical profile of backscatter intensity at full water depth, estimated by the echo intensity measured by the upward-looking ADCP (<b>a</b>,<b>c</b>) and downward-looking PCADP (<b>b</b>,<b>d</b>).</p> Full article ">Figure 8
<p>Volume concentration, particle size distribution (<b>a</b>,<b>c</b>), and water turbidity (<b>b</b>,<b>d</b>) at the bottom layer at M1 and M2 stations, respectively, measured by LISST (<b>a</b>,<b>c</b>) and OBS (<b>b</b>,<b>d</b>).</p> Full article ">Figure 9
<p>Comparison of wind speed and near-bottom turbulent kinetic energy (TKE) at M1 and M2 stations, respectively.</p> Full article ">Figure 10
<p>Scatter plots and fitting relations of current velocity at different heights from the bottom in different tidal phases during typical spring tide periods: (<b>a</b>,<b>b</b>) the periods of flood tide with ice cover on February 2 and 3; (<b>c</b>,<b>d</b>) the periods of flood tide without ice cover on February 17 and 18; (<b>e</b>,<b>f</b>) the periods of ebb tide with ice cover on February 3 and 4; (<b>g</b>,<b>h</b>) the periods of ebb tide without ice cover on February 18 and 19; (<b>i</b>,<b>j</b>) two periods of slack water with ice cover on February 3; (<b>k</b>,<b>l</b>) two periods of slack water without ice cover on February 18.</p> Full article ">Figure 11
<p>Scatter plots and fitting relations of current velocity at different heights from the bottom in different tidal phases during typical spring tide periods: (<b>a</b>) the flood tide period with ice cover on February 2; (<b>b</b>) the ebb tide period with ice cover on February 2; (<b>c</b>) the slack water period with ice cover on February 3; (<b>d</b>) the flood tide period without ice cover on February 17; (<b>e</b>) the ebb tide period without ice cover on February 18; (<b>f</b>) the slack water period without ice cover on February 18.</p> Full article ">
14 pages, 2427 KiB  
Article
Revisiting the Statistical Scaling of Annual Discharge Maxima at Daily Resolution with Respect to the Basin Size in the Light of Rainfall Climatology
by Anastasios Perdios and Andreas Langousis
Water 2020, 12(2), 610; https://doi.org/10.3390/w12020610 - 24 Feb 2020
Cited by 8 | Viewed by 3825
Abstract
Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the [...] Read more.
Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, an important question arises on how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing this question, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. To do so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: (a) the local rainfall climatology is an important contributor to the observed statistics of peak annual discharges, and (b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that commonly met in actual rainfields, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light on the approximate type of scaling of annual discharge maxima with the basin size. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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<p>Spatial distribution of the average SAAR values for the 805 considered catchments across the United Kingdom.</p> Full article ">Figure 2
<p>Spatial distribution of the mean value of the standardized discharge maxima <math display="inline"><semantics> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>∕</mo> <mi>A</mi> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>(<b>a</b>) Scatterplot (points) and linear least-squares (LS) fit (solid line) of the mean values <math display="inline"><semantics> <mrow> <mi>E</mi> <mrow> <mo>[</mo> <msup> <mi>Q</mi> <mo>′</mo> </msup> <mo>]</mo> </mrow> </mrow> </semantics></math> of the standardized discharges <math display="inline"><semantics> <mrow> <msup> <mi>Q</mi> <mo>′</mo> </msup> <mo>=</mo> <mi>Q</mi> <mo>∕</mo> <mi>A</mi> </mrow> </semantics></math> for the 805 considered catchments, with respect to their corresponding SAAR values. (<b>b</b>) Same as (a) but for the means <math display="inline"><semantics> <mrow> <mi>E</mi> <mrow> <mo>[</mo> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> of the standardized annual discharge maxima <math display="inline"><semantics> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>/</mo> <mi>A</mi> <mo>.</mo> </mrow> </semantics></math></p> Full article ">Figure 4
<p>Log-log plots of the empirical moments <math display="inline"><semantics> <mrow> <mi>E</mi> <mrow> <mo>[</mo> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> <mi>q</mi> </msup> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> of standardized annual discharge maxima <math display="inline"><semantics> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mo> </mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>∕</mo> <mi>A</mi> </mrow> </semantics></math>, as a function of the catchment area <span class="html-italic">A</span>, for different moment orders <math display="inline"><semantics> <mrow> <mi>q</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>:</mo> <mn>0.5</mn> <mo>:</mo> <mn>3</mn> </mrow> </semantics></math>. Black solid lines correspond to least-squares (LS) fits.</p> Full article ">Figure 5
<p>Empirical moment scaling functions <math display="inline"><semantics> <mrow> <mi>K</mi> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the standardized discharge maxima <math display="inline"><semantics> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mo> </mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>∕</mo> <mi>A</mi> </mrow> </semantics></math> (circles), and the amplification factor <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>/</mo> <mi>E</mi> <mrow> <mo>[</mo> <mi>Q</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> (stars).</p> Full article ">Figure 6
<p>Log-log plots of the empirical moments <math display="inline"><semantics> <mrow> <mi>E</mi> <mrow> <mo>[</mo> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>γ</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mi>q</mi> </msup> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> of the amplification factor <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>Q</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>/</mo> <mi>E</mi> <mrow> <mo>[</mo> <mi>Q</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, as a function of the catchment area <span class="html-italic">A</span>, for different moment orders <math display="inline"><semantics> <mrow> <mi>q</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.5</mn> <mo>:</mo> <mn>0.5</mn> <mo>:</mo> <mn>3</mn> </mrow> </semantics></math>. Black solid lines correspond to least-squares (LS) fits.</p> Full article ">
19 pages, 7533 KiB  
Article
A Borehole-Based Approach for Seawater Intrusion in Heterogeneous Coastal Aquifers, Eastern Part of Jeju Island, Korea
by Jehyun Shin and Seho Hwang
Water 2020, 12(2), 609; https://doi.org/10.3390/w12020609 - 24 Feb 2020
Cited by 17 | Viewed by 5058
Abstract
Understanding the basaltic aquifer system and seawater intrusion on the volcanic island of Jeju, Korea, has received significant attention over the years, and various methodologies have been suggested in the contributions. Nevertheless, it is still difficult to effectively characterize groundwater systems due to [...] Read more.
Understanding the basaltic aquifer system and seawater intrusion on the volcanic island of Jeju, Korea, has received significant attention over the years, and various methodologies have been suggested in the contributions. Nevertheless, it is still difficult to effectively characterize groundwater systems due to the long period of volcanic activity and the lithological variability of basalt. In this study, geophysical well logging in seawater intrusion monitoring boreholes detected a sudden decrease of electrical conductivity within the saltwater zone in the eastern part of Jeju Island. This anomalous condition cannot be explained by the Ghyben-Herzberg model, which has historically been considered as the basic groundwater model of Jeju Island. This paper focuses on fine-scale temporal and spatial variability of groundwater flow using electrical conductivity and temperature logs and borehole temperature monitoring by a thermal line sensor. On the basis of the results, we evaluate an alternative model to replace the traditional conceptual model in the eastern part of Jeju Island. It is revealed that the area consists of heterogeneous aquifer systems, and the behavior of freshwater and saltwater is understood by temperature monitoring over the entire depth of boreholes. Coastal aquifers flow through two or more independent channels with weak vertical connections. In addition, seawater intrusion does not occur continuously in the vertical direction from the bottom depth, but instead occurs through these multilayered aquifers. In particular, the multilayered aquifers that are responsible for flow pathway, as well as the freshwater–saltwater interface form mainly at lithological boundaries. Our preliminary conceptual model is expected to be improved and revised by various measurements of hydrodynamic parameters such as flowmeter or packer test. Full article
(This article belongs to the Section Hydrology)
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<p>Locations of seawater intrusion monitoring wells, tide stations, and automatic weather systems on Jeju Island.</p> Full article ">Figure 2
<p>Temporal variation of ocean tide level, groundwater level, electrical conductivity, and temperature in seawater intrusion monitoring wells. Boreholes (<b>a</b>) SS-1; (<b>b</b>) SS-2; (<b>c</b>) SS-3; and (<b>d</b>) SS-4.</p> Full article ">Figure 3
<p>Profiles of electrical conductivity, geology, and temperature at boreholes for Susan district. (<b>a</b>) SS-1; (<b>b</b>) SS-2; (<b>c</b>) SS-3; and (<b>d</b>) SS-4. The red circle expresses monitoring sensor’s location in each borehole. Red and black arrows indicate the variation of electrical conductivity and temperature, respectively.</p> Full article ">Figure 4
<p>Temperature variations of coastal aquifers in Susan district.</p> Full article ">Figure 5
<p>Schematic diagram of temperature monitoring using thermal line sensor system.</p> Full article ">Figure 6
<p>Temperature monitoring results with thermal line sensor in borehole SS-1. (<b>a</b>) Tide level; (<b>b</b>) temperature during Period A; (<b>c</b>) temperature during Period B; (<b>d</b>) temperature in Zone 1 during Period B; (<b>e</b>) temperature profiles at high and low tides; (<b>f</b>) electrical conductivity logs.</p> Full article ">Figure 7
<p>Temperature monitoring results with thermal line sensor in boreholes SS-2, 3, and 4. (<b>a</b>) SS-2 temperature during Period A; (<b>b</b>) SS-2 temperature during Period B; (<b>c</b>) SS-2 temperature at high and low tides; (<b>d</b>) SS-3 temperature during Period A; (<b>e</b>) SS-3 temperature during Period B; (<b>f</b>) SS-3 temperature at high and low tides; (<b>g</b>) SS-4 temperature during Period A; (<b>h</b>) SS-4 temperature during Period B; (<b>i</b>) SS-4 temperature at high and low tides.</p> Full article ">Figure 8
<p>Conceptual model of heterogeneous coastal aquifer system proposed from the data interpretation of geophysical well logging and borehole temperature monitoring. Solid black and blue lines indicate the bottom of the freshwater body and transition zone, respectively. Solid red line represents the decrease of electrical conductivity. Arrows denote the coastal aquifer groundwater flow during flooding.</p> Full article ">Figure 9
<p>Periodic electrical conductivity logs from 2002 to 2004 (Institute of Environmental Resource Research, Jeju Special Self-Governing Province). Boreholes (<b>a</b>) SS-1; (<b>b</b>) SS-2; (<b>c</b>) SS-3; and (<b>d</b>) SS-4.</p> Full article ">Figure 10
<p>Conceptual model of seawater intrusion on the eastern coast of Jeju Island.</p> Full article ">
17 pages, 15097 KiB  
Article
An Integrated Urban Flood Vulnerability Index for Sustainable Planning in Arid Zones of Developing Countries
by Carlos Salazar-Briones, José Mizael Ruiz-Gibert, Marcelo A. Lomelí-Banda and Alejandro Mungaray-Moctezuma
Water 2020, 12(2), 608; https://doi.org/10.3390/w12020608 - 24 Feb 2020
Cited by 38 | Viewed by 7399
Abstract
Floods are among the most recurring and devastating natural hazards, impacting human lives and causing severe economic damage. Urbanization can increase the risk of flooding due to increased peak discharge and volume. Over arid urban areas of developing countries, flood disaster management is [...] Read more.
Floods are among the most recurring and devastating natural hazards, impacting human lives and causing severe economic damage. Urbanization can increase the risk of flooding due to increased peak discharge and volume. Over arid urban areas of developing countries, flood disaster management is reactive, responding to prevailing disaster situations, mainly because of the lack of budget, equipment, facilities, and human resources. The approach required in a new city requires a different operative planning process, ruled by different kinds of specific indicators to be incorporated in the sustainable planning process. This study focuses on an approach to assess flood vulnerability as a planning tool using an integrated flood vulnerability index (FVI) with variables that are accessible in developing countries and arid urban areas. The research took place in the city of Mexicali, Baja, California. México. This index was determined by coupling the variables of three components: social, economic, and physical. The FVI reflects the status of an urban scale’s vulnerability. Variables were obtained from government data for the social and economic components, and a hydrological and hydraulic model approach as a physical component. The correlation of each variable to the flood was taken into account by using a general linear transformation. GIS was used as a tool for the development of spatial analysis. The results showed the spatial distribution of vulnerability at an urban district scale. It was found that 55% of the population is exposed to a vulnerability above the average value of the urban area. Integrating all the components will help decision-makers to implement strategies to improve the resilience of the area by attending the needs of the particular component that is more vulnerable. Full article
(This article belongs to the Special Issue Integrated Flood Management: Concepts, Methods, Tools and Results)
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<p>Flood vulnerability components and indicators processing methods.</p> Full article ">Figure 2
<p>Study area and selected primary geostatistical area (AGEB) scale for vulnerability calculation.</p> Full article ">Figure 3
<p>Social vulnerability values for the studied AGEB.</p> Full article ">Figure 4
<p>Spatial distribution of the AGEBS with a social vulnerability above the average value.</p> Full article ">Figure 5
<p>Economical vulnerability values for the studied AGEB.</p> Full article ">Figure 6
<p>Spatial distribution of the AGEBS with a economic vulnerability above the average value.</p> Full article ">Figure 7
<p>Physical vulnerability values for the studied AGEB.</p> Full article ">Figure 8
<p>Comparison of the physical vulnerability indicators.</p> Full article ">Figure 9
<p>Composed flood vulnerability index for an AGEB scale.</p> Full article ">Figure 10
<p>Composed flood vulnerability index values for the studied AGEB.</p> Full article ">Figure 11
<p>Assessment of AGEB units that showed an anomaly of above the mean vulnerability values.</p> Full article ">
16 pages, 7732 KiB  
Article
Effect of Wastewater Irrigation on Photosynthesis, Growth, and Anatomical Features of Two Wheat Cultivars (Triticum aestivum L.)
by Shokoofeh Hajihashemi, Sonia Mbarki, Milan Skalicky, Fariba Noedoost, Marzieh Raeisi and Marian Brestic
Water 2020, 12(2), 607; https://doi.org/10.3390/w12020607 - 24 Feb 2020
Cited by 59 | Viewed by 7825
Abstract
The wastewater from the Razi petrochemical complex contains high levels of salts and heavy metals. In the present research, the effects of different wastewater dilution levels (0, 25%, 50%, and 100%) were studied on two wheat cultivars—Chamran and Behrang. The wastewater contained high [...] Read more.
The wastewater from the Razi petrochemical complex contains high levels of salts and heavy metals. In the present research, the effects of different wastewater dilution levels (0, 25%, 50%, and 100%) were studied on two wheat cultivars—Chamran and Behrang. The wastewater contained high levels of NH4+, NO3-, PO43-, and SO42-, and Mg, Ca, K, Na, Cu, Zn, Fe, M, and Ni. The toxic levels of mineral elements in the wastewater resulted in a significant decline in the K, P, Si, and Zn content of leaves. Irrigation with the wastewater resulted in a significant reduction in photosynthetic characteristics including chlorophyll fluorescence (Fv/Fm and PIABS), intercellular CO2, net photosynthesis, water use efficiency, and photosynthetic pigments. The reduction in photosynthesis was followed by a significant decrease in the carbohydrate content and, subsequently, plant height, leaf area, and grain yield. Increasing the wastewater concentration reduced leaf thickness and root diameter, accounting for the decrease in xylem and phloem vessels, the root cortical parenchyma, and mesophyll thickness. The bulliform cell size increased under wastewater treatment, which may suggest induction of a defense system against water loss through leaf rolling. Based on the observed negative effect of wastewater on physiology, morphology, anatomy, and yield of two wheat cultivars, reusing wastewater with high levels of total suspended solids and salts for irrigation cannot be approved for wheat crops. Full article
(This article belongs to the Special Issue Advances in the Technologies for Water and Wastewater Treatment)
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<p>(<b>a</b>) Maximum quantum yield of photosystem II (F<sub>v</sub>/F<sub>m</sub>), (<b>b</b>) performance indices (PI<sub>ABS</sub>), (<b>c</b>) net photosynthesis (P<sub>N</sub>), (<b>d</b>) intercellular CO<sub>2</sub> (C<sub>i</sub>), (<b>e</b>) water use efficiency (WUE), and (<b>f</b>) transpiration rate (T<sub>r</sub>) of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. The same letters represent no significant difference at <span class="html-italic">p</span> ˂ 0.05. The means are the averages of 10 plants. The error bars show standard deviation.</p> Full article ">Figure 2
<p>(<b>a</b>) Chlorophyll <span class="html-italic">a</span> (Chl <span class="html-italic">a</span>), (<b>b</b>) chlorophyll <span class="html-italic">b</span> (Chl <span class="html-italic">b</span>), (<b>c</b>) chlorophylls total, (<b>d</b>) carotenoids (car), (<b>e</b>) water-soluble carbohydrates (WSC), and (<b>f</b>) glucose of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. The same letters represent no significant difference at <span class="html-italic">p</span> ˂ 0.05. The means are the averages of four values. The error bars show standard deviation.</p> Full article ">Figure 3
<p>(<b>a</b>) leaf area, (<b>b</b>) plant height, (<b>c</b>) spikelet number per plant, and (<b>d</b>) weight of 10 seeds of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. The same letters represent no significant difference at <span class="html-italic">p</span> ˂ 0.05 probability. The means are the averages of 10 plants. The error bars represent standard deviation.</p> Full article ">Figure 4
<p>Micrographs of leaf cross sections of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. BC, bulliform cells; Ph, phloem vessels; Sc, sclerenchyma; Vb, vascular bundle; X, xylem vessels.</p> Full article ">Figure 5
<p>Micrographs of root cross sections of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. CP, cortical parenchyma; CS, Casparian strips; VC, vascular cylinder; XV, xylem vessels.</p> Full article ">Figure 6
<p>(<b>a</b>) leaf thickness in the midrib vein, (<b>b</b>) diameter of vascular bundle, (<b>c</b>) width of xylem vessel area, (<b>d</b>) width of phloem vessel area, (<b>e</b>) width of sclerenchyma cell area, and (<b>f</b>) width of bulliform cells of two wheat cultivars, Chamran and Behrang, irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) wastewater from the Razi petrochemical complex. The same letters represent no significant difference at <span class="html-italic">p</span> ˂ 0.05 probability. The means are the averages of 10 values. The error bars show standard deviation.</p> Full article ">Figure 7
<p>(<b>a</b>) Root diameter, (<b>b</b>) cortical parenchyma width, (<b>c</b>) vascular cylinder diameter, (<b>d</b>) width of xylem vessels, and (<b>e</b>) Casparian strip width of two wheat cultivars (Chamran and Behrang)-irrigated with 0 (distilled water), 25%, 50%, and 100% (undiluted) of wastewater obtained from Razi petrochemical complex. The same letters represent no significant difference at <span class="html-italic">p</span> ˂ 0.05 probability. The means are the average of 10 values. The error bars show standard deviation.</p> Full article ">
21 pages, 2447 KiB  
Article
Mathematical Study on Wave Propagation through Emergent Vegetation
by Yuan-Jyh Lan
Water 2020, 12(2), 606; https://doi.org/10.3390/w12020606 - 23 Feb 2020
Cited by 1 | Viewed by 2941
Abstract
In this paper, the problem of the interaction between a periodic linear wave and offshore aquatic vegetation is investigated. The aquatic vegetation field is considered as a flexible permeable system. A vegetation medium theory is proposed based on Lan–Lee’s poro-elastomer theory, in which [...] Read more.
In this paper, the problem of the interaction between a periodic linear wave and offshore aquatic vegetation is investigated. The aquatic vegetation field is considered as a flexible permeable system. A vegetation medium theory is proposed based on Lan–Lee’s poro-elastomer theory, in which linearizing vegetation friction resistance is used to describe fluid motion in the vegetation medium. The study involves boundary conditions for free surface water in emergent vegetation media that have been of less concern in previous studies. The analytical solutions of the vegetation medium and wave fields are derived by the partitioning method combined with matching boundary conditions for neighboring regions. An estimation formula for a modification factor is proposed to evaluate the linear vegetation friction coefficient, which can reasonably compare the analytical solution with relevant past cases in terms of wave transmission. Wave reflection, transmission, and attenuation induced by the effects of the characteristics of the vegetation are studied. The results indicate that an increase in the drag coefficient, stem diameter, stem density, spatial coverage, and plant stiffness leads to the emergency vegetation inducing higher wave energy dissipation and reducing the wave transmission. Vegetation stiffness is a significant factor affecting the drag coefficient. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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Figure 1
<p>Comparison of the reflection and transmission coefficients versus the number of adopted evanescent wave modes.</p> Full article ">Figure 2
<p>Comparison of <math display="inline"><semantics> <mrow> <mrow> <mi>H</mi> <mo>/</mo> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> </mrow> </mrow> </mrow> </semantics></math> evolution in the vegetation region for the present analytical solution, SWAN–VEG numerical model [<a href="#B29-water-12-00606" class="html-bibr">29</a>], and random wave transformation model of Mendez and Losada [<a href="#B22-water-12-00606" class="html-bibr">22</a>] (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.4 m, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 2 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> </mrow> </semantics></math> 100 m, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.04 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math>10 steams/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 681.43 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math>1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>). (<b>a</b>) <span class="html-italic">T</span> = 10 s; (<b>b</b>) <span class="html-italic">T</span> = 8 s; (<b>c</b>) <span class="html-italic">T</span> = 6 s; (<b>d</b>) <span class="html-italic">T</span> = 4 s; (<b>e</b>) <span class="html-italic">T</span> = 2 s; (<b>f</b>) <span class="html-italic">T</span> = 1 s.</p> Full article ">Figure 3
<p>Comparison of <math display="inline"><semantics> <mrow> <mrow> <mi>H</mi> <mo>/</mo> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> </mrow> </mrow> </mrow> </semantics></math> evolution in the vegetation region for the present analytical solution, numerical wave models [<a href="#B29-water-12-00606" class="html-bibr">29</a>,<a href="#B30-water-12-00606" class="html-bibr">30</a>], and random wave transformation model [<a href="#B22-water-12-00606" class="html-bibr">22</a>] (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.2 m, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 3 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> </mrow> </semantics></math> 150 m, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1 steam/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math>0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 681.43 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math>1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math>4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>). (<b>a</b>) <span class="html-italic">T</span> = 6 s; (<b>b</b>) <span class="html-italic">T</span> = 4 s; (<b>c</b>) <span class="html-italic">T</span> = 3 s.</p> Full article ">Figure 4
<p>Comparison of <math display="inline"><semantics> <mrow> <mrow> <mi>H</mi> <mo>/</mo> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> </mrow> </mrow> </mrow> </semantics></math> evolution in the vegetation region for the present analytical solution and regular wave transformation model of Dalrymple et al. [<a href="#B43-water-12-00606" class="html-bibr">43</a>] (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.4 m, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 2 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> </mrow> </semantics></math> 100 m, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.04 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 10 steams/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 681.43 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>). (<b>a</b>) <span class="html-italic">T</span> = 10 s; (<b>b</b>) <span class="html-italic">T</span> = 8 s; (<b>c</b>) <span class="html-italic">T</span> = 6 s; (<b>d</b>) <span class="html-italic">T</span> = 4 s; (<b>e</b>) <span class="html-italic">T</span> = 2 s; (<b>f</b>) <span class="html-italic">T</span> = 1 s.</p> Full article ">Figure 5
<p>Comparison of <math display="inline"><semantics> <mrow> <mrow> <mi>H</mi> <mo>/</mo> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> </mrow> </mrow> </mrow> </semantics></math> evolution in the vegetation region for the present analytical solution and regular wave transformation model of Dalrymple et al. [<a href="#B43-water-12-00606" class="html-bibr">43</a>] (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.2 m, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 3 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> </mrow> </semantics></math> 150 m, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1 steam/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 681.43 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>). (<b>a</b>) <span class="html-italic">T</span> = 6 s; (<b>b</b>) <span class="html-italic">T</span> = 4 s; (<b>c</b>) <span class="html-italic">T</span> = 3 s.</p> Full article ">Figure 6
<p>Effect of the drag coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> </mrow> </semantics></math> of vegetation on <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 20, <math display="inline"><semantics> <mrow> <mrow> <mi>h</mi> <mo>/</mo> <mi>d</mi> </mrow> <mo>=</mo> </mrow> </semantics></math> 1.0285, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.012 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 194 steams/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 600 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>).</p> Full article ">Figure 7
<p>Effect of the plant stem diameter <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> </mrow> </semantics></math> of vegetation on <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 20, <math display="inline"><semantics> <mrow> <mrow> <mi>h</mi> <mo>/</mo> <mi>d</mi> </mrow> <mo>=</mo> </mrow> </semantics></math> 1.0285, <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1.0, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 194 steams/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 600 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>).</p> Full article ">Figure 8
<p>Effect of the stem density <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> </mrow> </semantics></math> of vegetation on <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3 m, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 20, <math display="inline"><semantics> <mrow> <mrow> <mi>h</mi> <mo>/</mo> <mi>d</mi> </mrow> <mo>=</mo> </mrow> </semantics></math> 1.0285, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.012 m, <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1.0, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math>0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 600 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>).</p> Full article ">Figure 9
<p>Effect of the dimensionless width <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>/</mo> <mi>d</mi> </mrow> </semantics></math> of vegetation on <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>i</mi> </msub> <mo>/</mo> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3, <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> </mrow> </semantics></math> 0.3 m, <math display="inline"><semantics> <mrow> <mrow> <mi>h</mi> <mo>/</mo> <mi>d</mi> </mrow> <mo>=</mo> </mrow> </semantics></math> 1.0285, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.012 m, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>v</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 194 steams/m<sup>2</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1.0, <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> </mrow> </semantics></math> 1, <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> </mrow> </semantics></math> 0.333, <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>’</mo> <mo>=</mo> </mrow> </semantics></math> 0.999, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>s</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 600 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>w</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 1000 kg/m<sup>3</sup>, <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics></math> 1.12 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/s, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> </mrow> </semantics></math> 4.35 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </mrow> </msup> </mrow> </semantics></math> m<sup>2</sup>/N, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> </mrow> </semantics></math> 1.29 × <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math> N/m<sup>2</sup>).</p> Full article ">Figure 10
<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </mrow> </semantics></math> for the experimental results of Bouma et al. [<a href="#B18-water-12-00606" class="html-bibr">18</a>] and present computations (the meaning of the solid line: <span class="html-italic">K<sub>t</sub></span> <sub>(Present results)</sub> − <span class="html-italic">K<sub>t</sub></span> <sub>(Exp. Results, Boumaet al. [<a href="#B18-water-12-00606" class="html-bibr">18</a>])</sub> = 0; the meaning of the range of dashed lines: the calculation error of <span class="html-italic">K<sub>t</sub></span> is within 5%).</p> Full article ">
17 pages, 13465 KiB  
Article
Flood Routing Process and High Dam Interception of Natural Discharge from the 2018 Baige Landslide-Dammed Lake
by Bin-Rui Gan, Xing-Guo Yang, Hai-Mei Liao and Jia-Wen Zhou
Water 2020, 12(2), 605; https://doi.org/10.3390/w12020605 - 23 Feb 2020
Cited by 7 | Viewed by 4304
Abstract
The outburst flood of the Baige landslide dam caused tremendous damage to infrastructure, unfinished hydraulic buildings, roads, and bridges that were built or under construction along the Jinsha River. Can downstream hydraulic buildings, such as high dams with flood control and discharge function, [...] Read more.
The outburst flood of the Baige landslide dam caused tremendous damage to infrastructure, unfinished hydraulic buildings, roads, and bridges that were built or under construction along the Jinsha River. Can downstream hydraulic buildings, such as high dams with flood control and discharge function, accommodate outburst floods or generate more serious losses due to wave overtopping? In this study, the unsteady flow of a one-dimensional hydraulic calculation was used to simulate natural flood discharge. Assuming a high dam (Yebatan arch dam) is constructed downstream, the flood processes were carried out in two forms of high dam interception (complete interception, comprehensive flood control of blocking and draining). Moreover, three-dimensional visualization of the inundation area was performed. Simulation results indicate that the Yebatan Hydropower Station can completely eliminate the outburst flood risk even under the most dangerous situations. This station can reduce the flood peak and delay the peak flood arrival time. Specifically, the flood peak decreased more obviously when it was closer to the upstream area, and the flood peak arrival time was more delayed when the flood spread further downstream. In addition, the downstream water depth was reduced by approximately 10 m, and the inundation area was reduced to half of the natural discharge. This phenomenon shows that hydraulic buildings such as high dams can reduce the inundation area of downstream farmlands and extend the evacuation time for downstream residents during the flood process, thus reducing the loss of life and property. Full article
(This article belongs to the Special Issue Hydrological Prediction and Flooding Risk Assessment)
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Figure 1

Figure 1
<p>Plane distribution map of hydropower stations on the upper reaches of the Jinsha River (<b>a</b>) and distribution map of hydropower stations in the longitudinal section of the river channel (<b>b</b>).</p> Full article ">Figure 2
<p>The flow chart illustrates the method used to simulate the flood process under natural discharge and high dam interception (ArcGIS is software of Geographic Information System; HEC-RAS is a tool of flow modeling).</p> Full article ">Figure 3
<p>Outburst flood process at the Baige landslide dam and four downstream power stations under natural discharge.</p> Full article ">Figure 4
<p>Layout of the Yebatan arch dam.</p> Full article ">Figure 5
<p>Actual outburst flood process curves and numerical simulation of flood process curves: (<b>a</b>) outburst flood curves at the Yebatan arch dam; (<b>b</b>) outburst flood curves at the Lawa hydropower station; (<b>c</b>) outburst flood curves at the Batang hydropower station; (<b>d</b>) outburst flood curves at the Suwalong hydropower station.</p> Full article ">Figure 6
<p>Water surface curves of the flood process in natural discharge (flood peak arrived at the location shown in red font).</p> Full article ">Figure 7
<p>Three-dimensional display of the maximum inundation area when naturally discharging (in the table, the inundation area of natural discharge).</p> Full article ">Figure 8
<p>Water surface curve (<b>left</b>) and three-dimensional display of maximum inundation area at complete interception (<b>right</b>).</p> Full article ">Figure 9
<p>Water surface curves of the flood process with high dam interception (flood peak arrived at the location shown in red font).</p> Full article ">Figure 10
<p>Outburst flood process at the Baige landslide dam and four downstream power stations with high dam interception.</p> Full article ">Figure 11
<p>Three-dimensional display of the maximum inundation area in the high dam interception (in the table, the inundation area with the high dam interception).</p> Full article ">Figure 12
<p>Comparison of water depth curves and outburst flood curves: (<b>a</b>) curves at the Baige landslide dam; (<b>b</b>) curves at the Yebatan arch dam; (<b>c</b>) curves at the Lawa hydropower station; (<b>d</b>) curves at the Batang hydropower station; (<b>e</b>) curves at the Suwalong hydropower station.</p> Full article ">Figure 13
<p>Comparison of outburst flood process curves between natural discharge and high dam interception.</p> Full article ">Figure 14
<p>The maximum water depth of all sections in the natural discharge and high dam interception.</p> Full article ">Figure 15
<p>Comparison of the downstream inundation area of the Yebatan arch dam for the natural discharge and high dam interception.</p> Full article ">
14 pages, 3120 KiB  
Article
Estimation and Mapping of the Transmissivity of the Nubian Sandstone Aquifer in the Kharga Oasis, Egypt
by Mustafa El-Rawy and Florimond De Smedt
Water 2020, 12(2), 604; https://doi.org/10.3390/w12020604 - 23 Feb 2020
Cited by 15 | Viewed by 5761
Abstract
The Nubian sandstone aquifer is the only water source for domestic use and irrigation in the Kharga oasis, Egypt. In this study, 46 pumping tests are analyzed to estimate the transmissivity of the aquifer and to derive a spatial distribution map by geostatistical [...] Read more.
The Nubian sandstone aquifer is the only water source for domestic use and irrigation in the Kharga oasis, Egypt. In this study, 46 pumping tests are analyzed to estimate the transmissivity of the aquifer and to derive a spatial distribution map by geostatistical analysis and kriging interpolation. The resulting transmissivity values are log-normally distributed and spatially correlated over a distance of about 20 km. Representative values for the transmissivity are a geometric average of about 400 m2/d and a 95% confidence interval of 100–1475 m2/d. There is no regional trend in the spatial distribution of the transmissivity, but there are local clusters with higher or lower transmissivity values. The error map indicates that the highest prediction accuracy is obtained along the central north-south traffic route along which most agricultural areas and major well sites are located. This study can contribute to a better understanding of the hydraulic properties of the Nubian sandstone aquifer in the Kharga oasis for an effective management strategy. Full article
(This article belongs to the Special Issue Sustainable Management of Aquifers in Semi-Arid Tropics)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study area and pumping test wells.</p> Full article ">Figure 2
<p>Technical log of well 11 (<b>a</b>); schematic geological and technical log of the wells, with orders of magnitude of the depth and diameter of the wells (<b>b</b>).</p> Full article ">Figure 3
<p>Plots showing drawdown against log-time for well 42 (<b>a</b>), slope of drawdown against log-time for well 42 (<b>b</b>), drawdown against log-time for well 19 (<b>c</b>) and slope of drawdown against log-time for well 19 (<b>d</b>); the solid line shows the straight-line fit to estimate the transmissivity.</p> Full article ">Figure 4
<p>Cumulative frequency distribution of the estimated transmissivity and fitted log-normal distribution.</p> Full article ">Figure 5
<p>Transmissivity plotted against latitude (<b>a</b>) and against longitude (<b>b</b>); the dashed line represents the geometric mean transmissivity value of 385 m<sup>2</sup>/d.</p> Full article ">Figure 6
<p>Empirical variogram in different directions and fitted omnidirectional exponential model variogram with parameters given in <a href="#water-12-00604-t002" class="html-table">Table 2</a> (<b>a</b>); verification of the variogram model by cross-validation, showing the quantiles of the standardized errors and the corresponding quantiles from a standard normal distribution (<b>b</b>).</p> Full article ">Figure 7
<p>Map with the spatial distribution of the transmissivity of the Nubian sandstone aquifer in the Kharga oasis obtained by kriging interpolation (<b>a</b>) and the corresponding error map showing the spatial distribution of the estimated kriging variance divided by sample variance (<b>b</b>).</p> Full article ">
17 pages, 4792 KiB  
Article
Towards an Assessment of the Ephemeral Gully Erosion Potential in Greece Using Google Earth
by Christos Karydas and Panos Panagos
Water 2020, 12(2), 603; https://doi.org/10.3390/w12020603 - 23 Feb 2020
Cited by 33 | Viewed by 5807
Abstract
Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout [...] Read more.
Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout the country. The almost 30-ha sampling surfaces were examined with visual interpretation of multi-temporal imagery from the online Google Earth for the period 2002–2019. In parallel, rill and sheet erosion signs, land uses, and presence of terraces and other anti-erosion features, were recorded within every sample. One hundred fifty-three ephemeral gullies were identified in total, inside 22 examined agricultural surfaces. The mean length of the gullies was 55.6 m, with an average slope degree of 9.7%. Vineyards showed the largest proportion of gullies followed by olive groves and arable land, while pastures exhibited limited presence of gullies. Spatial clusters of high gully severity were observed in the north and east of the country. In 77% of the surfaces with gullies, there were no terraces, although most of these surfaces were situated in slopes higher than 8%. It was the first time to use visual interpretation with Google Earth image time-series on a country scale producing a gully erosion inventory. Soil conservation practices such as contour farming and terraces could mitigate the risk of gully erosion in agricultural areas. Full article
(This article belongs to the Special Issue The Effect of Hydrology on Soil Erosion)
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Figure 1

Figure 1
<p>The random sampling scheme within the agricultural land of Greece.</p> Full article ">Figure 2
<p>Examples of ephemeral gully detection. The sampled surfaces are denoted by white circles and gullies by cyan lines in the far views (geographic coordinates and image date in brackets) (<b>a</b>) sparse gullies formed towards a torrent in sloping olive plantation near Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>b</b>) close view to case (<b>a</b>); (<b>c</b>) the longest identified gully near Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>d</b>) a close view to 0.6-m wide sections of the gully of case (<b>c</b>); (<b>e</b>) long parallel gullies in an arable field out of Sitia (35°11′42.82″ N/26°6′41.08″ E, 12 April 2013); (<b>f</b>) intensive rill and sheet erosion signs close to lake Doirani (41°8′26.07″ N/22°46′18.52″ E, 4 September 2013).</p> Full article ">Figure 2 Cont.
<p>Examples of ephemeral gully detection. The sampled surfaces are denoted by white circles and gullies by cyan lines in the far views (geographic coordinates and image date in brackets) (<b>a</b>) sparse gullies formed towards a torrent in sloping olive plantation near Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>b</b>) close view to case (<b>a</b>); (<b>c</b>) the longest identified gully near Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>d</b>) a close view to 0.6-m wide sections of the gully of case (<b>c</b>); (<b>e</b>) long parallel gullies in an arable field out of Sitia (35°11′42.82″ N/26°6′41.08″ E, 12 April 2013); (<b>f</b>) intensive rill and sheet erosion signs close to lake Doirani (41°8′26.07″ N/22°46′18.52″ E, 4 September 2013).</p> Full article ">Figure 3
<p>Geographic distribution of the total gully length per randomly sampled site (graduated symbol in five categories of magnitude).</p> Full article ">Figure 4
<p>Relative proportion of gullies, rills, and sheet erosion in the randomly sampled surfaces; five categories of severity were considered for gullies, and three for rills and sheet erosion.</p> Full article ">Figure 5
<p>Spatial independence of gully length visualized: (<b>a</b>) at the global scale using the Getis-Ord Gi* statistic (percentages indicate level of confidence); and (<b>b</b>) at the local scale using Anselin Local Moran’s I statistic.</p> Full article ">Figure 6
<p>Share of the main agricultural land uses in Greece found to contain ephemeral gullies (CORINE coding in brackets).</p> Full article ">Figure 7
<p>Number of samples found with and without ephemeral gullies (CORINE coding in brackets).</p> Full article ">Figure 8
<p>Trend plots: negative trend between total gully length and number of gullies (<b>a</b>) and between gully length and slope degree (<b>b</b>); positive trend between number of gullies and slope degree (<b>c</b>) and sheet erosion grade and elevation (<b>d</b>).</p> Full article ">Figure 8 Cont.
<p>Trend plots: negative trend between total gully length and number of gullies (<b>a</b>) and between gully length and slope degree (<b>b</b>); positive trend between number of gullies and slope degree (<b>c</b>) and sheet erosion grade and elevation (<b>d</b>).</p> Full article ">Figure 9
<p>Indicative cases of detected gullies overlaid on the drainage network (<b>a</b>) variety of gullies in a 4.5% slope vines - arable land complex, close to Thiva (38°17′8.45″ N/23°27′1.68″ E, 20 February 2014); (<b>b</b>) the longest detected gully in a 4.4% slope naked arable soil, close to Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>c</b>) short gullies in a 2% slope arable land, close to Agrinio (38°33′59.95″ N/21°23′35.14″ E, 9 September 2009); (<b>d</b>) moderate gullies in 11.4% slope olive groves, close to Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>e</b>) highly dense gully pattern in a 29.2% slope olive-vines complex, close to Egio (38°33′59.95″ N/21°23′35.14″ E, 9 July 2009); (<b>f</b>) legend.</p> Full article ">Figure 9 Cont.
<p>Indicative cases of detected gullies overlaid on the drainage network (<b>a</b>) variety of gullies in a 4.5% slope vines - arable land complex, close to Thiva (38°17′8.45″ N/23°27′1.68″ E, 20 February 2014); (<b>b</b>) the longest detected gully in a 4.4% slope naked arable soil, close to Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>c</b>) short gullies in a 2% slope arable land, close to Agrinio (38°33′59.95″ N/21°23′35.14″ E, 9 September 2009); (<b>d</b>) moderate gullies in 11.4% slope olive groves, close to Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>e</b>) highly dense gully pattern in a 29.2% slope olive-vines complex, close to Egio (38°33′59.95″ N/21°23′35.14″ E, 9 July 2009); (<b>f</b>) legend.</p> Full article ">
19 pages, 8712 KiB  
Article
Climate Change Impacts on Cold Season Runoff in the Headwaters of the Yellow River Considering Frozen Ground Degradation
by Pan Wu, Sihai Liang, Xu-Sheng Wang, Jeffrey M. McKenzie and Yuqing Feng
Water 2020, 12(2), 602; https://doi.org/10.3390/w12020602 - 22 Feb 2020
Cited by 12 | Viewed by 4728
Abstract
Climate change has effects on hydrological change in multiple aspects, particularly in the headwaters of the Yellow River (HWYR), which is widely covered by climate-sensitive frozen ground. In this study, the annual runoff was partitioned into four runoff compositions: winter baseflow, snowmelt runoff, [...] Read more.
Climate change has effects on hydrological change in multiple aspects, particularly in the headwaters of the Yellow River (HWYR), which is widely covered by climate-sensitive frozen ground. In this study, the annual runoff was partitioned into four runoff compositions: winter baseflow, snowmelt runoff, rainy season runoff, and recession flow. In addition, the effects of global warming, precipitation change, and frozen ground degradation were considered in long-term variation analyses of the runoff compositions. The moving t-test was employed to detect change points of the hydrometeorological data series from 1961 to 2013, and flow duration curves were used to analyze daily runoff regime change in different periods. It was found that the abrupt change points of cold season runoff, such as recession flow, winter baseflow, and snowmelt runoff, are different from that of the rainy season runoff. The increase in winter baseflow and decrease in snowmelt runoff at the end of 1990s was closely related to global warming. In the 21st century, winter baseflow presented a larger relative increase compared to rainy season runoff. The correlation analyses indicate that winter baseflow and snowmelt runoff are mainly controlled by water-resource-related factors, such as rainy season runoff and the accumulated precipitation in cold season. To analyze the global warming impacts, two runoff coefficients—winter baseflow discharge rate (Rw) and direct snowmelt runoff coefficients (Rs)—were proposed, and their correlation with freezing–thawing indices were analyzed. The increase of Rw is related to the increase in the air temperature thawing index (DDT), but Rs is mainly controlled by the air temperature freezing index (DDF). Meanwhile, the direct snowmelt runoff coefficient (Rs) is significantly and positively correlated to DDF and has decreased at a rate of 0.0011/year since 1980. Under global warming, the direct snowmelt runoff (runoff increment between March to May) of the HWYR could decrease continuously in the future due to the decrease of accumulative snow in cold season and frozen ground degradation. This study provides a better understanding of the long-term runoff characteristic changes in the HWYR. Full article
(This article belongs to the Special Issue Climate Change Effects on Hydrological Processes, Water Resources, Ecosystems and Agriculture)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Relative location of the study area in the Yellow River basin, distribution of the meteorological stations, and the outlet hydrological station; (<b>b</b>) frozen ground distribution in the 1980s; (<b>c</b>) frozen ground distribution in the 1990s.</p> Full article ">Figure 2
<p>Snow, sleet, and rain distribution along with air temperature; (<b>a</b>) Jimai station, (<b>b</b>) Maduo station.</p> Full article ">Figure 3
<p>(<b>a</b>) The change points of annual mean runoff detected by moving <span class="html-italic">t</span>-test (<b>b</b>) comparison of daily runoff regime of the three periods and the definition of hydrological year.</p> Full article ">Figure 4
<p>(<b>a</b>) Comparison of flow duration curves of the three periods (<b>b</b>) Relative changes of different percentile runoff in different periods.</p> Full article ">Figure 5
<p>The moving <span class="html-italic">t</span>-test results of different runoff compositions.</p> Full article ">Figure 6
<p>(<b>a</b>) Intra-annual variation of snow and snow cover area; (<b>b</b>) inter-annual variation of annual accumulative snow in cold season (ASC).</p> Full article ">Figure 7
<p>The change points of precipitation intensity (I), rain, and precipitation (P); and (<b>b</b>) cold season precipitation detected by moving <span class="html-italic">t</span>-test.</p> Full article ">Figure 8
<p>Change points of freezing–thawing indices and annual mean temperature detected by moving <span class="html-italic">t</span>-test.</p> Full article ">Figure 9
<p>(<b>a</b>) Inter-annual variation of rainy season runoff and winter baseflow (<b>b</b>) correlation of winter baseflow and rainy season runoff (<b>c</b>) Inter-annual variation of freezing–thawing indices (DDF, DDT) and winter groundwater discharge rate (<span class="html-italic">R</span><sub>w</sub>) (<b>d</b>) The correlation between freezing–thawing indices (DDF, DDT) and winter groundwater discharge rate (<span class="html-italic">R</span><sub>w</sub>).</p> Full article ">Figure 10
<p>The inter-annual variation of (<b>a</b>) direct snowmelt runoff and (<b>b</b>) direct snowmelt runoff coefficient (<span class="html-italic">R</span><sub>s</sub>).</p> Full article ">Figure 11
<p>The correlation between direct snowmelt runoff coefficient and freezing–thawing indices (DDF (<b>a</b>), DDT (<b>b</b>)).</p> Full article ">
19 pages, 6050 KiB  
Article
Transient-Flow Induced Compressed Air Energy Storage (TI-CAES) System towards New Energy Concept
by Mohsen Besharat, Avin Dadfar, Maria Teresa Viseu, Bruno Brunone and Helena M. Ramos
Water 2020, 12(2), 601; https://doi.org/10.3390/w12020601 - 22 Feb 2020
Cited by 10 | Viewed by 5459
Abstract
In recent years, interest has increased in new renewable energy solutions for climate change mitigation and increasing the efficiency and sustainability of water systems. Hydropower still has the biggest share due to its compatibility, reliability and flexibility. This study presents one such technology [...] Read more.
In recent years, interest has increased in new renewable energy solutions for climate change mitigation and increasing the efficiency and sustainability of water systems. Hydropower still has the biggest share due to its compatibility, reliability and flexibility. This study presents one such technology recently examined at Instituto Superior Técnico based on a transient-flow induced compressed air energy storage (TI-CAES) system, which takes advantage of a compressed air vessel (CAV). The CAV can produce extra required pressure head, by compressing air, to be used for either hydropower generation using a water turbine in a gravity system or to be exploited in a pumping system. The results show a controlled behaviour of the system in storing the pressure surge as compressed air inside a vessel. Considerable power values are achieved as well, while the input work is practically neglected. Higher power values are attained for bigger air volumes. The TI-CAES offers an efficient and flexible solution that can be exploited in exiting water systems without putting the system at risk. The induced transients in the compressed air allow a constant outflow discharge characteristic, making the energy storage available in the CAV to be used as a pump storage hydropower solution. Full article
(This article belongs to the Special Issue Environmental Hydraulics Research)
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<p>(<b>a</b>) Hydropower capacity growth, (<b>b</b>) hydropower rank in the global electricity generation [<a href="#B1-water-12-00601" class="html-bibr">1</a>]</p> Full article ">Figure 2
<p>Experimental apparatus as part of the transient flow-induced compressed air energy storage (TI-CAES) system and energy conceptual ideas: (<b>a</b>) electricity dispatch; (<b>b</b>) pumping system.</p> Full article ">Figure 3
<p>Experimental pressure variation in charge/discharge stages of the CAV, for Re = 155,000: (<b>a</b>) volume fraction ratio (VFR) = 8.33%; (<b>b</b>) VFR = 66.67%.</p> Full article ">Figure 4
<p>The pressure–volume diagram.</p> Full article ">Figure 5
<p>The work done by the air expansion is the area below the pressure–volume graph in different VFRs.</p> Full article ">Figure 6
<p>The concept of average and real-time air pressure: (<b>a</b>) average pressure for different VFR and Re numbers; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> for VFR = 3.33% and Re = 155,000; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> for VFR = 25.00% and Re = 155,000; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> for VFR = 66.67% and Re = 155,000.</p> Full article ">Figure 7
<p>The hydraulic power and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> changing during transient for Re = 155,000 and: (<b>a</b>) VFR = 3.33%; (<b>b</b>) VFR = 16.67%; (<b>c</b>) VFR = 33.33%; (<b>d</b>) VFR = 66.67%.</p> Full article ">Figure 7 Cont.
<p>The hydraulic power and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> changing during transient for Re = 155,000 and: (<b>a</b>) VFR = 3.33%; (<b>b</b>) VFR = 16.67%; (<b>c</b>) VFR = 33.33%; (<b>d</b>) VFR = 66.67%.</p> Full article ">Figure 8
<p>The power production for experimental CAV volume based on integrated pressure values.</p> Full article ">Figure 9
<p>Velocity profiles along time: (<b>a</b>) Re = 58,000; (<b>b</b>) Re = 93,000; (<b>c</b>) Re = 132,000; (<b>d</b>) Re = 155,000.</p> Full article ">Figure 10
<p>Difference between the transient mean velocity (V<sub>1</sub>) and the initial mean velocity (V<sub>0</sub>) over the maximum initial velocity (V<sub>max</sub>)<sub>0</sub> for all the tested conditions and time steps after starting the transient event as: (<b>a</b>) 0.8; (<b>b</b>) 1.0; (<b>c</b>) 1.2; (<b>d</b>) 1.4; (<b>e</b>) for more time.</p> Full article ">Figure 11
<p>Hydraulic prototype power for different length-scales and VFRs.</p> Full article ">Figure 12
<p>Dimensionless power for different VFR and Re numbers in TI-CAES system.</p> Full article ">
18 pages, 12635 KiB  
Article
Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows
by Zhenzhu Meng, Yating Hu and Christophe Ancey
Water 2020, 12(2), 600; https://doi.org/10.3390/w12020600 - 22 Feb 2020
Cited by 30 | Viewed by 5165
Abstract
When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave [...] Read more.
When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model. Full article
(This article belongs to the Special Issue Non-Newtonian Fluids in Environmental Hydraulics: Modelling and Applications)
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<p>Two dimensional physical model of a landslide generating wave: (<b>a</b>) the slide material is at rest and then starts moving (stage I), (<b>b</b>) the slide material moves down the slope and reaches the shoreline (stage II), and (<b>c</b>) the slide material intrudes into the body of water and generates waves (stage III).</p> Full article ">Figure 2
<p>The experimental facility.</p> Full article ">Figure 3
<p>A biological neuron in comparison to an artificial neural network: (<b>a</b>) human neuron; (<b>b</b>) artificial neuron; (<b>c</b>) biological synapse; and (<b>d</b>) ANN synapses [<a href="#B39-water-12-00600" class="html-bibr">39</a>].</p> Full article ">Figure 4
<p>Variation of <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> versus the number of neurons in the hidden layer.</p> Full article ">Figure 5
<p>Variations in (<b>a</b>) the gradient, (<b>b</b>) the number of validation fails, and (<b>c</b>) MSE, against epochs.</p> Full article ">Figure 6
<p>Error histogram of <math display="inline"><semantics> <msub> <mi>A</mi> <mi>m</mi> </msub> </semantics></math> with 20 bins. The red part denotes test data and the grey part denotes training data.</p> Full article ">Figure 7
<p>Q-Q plot of observed and predicted (<b>a</b>) <math display="inline"><semantics> <msub> <mi>A</mi> <mi>m</mi> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>H</mi> <mi>m</mi> </msub> </semantics></math>, for the empirical equations and the ANN model. Training data and test data in the ANN model are displayed separately.</p> Full article ">Figure 8
<p>Variations in wave amplitude <math display="inline"><semantics> <msub> <mi>a</mi> <mi>m</mi> </msub> </semantics></math> against <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>I</mi> </msub> <msubsup> <mi>l</mi> <mi>s</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>, with the water depth <math display="inline"><semantics> <msub> <mi>h</mi> <mn>0</mn> </msub> </semantics></math> = 0.2 m and slope angle <math display="inline"><semantics> <mi>θ</mi> </semantics></math> = 45<math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>.</p> Full article ">Figure 9
<p>Raw images of landslides intruding into a body of water, as recorded by a high-speed camera: (<b>a</b>) Carbopol, (<b>b</b>) mixture of 50% Carbopol and 50% polymer–water balls, and (<b>c</b>) polymer–water balls.</p> Full article ">Figure 10
<p>Effects of slide mass material composition on the scaled maximum wave amplitude <math display="inline"><semantics> <msub> <mi>A</mi> <mi>m</mi> </msub> </semantics></math>.</p> Full article ">Figure 11
<p>Predicted (<b>a</b>) <math display="inline"><semantics> <msub> <mi>A</mi> <mi>m</mi> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>H</mi> <mi>m</mi> </msub> </semantics></math> with a six–eight–two ANN model versus experimental data. Training data and test data in the ANN model are displayed separately.</p> Full article ">Figure 12
<p>Correlation matrix of explanatory variables <math display="inline"><semantics> <msub> <mo>Π</mo> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mo>Π</mo> <mn>2</mn> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mo>Π</mo> <mn>3</mn> </msub> </semantics></math> in Equation (<a href="#FD20-water-12-00600" class="html-disp-formula">20</a>).</p> Full article ">
13 pages, 2918 KiB  
Article
Ammonium-Nitrogen (NH4+-N) Removal from Groundwater by a Dropping Nitrification Reactor: Characterization of NH4+-N Transformation and Bacterial Community in the Reactor
by Amit Kumar Maharjan, Tatsuru Kamei, Iswar Man Amatya, Kazuhiro Mori, Futaba Kazama and Tadashi Toyama
Water 2020, 12(2), 599; https://doi.org/10.3390/w12020599 - 22 Feb 2020
Cited by 22 | Viewed by 10075
Abstract
A dropping nitrification reactor was proposed as a low-cost and energy-saving option for the removal of NH4+-N from contaminated groundwater. The objectives of this study were to investigate NH4+-N removal performance and the nitrogen removal pathway and [...] Read more.
A dropping nitrification reactor was proposed as a low-cost and energy-saving option for the removal of NH4+-N from contaminated groundwater. The objectives of this study were to investigate NH4+-N removal performance and the nitrogen removal pathway and to characterize the microbial communities in the reactor. Polyolefin sponge cubes (10 mm × 10 mm × 10 mm) were connected diagonally in a nylon thread to produce 1 m long dropping nitrification units. Synthetic groundwater containing 50 mg L−1 NH4+-N was added from the top of the hanging units at a flow rate of 4.32 L day−1 for 56 days. Nitrogen-oxidizing microorganisms in the reactor removed 50.8–68.7% of the NH4+-N in the groundwater, which was aerated with atmospheric oxygen as it flowed downwards through the sponge units. Nitrogen transformation and the functional bacteria contributing to it were stratified in the sponge units. Nitrosomonadales-like AOB predominated and transformed NH4+-N to NO2−-N in the upper part of the reactor. Nitrospirales-like NOB predominated and transformed NO2−-N to NO3−-N in the lower part of the reactor. The dropping nitrification reactor could be a promising technology for oxidizing NH4+-N in groundwater and other similar contaminated wastewaters. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Changes in pH (<b>a</b>), redox potential (ORP) (<b>b</b>), and dissolved oxygen (DO) (<b>c</b>) in the influent and effluent samples over 56 days of operation. Effluent values are means ± SD (<span class="html-italic">n</span> = 4).</p> Full article ">Figure 2
<p>Changes in NH<sub>4</sub><sup>+</sup>-N, NO<sub>2</sub><sup>−</sup>-N, and NO<sub>3</sub><sup>−</sup>-N concentration and NH<sub>4</sub><sup>+</sup>-N removal efficiency over 56 days of operation. Values are means ± SD (<span class="html-italic">n</span> = 4).</p> Full article ">Figure 3
<p>Changes in the abundances of 16S rRNA, <span class="html-italic">amoA</span>, <span class="html-italic">nxrA</span>, <span class="html-italic">nirK</span>, and <span class="html-italic">nirS</span> in the dropping nitrification units over 56 days of operation. Average numbers of gene copies ± SD are shown for duplicate experiments.</p> Full article ">Figure 4
<p>Profiles of NH<sub>4</sub><sup>+</sup>-N, NO<sub>2</sub><sup>−</sup>-N, NO<sub>3</sub><sup>−</sup>-N, and functional genes along the single axis of the dropping flow every 2 weeks. NH<sub>4</sub><sup>+</sup>-N, NO<sub>2</sub><sup>−</sup>-N, and NO<sub>3</sub><sup>−</sup>-N concentrations indicated are the averages ± SD of four replicate reactors. The gene copies shown represent the averages ± SD of duplicate experiments.</p> Full article ">Figure 5
<p>First-order kinetic constant (<span class="html-italic">k<sub>1</sub></span>) for the decrease in NH<sub>4</sub><sup>+</sup>-N concentration along the single axis of the dropping nitrification reactor over 56 days of operation. Values are means ± SD (<span class="html-italic">n</span> = 4).</p> Full article ">Figure 6
<p>Bacterial community structures at the class (<b>a</b>) and order (<b>b</b>) levels along the single axis of the dropping flow on the 56th day of operation.</p> Full article ">
14 pages, 1851 KiB  
Article
The Effect of Soil Iron on the Estimation of Soil Water Content Using Dielectric Sensors
by George Kargas, Paraskevi Londra, Marianthi Anastasatou and Nick Moustakas
Water 2020, 12(2), 598; https://doi.org/10.3390/w12020598 - 22 Feb 2020
Cited by 11 | Viewed by 3626
Abstract
Nowadays, the estimation of volumetric soil water content (θ) through apparent dielectric permittivity (εa) is the most widely used method. The purpose of this study is to investigate the effect of the high iron content of two sandy loam soils on [...] Read more.
Nowadays, the estimation of volumetric soil water content (θ) through apparent dielectric permittivity (εa) is the most widely used method. The purpose of this study is to investigate the effect of the high iron content of two sandy loam soils on estimating their water content using two dielectric sensors. These sensors are the WET sensor operating at 20 MHz and the ML2 sensor operating at 100 MHz. Experiments on specific soil columns, in the laboratory, by mixing different amounts of water in the soils to obtain a range of θ values under constant temperature conditions were conducted. Analysis of the results showed that both sensors, based on manufacturer calibration, led to overestimation of θ. This overestimation is due to the high measured values of εa by both sensors used. The WET sensor, operating at a lower frequency and being strongly affected by soil characteristics, showed the greatest overestimation. The difference of εa values between the two sensors ranged from 14 to 19 units at the maximum actual soil water content (θm). Compared to the Topp equation, the WET sensor measures 2.3 to 2.8 fold higher value of εa. From the results, it was shown that the relationship θm-εa0.5 remained linear even in the case of these soils with high iron content and the multi-point calibration (CALALL) is a good option where individual calibration is needed. Full article
(This article belongs to the Special Issue Study of the Soil Water Movement in Irrigated Agriculture)
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<p>XRD diagram of soil 1.</p> Full article ">Figure 2
<p>Comparative presentation of relationships θ<sub>m</sub>-ε<sub>a</sub> obtained by the WET sensor, the ML2 sensor and the TOPP equation for soil 1.</p> Full article ">Figure 3
<p>Comparative presentation of relationships θ<sub>m</sub>-ε<sub>a</sub> obtained by the WET sensor, the ML2 sensor and the TOPP equation for soil 2.</p> Full article ">Figure 4
<p>Comparative presentation of the relationships θ<sub>m</sub>-ε<sub>a</sub> obtained by the WET sensor, the ML2 sensor and the Topp equation for a sandy loam soil (SL) studied by Kargas et al. (2014) [<a href="#B6-water-12-00598" class="html-bibr">6</a>] with low iron content.</p> Full article ">Figure 5
<p>Comparative presentation of the relationships θ<sub>m</sub>-ε<sub>a</sub><sup>0.5</sup> obtained by the WET and ML2 sensors for soil 1. The corresponding relationships using the manufacturer calibration are also given for both sensors.</p> Full article ">Figure 6
<p>Comparative presentation of the relationships θ<sub>m</sub>-ε<sub>a</sub><sup>0.5</sup> obtained by the WET and ML2 sensors for soil 2. The corresponding relationships using the manufacturer calibration are also given for both sensors.</p> Full article ">Figure 7
<p>Relationship between the apparent dielectric permittivity ε<sub>a</sub> and the actual soil water content θ<sub>m</sub> for five different ratios of the sand-soil mixture studied using the WET sensor.</p> Full article ">Figure 8
<p>Relationship between the apparent dielectric permittivity ε<sub>a</sub> and the percentage of soil 1 in the soil mixture studied at the maximum actual soil water content using the WET sensor.</p> Full article ">
15 pages, 4625 KiB  
Article
Changes in Planktivory and Herbivory Regimes in a Shallow South American Lake (Lake Blanca Chica, Argentina) Over the Last 250 Years
by David Carrozzo, Simona Musazzi, Andrea Lami, Francisco E. Córdoba and María de los Ángeles González Sagrario
Water 2020, 12(2), 597; https://doi.org/10.3390/w12020597 - 22 Feb 2020
Cited by 4 | Viewed by 3326
Abstract
Shallow lakes are vulnerable ecosystems impacted by human activities and climate change. The Cladocera occupy a central role in food webs and are an excellent paleoecological indicator of food web structure and trophic status. We conducted a paleolimnological study in Lake Blanca Chica [...] Read more.
Shallow lakes are vulnerable ecosystems impacted by human activities and climate change. The Cladocera occupy a central role in food webs and are an excellent paleoecological indicator of food web structure and trophic status. We conducted a paleolimnological study in Lake Blanca Chica (Argentina) to detect changes on the planktivory and herbivory regimes over the last 250 years. Generalized additive models were fitted to the time series of fish predation indicators (ephippial abundance and size, mucrone size, fish scales, and the planktivory index) and pheophorbide a concentration. The cladoceran assemblage changed from littoral-benthic to pelagic species dominance and zooplankton switched from large-bodied (Daphnia) to small-bodied grazers (Bosmina) ca. 1900 due to increased predation. The shift in planktivory regime (ca. 1920–1930), indicated by fish scales and the planktivory index, as well as herbivory (ca. 1920–1950), was triggered by eutrophication. Changes in planktivory affected the size structure of Bosmina, reducing its body size. This study describes the baseline for the lake as well as the profound changes in the composition and size structure of the zooplankton community due to increased predation and the shift in the planktivory regime. These findings will provide a reference status for future management strategies of this ecosystem. Full article
(This article belongs to the Special Issue Zooplankton Diversity and Pelagic Food Webs: Investigating Present and Past with Different Techniques)
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<p>Location of Lake Blanca Chica in the the Argentinean Pampa Plain, South America.</p> Full article ">Figure 2
<p>Generalized additive model (GAM) fitted to the time series of pheophorbide <span class="html-italic">a</span> (expressed as ƞ Molesg OM<sup>-1</sup>)in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM-fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">Figure 3
<p>Size of the ephippia of <span class="html-italic">Daphnia</span> species and <span class="html-italic">Moina</span> sp. and the mucrone of <span class="html-italic">Bosmina huaronensis</span> in the sedimentary record of Lake Blanca Chica.</p> Full article ">Figure 4
<p>GAM fitted to the time series of <span class="html-italic">Daphnia spinulata</span> ephippia abundance (left pannel) and ephippia size (right pannel) in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">Figure 5
<p>GAM fitted to the time series of <span class="html-italic">Daphnia obtuse</span> ephippia abundance (left pannel) and ephippia size (right pannel) in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">Figure 6
<p>GAM fitted to the time series of <span class="html-italic">Bosmina huaronensis</span> abundance (chitinous remians) (left pannel) and mucrone size (right pannel)in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">Figure 7
<p>GAM fitted to the time series of fish scale abundance in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">Figure 8
<p>GAM fitted to the planktivory index (<span class="html-italic">Daphnia</span>/(<span class="html-italic">Daphnia</span> + <span class="html-italic">Bosmina</span>)) estimated from the ephippial assemblage in the sedimentary archive of Lake Blanca Chica. (<b>a</b>) Observed values, GAM-based trend fitted and its simultaneous interval; (<b>b</b>) estimated first derivative of the GAM fitted trend and the 95% simultaneous interval; (<b>c</b>) period of transition.</p> Full article ">
18 pages, 3910 KiB  
Review
An Integrated Review of River Bars for Engineering, Management and Transdisciplinary Research
by Alessandra Crosato and Erik Mosselman
Water 2020, 12(2), 596; https://doi.org/10.3390/w12020596 - 21 Feb 2020
Cited by 39 | Viewed by 6779
Abstract
River training and river restoration often imply modifying the patterns and dimensions of bars, channels, and pools. Research since the 1980s has greatly advanced and matured our knowledge on the formation and behavior of river bars, thanks to field work, laboratory experiments, theoretical [...] Read more.
River training and river restoration often imply modifying the patterns and dimensions of bars, channels, and pools. Research since the 1980s has greatly advanced and matured our knowledge on the formation and behavior of river bars, thanks to field work, laboratory experiments, theoretical analyses, and numerical modelling by several research groups. However, this knowledge is not easily accessible to design engineers, river managers, and ecologists who need to apply it. This is mainly due to confusing differences in terminology as well as to difficult mathematical theories. Moreover, existing scientific publications generally focus on specific aspects, so an overall review of the findings and their applications is still lacking. In many cases, the knowledge achieved so far would allow minimizing hard engineering interventions and thus obtaining more natural rivers. We present an integrated review of the major findings of river bar studies. Our aim is to provide accessible state-of-the-art knowledge for nature-based bar management and successful river training and river restoration. To this end we review the results from analytical, numerical, experimental, and field studies, explain the background of bar theories, and discuss applications in river engineering and river restoration. Full article
(This article belongs to the Special Issue Studies on River Training)
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<p>(<b>a</b>) Gravel-bars under a bridge, Tagliamento River, Italy (courtesy of Paolo Reggiani); (<b>b</b>) sediment sorting on a bar in the River Adige, at Castelvecchio Bridge, Verona, Italy.</p> Full article ">Figure 2
<p>(<b>a</b>) Rhine River, the Netherlands. Point bar inside a river bend, forced by channel curvature, hinders navigation. Flow direction from top to bottom. (<b>b</b>) Cauca River, Colombia. Compound central bar forced by a local width expansion. Flow direction from bottom to top.</p> Full article ">Figure 3
<p>Tagliamento River, Italy. Multiple free bars have merged into large compound bars.</p> Full article ">Figure 4
<p>Bar classification after Duró et al. [<a href="#B19-water-12-00596" class="html-bibr">19</a>].</p> Full article ">Figure 5
<p>Longitudinal and transverse bed oscillations caused by the presence of alternate and central (periodic) bars. Alternate bars: one row of alternate bars in the channel (<span class="html-italic">m</span> = 1). Central bars: two parallel rows of alternate bars in the channel (<span class="html-italic">m</span> = 2).</p> Full article ">Figure 6
<p>(<b>a</b>) Free bars migrating in downstream direction in the laboratory. Flow direction from bottom to top (courtesy of Andrés Vargas-Luna). (<b>b</b>) Comparison between the shape and size of free bars migrating in downstream direction (channel above) and of steady hybrid bars (channel below) obtained with identical boundary conditions in a 2D numerical model constructed with the Delft3D code (courtesy of Le Thai Binh). Flow direction from left to right.</p> Full article ">Figure 7
<p>Alternate bars enhance opposite bank erosion (courtesy of Andrés Vargas-Luna).</p> Full article ">
12 pages, 2684 KiB  
Article
Electrocoagulation: A Promising Method to Treat and Reuse Mineral Processing Wastewater with High COD
by Gaogui Jing, Shuai Ren, Yuesheng Gao, Wei Sun and Zhiyong Gao
Water 2020, 12(2), 595; https://doi.org/10.3390/w12020595 - 21 Feb 2020
Cited by 53 | Viewed by 6125
Abstract
Mineral processing wastewater contains large amounts of reagents which can lead to severe environmental problems, such as high chemical oxygen demand (COD). Inspired by the wastewater treatment in such industries as those of textiles, food, and petrochemistry, in the present work, electrocoagulation (EC) [...] Read more.
Mineral processing wastewater contains large amounts of reagents which can lead to severe environmental problems, such as high chemical oxygen demand (COD). Inspired by the wastewater treatment in such industries as those of textiles, food, and petrochemistry, in the present work, electrocoagulation (EC) is applied for the first time to explore its feasibility in the treatment of wastewater with an initial COD of 424.29 mg/L from a Pb/Zn sulfide mineral flotation plant and its effect on water reuse. Typical parameters, such as anode materials, current density, initial pH, and additives, were characterized to evaluate the performance of the EC method. The results showed that, under optimal conditions, i.e., iron anode, pH 7.1, electrolysis time 70 min, 19.23 mA/cm2 current density, and 4.1 g/L activated carbon, the initial COD can be reduced to 72.9 mg/L, corresponding to a removal rate of 82.8%. In addition, compared with the untreated wastewater, EC-treated wastewater was found to benefit the recovery of galena and sphalerite, with galena recovery increasing from 25.01% to 36.06% and sphalerite recovery increasing from 59.99% to 65.33%. This study confirmed that EC is a promising method for the treatment and reuse of high-COD-containing wastewater in the mining industry, and it possesses great potential for wide industrial applications. Full article
(This article belongs to the Special Issue Wastewater Treatment, Valorization and Reuse)
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Full article ">Figure 1
<p>Electrocoagulation (EC) experimental set-up: (1) Magnetic stirrer; (2) DC power supply; (3) electrolytic cell; (4) anode plate; (5) cathode plate; (6) magnetic bar-stirrer.</p> Full article ">Figure 2
<p>Flowsheet and corresponding experimental conditions of batch flotation tests using different types of water.</p> Full article ">Figure 3
<p>Effect of anode materials for EC treatment on mixed wastewater COD value (cathode material: Stainless steel, current density: 13.74 mA/cm<sup>2</sup>, pH: 7.1, electrolysis time: 50 min).</p> Full article ">Figure 4
<p>Effect of current density on mixed wastewater COD removal rate (anode material: Fe, cathode material: Stainless steel, pH: 7.1, electrolysis time: 50 min).</p> Full article ">Figure 5
<p>Effect of initial pH value on mixed wastewater COD removal rate (anode material: Fe, cathode material: Stainless steel, current density: 19.23 mA/cm<sup>2</sup>, electrolysis time: 50 min).</p> Full article ">Figure 6
<p>Effect of electrolysis time on mixed wastewater COD removal rate (anode material: Fe, cathode material: Stainless steel, current density: 19.23 mA/cm<sup>2</sup>, pH: 7.1).</p> Full article ">Figure 7
<p>Effect of addition of Na<sub>2</sub>SO<sub>4</sub> on mixed wastewater COD removal rate (anode material: Fe, cathode material: Stainless steel, pH: 7.1, electrolysis time: 70 min).</p> Full article ">Figure 8
<p>Effect of granular activated carbon (GAC) treatment on mixed wastewater COD removal rate (anode material: Fe, cathode material: Stainless steel, current density: 19.27 mA/cm<sup>2</sup>, pH: 7.1, electrolysis time: 70 min).</p> Full article ">Figure 9
<p>Effect of EC treatment on the COD values and COD removal rates of four different types of wastewaters.</p> Full article ">Figure 10
<p>The grade and recovery of Pb (<b>a</b>) and Zn (<b>b</b>) sulfide flotation concentrates using different types of water.</p> Full article ">Figure 10 Cont.
<p>The grade and recovery of Pb (<b>a</b>) and Zn (<b>b</b>) sulfide flotation concentrates using different types of water.</p> Full article ">
13 pages, 15936 KiB  
Article
Preliminary Characterization of Underground Hydrological Processes under Multiple Rainfall Conditions and Rocky Desertification Degrees in Karst Regions of Southwest China
by Guijing Li, Matteo Rubinato, Long Wan, Bin Wu, Jiufu Luo, Jianmei Fang and Jinxing Zhou
Water 2020, 12(2), 594; https://doi.org/10.3390/w12020594 - 21 Feb 2020
Cited by 7 | Viewed by 3408
Abstract
Karst regions are widely distributed in Southwest China and due to the complexity of their geologic structure, it is very challenging to collect data useful to provide a better understanding of surface, underground and fissure flows, needed to calibrate and validate numerical models. [...] Read more.
Karst regions are widely distributed in Southwest China and due to the complexity of their geologic structure, it is very challenging to collect data useful to provide a better understanding of surface, underground and fissure flows, needed to calibrate and validate numerical models. Without characterizing these features, it is very problematic to fully establish rainfall–runoff processes associated with soil loss in karst landscapes. Water infiltrated rapidly to the underground in rocky desertification areas. To fill this gap, this experimental work was completed to preliminarily determine the output characteristics of subsurface and underground fissure flows and their relationships with rainfall intensities (30 mm h−1, 60 mm h−1 and 90 mm h−1) and bedrock degrees (30%, 40% and 50%), as well as the role of underground fissure flow in the near-surface rainfall–runoff process. Results indicated that under light rainfall conditions (30 mm h−1), the hydrological processes observed were typical of Dunne overland flows; however, under moderate (60 mm h−1) and high rainfall conditions (90 mm h−1), hydrological processes were typical of Horton overland flows. Furthermore, results confirmed that the generation of underground runoff for moderate rocky desertification (MRD) and severe rocky desertification (SRD) happened 18.18% and 45.45% later than the timing recorded for the light rocky desertification (LRD) scenario. Additionally, results established that the maximum rate of underground runoff increased with the increase of bedrock degrees and the amount of cumulative underground runoff measured under different rocky desertification was SRD > MRD > LRD. In terms of flow characterization, for the LRD configuration under light rainfall intensity the underground runoff was mainly associated with soil water, which was accounting for about 85%–95%. However, under moderate and high rainfall intensities, the underground flow was mainly generated from fissure flow. Full article
(This article belongs to the Special Issue Advances in Modelling and Prediction on the Impact of Human Activities and Extreme Events on Environments)
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<p>Geographical location of the study site and bed rock configurations tested (1, 2 and 3), whose characteristics are displayed in <a href="#water-12-00594-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>Dynamics of underground runoff for light rocky desertification (LRD) (<b>a</b>,<b>d</b>,<b>g</b>), moderate rocky desertification (MRD) (<b>b</b>,<b>e</b>,<b>h</b>) and severe rocky desertification (SRD) (<b>c</b>,<b>f</b>,<b>i</b>) under 30 mm/h (<b>a</b>–<b>c</b>), 60 mm/h (<b>d</b>–<b>f</b>) and 90 mm/h (<b>g</b>–<b>i</b>).</p> Full article ">Figure 3
<p>Time to underground runoff under different rainfall intensities. Note: In the figure, the capital letter represented the significant difference between different rainfall intensity under a certain degree of rocky desertification (<span class="html-italic">p</span> &lt; 0.05), and the lowercase letter represented the significant difference between different degrees of rocky desertification under a certain degree of rainfall intensity (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Maximun underground runoff rate under different rainfall intensities.</p> Full article ">Figure 5
<p>Cumulative underground runoff under different rainfall intensities.</p> Full article ">Figure 6
<p>Dynamics of volumetric water content (VWC) for LRD (<b>a</b>,<b>d</b>,<b>g</b>), MRD (<b>b</b>,<b>e</b>,<b>h</b>) and SRD (<b>c</b>,<b>f</b>,<b>i</b>) under 30 mm/h (<b>a</b>–<b>c</b>), 60 mm/h (<b>d</b>–<b>f</b>), 90 mm/h (<b>g</b>–<b>i</b>).</p> Full article ">Figure 7
<p>Fissure flow ratio under different rainfall intensities.</p> Full article ">
18 pages, 5477 KiB  
Article
Understanding Complexity in Freshwater Management: Practitioners’ Perspectives in The Netherlands
by Guido Rutten, Steve Cinderby and Jennie Barron
Water 2020, 12(2), 593; https://doi.org/10.3390/w12020593 - 21 Feb 2020
Cited by 4 | Viewed by 3816
Abstract
Ecosystems have been stabilized by human interventions to optimize delivery of certain ecosystem services, while at the same time awareness has grown that these systems are inherently dynamic rather than steady state. Applied research fields have emerged that try to increase adaptive capacity [...] Read more.
Ecosystems have been stabilized by human interventions to optimize delivery of certain ecosystem services, while at the same time awareness has grown that these systems are inherently dynamic rather than steady state. Applied research fields have emerged that try to increase adaptive capacity in these ecosystems, using concepts deriving from the theory of complex adaptive systems. How are these concepts of complexity interpreted and applied by practitioners? This study applies a mixed-methods approach to analyze the case of freshwater management in The Netherlands, where a management paradigm promoting nature-fixating interventions is recently being replaced with a new paradigm of nature-based solutions. We find that practitioners have widely varying interpretations of concepts and of how the ecosystems they work in have evolved over time when described with complex system attributes. This study allows for the emergence of key complexity-related considerations among practitioners that are not often discussed in literature: (i) the need for physical and institutional space for self-organization of nature; (ii) the importance of dependency and demand management; and (iii) trade-offs between robustness and flexibility. This study, furthermore, stresses the importance of using practitioners’ views to guide applied research and practice in this field. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>Ranking matrix used for Q methodology.</p> Full article ">Figure 2
<p>Q statements used in study.</p> Full article ">Figure 3
<p>Layout of the water management system in The Netherlands, divided into five major areas here: the main rivers (Rhine and Meuse) and associated canals and water control structures (weirs); the Ijsselmeer area, an artificial lake created in the 1930s; the southwest Delta with fully closed, semi-closed and open river arms; the closed coastal zone along the North Sea and the tidal mudflat areas of the Wadden Sea in the north; and the “high grounds” areas with small waterways. [<a href="#B41-water-12-00593" class="html-bibr">41</a>].</p> Full article ">Figure 4
<p>Sequence of priorities in allocation of freshwater [<a href="#B45-water-12-00593" class="html-bibr">45</a>].</p> Full article ">Figure 5
<p>(<b>a</b>) National water demand by usage, with water quality (<span class="html-italic">Y</span>-axis) as a relative aggregate score (10 = highest) [<a href="#B49-water-12-00593" class="html-bibr">49</a>]; (<b>b</b>) Supply to regional water networks by purpose; (<b>c</b>) Trend in drinking water demand, [<a href="#B54-water-12-00593" class="html-bibr">54</a>]; (<b>d</b>) Horticultural and other agricultural (without horticulture and livestock) output [<a href="#B55-water-12-00593" class="html-bibr">55</a>]; (<b>e</b>) Agricultural Value Added in 2016 constant Euro (bars, left scale) and as percentage of Gross Domestic Product (GDP) (line; right scale). Agriculture includes fisheries. Data sources: 1900–1940 [<a href="#B55-water-12-00593" class="html-bibr">55</a>]; 1968–2016 World Bank and OECD [<a href="#B56-water-12-00593" class="html-bibr">56</a>]; conversion historic value in Guilder to 2016 Euro using [<a href="#B57-water-12-00593" class="html-bibr">57</a>].</p> Full article ">Figure 6
<p>Trends of robustness over time, as perceived by participants, with <span class="html-italic">y</span>-axis as an index scale, 2017 = 100. Blue = robustness is a function of water supply, orange = robustness is a function of water supply a demand, green = robustness is a function of water supply, demand and economic and social dependency. Some lines do not cover the entire timeframe.</p> Full article ">Figure 7
<p>Sketched trends of flexibility over time, with y axis as an index scale, 2017 = 100. Blue = flexibility is the ability to control or steer the system, orange = flexibility is the ability to deal with extreme situations, green = flexibility is keeping options for the future open. Some lines do not cover the entire timeframe.</p> Full article ">Figure 8
<p>Responses to selected numbered Q statements.</p> Full article ">Figure 9
<p>Representative mapping and narrative of robustness as a function of supply and demand, with interventions increasing robustness and increasing demand and climate change as slow variables. Small scale interventions can be minor canals or pumping stations. Source: author.</p> Full article ">
16 pages, 3807 KiB  
Article
Nutrient Recovery from Anaerobically Treated Blackwater and Improving Its Effluent Quality through Microalgae Biomass Production
by Melesse Eshetu Moges, Arve Heistad and Thorsten Heidorn
Water 2020, 12(2), 592; https://doi.org/10.3390/w12020592 - 21 Feb 2020
Cited by 20 | Viewed by 5111
Abstract
The blackwater stream of domestic wastewater contains energy and the majority of nutrients that can contribute to a circular economy. Hygienically safe and odor-free nutrient solution produced from anaerobically treated source-separated blackwater through an integrated post-treatment unit can be used as a source [...] Read more.
The blackwater stream of domestic wastewater contains energy and the majority of nutrients that can contribute to a circular economy. Hygienically safe and odor-free nutrient solution produced from anaerobically treated source-separated blackwater through an integrated post-treatment unit can be used as a source of liquid fertilizer. However, the high water content in the liquid fertilizer represents a storage or transportation challenge when utilized on agricultural areas, which are often situated far from the urban areas. Integration of microalgae into treated source-separated blackwater (BW) has been shown to effectively assimilate and recover phosphorus (P) and nitrogen (N) in the form of green biomass to be used as slow release biofertilizer and hence close the nutrient loop. With this objective, a lab-scale flat panel photobioreactor was used to cultivate Chlorella sorokiniana strain NIVA CHL 176 in a chemostat mode of operation. The growth of C. sorokiniana on treated source-separated blackwater as a substrate was monitored by measuring dry biomass concentration at a dilution rate of 1.38 d−1, temperature of 37 °C and pH of 7. The results indicate that the N and P recovery rates of C. sorokiniana were 99 mg N L−1d−1 and 8 mg P L−1d−1 for 10% treated BW and reached 213 mg N L−1d−1 and 35 mg P L−1d−1, respectively when using 20% treated BW as a substrate. The corresponding biomass yield on light, N and P on the 20% treated BW substrate were 0.37 g (mol photon)−1, 9.1 g g−1 and 54.1 g g−1, respectively, and up to 99% of N and P were removed from the blackwater. Full article
(This article belongs to the Special Issue Hybrid Systems Using Different Technologies for Wastewater Treatment and Reuse)
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Graphical abstract

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<p>Schematic experimental set-up of the chemostat.</p> Full article ">Figure 2
<p>Dry biomass concentration of <span class="html-italic">Chlorella sorokiniana</span> (X) in g L<sup>−1</sup> as grown in the defined medium and the concentrations of (<b>a</b>) NO<sub>3</sub>−N and (<b>b</b>) PO<sub>4</sub>−P in the effluent in mg L<sup>−1</sup>.</p> Full article ">Figure 3
<p>N and P removal efficiency of <span class="html-italic">C. sorokiniana</span> as grown in the defined medium.</p> Full article ">Figure 4
<p>Dry biomass concentration X in g L<sup>−1</sup> without Mg and without extra P, with Mg and extra P and with Mg but not extra P (□), N concentration in the effluent (▲), and P concentration in the effluent (-o-) using 10% treated source-separated blackwater as a substrate.</p> Full article ">Figure 5
<p>Change in NH<sub>4</sub>-N, NO<sub>2</sub>-N, and NO<sub>3</sub>-N concentrations in the substrate during the 6th day feeding period.</p> Full article ">Figure 6
<p>NH<sub>4</sub>-N, NO<sub>2</sub>-N, and NO<sub>3</sub>-N concentrations in the effluent culture during the 6th day feeding period.</p> Full article ">Figure 7
<p>The response of <span class="html-italic">Chlorella sorokiniana</span> to anaerobic substrate condition. Photobioreactor (PBR) I substrate was kept anaerobic during the entire 10 days of the experiment, while in PBR II the substrate was kept aerobic in the first 5 days and anaerobic thereafter.</p> Full article ">Figure 8
<p>Nitrite concentrations in the anaerobic substrate PBR I (o), and aerobic and anaerobic substrate in PBR II (●).</p> Full article ">Figure 9
<p>Average influent and effluent concentrations of NH<sub>4</sub>-N, NO<sub>2</sub>-N and NO<sub>3</sub>-N.</p> Full article ">Figure 10
<p>N and P removal efficiency of <span class="html-italic">C. sorokiniana.</span></p> Full article ">
12 pages, 3552 KiB  
Article
An Experimental Method for Generating Shear-Free Turbulence Using Horizontal Oscillating Grids
by Wenjie Li, Peng Zhang, Shengfa Yang, Xuhui Fu and Yi Xiao
Water 2020, 12(2), 591; https://doi.org/10.3390/w12020591 - 21 Feb 2020
Cited by 2 | Viewed by 2813
Abstract
An experimental apparatus driven by horizontal oscillating grids in a water tank is proposed for generating shear-free turbulence, which is measured using Particle Image Velocimetry (PIV). The performances of the proposed apparatus are investigated through the instantaneous and root-mean-square (RMS) velocity, Reynolds stress, [...] Read more.
An experimental apparatus driven by horizontal oscillating grids in a water tank is proposed for generating shear-free turbulence, which is measured using Particle Image Velocimetry (PIV). The performances of the proposed apparatus are investigated through the instantaneous and root-mean-square (RMS) velocity, Reynolds stress, length and time scale, frequency spectra and dissipation rate. Results indicate that the turbulence at the core region of the water tank, probably 8 cm in length, is identified to be shear-free. The main advantage of the turbulence driven by horizontal oscillating mode is that the ratios of the longitudinal turbulent intensities to the vertical values are between 1.5 and 2.0, consistent with those ratios in open-channel flows. Additionally, the range of the length scale can span the typical sizes of suspended particles in natural environments, and the dissipation rate also agrees with those found in natural environments. For convenience of experimental use, a formula is suggested to calculate the RMS flow velocity, which is linearly proportional to the product of oscillating stroke and frequency. The proposed experimental method in this study appears to be more appropriate than the traditional vertical oscillating mode for studying the fundamental mechanisms of vertical migratory behavior of suspended particles and contaminants in turbulent flows. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>Sketch map of the experimental system.</p> Full article ">Figure 2
<p>Plan view of the imaged locations.</p> Full article ">Figure 3
<p>Measured flow velocity of one point: (<b>a</b>) instantaneous velocity; (<b>b</b>) probability density.</p> Full article ">Figure 4
<p>Variation of the RMS velocity with the sampling size when the sampling frequency is 1.5 Hz: (<b>a</b>) RMS velocity in the X direction; (<b>b</b>) RMS velocity in the Z direction.</p> Full article ">Figure 5
<p>Variation of the RMS velocity with the sampling size when the sampling frequency is 1000 Hz: (<b>a</b>) RMS velocity in the X direction; (<b>b</b>) RMS velocity in the Z direction.</p> Full article ">Figure 6
<p>Distribution of the measured flow velocity when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz: (<b>a</b>) instantaneous velocity field of zone B2; (<b>b</b>) mean velocity along the X direction.</p> Full article ">Figure 7
<p>The RMS velocity when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz: (<b>a</b>) <span class="html-italic">u</span> component; (<b>b</b>) <span class="html-italic">w</span> component.</p> Full article ">Figure 8
<p>Variation of the ratio of <span class="html-italic">u</span>/<span class="html-italic">w</span> along the X direction when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz.</p> Full article ">Figure 9
<p>The spatial distribution of Reynolds stress and the variations when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz.</p> Full article ">Figure 10
<p>Spatial distribution of the integral scale at different X position when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz: (<b>a</b>) integral length scale; (<b>b</b>) integral time scale.</p> Full article ">Figure 11
<p>Frequency spectra at different elevations when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz.</p> Full article ">Figure 12
<p>Dissipation rate contours at different zones when <span class="html-italic">s</span> = 0.5 cm and <span class="html-italic">f</span> = 3 Hz.</p> Full article ">Figure 13
<p>Turbulent intensity level (nondimensionalized using their reference values at X = 0, <span class="html-italic">fs</span> = 0.5 cm/s) under different strokes and frequencies: (<b>a</b>) <span class="html-italic">u</span> component; (<b>b</b>) <span class="html-italic">w</span> component; (<b>c</b>) <span class="html-italic">q</span>; (<b>d</b>) <span class="html-italic">u</span>/<span class="html-italic">w</span>.</p> Full article ">
15 pages, 2800 KiB  
Article
Visualization Framework for High-Dimensional Spatio-Temporal Hydrological Gridded Datasets using Machine-Learning Techniques
by Abeer Mazher
Water 2020, 12(2), 590; https://doi.org/10.3390/w12020590 - 21 Feb 2020
Cited by 21 | Viewed by 5137
Abstract
Numerical modelling increasingly generates massive, high-dimensional spatio-temporal datasets. Exploring such datasets relies on effective visualization. This study presents a generic workflow to (i) project high-dimensional spatio-temporal data on a two-dimensional (2D) plane accurately (ii) compare dimensionality reduction techniques (DRTs) in terms of resolution [...] Read more.
Numerical modelling increasingly generates massive, high-dimensional spatio-temporal datasets. Exploring such datasets relies on effective visualization. This study presents a generic workflow to (i) project high-dimensional spatio-temporal data on a two-dimensional (2D) plane accurately (ii) compare dimensionality reduction techniques (DRTs) in terms of resolution and computational efficiency (iii) represent 2D projection spatially using a 2D perceptually uniform background color map. Machine learning (ML) based DRTs for data visualization i.e., principal component analysis (PCA), generative topographic mapping (GTM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are compared in terms of accuracy, resolution and computational efficiency to handle massive datasets. The accuracy of visualization is evaluated using a quality metric based on a co-ranking framework. The workflow is applied to an output of an Australian Water Resource Assessment (AWRA) model for Tasmania, Australia. The dataset consists of daily time series of nine components of the water balance at a 5 km grid cell resolution for the year 2017. The case study shows that PCA allows rapid visualization of global data structures, while t-SNE and UMAP allows more accurate representation of local trends. Furthermore, UMAP is computationally more efficient than t-SNE and least affected by the outliers compared to GTM. Full article
(This article belongs to the Section Hydrology)
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<p>(<b>a</b>) Water balance map of Tasmania region for Australian Water Resource Assessment (AWRA) model output from January–September 2017, (<b>b</b>) each pixel shows daily time series of nine hydrological AWRA components from January–September 2017 and (<b>c</b>) hydrological components of AWRA model output.</p> Full article ">Figure 2
<p>(<b>a</b>) Three-dimensional arrays of hydrological variables at the spatial grid scale of (−43.71 &lt; latitude &lt; −40.14) and (144.49 &lt; longitude &lt; 149.41) of size (72 × 79), (<b>b</b>) a spatial grid e.g., (X<sub>1</sub>, Y<sub>1</sub>) consists of (9 × 273) observations and (<b>c</b>) three-dimensional arrays are normalized (Nor) and append to each other to form a single three-dimensional array of size (72 × 79) by (9 × 273).</p> Full article ">Figure 3
<p>Visualization workflow for high-dimensional spatio-temporal gridded datasets.</p> Full article ">Figure 4
<p>For principal component analysis (PCA), generative topographic mapping (GTM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), (<b>a</b>) color plot shows a projection of high-dimensional data on 2D plane by preserving topology and maximize distances between projected and high-dimensional data for AWRA model output for Tasmania, 2017, (<b>b</b>) spatial map of Tasmania provides a quick spatial visualization of high-dimensional data features and (<b>c</b>) time series colored according to their position in 2D space. The subplots show 20 randomly selected time series from within 9 equally sized regions of the 2D space.</p> Full article ">Figure 5
<p>Q<sub>NX</sub> visualization quantification curves PCA, GTM, t-SNE and UMAP. PCA provides better global patterns (K &gt; 120), whereas, GTM performance is poor in comparison. Further, t-SNE captures the local structures better (K &lt; 120) and UMAP captures better global structure between (250 &lt; K &lt; 550).</p> Full article ">
13 pages, 2696 KiB  
Article
Effect of Heavy Metal Ions on Steroid Estrogen Removal and Transport in SAT Using DLLME as a Detection Method of Steroid Estrogen
by Ge Zhang, Yuesuo Yang, Ying Lu, Yu Chen, Wenbo Li and Siyuan Wang
Water 2020, 12(2), 589; https://doi.org/10.3390/w12020589 - 21 Feb 2020
Cited by 7 | Viewed by 3516
Abstract
Environmental endocrine-disrupting chemicals have become a global environmental problem, and the distribution, transport, and fate of estrogens in soil and water environments closely relate to human and ecological health as well as to the remediation scheme design. A new micro-extraction technique termed dispersive [...] Read more.
Environmental endocrine-disrupting chemicals have become a global environmental problem, and the distribution, transport, and fate of estrogens in soil and water environments closely relate to human and ecological health as well as to the remediation scheme design. A new micro-extraction technique termed dispersive liquid–liquid micro-extraction (DLLME) combined with high-performance liquid chromatography with fluorescence detector (HPLC-FLD) was developed for the determination of the concentration of steroid estrogens in water samples. The detection limits of HPLC-FLD and DLLME-HPLC/FLD were 0.68–1.73 μg L−1 and 7.16–69.22 ng L−1, respectively. Based on this method, the isothermal adsorption of 17β-E2 on sand and a breakthrough experiment of 17β-E2 and Cu2+ in a soil aquifer treatment (SAT) system were studied. The 17β-E2 adsorption capacity of sand in 17β-E2 solution was detected to be larger than that in a mixed solution of 17β-E2 and Cu(NO3)2 solution, and the breakthrough curves of 17β-E2 and Cu2+ in the mixed solution shifted forward in sand column experiments. Both suggested that the competitive adsorption of 17β-E2 and Cu2+ in the mixed solution might occur on the surface of the sand. In the process of the removal of 17β-E2 in wastewater by SAT, the existence of Cu2+ slightly inhibited the adsorption of 17β-E2 and accelerated the breakthrough of 17β-E2. These results ought to be a warning for SAT application for 17β-E2 removal in water where heavy metals coexist. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Dispersive liquid–liquid micro-extravtion (DLLME) operation schematic diagram.</p> Full article ">Figure 2
<p>Soil aquifer treatment (SAT) soil column setup.</p> Full article ">Figure 3
<p>Effect of 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM]PF6) volume on (<b>a</b>) sedimentation phase volume; and effect of (<b>b</b>) [BMIM]PF6 volume, (<b>c</b>) methanol volume, (<b>d</b>) and ultrasonication time on enrichment factor (EF) and effect of centrifugal rotational speed on recovery.</p> Full article ">Figure 4
<p>Standard curve of (<b>a</b>) 17β-E2 and (<b>b</b>) E3 in HPLC.</p> Full article ">Figure 5
<p>Isotherm plots for 17β-E2 adsorption by sand in different solutions (<b>a</b>) Freundlich fit and (<b>b</b>) Langmuir fit.</p> Full article ">Figure 6
<p>Breakthrough curves of (<b>a</b>) a tracer, (<b>b</b>) 17β-E2 and Cu<sup>2+</sup> in mixed solution, (<b>c</b>) Cu<sup>2+</sup> in Cu(NO<sub>3</sub>)<sub>2</sub> and mixed solution, and (<b>d</b>) 17β-E2 in 17β-E2 and mixed solution.</p> Full article ">
12 pages, 2698 KiB  
Article
Wastewater Treatment by Novel Polyamide/Polyethylenimine Nanofibers with Immobilized Laccase
by Milena Maryšková, Markéta Schaabová, Hana Tománková, Vít Novotný and Miroslava Rysová
Water 2020, 12(2), 588; https://doi.org/10.3390/w12020588 - 21 Feb 2020
Cited by 19 | Viewed by 4068
Abstract
Endocrine-disrupting chemicals are highly resistant organic compounds, commonly occurring in the aquatic environment, that can interfere with the endocrine system of animals and humans, causing serious chronic diseases. In recent decades, enzymes from oxidoreductases have been studied for their potential to degrade these [...] Read more.
Endocrine-disrupting chemicals are highly resistant organic compounds, commonly occurring in the aquatic environment, that can interfere with the endocrine system of animals and humans, causing serious chronic diseases. In recent decades, enzymes from oxidoreductases have been studied for their potential to degrade these compounds effectively. In order to use such enzymes repeatedly, it is necessary to ensure their insolubility in water, a method termed enzyme immobilization. We developed novel polyamide/polyethylenimine (PA/PEI) nanofibers as a promising support material for the immobilization of various biomolecules. Our nanofibers are highly suitable due to a unique combination of mechanical endurance provided by polyamide 6 and their affinity toward biomolecules, ensured by numerous PEI amino groups. Enzyme laccase was successfully immobilized onto PA/PEI nanofibers using a simple and fast method, providing exceptional activity and stability of the attached enzyme. We then tested the degradation ability of the PA/PEI-laccase samples on a highly concentrated mixture of endocrine-disrupting chemicals in real wastewater with adjusted pH. The results indicate that the samples were a suitable material for wastewater treatment by degrading a highly concentrated mixture of bisphenol A, 17α-ethinylestradiol, triclosan, and diclofenac, in real wastewater effluent. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Comparison of pristine polyamide 6/polyethylenimine (PA/PEI) nanofibers (<b>a</b>) and PA/PEI with immobilized <span class="html-italic">T. versicolor</span> laccase (<b>b</b>).</p> Full article ">Figure 2
<p>Quantification of available amino groups of polyamide 6 (PA6) and polyamide 6/polyethylenimine (PA/PEI) nanofibers (<b>a</b>). Color difference according to the amount of methyl orange (MO) attached to the nanofibers (<b>b</b>).</p> Full article ">Figure 3
<p>Parameters influencing laccase immobilization—concentration McIlvaine’s buffer solution (<b>a</b>) (30-min oxidation with 1mM NaIO<sub>4</sub>, 1-h immobilization using 1 mg/mL stock solution at pH 4), concentration of laccase solution; (<b>b</b>) (30-min oxidation with 1mM NaIO<sub>4</sub>, 1-h immobilization using 1 mg/mL stock solution at 20% pH 4 buffer), pH; (<b>c</b>) (30-min oxidation with 1mM NaIO<sub>4</sub>, 1-h immobilization using 2 mg/mL stock solution at 20% pH 4 buffer), concentration of sodium periodate; (<b>d</b>) (30-min oxidation, 1-h immobilization using 2 mg/mL stock solution at 20% pH 7 buffer), oxidation; (<b>e</b>) (oxidation with 1mM NaIO<sub>4</sub>, 1-h immobilization using 2 mg/mL stock solution at 20% pH 7 buffer), and immobilization time; and (<b>f</b>) (30-min oxidation with 1mM NaIO<sub>4</sub>, immobilization using 2 mg/mL stock solution at 20% pH 7 buffer).</p> Full article ">Figure 4
<p>Statistical analysis of studied immobilization parameters using the multiple comparison test: optimal buffer concentration 10% and 20% (<b>a</b>); optimal pH 6 and 7 (<b>b</b>); optimal concentration of NaIO<sub>4</sub> 1mM and 5mM (<b>c</b>); optimal oxidation time 30, 45, and 60 min (<b>d</b>); and optimal immobilization time 30 min and 2 h (<b>e</b>).</p> Full article ">Figure 5
<p>Storage stability of free and immobilized laccase. PA/PEI nanofibers loaded with immobilized laccase were incubated deionized water at 4 °C. Free laccase solution was stored under the same conditions for comparison with the immobilized laccase.</p> Full article ">Figure 6
<p>Degradation efficiency of immobilized laccase (PA/PEI-laccase) towards a mixture of 10 mg/mL of bisphenol A (BPA), 17α-ethinylestradiol (EE2), triclosan (TCS), and diclofenac (DCF), in deionized water (DIW), wastewater effluent (WASTE), and wastewater infused with 2.5% (<span class="html-italic">v</span>/<span class="html-italic">v</span>) of McIlvaine’s buffer of pH 7 (WASTE + BUFFER).</p> Full article ">
19 pages, 3701 KiB  
Article
Adsorption of Methylene Blue in Water onto Activated Carbon by Surfactant Modification
by Yu Kuang, Xiaoping Zhang and Shaoqi Zhou
Water 2020, 12(2), 587; https://doi.org/10.3390/w12020587 - 21 Feb 2020
Cited by 406 | Viewed by 22620
Abstract
In this paper, the enhanced adsorption of methylene blue (MB) dye ion on the activated carbon (AC) modified by three surfactants in aqueous solution was researched. Anionic surfactants—sodium lauryl sulfate (SLS) and sodium dodecyl sulfonate (SDS)—and cationic surfactant—hexadecyl trimethyl ammonium bromide (CTAB)—were used [...] Read more.
In this paper, the enhanced adsorption of methylene blue (MB) dye ion on the activated carbon (AC) modified by three surfactants in aqueous solution was researched. Anionic surfactants—sodium lauryl sulfate (SLS) and sodium dodecyl sulfonate (SDS)—and cationic surfactant—hexadecyl trimethyl ammonium bromide (CTAB)—were used for the modification of AC. This work showed that the adsorption performance of cationic dye by activated carbon modified by anionic surfactants (SLS) was significantly improved, whereas the adsorption performance of cationic dye by activated carbon modified by cationic surfactant (CTAB) was reduced. In addition, the effects of initial MB concentration, AC dosage, pH, reaction time, temperature, real water samples, and additive salts on the adsorption were studied. When Na+, K+, Ca2+, NH4+, and Mg2+ were present in the MB dye solution, the effect of these cations was negligible on the adsorption (<5%). The presence of NO2- improved the adsorption performance significantly, whereas the removal rate of MB was reduced in the presence of competitive cation (Fe2+). It was found that the isotherm data had a good correlation with the Langmuir isotherm through analyzing the experimental data by various models. The dynamics of adsorption were better described by the pseudo-second-order model and the adsorption process was endothermic and spontaneous. The results showed that AC modified by anionic surfactant was effective for the adsorption of MB dye in both modeling water and real water. Full article
(This article belongs to the Special Issue Adsorbents for Water and Wastewater Treatment and Resource Recovery)
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<p>The chemical structure of methylene blue (MB).</p> Full article ">Figure 2
<p>MB removal by using different activated carbons (ACs) (initial MB concentration = 50 mg·L<sup>−1</sup>, S/L = 0.15 g·L<sup>−1</sup>, T = 298 K, pH = 5.0).</p> Full article ">Figure 3
<p>The chemical adsorption between MB dye and the surface functional groups on sodium lauryl sulfate (SLS)-C.</p> Full article ">Figure 4
<p>FE-SEM pictures of (<b>a</b>) Virgin-C and (<b>b</b>) SLS-C.</p> Full article ">Figure 5
<p>The adsorption rate (<b>a</b>) and the adsorption capacity (q<sub>e</sub>) (<b>b</b>) of MB adsorption on the SLS-C sample at various pH values (S/L = 0.15 g·L<sup>−</sup><sup>1</sup>, contact time = 120 min, 25 °C).</p> Full article ">Figure 6
<p>The zeta potential of Virgin-C and SLS-C.</p> Full article ">Figure 7
<p>Effect of adsorbent dose on MB adsorption on SLS-C sample (contact time = 120 min).</p> Full article ">Figure 8
<p>Effect of contact time on adsorption of MB on SLS-C at pH = 5.0, S/L = 0.15 g·L<sup>−1</sup>. (<b>a</b>) The adsorption rate; (<b>b</b>) the adsorption capacity.</p> Full article ">Figure 9
<p>(<b>a</b>) Pseudo-second order plots and (<b>b</b>) the adsorption capacity (<math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mi>e</mi> </msub> </mrow> </semantics></math>) for the adsorption of MB on SLS-C at various initial concentrations, pH = 5.0, S/L = 0.15 g·L<sup>−1</sup>, T = 25 °C.</p> Full article ">Figure 10
<p>(<b>a</b>) Langmuir, (<b>b</b>) Freundlich, and (<b>c</b>) Temkin isotherm plots for adsorption of MB on SLS-C.</p> Full article ">Figure 11
<p>Influence of temperature on MB removal efficiency by SLS-C (adsorbent dosage S/L = 0.15 g·L<sup>−1</sup>, pH = 5.0, contact time = 120 min).</p> Full article ">Figure 12
<p>Thermodynamics parameters on the adsorption of MB by SLS-C.</p> Full article ">
15 pages, 8454 KiB  
Article
The Susceptibility of Juvenile American Shad to Rapid Decompression and Fluid Shear Exposure Associated with Simulated Hydroturbine Passage
by Brett D. Pflugrath, Ryan A. Harnish, Briana Rhode, Kristin Engbrecht, Bernardo Beirão, Robert P. Mueller, Erin L. McCann, John R. Stephenson and Alison H. Colotelo
Water 2020, 12(2), 586; https://doi.org/10.3390/w12020586 - 20 Feb 2020
Cited by 7 | Viewed by 3660
Abstract
Throughout many areas of their native range, American shad (Alosa sapidissima) and other Alosine populations are in decline. Though several conditions have influenced these declines, hydropower facilities have had significant negative effects on American shad populations. Hydropower facilities expose ocean-migrating American [...] Read more.
Throughout many areas of their native range, American shad (Alosa sapidissima) and other Alosine populations are in decline. Though several conditions have influenced these declines, hydropower facilities have had significant negative effects on American shad populations. Hydropower facilities expose ocean-migrating American shad to physical stressors during passage through hydropower facilities, including strike, rapid decompression, and fluid shear. In this laboratory-based study, juvenile American shad were exposed separately to rapid decompression and fluid shear to determine their susceptibility to these stressors and develop dose–response models. These dose–response relationships can help guide the development and/or operation of hydropower turbines and facilities to reduce the negative effects to American shad. Relative to other species, juvenile American shad have a high susceptibility to both rapid decompression and fluid shear. Reducing or preventing exposure to these stressors at hydropower facilities may be a potential method to assist in the effort to restore American shad populations. Full article
(This article belongs to the Special Issue Addressing the Environmental Impacts of Hydropower)
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<p>Diagram (<b>left</b>) and image captured from high speed video (<b>right</b>) display how fish were exposed to fluid shear by passing down the induction tube and into the water jet.</p> Full article ">Figure 2
<p>The probability of minor injury (grey), major injury (black), and mortality (red), as a function of strain rate (s<sup>−1</sup>) or acceleration (m s<sup>−2</sup>) for American shad exposed to fluid shear. Curves are a graphical representation of Equation (1) using the coefficients from <a href="#water-12-00586-t004" class="html-table">Table 4</a> and dotted lines of the corresponding color represent the upper and lower 95% confidence intervals.</p> Full article ">Figure 3
<p>A graphical representation of Equation (1) using the coefficients from <a href="#water-12-00586-t006" class="html-table">Table 6</a> to estimate the probability of American shad injury (grey), mortal injury (black), and immediate mortality (red), as a function of rapid decompression expressed as LRP. Dotted lines of the corresponding color represent the upper and lower 95% confidence intervals.</p> Full article ">Figure 4
<p>Comparison between the susceptibility of juvenile American shad (black) and juvenile Chinook salmon (green) to fluid shear (top) and rapid decompression (bottom). Curves for juvenile Chinook salmon exposed to fluid shear and rapid decompression were extracted from Deng et al. [<a href="#B37-water-12-00586" class="html-bibr">37</a>] and Brown et al. [<a href="#B31-water-12-00586" class="html-bibr">31</a>], respectively. Dotted lines of the corresponding color represent the upper and lower 95% confidence intervals.</p> Full article ">
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