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Water, Volume 10, Issue 6 (June 2018) – 148 articles

Cover Story (view full-size image): Agricultural intensification has the undesirable effect of degrading water quality. Water quality trading can enable cooperative solutions between urban residents and upstream rural residents through the installation of agricultural green infrastructure in the form of riparian buffers. Analysis of the Raccoon River Watershed in Iowa, USA reveals that agricultural green infrastructure is similar in cost to centralized gray infrastructure and offers more indirect, non-quantified benefits. View this paper.
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22 pages, 3782 KiB  
Review
Reuse and Recycling of Livestock and Municipal Wastewater in Chilean Agriculture: A Preliminary Assessment
by Cristina-Alejandra Villamar, Ismael Vera-Puerto, Diego Rivera and Felipe De la Hoz
Water 2018, 10(6), 817; https://doi.org/10.3390/w10060817 - 20 Jun 2018
Cited by 54 | Viewed by 10320
Abstract
Chile is an agricultural power, but also one of the most vulnerable countries to climate change and water shortage. About 50% of the irrigated agriculture land in Chile is in the central zone, thanks to its agricultural-climatic characteristics that provide an adequate water [...] Read more.
Chile is an agricultural power, but also one of the most vulnerable countries to climate change and water shortage. About 50% of the irrigated agriculture land in Chile is in the central zone, thanks to its agricultural-climatic characteristics that provide an adequate water supply (100–4000 m3/s). However, the vulnerability scenario in this zone is high due to the seasonal availability of water resources. Therefore, opportunities to use non-conventional alternative sources (e.g., wastewater) become an appealing and feasible option due to the high population and animal density (>76%) in this part of the country. Moreover, the physicochemical characteristics of the municipal and livestock wastewater suggest that there are potential opportunities to recycle nutrients for agricultural production. In Chile, wastewater reuse opportunities are noted by the wide coverage of wastewater treatment programs, with municipal and intensified livestock production taking up most of the percentage (>99%). Nevertheless, more than 70% of wastewater treatment systems reach biological secondary treatment, which suggests reuse possibilities only for non-food crops. Therefore, this paper is focused on a preliminary analysis of the potential of reusing and recycling municipal and livestock wastewater for Chilean agriculture. There is some reuse work occurring in Chile, specifically in the use of municipal and livestock wastewater for cereal crops (animal feed), forests, and grasslands. However, aspects related to the long-term effects of these practices have not yet been evaluated. Therefore, municipal and livestock wastewater in Chile could be re-valued in agriculture, but the current quality and condition of treated wastewater do not ensure its safe use in food crops. In addition, state policies are needed to provide sustainability (circular and ethic economy) to water reusing/recycling in agriculture. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
►▼ Show Figures

Figure 1

Figure 1
<p>Maps geopolitical, climatic and water supply of Continental Chile.</p> Full article ">Figure 2
<p>Chilean vulnerability maps of water resources availability and accessibility. Information based on Vulnerability Atlas from MA [<a href="#B21-water-10-00817" class="html-bibr">21</a>].</p> Full article ">Figure 3
<p>Production type and per capita wastewater generated within Chilean livestock intensified farms. The information is based on the Environmental Impact Statements of farms with more than 300 heads SEA [<a href="#B29-water-10-00817" class="html-bibr">29</a>]. Swine: (<span class="html-fig-inline" id="water-10-00817-i001"> <img alt="Water 10 00817 i001" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i001.png"/></span>) Fattening, (<span class="html-fig-inline" id="water-10-00817-i002"> <img alt="Water 10 00817 i002" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i002.png"/></span>) Breeding-Fattening, (<span class="html-fig-inline" id="water-10-00817-i003"> <img alt="Water 10 00817 i003" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i003.png"/></span>) Full-Cycle, (<span class="html-fig-inline" id="water-10-00817-i004"> <img alt="Water 10 00817 i004" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i004.png"/></span>) Maternity. Cattle: (<span class="html-fig-inline" id="water-10-00817-i005"> <img alt="Water 10 00817 i005" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i005.png"/></span>) Dairy, (<span class="html-fig-inline" id="water-10-00817-i006"> <img alt="Water 10 00817 i006" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i006.png"/></span>) Beef.</p> Full article ">Figure 4
<p>Livestock treatment technology applied on Chilean intensified farms. Swine: (<b>a</b>) secondary and (<b>b</b>) tertiary, Cattle: (<b>c</b>) secondary treatments. The information is based on Environmental Impact Statements of farms with more than 300 heads SEA [<a href="#B29-water-10-00817" class="html-bibr">29</a>].</p> Full article ">Figure 5
<p>Municipal wastewater treatment technology applied in Chile. (<b>a</b>) North area, 48 WWTP; (<b>b</b>) Central area, 230 WWTP; (<b>c</b>) South area, 11 WWTP.</p> Full article ">Figure 6
<p>Availability/quality maps of non-reclaimed and reclaimed wastewater from municipalities and livestock farms with respect to irrigated areas. (<span class="html-fig-inline" id="water-10-00817-i007"> <img alt="Water 10 00817 i007" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i007.png"/></span>) NR: Not suitable for reuse, (<span class="html-fig-inline" id="water-10-00817-i008"> <img alt="Water 10 00817 i008" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i008.png"/></span>) A Class: Surface irrigation of orchards and vineyards, irrigation of non-food crops and landscape Restrictions (<span class="html-fig-inline" id="water-10-00817-i009"> <img alt="Water 10 00817 i009" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i009.png"/></span>) B Class: Irrigation of gardens and golf courses and (<span class="html-fig-inline" id="water-10-00817-i010"> <img alt="Water 10 00817 i010" src="/water/water-10-00817/article_deploy/html/images/water-10-00817-i010.png"/></span>) Dry: Dry treatment. Restrictions used are described by Bastian and Murray [<a href="#B19-water-10-00817" class="html-bibr">19</a>].</p> Full article ">
24 pages, 5140 KiB  
Article
Phosphorus Fluxes from Three Coastal Watersheds under Varied Agriculture Intensities to the Northern Gulf of Mexico
by Songjie He and Y. Jun Xu
Water 2018, 10(6), 816; https://doi.org/10.3390/w10060816 - 20 Jun 2018
Cited by 3 | Viewed by 4865
Abstract
This study aims to evaluate recent total phosphorus (TP) and dissolved inorganic phosphorus (DIP) transport from three coastal rivers—the Calcasieu, Mermentau, and Vermilion Rivers—that drain watersheds with varied agriculture intensities (21%, 67%, and 61%, respectively) into the northern Gulf of Mexico, one of [...] Read more.
This study aims to evaluate recent total phosphorus (TP) and dissolved inorganic phosphorus (DIP) transport from three coastal rivers—the Calcasieu, Mermentau, and Vermilion Rivers—that drain watersheds with varied agriculture intensities (21%, 67%, and 61%, respectively) into the northern Gulf of Mexico, one of the world’s largest summer hypoxic zones. The study also examined the spatial trends of TP and DIP from freshwater to saltwater along an 88-km estuarine reach with salinity increasing from 0.02 to 29.50. The results showed that from 1990–2009 to 2010–2017, the TP fluxes for one of the agriculture-intensive rivers increased while no significant change was found for the other two rivers. Change in river discharge was the main reason for this TP flux trend. The two more agriculture-intensive river basins showed consistently higher TP and DIP concentrations and fluxes, as well as higher DIP:TP ratios than the river draining less agriculture-intensive land, confirming the strong effect of land uses on phosphorus input and speciation. Longitudinal profiles of DIP along the salinity gradient of the estuarine reach displayed characteristic input behavior. Desorption of DIP from suspended solids and river bed sediments, urban inputs, as well as stronger calcium carbonate and phosphorus co-precipitation at the marine endmember could be the reasons for such mixing dynamics. Full article
(This article belongs to the Special Issue Recent Progress in River Biogeochemistry Research)
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Figure 1

Figure 1
<p>Geographic location of the Calcasieu, Mermentau, and Vermilion Rivers entering the Northern Gulf of Mexico, and the locations of eight sampling sites and United States Geological Survey (USGS) discharge gauging sites. The Vermilion and Mermentau River Basins are agriculture-intensive (i.e., 67% and 61% agricultural land use), while the Calcasieu River Basin is much less agriculture-intensive (i.e., 26%).</p> Full article ">Figure 2
<p>Daily (<b>a</b>–<b>c</b>) and monthly (<b>d</b>–<b>f</b>) discharge and mass fluxes of TP and DIP in the Calcasieu (Site 1), Mermentau, and Vermilion Rivers from April 2014 to February 2016; discharge data at sites 1, 7, and 8 were used to represent the three rivers; Discharge at site 1 was calculated using discharge data at Kinder (USGS 08015500). Discharges at sites 7 and 8 were downloaded from USGS gage stations (USGS 08012150 and USGS 07386980); dashed lines in (<b>a</b>) mark the sampling dates; phosphorus concentrations and discharges at sites 1, 7, and 8 were used to calculate mass fluxes for the Calcasieu, Mermentau, and Vermilion Rivers, respectively; missing data for the Mermentau and Vermilion Rivers are due to availability of phosphorus concentration and discharge data.</p> Full article ">Figure 3
<p>Temporal trends of salinity (<b>a</b>), water temperature (<b>b</b>), dissolved oxygen (DO) concentration (<b>b</b>), pH (<b>c</b>), total suspended solids (TSS) (<b>d</b>), fluorescence (<b>e</b>), and turbidity (<b>f</b>) at six sampling sites along the Calcasieu River from April 2014 to February 2016.</p> Full article ">Figure 4
<p>Concentrations of TP and DIP in the Calcasieu, Mermentau, and Vermilion Rivers from April 2014 to February 2016; The Mermentau and Vermilion Rivers were only sampled from April 2014 to July 2015; Data for the Calcasieu, Mermentau, and Vermilion Rivers in (<b>a</b>) are data for sites 1, 7, and 8; (<b>b</b>) TP concentration in the Calcasieu River; (<b>c</b>) DIP concentration in the Calcasieu River.</p> Full article ">Figure 5
<p>Relationships between TP and salinity in the Calcasieu River entering the Northern Gulf of Mexico in the southern United States. Hollow circles represent actual measurements. Corresponding conservative mixing values are denoted by stars. Lines represent conservative-mixing models (see Equations (2) and (3)). Site 1 was used as the river endmember, and site 6 was used as the marine endmember.</p> Full article ">Figure 6
<p>Relationships between DIP and salinity in the Calcasieu River entering the Northern Gulf of Mexico in the southern United States. Hollow circles represent actual measurements. Corresponding conservative mixing values are denoted by stars. Lines represent conservative-mixing models (see Equations (2) and (3)). Site 1 was used as the river endmember, and site 6 was used as the marine endmember.</p> Full article ">Figure A1
<p>Relationship between measured TP and DIP fluxes and estimated TP and DIP fluxes using regression equations (Equation (1)) for the Calcasieu (Site 1), Mermentau, and Vermilion Rivers from April 2014 to February 2016. (<b>a</b>) Relationship between measured and estimated TP flux in the Calcasieu River; (<b>b</b>) Relationship between measured and estimated TP flux in the Mermentau River; (<b>c</b>) Relationship between measured and estimated TP flux in the Vermilion River; (<b>d</b>) Relationship between measured and estimated DIP flux in the Calcasieu River; (<b>e</b>) Relationship between measured and estimated DIP flux in the Mermentau River; (<b>f</b>) Relationship between measured and estimated DIP flux in the Vermilion River.</p> Full article ">Figure A2
<p>Relationship between measured TP fluxes and estimated TP fluxes using regression equations (Equation (1)) for the Calcasieu (Kinder, (<b>a</b>)), Mermentau (<b>b</b>), and Vermilion (<b>c</b>) Rivers from 2010 to 2017.</p> Full article ">Figure A3
<p>Statistical performance of the rating curves (<a href="#water-10-00816-t001" class="html-table">Table 1</a>) for estimating TP and DIP daily fluxes of the Calcasieu (Site 1), Mermentau, and Vermilion Rivers from April 2014 to February 2016 using data collected from this study.</p> Full article ">Figure A4
<p>Statistical performance of the rating curves (<a href="#water-10-00816-t001" class="html-table">Table 1</a>) for estimating TP daily fluxes of the Calcasieu (Kinder), Mermentau, and Vermilion Rivers from 2010 to 2017 using data collected from LDEQ.</p> Full article ">Figure A5
<p>The relationship between discharge at Kinder (United States Geological Survey (USGS) station number: 08015500) and precipitation at Oberlin (National Oceanic and Atmospheric Administration (NOAA) station number: USC00166938). Each dot in the figure represent a pair of mean discharge and precipitation during a certain year from 1990 to 2017.</p> Full article ">Figure A6
<p>The relationship between discharge and TP concentration for the Calcasieu (<b>a</b>), Mermentau (<b>b</b>), and Vermilion (<b>c</b>) Rivers from 2010 to 2017 using discharge data from USGS and TP concentration data from Louisiana Department of Environmental Quality (LDEQ).</p> Full article ">
11 pages, 522 KiB  
Technical Note
Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm
by Jing Zhang, Zhenhua Wang and Xiong Luo
Water 2018, 10(6), 815; https://doi.org/10.3390/w10060815 - 20 Jun 2018
Cited by 57 | Viewed by 5857
Abstract
This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions [...] Read more.
This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions for optimization problems. In this paper, parameter estimation of the van Genuchten model based on nine soil samples, covering eight soil textures, is conducted. The optimization problem that minimizes the difference between the measured and the estimated water content is formulated, and the SSA is applied to solve this problem. To validate the competitive advantage of the SSA, the experimental results are compared with Particle Swarm Optimization algorithm, the Differential Evolution algorithm and the RETC program, which indicates that SSA performs better than the three methods. Full article
(This article belongs to the Special Issue Data-Driven Methods for Agricultural Water Management)
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Figure 1

Figure 1
<p>The flowchart of the salp swarm algorithm (SSA).</p> Full article ">Figure 2
<p>The estimated SWRCs of eight soil samples using SSA. (<b>a</b>) Sample 3020, (<b>b</b>) Sample 1120, (<b>c</b>) Sample 1102, (<b>d</b>) Sample 1330, (<b>e</b>) Sample 1162, (<b>f</b>) Sample 2400, (<b>g</b>) Sample 1361, and (<b>h</b>) Sample 1173.</p> Full article ">
26 pages, 10512 KiB  
Article
The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin
by Guangwen Shao, Yiqing Guan, Danrong Zhang, Baikui Yu and Jie Zhu
Water 2018, 10(6), 814; https://doi.org/10.3390/w10060814 - 20 Jun 2018
Cited by 51 | Viewed by 6787
Abstract
The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970–2014. These changes are expected to impact hydrological processes in the basin. The Mann–Kendall (MK) test and [...] Read more.
The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970–2014. These changes are expected to impact hydrological processes in the basin. The Mann–Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008–2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series; however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970–1985, 1986–2000, and 2001–2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 °C, respectively, for the sub-period 1970–1985, 272 mm, 2.58 m/s, and 8.4 °C, respectively, for the sub-period 1986–2000, and 391 mm, 2.2 m/s, and 9.35 °C, respectively, for the sub-period 2001–2014. The simulated mean annual streamflow was 35.09 mm/year during the period 1970–1985. Considering the impact of the climate variability, the simulated mean annual streamflow values were 32.94 mm/year (1986–2000) and 36.78 mm/year (2001–2014). Compared to the period 1970–1985, the simulated mean annual streamflow reduced by 2.15 mm/year for the period 1986–2000 and increased by 1.69 mm/year for the period 2001–2014. The main variations of land use from 1970 to 2014 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986–2000 and 2001–2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986–2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001–2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau. Full article
(This article belongs to the Special Issue Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia)
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Figure 1

Figure 1
<p>The location of the Hailiutu River basin and its digital elevation model with hydrometeorological stations.</p> Full article ">Figure 2
<p>The (<b>a</b>) slope classes and (<b>b</b>) soil types of the Hailiutu River basin.</p> Full article ">Figure 3
<p>The land use patterns of the Hailiutu River basin for the years 1986, 1995, and 2010.</p> Full article ">Figure 4
<p>The flowchart for assessing the impacts of climate variability and land use change (refer to Yin et al. [<a href="#B21-water-10-00814" class="html-bibr">21</a>]). SWAT: Soil Water Assessment Tool.</p> Full article ">Figure 5
<p>Observed and simulated monthly streamflow of the Hailiutu River basin.</p> Full article ">Figure 6
<p>The (<b>a</b>) temporal variation of annual streamflow; (<b>b</b>) precipitation; (<b>c</b>) wind speed; (<b>d</b>) maximum temperature; (<b>e</b>) minimum temperature; and (<b>f</b>) average temperature of the Hailiutu River basin. The dashed lines are the step trends.</p> Full article ">Figure 7
<p>Change variation of (<b>a</b>) mean monthly streamflow; (<b>b</b>) precipitation; (<b>c</b>) wind speed; (<b>d</b>) maximum temperature; (<b>e</b>) minimum temperature; and (<b>f</b>) average temperature of the Hailiutu River basin.</p> Full article ">Figure 8
<p>The variation of land used in the three eras. The values in the brackets are the percentages for each type of land use.</p> Full article ">Figure 9
<p>The evaluation of precipitation from CMADS. (<b>a</b>) A scattered plot of observed precipitation and CMADS precipitation; (<b>b</b>) the duration curve of observed precipitation and CMADS precipitation.</p> Full article ">Figure 10
<p>The comparison of monthly precipitation obtained by different precipitation datasets.</p> Full article ">Figure 11
<p>The spatial distribution of areal precipitation obtained by (<b>a</b>) OBS; (<b>b</b>) CMADS; and (<b>c</b>) OBS+CMADS.</p> Full article ">Figure 12
<p>The box plots for the criteria of NSE (<b>top</b>), R<sup>2</sup> (<b>medium</b>) and PBIAS (<b>bottom</b>) during calibration period (<b>left</b>) and validation period (<b>right</b>). The square symbol and middle line in the box represent the mean value and median value, respectively. Each box ranges from the lower (25th) to upper quartile (75th). PBIAS: percent bias.</p> Full article ">Figure 13
<p>Simulation results of SWAT with (<b>a</b>) OBS; (<b>b</b>) CMADS; (<b>c</b>) OBS+CMADS in the Hailiutu River basin during the period 2008–2014.</p> Full article ">
15 pages, 1380 KiB  
Article
Extrapolation of Leaf Measurements to Obtain the Whole-Canopy Transpiration of C3 and C4 Xerophytic Shrubs
by Yanxia Jin, Xinping Wang, Yafeng Zhang, Yanxia Pan and Rui Hu
Water 2018, 10(6), 813; https://doi.org/10.3390/w10060813 - 20 Jun 2018
Cited by 4 | Viewed by 4207
Abstract
Quantifying the water balance within areas with sparse vegetation requires frequent measurement of transpiration in water-limited, arid, desert ecosystems. Field experiments were conducted in Shapotou, northwestern China, to examine the feasibility of up-scaling the transpiration of C3 and C4 xerophytic shrubs [...] Read more.
Quantifying the water balance within areas with sparse vegetation requires frequent measurement of transpiration in water-limited, arid, desert ecosystems. Field experiments were conducted in Shapotou, northwestern China, to examine the feasibility of up-scaling the transpiration of C3 and C4 xerophytic shrubs (Reaumuria soongorica and Salsola passerina, respectively) from the leaf to the canopy level throughout the growing season in 2015. The large weighing lysimeter method and LI-6400XT portable photosynthesis system were used to make relatively long-term measurements of transpiration. The results indicated that meteorological factors coupled with stomatal conductance affected the transpiration rate of the two shrubs at the leaf level, and that the vapor pressure deficit other than net radiation and the air temperature affected the transpiration rate of S. passerina at the canopy level. Precipitation and vegetation characteristics determined the transpiration amount of the C3 and C4 xerophytic shrubs. The leaf gas exchange measurements were arithmetically scaled up to the canopy level based on the leaf area. The validity of the extrapolation was evaluated by comparing the upscale values of transpiration with the calculated values obtained from lysimeter measurement. The up-scaling approach accurately (±0.005 mm h−1, RMSE = 35%) obtained canopy transpiration from the leaf measurements. Our study suggests that the up-scaling method based on leaf area can be adopted to determine the canopy transpiration of C3 and C4 xerophytic shrubs in arid desert environments. Full article
(This article belongs to the Section Hydrology)
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Figure 1

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<p>Precipitation and precipitation distribution in 2015.</p> Full article ">Figure 2
<p>Seasonal changes in soil matric potential (<b>A</b>) and soil temperature (<b>B</b>) at a soil depth of 0–40 cm for <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina</span>. Error bars are standard errors of the mean (<span class="html-italic">n</span> = 30 or 31). Different lowercase letters indicate significant differences among the months at <span class="html-italic">p</span> &lt; 0.05. pF = log(-hPa); pF &gt; 4.2, no plant-available water; pF = 4.2, permanent wilting point; pF = 1.8–2.5, field capacity.</p> Full article ">Figure 3
<p>Diurnal changes in the average transpiration rate for <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina</span> during the experimental period.</p> Full article ">Figure 4
<p>Seasonal changes of the hourly transpiration rate of <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina</span> by Lysimeter measurement (<b>A</b>) and LI-6400XT extrapolation (<b>B</b>) during the experimental period. Asterisks indicate significant differences between <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina</span> during the experimental period: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 5
<p>Seasonal changes in stomatal limitation (<b>A</b>) and instantaneous water use efficiency (<b>B</b>) of <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina.</span> Different lowercase letters indicate significant differences among the months (<span class="html-italic">p</span> &lt; 0.05). Asterisks indicate significant differences between <span class="html-italic">R. soongorica</span> and <span class="html-italic">S. passerina</span> in stomatal limitation and instantaneous water use efficiency during the experimental period: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 6
<p>Relationships between Lysimeter measurements (<b>A</b>) and LI-6400XT extrapolation (<b>B</b>).</p> Full article ">
21 pages, 6621 KiB  
Article
An Improved Approach for Evapotranspiration Estimation Using Water Balance Equation: Case Study of Yangtze River Basin
by Qiong Li, Zhicai Luo, Bo Zhong and Hao Zhou
Water 2018, 10(6), 812; https://doi.org/10.3390/w10060812 - 19 Jun 2018
Cited by 20 | Viewed by 5852
Abstract
Evapotranspiration (ET) is a critical component of the water cycle, and it plays an important role in global water exchange and energy flow. However, accurate estimation and numerical simulation of regional ET remain difficult. In this work, based on the water balance equation, [...] Read more.
Evapotranspiration (ET) is a critical component of the water cycle, and it plays an important role in global water exchange and energy flow. However, accurate estimation and numerical simulation of regional ET remain difficult. In this work, based on the water balance equation, an improved regional ET estimating approach was developed by using Gravity Recovery and Climate Experiment (GRACE), daily precipitation, and discharge data. Firstly, the method and algorithm were validated by simulation study. Compared with ET estimated from previous methods, the result derived from our method present significant improvement, with the correlation coefficient great than 0.9. Secondly, using our improved method, the spatially averaged ET over the Yangtze River Basin (YRB) was computed for the period 2003–2013. The ET estimations were in good consistency with different ET products, and the mean annual value of ET estimation over the YRB was close to the difference between precipitation and discharge over the YRB. Thirdly, the comparison between ET estimation and independent estimates of meteorological factors and soil moisture over the entire YRB were conducted through the entire YRB. The analysis indicated that near-surface temperature, as responsive to atmospheric demand, was the limiting factor of time variation of ET, with the correlation coefficients of 0.69. We also analyzed the relationship between the mean annual ET and atmospheric demand for seven subcatchments of the YRB, which indicated that the spatial distribution characteristics of ET estimated by our method were in accord with atmospheric conditions. These results indicated the good performance of our improved approach in estimating ET variations over the YRB. It also demonstrates the applicability of GRACE to the analysis of hydrological features such as regional ET. Full article
(This article belongs to the Section Hydrology)
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Figure 1

Figure 1
<p>(<b>a</b>) Boundaries of the Yangtze River Basin (YRB) and its 11 subwatersheds, and (<b>b</b>) YRB topography and the locations of in situ measurements.</p> Full article ">Figure 2
<p>Monthly evapotranspiration (ET) estimated from simulated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and daily <span class="html-italic">P</span> and <span class="html-italic">R</span> observations (<b>a</b>) without error and (<b>b</b>) with error, using the method introduced by Ramillien et al. (2006).</p> Full article ">Figure 3
<p>(<b>a</b>) Monthly ET estimated from simulated GRACE TWS and daily <span class="html-italic">P</span> and <span class="html-italic">R</span> observations without error using the method introduced in this study; (<b>b</b>) The regularization factor determined by the generalized cross validation (GCV) method; (<b>c</b>) The correlation (<span class="html-italic">R</span><sup>2</sup>) and RMSE between the simulated ET observations and ET estimations using our method.</p> Full article ">Figure 4
<p>(<b>a</b>) Monthly ET estimated from simulated GRACE TWS and daily <span class="html-italic">P</span> and <span class="html-italic">R</span> observations with error using the method introduced in this study; (<b>b</b>) The regularization factor determined by the GCV method; (<b>c</b>) The correlation (<span class="html-italic">R</span><sup>2</sup>) and RMSE between the simulated ET observations and ET estimations using our method.</p> Full article ">Figure 5
<p>(<b>a</b>) Time series of monthly precipitation and runoff from the Datong gauge station, and TWS estimated from GRACE; (<b>b</b>,<b>c</b>) ET estimations based on different methods or products over the entire YRB. ET_NOAH, ET_CLM, ET_MOSAIC, and ET_VIC are the ET results obtained from the Global Land Data Assimilation System (GLDAS) products of the four land surface models (LSMs) (Noah, CLM, Mosaic, and VIC), and ET_ MODIS and ET_ in situ are the ET results from Moderate Resolution Imaging Spectroradiometer (MODIS) and in situ observations, respectively.</p> Full article ">Figure 6
<p>The non-seasonal TWS anomaly from GRACE, ET from the water balance method, and precipitation from the Global Precipitation Climatology Project (GPCP) dataset 2003–2013, smoothed using the 13-month moving average filter.</p> Full article ">Figure 7
<p>Time series smoothed using 13-month moving average filter: (<b>a</b>) ET and temperature (TEM), (<b>b</b>) ET and relative humidity (RHU), (<b>c</b>) ET and wind speed (WIN) and (<b>d</b>) ET and soil moisture (SM).</p> Full article ">Figure 8
<p>Limitation factors of ET in the seven subcatchments, i.e., TEM, RHU, and WIN. And the name for each subcatchment are Jinshajiang (JSJ), Minjiang(MJ), Jialinjiang (JLJ), Hanjiang (HJ), Wujiang (WJ), Dongting Lake (DTL), and Poyang Lake (PYL). For each box, the central mark is the median, edges represent the 25th and 75th percentiles, and whiskers extend to the furthest data points not considered outliers (outliers are plotted individually). (<b>a</b>) Blue boxes denote precipitation (<span class="html-italic">P</span>) for each region, and red boxes denote ET for each subcatchment; Boxes in (<b>b</b>–<b>d</b>) denote temperature (TEM), relative humidity (RHU), and wind speed (WIN) for each subcatchment, respectively.</p> Full article ">
18 pages, 8775 KiB  
Article
Simulation of Fluid and Complex Obstacle Coupling Based on Narrow Band FLIP Method
by Changjun Zou and Yong Yin
Water 2018, 10(6), 811; https://doi.org/10.3390/w10060811 - 19 Jun 2018
Cited by 1 | Viewed by 4812
Abstract
With the continuous development of fluid simulation theory and technology, there are increasingly higher requirements for simulation of complex fluid interaction. Fluid simulation based on the Eulerian method is limited by the grid resolution, and the sawtooth phenomenon occurs near the obstacle boundary. [...] Read more.
With the continuous development of fluid simulation theory and technology, there are increasingly higher requirements for simulation of complex fluid interaction. Fluid simulation based on the Eulerian method is limited by the grid resolution, and the sawtooth phenomenon occurs near the obstacle boundary. To enhance the fluid interaction performance with complex obstacle, an advanced fluid interaction method was proposed based on NBFLIP. Improved from FLIP method, the NBFLIP method combines the advantages of Euler method and Lagrangian method. The SDF method is proposed in complex obstacle discretion, with an expectation to facilitate the processing with obstacle boundary and efficiency improvement. Compared with FLIP method, particle number in NBFLIP method is reduced by 86.2% and the average running time per frame is reduced by 36.1%. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>NBFLIP Flow Chart.</p> Full article ">Figure 2
<p>Breakdown of the Narrow Band [<a href="#B3-water-10-00811" class="html-bibr">3</a>].</p> Full article ">Figure 3
<p>Flow Chart for SDF Calculation.</p> Full article ">Figure 4
<p>Rendering of original obstacle.</p> Full article ">Figure 5
<p>Zero contour surface result of 3D obstacle SDF.</p> Full article ">Figure 6
<p>Contour line of SDF on obstacle profile.</p> Full article ">Figure 7
<p>Solid Obstacle Process.</p> Full article ">Figure 8
<p>Dam Break Initial Setting.</p> Full article ">Figure 9
<p>Dam Break Results from Reference [<a href="#B17-water-10-00811" class="html-bibr">17</a>].</p> Full article ">Figure 10
<p>Our Simulation Result.</p> Full article ">Figure 11
<p>Simulation Result from Reference [<a href="#B18-water-10-00811" class="html-bibr">18</a>].</p> Full article ">Figure 12
<p>Our Simulation Result.</p> Full article ">Figure 13
<p>Bunny Drop on Surface.</p> Full article ">Figure 14
<p>Simulation without Obstacle.</p> Full article ">Figure 15
<p>Fluid Simulation with Obstacle.</p> Full article ">Figure 16
<p>Rendering with and without Obstacle.</p> Full article ">Figure 17
<p>Fluid Interaction with Moving Obstacle.</p> Full article ">Figure 18
<p>Particle Number Comparison.</p> Full article ">Figure 19
<p>Energy Comparison.</p> Full article ">Figure 20
<p>Particles in NBFLIP.</p> Full article ">Figure 21
<p>Particles in FLIP.</p> Full article ">Figure 22
<p>Running Time Comparison</p> Full article ">
23 pages, 2641 KiB  
Article
End Use Level Water and Energy Interactions: A Large Non-Residential Building Case Study
by Sudeep Nair, Hafiz Hashim, Louise Hannon and Eoghan Clifford
Water 2018, 10(6), 810; https://doi.org/10.3390/w10060810 - 19 Jun 2018
Cited by 9 | Viewed by 5729
Abstract
Within the European Union, buildings account for around 40% of the energy use and 36% of CO2 emissions, thus representing a significant challenge in the context of recent EU directives that require all new buildings to be nearly zero-energy by 2020. Reduced [...] Read more.
Within the European Union, buildings account for around 40% of the energy use and 36% of CO2 emissions, thus representing a significant challenge in the context of recent EU directives that require all new buildings to be nearly zero-energy by 2020. Reduced consumption of water, and hot water in particular, provides a significant opportunity to reduce energy consumption. While there have been numerous studies pertaining to the water-energy nexus of residential buildings, the complexity of water networks in larger buildings has meant that this area has been relatively unexplored. The paper presents a comprehensive investigation of the hot water use profile, associated energy use, on-site pumping energy use, carbon emissions, and solar energy harvesting potential in an Irish university building over periods before and after water conservation efforts. Total water-related energy consumption (including the heating and pumping losses) were analysed using the WHAM model and modified pumping energy expressions. The results revealed that water heating including losses contributed to as high as 30% of total building energy consumption, and stringent water conservation measures reduced the average hot water use rate by 8.5 m3/day. It was found that 10% of the total pumping energy was constituted by pump start-ups. Simulation results for solar harvesting potential in the study site found that around 60% of water heating energy demand could be met by solar energy in the new water demand scenario. The study results can act as a benchmark for similar buildings, and the model combination can be emulated in future studies. Full article
(This article belongs to the Special Issue Carbon Footprint of Water Supply and Wastewater Treatment)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Conceptual diagram of the study boundary.</p> Full article ">Figure 2
<p>Simplified schematic of water network considered for pumping energy calculation in the building. The dashed line indicates systems not considered in this study.</p> Full article ">Figure 3
<p>HWS system in the building.</p> Full article ">Figure 4
<p>Building water-energy use estimation methodology.</p> Full article ">Figure 5
<p>Hot water usage rate in the building during March 2016 to January 2017.</p> Full article ">Figure 6
<p>Weekday and weekend averages for first semester, summer and second semester periods.</p> Full article ">Figure 7
<p>Ratio of hot water use to total water use for different profiles in different months in 2016.</p> Full article ">Figure 8
<p>Hot water usage rate in the building during 1st semester in 2018.</p> Full article ">Figure 9
<p>Hourly average profile of hot water consumption for weekday, weekend and combined usage during first semester of 2018.</p> Full article ">Figure 10
<p>Energy used for water heating in the building, (<b>a</b>) during 2016 and (<b>b</b>) 2018.</p> Full article ">Figure 11
<p>Energy used for water pumping in the building in 2016, (<b>a</b>) for GWS and (<b>b</b>) other CWS uses.</p> Full article ">Figure 12
<p>Monthly pump start-up energy usages for GWS and other CWS uses.</p> Full article ">Figure 13
<p>Average daily total energy use in the building in 2016.</p> Full article ">Figure 14
<p>Contribution of solar and top up (grid) energy for water heating in the building for scenarios: (<b>a</b>) for the months in 2016 with module size of 1300 m<sup>2</sup>, (<b>b</b>) for the months in 2016 with module size of 650 m<sup>2</sup>, (<b>c</b>) for the three months of 2018 with module size of 1300 m<sup>2</sup>, (<b>d</b>) for the three months of 2018 with module size of 650 m<sup>2</sup> and (<b>e</b>) for the three months of 2018 without restriction on the module size. It was assumed that the total roof area could be covered by solar modules.</p> Full article ">
19 pages, 8048 KiB  
Article
Multi-Source Uncertainty Analysis in Simulating Floodplain Inundation under Climate Change
by Nadine Maier, Lutz Breuer, Alejandro Chamorro, Philipp Kraft and Tobias Houska
Water 2018, 10(6), 809; https://doi.org/10.3390/w10060809 - 19 Jun 2018
Cited by 3 | Viewed by 4804
Abstract
Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they harbor highly specialized floodplain plant species depending on different inundation characteristics. Climate change will most likely alter those characteristics. This study investigates the potential impact of climate change on [...] Read more.
Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they harbor highly specialized floodplain plant species depending on different inundation characteristics. Climate change will most likely alter those characteristics. This study investigates the potential impact of climate change on the inundation characteristics of a floodplain of the Rhine River in Hesse, Germany. We report on the cascading uncertainty introduced through climate projections, climate model structure, and parameter uncertainty. The established modeling framework integrates projections of two general circulation models (GCMs), three emission scenarios, a rainfall–runoff model, and a coupled surface water–groundwater model. Our results indicate large spatial and quantitative uncertainties in the simulated inundation characteristics, which are mainly attributed to the GCMs. Overall, a shift in the inundation pattern, possible in both directions, and an increase in inundation extent are simulated. This can cause significant changes in the habitats of species adapted to these highly-endangered ecosystems. Full article
(This article belongs to the Section Hydrology)
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<p>Geographic location of the study area (<b>lower left</b>), digital elevation of the larger nature reserve with the study area (<b>middle</b>), and the setup for the surface water–groundwater model in the catchment modeling framework (CMF) (<b>right</b>). The irregular polygons are based on similar elevation and land use.</p> Full article ">Figure 2
<p>Flowchart showing the steps within the model framework. The steps within the dotted box represent steps forced by historical data and steps within the dashed box represent future projections. Models are depicted by grey boxes.</p> Full article ">Figure 3
<p>Monthly boxplots of HBV-simulated water levels of the Rhine using the best-performing model parameter sets for representative concentration pathway (RCP) 2.6 (<b>bluish</b>, <b>top</b>), RCP 4.5 (<b>greenish</b>, <b>middle</b>), RCP 8.5 (<b>reddish</b>, <b>bottom</b>) and for two different periods (<b>top</b>: mid-century 2021–2050, <b>bottom</b>: end-century 2071–2100). Black boxplots represent observations for the period 1990–2015. The linear trend of the water level change is given below each boxplot for the time from 1990 to 2015 (under black boxplots), from 2021 to 2050 (upper figure of each RCP group, under colored boxplots), and from 2071 to 2100 (lower figure of each RCP group, under colored boxplots). Points indicate no trend; upward arrows indicate an increasing trend and downward arrows a decreasing trend at the significance level of <span class="html-italic">p</span> &lt; 0.05 based on the Mann–Kendall Test. For significant trends, the absolute change for the time period is given in meters.</p> Full article ">Figure 4
<p>Spatial distribution of average inundation days (per year) for the mid-century (2021–2050) and end-century (2071–2100) compared to the past (2002–2015). Results are shown for the different combinations of GCMs and RCPs as the difference of inundation days for the future period minus those from the past. Results shown for the CMF model are forced by simulated water levels of the Rhine River, using the best-performing HBV parameter set for the calibration period.</p> Full article ">Figure 5
<p>Spatial distribution of average inundation duration (days per year) for the mid-century (2021–2050) and end-century (2071–2100) compared to the past (2002–2015). Results are shown for the combinations of GCMs and RCPs as the difference of average inundation duration for the future period minus those from the past. Results shown for the CMF model are forced by simulated water levels of the Rhine River, using the best-performing HBV parameter set for the calibration period.</p> Full article ">Figure 6
<p>Uncertainty of the annual inundation days per time period separated by the three sources of uncertainty: GCMs, RCPs, and the parameter and predictive uncertainty of the HBV model. Each component represents the mean of all possible combinations, whereas each combination indicates the average annual flooding days. The filling patterns depict the mean annual inundation days from all simulations. The top panels show results for the mid-century, bottom panels for the end-century conditions. (<b>A</b>,<b>B</b>) indicate sites that are specifically referred to in the text.</p> Full article ">Figure A1
<p>Example of the quantification of differences in the mean annual inundation days for the polygons A and B (<a href="#water-10-00809-f003" class="html-fig">Figure 3</a>) for the mid-century and the end-century, represented in the form of tree diagrams. Each node at the bottom represents the mean annual inundation days of the two polygons (A and B) of a simulation. The nodes one and two levels higher depict the mean of the two lower nodes. Only the node in the top depicts the difference between the two lower nodes (depicted by dashed lines); i.e., the top node indicates the difference between the mean annual inundation days and represents the quantitative uncertainty. Abbreviations used: HG = HadGEM, ME = MPI-ESM, 2.6 = RCP 2.6, 8.5 = RCP 8.5, PrU = predictive uncertainty, PaU = parameter uncertainty.</p> Full article ">Figure A2
<p>Performance of the HBV model, shown for the best performing parameter set for the years 2001 to 2013 (<b>top</b>) and for the year 1007 with the parameter and the predictive uncertainty band (<b>bottom</b>).</p> Full article ">Figure A3
<p>Projected water levels of the Rhine river and the associated uncertainty bands (parameter and predictive uncertainty), exemplarily shown for the years 2040 to 2050 (<b>top</b>) and for the year 2045 (<b>bottom</b>).</p> Full article ">
20 pages, 3122 KiB  
Article
Research on Cascade Reservoirs’ Short-Term Optimal Operation under the Effect of Reverse Regulation
by Changming Ji, Hongjie Yu, Jiajie Wu, Xiaoran Yan and Rui Li
Water 2018, 10(6), 808; https://doi.org/10.3390/w10060808 - 19 Jun 2018
Cited by 14 | Viewed by 4716
Abstract
Currently research on joint operation of a large reservoir and its re-regulating reservoir focuses on either water quantity regulation or water head regulation. The accuracy of relevant models is in need of improvement if the influence of factors such as water flow hysteresis [...] Read more.
Currently research on joint operation of a large reservoir and its re-regulating reservoir focuses on either water quantity regulation or water head regulation. The accuracy of relevant models is in need of improvement if the influence of factors such as water flow hysteresis and the aftereffect of tail water level variation are taken into consideration. In this paper, given the actual production of Pankou-Xiaoxuan cascade hydropower stations that combines two operation modes (‘electricity to water’ and ‘water to electricity’), a coupling model of their short-term optimal operation is developed, which considers Xiaoxuan reservoir’s regulating effect on Pankou reservoir’s outflow volume and water head. Factors such as water flow hysteresis and the aftereffect of tail water level variation are also considered to enhance the model’s accuracy. The Backward Propagation (BP) neural network is employed for precise calculation of the downstream reservoir’s inflow and the upstream reservoir’s tail water level. Besides, we put forth Accompanying Progressive Optimality Algorithm (APOA) to solve the coupling model with aftereffect. An example is given to verify the scientificity of the proposed model and the advantages of APOA. Through analysis of the model calculation results, the optimal operation rules of the cascade reservoirs are obtained in terms of water quantity regulation and water head regulation, which can provide scientific reference for cascade reservoirs’ optimal operation. Full article
(This article belongs to the Special Issue Adaptive Catchment Management and Reservoir Operation)
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<p>Schematic diagram of the cascade reservoirs’ hydraulic connection.</p> Full article ">Figure 2
<p>Pankou’s outflow and Xiaoxuan’s inflow.</p> Full article ">Figure 3
<p>Calculation results of Xiaoxuan’s inflow.</p> Full article ">Figure 4
<p>Pankou’s stage-discharge curve.</p> Full article ">Figure 5
<p>Calculation results of Pankou’s tail water level.</p> Full article ">Figure 6
<p>Schematic diagram of APOA.</p> Full article ">Figure 7
<p>The location of Pankou-Xiaoxuan cascade reservoirs.</p> Full article ">Figure 8
<p>Comparison of Pankou’s calculated results between POA and APOA. (<b>a</b>) Pankou’s outflow process; (<b>b</b>) Pankou’s tail water level process.</p> Full article ">Figure 9
<p>Comparison of Xiaoxuan’s calculated results between POA and APOA. (<b>a</b>) Xiaoxuan’s inflow process; (<b>b</b>) Xiaoxuan’s water level process; (<b>c</b>) Xiaoxuan’s output process.</p> Full article ">Figure 10
<p>Comparison between the optimal and the actual operation schemes. (<b>a</b>) Pankou; (<b>b</b>) Xiaoxuan.</p> Full article ">Figure 11
<p>Comparison between optimal and actual operation schemes of Xiaoxuan, (<b>a</b>) 1 July; (<b>b</b>) 12 June.</p> Full article ">
21 pages, 2168 KiB  
Article
Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm
by Mohammad Ehteram, Faridah Binti Othman, Zaher Mundher Yaseen, Haitham Abdulmohsin Afan, Mohammed Falah Allawi, Marlinda Bt. Abdul Malek, Ali Najah Ahmed, Shamsuddin Shahid, Vijay P. Singh and Ahmed El-Shafie
Water 2018, 10(6), 807; https://doi.org/10.3390/w10060807 - 19 Jun 2018
Cited by 53 | Viewed by 6923
Abstract
Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must [...] Read more.
Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time. Full article
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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<p>Bat Algorithm procedure.</p> Full article ">Figure 2
<p>Hybrid algorithm procedure.</p> Full article ">Figure 3
<p>Simulated discharges based on different methods for the Wilson flood.</p> Full article ">Figure 4
<p>Simulated discharges by different algorithms for Karahan flood.</p> Full article ">Figure 5
<p>Extracted hydrograph for Viessman and Lewis flood based on 4PMM.</p> Full article ">
19 pages, 5551 KiB  
Article
Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network
by Siyu Chen, Guohua Fang, Xianfeng Huang and Yuhong Zhang
Water 2018, 10(6), 806; https://doi.org/10.3390/w10060806 - 18 Jun 2018
Cited by 63 | Viewed by 8633
Abstract
Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction [...] Read more.
Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field. Full article
(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)
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<p>Experimental area in Jiangsu Province.</p> Full article ">Figure 2
<p>Architecture of the typical BP neural network.</p> Full article ">Figure 3
<p>The flow chart of IABC-BP neural network.</p> Full article ">Figure 4
<p>Performance of IABC-BP models with various parameter settings: (<b>a</b>) MSE distribution at the entire range (3–10) of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>h</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) MSE distribution at the selected range (4–8) of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>h</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The 3-7-3 BP neural network model.</p> Full article ">Figure 6
<p>The comparison of fitness among the four models. (<b>a</b>) The convergence performance from the first iteration time to the last of four models; (<b>b</b>) The convergence performance from the first iteration time to the twentieth iteration time.</p> Full article ">Figure 7
<p>Comparison of DO among the four models.</p> Full article ">Figure 8
<p>Comparison of BOD<sub>5</sub> among the four models.</p> Full article ">Figure 9
<p>Comparison of COD<sub>M</sub><sub>n</sub> among the four models.</p> Full article ">Figure 10
<p>The predicted BP value and its error percentage.</p> Full article ">Figure 11
<p>The predicted PSO-BP value and its error percentage.</p> Full article ">Figure 12
<p>The predicted ABC-BP value and its error percentage.</p> Full article ">Figure 13
<p>The predicted IABC-BP value and its error percentage.</p> Full article ">
11 pages, 1162 KiB  
Article
Presence of Antibiotic-Resistant Escherichia coli in Wastewater Treatment Plant Effluents Utilized as Water Reuse for Irrigation
by Asli Aslan, Zachariah Cole, Anunay Bhattacharya and Oghenekpaobor Oyibo
Water 2018, 10(6), 805; https://doi.org/10.3390/w10060805 - 18 Jun 2018
Cited by 31 | Viewed by 12024
Abstract
Providing safe water through water reuse is becoming a global necessity. One concern with water reuse is the introduction of unregulated contaminants to the environment that cannot be easily removed by conventional wastewater treatment plants (WWTP). The occurrence of ampicillin, sulfamethoxazole, ciprofloxacin, and [...] Read more.
Providing safe water through water reuse is becoming a global necessity. One concern with water reuse is the introduction of unregulated contaminants to the environment that cannot be easily removed by conventional wastewater treatment plants (WWTP). The occurrence of ampicillin, sulfamethoxazole, ciprofloxacin, and tetracycline-resistant Escherichia coli through the treatment stages of a WWTP (raw sewage, post-secondary, post-UV and post-chlorination) was investigated from January to May 2016. The highest concentrations of antibiotic resistant E. coli in the effluent were detected in April after rainfall. Ampicillin-resistant E. coli was the most common at the post UV and chlorination stages comprising 63% of the total E. coli population. The minimum inhibitory concentration (MIC) analysis showed that one in five isolates was resistant to three or more antibiotics, and the majority of these E. coli were resistant to ampicillin, followed by sulfamethoxazole and ciprofloxacin. The highest MIC was detected at the finished water after application of multiple disinfection methods. Tetracycline resistance was the least observed among others, indicating that certain drug families may respond to wastewater treatment differently. Currently, there are no policies to enforce the monitoring of antibiotic-resistant pathogen removal in WWTP. Better guidelines are needed to better regulate reuse water and prevent health risk upon exposure to antibiotic-resistant bacteria. Full article
(This article belongs to the Special Issue Antimicrobial Resistance in Environmental Waters)
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<p><span class="html-italic">Escherichia coli</span> growth on control (<b>A</b>), ampicillin (<b>B</b>), ciprofloxacin (<b>C</b>), sulfamethoxazole (<b>D</b>) and tetracycline (<b>E</b>) supplemented media.</p> Full article ">Figure 2
<p>Cumulative precipitation (7 days total) before each sampling event.</p> Full article ">
22 pages, 29622 KiB  
Article
Delineation of Suitable Zones for the Application of Managed Aquifer Recharge (MAR) in Coastal Aquifers Using Quantitative Parameters and the Analytical Hierarchy Process
by Nerantzis Kazakis
Water 2018, 10(6), 804; https://doi.org/10.3390/w10060804 - 18 Jun 2018
Cited by 48 | Viewed by 8820
Abstract
Coastal aquifer salinization is usually related to groundwater overexploitation and water table decline. Managed Aquifer Recharge (MAR) can be applied as a measure to reverse and prevent this phenomenon. A detailed literature review was performed to identify the various methods and parameters commonly [...] Read more.
Coastal aquifer salinization is usually related to groundwater overexploitation and water table decline. Managed Aquifer Recharge (MAR) can be applied as a measure to reverse and prevent this phenomenon. A detailed literature review was performed to identify the various methods and parameters commonly used to determine suitable sites of MAR application. Based on the review results, a new multi-criteria index (SuSAM) that is compatible to coastal aquifers was developed to delineate suitable zones for MAR application. New parameters were introduced into the index, such as distance from the shore and hydraulic resistance of the vadose zone, while factor weights were determined using the Analytical Hierarchy Process (AHP) and single sensitivity analysis. The applicability of the new index was examined in the coastal aquifer of the Anthemountas basin located in northern Greece. The most suitable areas for MAR application cover 28% of the aquifer’s surface area, while 16% of the area was characterized as non-suitable for MAR application. The new method constitutes the first step of the managed aquifer recharge concept for the delineation of MAR-suitable zones in coastal aquifers. Full article
(This article belongs to the Special Issue Salinization of Coastal Aquifer Systems)
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Graphical abstract
Full article ">Figure 1
<p>Location and morphological map of the study area of Anthemountas basin.</p> Full article ">Figure 2
<p>Flow chart of the site suitability index to apply MAR (SuSAM).</p> Full article ">Figure 3
<p>Thematic maps of the site suitability index to apply MAR in the coastal aquifer of Anthemountas basin.</p> Full article ">Figure 4
<p>Map of MAR application suitability in the coastal aquifer of Anthemountas basin.</p> Full article ">Figure 5
<p>Distribution of MAR suitable areas in study area.</p> Full article ">
17 pages, 4914 KiB  
Article
Modeling the Spatial and Seasonal Variations of Groundwater Head in an Urbanized Area under Low Impact Development
by Yu Zheng, Sidian Chen, Huapeng Qin and Jiu Jimmy Jiao
Water 2018, 10(6), 803; https://doi.org/10.3390/w10060803 - 17 Jun 2018
Cited by 16 | Viewed by 4978
Abstract
Increasing impervious land cover has great impacts on groundwater regimes in urbanized areas. Low impact development (LID) is generally regarded as a sustainable solution for groundwater conservation. However, the effects of LID on the spatial-temporal distribution of groundwater are not yet fully understood. [...] Read more.
Increasing impervious land cover has great impacts on groundwater regimes in urbanized areas. Low impact development (LID) is generally regarded as a sustainable solution for groundwater conservation. However, the effects of LID on the spatial-temporal distribution of groundwater are not yet fully understood. In this case study, a coupled Storm Water Management Model (SWMM) and Finite Element Subsurface FLOW system (FEFLOW) model was used to simulate surface and groundwater flow in an urbanized area in Shenzhen, China. After verification, the model was used to analyze the spatial-seasonal variations of groundwater head and hydrological processes under different LID scenarios. The results indicate that if the runoff from 7.5% and 15% of impervious area is treated by LID facilities, the annual surface runoff decreases by 5% and 9%, respectively, and the spatial average groundwater head relative to sea level pressure increases by 0.9 m and 1.7 m in the study area, respectively. The rise in groundwater head generally decreases from the recharge zones to the discharge zones surrounded by the streams and coastal waters. However, the groundwater head change is determined not only by the location in the catchment, but also by the hydraulic conductivity of underlying aquifer and LID infiltration intensity. Moreover, LID significantly enhances groundwater recharge and aquifer storage in the wet seasons; in turn it increases aquifer release and groundwater discharge in the dry seasons. However, LID has the potential to increase the risk of groundwater flooding during wet seasons in areas with poor aquifer drainage capacity and shallow groundwater depth. The findings from this study provide the basis for further assessing the benefit and risk of LID infiltration for groundwater supplementation in the urbanized areas. Full article
(This article belongs to the Section Urban Water Management)
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<p>Study area and land use.</p> Full article ">Figure 2
<p>The geological division (Zones 1–4) in the study area (<b>a</b>) and the two typical cross sections of geological formation (<b>b</b>).</p> Full article ">Figure 3
<p>The divided subcatchments of the Storm Water Management Model (SWMM).</p> Full article ">Figure 4
<p>The divided elements and boundary condition of the Finite Element Subsurface FLOW system (FEFLOW) model.</p> Full article ">Figure 5
<p>The comparison of observed and simulated groundwater head of 15 monitoring wells at the end of September 2008.</p> Full article ">Figure 6
<p>The monthly variation of observed and simulated groundwater head of (<b>a</b>) Well A and (<b>b</b>) Well B during the period from September 2008 to December 2009.</p> Full article ">Figure 7
<p>The low impact development (LID) implementation intensity in each subcatchment under Scenario 1.</p> Full article ">Figure 8
<p>The increase in groundwater head under (<b>a</b>) Scenarios 1 and (<b>b</b>) Scenario 2. The increase in groundwater (GW) head = GW head relative to sea level pressure for Scenario 1 or Scenario 2 − GW head relative to sea level pressure for the base case.</p> Full article ">Figure 9
<p>The 10-year cumulated increase in groundwater (<b>a</b>) net recharge and (<b>b</b>) discharge due to LID for the Scenario 1.</p> Full article ">Figure 10
<p>The correlation of the main factors with increased cumulated (<b>a</b>) net groundwater recharge and (<b>b</b>) discharge.</p> Full article ">Figure 11
<p>The seasonal variations of groundwater system under three LID scenarios in the equilibrium state. (<b>a</b>) groundwater head; (<b>b</b>) aquifer water exchange; (<b>c</b>) net groundwater recharge; and (<b>d</b>) groundwater discharge.</p> Full article ">
3 pages, 159 KiB  
Editorial
The Impact of Climate on Hydrological Extremes
by Salvatore Manfreda, Vito Iacobellis, Andrea Gioia, Mauro Fiorentino and Krzysztof Kochanek
Water 2018, 10(6), 802; https://doi.org/10.3390/w10060802 - 17 Jun 2018
Cited by 12 | Viewed by 4276
Abstract
High and low flows and associated floods and droughts are extreme hydrological phenomena mainly caused by meteorological anomalies and modified by catchment processes and human activities. They exert increasing on human, economic, and natural environmental systems around the world. In this context, global [...] Read more.
High and low flows and associated floods and droughts are extreme hydrological phenomena mainly caused by meteorological anomalies and modified by catchment processes and human activities. They exert increasing on human, economic, and natural environmental systems around the world. In this context, global climate change along with local fluctuations may eventually trigger a disproportionate response in hydrological extremes. This special issue focuses on observed extreme events in the recent past, how these extremes are linked to a changing global/regional climate, and the manner in which they may shift in the coming years. Full article
(This article belongs to the Special Issue Impact of Climate on Hydrological Extremes)
11 pages, 4806 KiB  
Article
Effects of the Notch Angle, Notch Length and Injection Rate on Hydraulic Fracturing under True Triaxial Stress: An Experimental Study
by Yulong Chen, Qingxiang Meng and Jianwei Zhang
Water 2018, 10(6), 801; https://doi.org/10.3390/w10060801 - 17 Jun 2018
Cited by 9 | Viewed by 4683
Abstract
This study focused on the effects of the notch angle, notch length, and injection rate on hydraulic fracturing. True triaxial hydraulic fracturing experiments were conducted with 300 × 300 × 300 mm cement mortar blocks. The test results showed that the fracture initiation [...] Read more.
This study focused on the effects of the notch angle, notch length, and injection rate on hydraulic fracturing. True triaxial hydraulic fracturing experiments were conducted with 300 × 300 × 300 mm cement mortar blocks. The test results showed that the fracture initiation pressure decreased as the notch length and injection rate increased, whereas, the fracture initiation pressure decreased as the notch angle decreased. Furthermore, the direction of the hydraulic fracture was always along the direction of the maximum principle stress. Full article
(This article belongs to the Special Issue Thermo-Hydro-Mechanical-Chemical Processes of Deep Underground Reservoirs)
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<p>Schematic of the true triaxial hydraulic fracturing test system.</p> Full article ">Figure 2
<p>Diagram of the specimens (units: mm): (<b>a</b>) 3D illustration, (<b>b</b>) size, and (<b>c</b>) photo.</p> Full article ">Figure 3
<p>Hydraulic pressure in the hydraulic fracturing process.</p> Full article ">Figure 4
<p>Effect of the notch angle on water pressure when <span class="html-italic">l</span> = 15 mm and <span class="html-italic">v</span> = 0.2 mm/s.</p> Full article ">Figure 5
<p>Effect of the notch angle on water pressure when <span class="html-italic">l</span> = 30 mm and <span class="html-italic">v</span> = 0.4 mm/s.</p> Full article ">Figure 6
<p>Effect of the notch length on the water pressure when <span class="html-italic">θ</span> = 90° and <span class="html-italic">v</span> = 0.2 mm/s.</p> Full article ">Figure 7
<p>Effect of the notch length on the water pressure when <span class="html-italic">θ</span> = 45° and <span class="html-italic">v</span> = 0.4 mm/s.</p> Full article ">Figure 8
<p>Effect of the injection rate on the water pressure when <span class="html-italic">θ</span> = 90° and <span class="html-italic">l</span> = 30 mm.</p> Full article ">Figure 9
<p>Effect of the injection rate on the water pressure when <span class="html-italic">θ</span> = 45° and <span class="html-italic">l</span> = 15 mm/s.</p> Full article ">Figure 10
<p>Hydraulic fracture configurations for the six specimens.</p> Full article ">Figure 10 Cont.
<p>Hydraulic fracture configurations for the six specimens.</p> Full article ">Figure 10 Cont.
<p>Hydraulic fracture configurations for the six specimens.</p> Full article ">
14 pages, 4309 KiB  
Article
Quantification of Seasonal Precipitation over the upper Chao Phraya River Basin in the Past Fifty Years Based on Monsoon and El Niño/Southern Oscillation Related Climate Indices
by Tsuyoshi Kinouchi, Gakuji Yamamoto, Atchara Komsai and Winai Liengcharernsit
Water 2018, 10(6), 800; https://doi.org/10.3390/w10060800 - 17 Jun 2018
Cited by 10 | Viewed by 4799
Abstract
For better water resources management, we proposed a method to estimate basin-scale seasonal rainfall over selected areas of the Chao Phraya River Basin, Thailand, from existing climate indices that represent variations in the Asian summer monsoon, the El Niño/Southern Oscillation, and sea surface [...] Read more.
For better water resources management, we proposed a method to estimate basin-scale seasonal rainfall over selected areas of the Chao Phraya River Basin, Thailand, from existing climate indices that represent variations in the Asian summer monsoon, the El Niño/Southern Oscillation, and sea surface temperatures (SST) in the Pacific Ocean. The basin-scale seasonal rainfall between 1965 and 2015 was calculated for the upper Ping River Basin (PRB) and the upper Nan River Basin (NRB) from a gridded rainfall dataset and rainfall data collected at several gauging stations. The corresponding climate indices, i.e., the Equatorial-Southern Oscillation Index (EQ-SOI), Indian Monsoon Index (IMI), and SST-related indices, were examined to quantify seasonal rainfall. Based on variations in the rainfall anomaly and each climate index, we found that IMI is the primary variable that can explain variations in seasonal rainfall when EQ-SOI is negative. Through a multiple regression analysis, we found that EQ-SOI and two SST-related indices, i.e., Pacific Decadal Oscillation Index (PDO) and SST anomalies in the tropical western Pacific (SSTNW), can quantify the seasonal rainfall for years with positive EQ-SOI. The seasonal rainfall calculated for 1975 to 2015 based on the proposed method was highly correlated with the observed rainfall, with correlation coefficients of 0.8 and 0.86 for PRB and NRB, respectively. These results suggest that the existing indices are useful for quantifying basin-scale seasonal rainfall, provided a proper classification and combination of the climate indices are introduced. The developed method could forecast seasonal rainfall over the target basins if well-forecasted climate indices are provided with sufficient leading time. Full article
(This article belongs to the Special Issue Adaptive Catchment Management and Reservoir Operation)
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<p>Location of the upper Ping River Basin (PRB) and the upper Nan River Basin (NRB). The inset shows the area of the Chao Phraya River Basin (CPRB). The Upper Chao Phraya River Basin (UCPRB) is defined to cover the area north of 16° N. The black dots indicate the locations of rain gauges used to estimate areal rainfall since 2001.</p> Full article ">Figure 2
<p>Mean monthly areal-averaged rainfall for the period from 1975 to 2015 over (<b>a</b>) PRB and (<b>b</b>) NRB.</p> Full article ">Figure 3
<p>Domains related with each climate index finally employed in the proposed method (the domain related with PDO is not shown).</p> Full article ">Figure 4
<p>Anomalies of the seasonal rainfall and finally selected climate indices for the period between 1965 and 2015. (<b>a</b>) Rainfall anomaly in PRB; (<b>b</b>) Rainfall anomaly in NRB; (<b>c</b>) EQ-SOI; (<b>d</b>) IMI; (<b>e</b>) PDO; and (<b>f</b>) SST<sub>NW</sub> (SST anomaly over NINO.WEST). Each decade is separated by dashed lines. The rainfall anomaly is calculated as the deviation from the mean rainfall for the period from 1975 to 2015.</p> Full article ">Figure 5
<p>The relation between the seasonal rainfall and IMI for (<b>a</b>) PRB and (<b>b</b>) NRB. Two dashed lines are separated from the solid line (Equation (1)) by the standard deviation of the error between the observed and estimated seasonal rainfalls. The gray circles are the data used for calibrating the parameters, and the cross marks indicate data for 1965, 1969, and 1972, which were not used for calibration.</p> Full article ">Figure 6
<p>Comparison of observed and estimated seasonal rainfall for (<b>a</b>) PRB and (<b>b</b>) NRB for calibration and validation years with EQ-SOI &lt; 0.02. Equation (2) with calibrated parameters (<a href="#water-10-00800-t002" class="html-table">Table 2</a>) was applied. The dashed line indicates the mean rainfall for the period from 1975 to 2015.</p> Full article ">Figure 7
<p>Comparison of observed and estimated seasonal rainfall for (<b>a</b>) PRB and (<b>b</b>) NRB for years with EQ-SOI &gt; 0.02. Equation (3) with NINO.WEST was applied. The dashed line indicates the mean rainfall for the period from 1975 to 2015.</p> Full article ">Figure 8
<p>Same as <a href="#water-10-00800-f007" class="html-fig">Figure 7</a> but for validation years between 1966 and 1974 for (<b>a</b>) PRB and (<b>b</b>) NRB.</p> Full article ">Figure 9
<p>Relationship between seasonal rainfall and (<b>a</b>) EQ-SOI; (<b>b</b>) PDO and (<b>c</b>) SST<sub>NW</sub> for (left) PRB and (right) NRB between 1965 and 2015 (when EQ-SOI is positive).</p> Full article ">Figure 10
<p>Relationship between seasonal rainfall and (<b>a</b>) PDO and (<b>b</b>) SST<sub>NW</sub> for (left) PRB and (right) NRB. The vertical axis indicates the seasonal rainfall anomaly divided by EQ-SOI. The solid and dashed curves in each panel are given by Equation (3). Each symbol represents the data when EQ-SOI is positive.</p> Full article ">
19 pages, 5686 KiB  
Article
Research on Characteristics of Groundwater Recharge in the Weishan Irrigated District Based on a Bromide Tracer
by Xin Cong, Zhenghe Xu and Tong Wang
Water 2018, 10(6), 799; https://doi.org/10.3390/w10060799 - 17 Jun 2018
Cited by 5 | Viewed by 4120
Abstract
Bromide was used as tracer in the Weishan Irrigated District to determine the groundwater recharge as well as to evaluate the impacts of different irrigation basin locations, irrigation regimes, and crop types on the recharge. The comprehensive recharge coefficient and the Kriging Spatial [...] Read more.
Bromide was used as tracer in the Weishan Irrigated District to determine the groundwater recharge as well as to evaluate the impacts of different irrigation basin locations, irrigation regimes, and crop types on the recharge. The comprehensive recharge coefficient and the Kriging Spatial Interpolation methods were used to distinguish the effects of precipitation and surface water irrigation on the groundwater recharge rate. The results show that the recharge rates ranged from 85.8 to 243 mm/a, with an average of 168 mm/a. The average recharge rate in the upstream district is greater in the downstream and the average recharge rate of irrigated land (193 mm/a) is greater than non-irrigated land (110 mm/a). The recharge rates in fields of winter wheat-summer maize and cotton with irrigation are 210 mm/a and 140 mm/a, respectively, while they are 115 mm/a and 94.1 mm/a under no irrigation conditions. The comprehensive recharge coefficient of groundwater in the upstream irrigation area is larger than that in the downstream. By comparing the spatial distribution of the groundwater level and the comprehensive recharge coefficient, it is found that there is a positive relationship between the groundwater level and the comprehensive recharge coefficient. The results of this study can provide reference and guidance to a water resources analysis of the Weishan Irrigated District. Full article
(This article belongs to the Section Hydrology)
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<p>Location of the (<b>a</b>) study area and (<b>b</b>) sampling points and tracer injection.</p> Full article ">Figure 2
<p>The principles diagram of the tracer experiment.</p> Full article ">Figure 3
<p>Schematic figure of sampling and tracer injecting points location.</p> Full article ">Figure 4
<p>Bromide concentration and moisture content in typical sites.</p> Full article ">Figure 4 Cont.
<p>Bromide concentration and moisture content in typical sites.</p> Full article ">Figure 5
<p>The recharge rate of groundwater in different periods.</p> Full article ">Figure 6
<p>The recharge rate of groundwater in different irrigated district location.</p> Full article ">Figure 7
<p>The recharge rate of groundwater in different periods in irrigated cropland and non-irrigated land.</p> Full article ">Figure 8
<p>Recharge versus precipitation and irrigation.</p> Full article ">Figure 9
<p>The recharge coefficient of groundwater in different periods.</p> Full article ">Figure 10
<p>Spatial distribution map of comprehensive recharge coefficient.</p> Full article ">Figure 11
<p>Spatial distribution map of (<b>a</b>) groundwater level and (<b>b</b>) evapotranspiration.</p> Full article ">Figure 12
<p>Groundwater level of sampling points.</p> Full article ">Figure 13
<p>Correlation between groundwater level and comprehensive recharge coefficient.</p> Full article ">
19 pages, 4170 KiB  
Article
The Assessment of Green Water Based on the SWAT Model: A Case Study in the Hai River Basin, China
by Kui Zhu, Zibo Xie, Yong Zhao, Fan Lu, Xinyi Song, Lu Li and Xiaomeng Song
Water 2018, 10(6), 798; https://doi.org/10.3390/w10060798 - 16 Jun 2018
Cited by 16 | Viewed by 6505
Abstract
Green water accounts for two-thirds of precipitation, and the proportion could be even higher in dry years. Conflicts between water supply and demand have gradually become severe in the Hai River Basin (HRB) due to the socio-economic development. Thus, the exploitation and the [...] Read more.
Green water accounts for two-thirds of precipitation, and the proportion could be even higher in dry years. Conflicts between water supply and demand have gradually become severe in the Hai River Basin (HRB) due to the socio-economic development. Thus, the exploitation and the utilization of green water have attracted increasing attention. By gathering the related hydrological, meteorological, and geographic data, the spatiotemporal distribution of green water in HRB and the impacts of land use types on green water are analyzed based on the SWAT (Soil and Water Assessment Tool) model in this study. Furthermore, three new indices are proposed for evaluation, including the maximum possible storage of green water (MSGW), the consumed green water (CGW), and the utilizable green water (UGW). The results show that (1) the MSGW is relatively low in plain areas and its spatial distribution is significantly associated with the soil type; (2) according to the evaluation results of CGW and UGW in HRB, a further improvement of utilization efficiency of green water could be achieved; (3) in general, the utilization efficiency of precipitation in farmlands is higher than other land use types, which means that the planting of appropriate plants could be helpful to enhance the utilization efficiency of green water. Our results summarize the spatiotemporal distribution of green water resource and provide a reference for water resources management in other water-short agricultural areas. Full article
(This article belongs to the Section Hydrology)
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<p>The location of the Hai River basin (HRB) and its hydrological regions.</p> Full article ">Figure 2
<p>The distribution of the river net, sub-basins, and the locations of meteorological, precipitations and hydrologic stations which were used in the model simulations in HRB.</p> Full article ">Figure 3
<p>The spatial distribution of soil types (<b>a</b>) and land use types (<b>b</b>) in HRB.</p> Full article ">Figure 4
<p>The comparison of the observed and simulated runoff at the Zhangjiakou station (<b>a</b>) and the Dongyanghe station (<b>b</b>) (1995–2004).</p> Full article ">Figure 5
<p>The annual precipitation, blue water, green water, and soil water variation in HRB (1995–2004) (unit: mm).</p> Full article ">Figure 6
<p>The spatial distribution of the maximum possible storage of green water (MSGW) in HRB.</p> Full article ">Figure 7
<p>The components of green water and blue water in HRB (1995–2004) (unit: mm).</p> Full article ">Figure 8
<p>The spatial distribution of the consumed green water (<b>a</b>) and utilizable green water (<b>b</b>) in HRB.</p> Full article ">
29 pages, 1374 KiB  
Review
Disinfection Methods for Swimming Pool Water: Byproduct Formation and Control
by Huma Ilyas, Ilyas Masih and Jan Peter Van der Hoek
Water 2018, 10(6), 797; https://doi.org/10.3390/w10060797 - 16 Jun 2018
Cited by 34 | Viewed by 15665
Abstract
This paper presents a comprehensive and critical comparison of 10 disinfection methods of swimming pool water: chlorination, electrochemically generated mixed oxidants (EGMO), ultraviolet (UV) irradiation, UV/chlorine, UV/hydrogen peroxide (H2O2), UV/H2O2/chlorine, ozone (O3)/chlorine, O [...] Read more.
This paper presents a comprehensive and critical comparison of 10 disinfection methods of swimming pool water: chlorination, electrochemically generated mixed oxidants (EGMO), ultraviolet (UV) irradiation, UV/chlorine, UV/hydrogen peroxide (H2O2), UV/H2O2/chlorine, ozone (O3)/chlorine, O3/H2O2/chlorine, O3/UV and O3/UV/chlorine for the formation, control and elimination of potentially toxic disinfection byproducts (DBPs): trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs), trihaloacetaldehydes (THAs) and chloramines (CAMs). The statistical comparison is carried out using data on 32 swimming pools accumulated from the reviewed studies. The results indicate that O3/UV and O3/UV/chlorine are the most promising methods, as the concentration of the studied DBPs (THMs and HANs) with these methods was reduced considerably compared with chlorination, EGMO, UV irradiation, UV/chlorine and O3/chlorine. However, the concentration of the studied DBPs including HAAs and CAMs remained much higher with O3/chlorine compared with the limits set by the WHO for drinking water quality. Moreover, the enhancement in the formation of THMs, HANs and CH with UV/chlorine compared with UV irradiation and the increase in the level of HANs with O3/UV/chlorine compared with O3/UV indicate the complexity of the combined processes, which should be optimized to control the toxicity and improve the quality of swimming pool water. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Mean and standard deviation of THMs with different methods of disinfection. Note: “<b>a</b>” shows that chlorination, EGMO, UV irradiation, UV/chlorine and ozone/chlorine are not significantly different from each other; “<b>b</b>” shows that EGMO is significantly different from UV irradiation and ozone/chlorine; “<b>c</b>” shows that UV irradiation, UV/chlorine and ozone/chlorine are not significantly different from each other; “<b>d</b>” shows that ozone/UV/chlorine is significantly different from chlorination, EGMO, UV irradiation, UV/chlorine and ozone/chlorine at α = 0.05 (<span class="html-italic">p</span> &lt; 0.05); Statistical analysis to compare the means in case of UV/hydrogen peroxide, UV/hydrogen peroxide/chlorine and ozone/UV was not carried out because of fewer data points.</p> Full article ">Figure 2
<p>Mean and standard deviation of HAAs with different methods of disinfection. Note: “<b>a</b>” shows that chlorination, EGMO and UV/chlorine are not significantly different from each other; ”<b>b</b>“ shows that UV/chlorine is significantly different from EGMO at α = 0.05 (<span class="html-italic">p</span> &lt; 0.05). Statistical analysis to compare the means in the case of ozone/chlorine was not carried out because of fewer data points.</p> Full article ">Figure 3
<p>Mean and standard deviation of HANs with different methods of disinfection. Note: “<b>a</b>” shows that chlorination is not significantly different from EGMO, UV/chlorine and ozone/chlorine; “<b>b</b>” shows that EGMO and UV/chlorine are significantly different from ozone/chlorine; “<b>c</b>” shows that ozone/chlorine and ozone/UV/chlorine are not significantly different from each other; “<b>d</b>” shows that ozone/UV/chlorine is significantly different from chlorination, EGMO and UV/chlorine at α = 0.05 (<span class="html-italic">p</span> &lt; 0.05). Statistical analysis to compare the means in case of UV irradiation, UV/hydrogen peroxide, UV/hydrogen peroxide/chlorine and ozone/UV was not carried out because of fewer data points.</p> Full article ">Figure 4
<p>Mean and standard deviation of THAs with different methods of disinfection. Note: “<b>a</b>” shows that chlorination and UV/chlorine are not significantly different at α = 0.05 (<span class="html-italic">p</span> &lt; 0.05). Statistical analysis to compare the means in the case of ozone/chlorine was not carried out because of fewer data points.</p> Full article ">Figure 5
<p>Mean and standard deviation of CAMs with different methods of disinfection. Note: a statistical analysis to compare the means was not carried out because of fewer data points.</p> Full article ">Figure 6
<p>A graphical summary of concentrations of DBPs with different disinfection methods examined in this study.</p> Full article ">
19 pages, 3807 KiB  
Article
Recent Glacier Mass Balance and Area Changes from DEMs and Landsat Images in Upper Reach of Shule River Basin, Northeastern Edge of Tibetan Plateau during 2000 to 2015
by Xiaowen Zhang, Haojie Li, Zhihua Zhang, Qianxin Wu and Shiqiang Zhang
Water 2018, 10(6), 796; https://doi.org/10.3390/w10060796 - 16 Jun 2018
Cited by 11 | Viewed by 4957
Abstract
Glacier changes in the Upper Reach of the Shule River Basin (URSRB) serve as a good indicator of climate change in the western part of the Qilian Mountains, located on the northeastern edge of the Tibetan Plateau. However, information on recent glacier changes [...] Read more.
Glacier changes in the Upper Reach of the Shule River Basin (URSRB) serve as a good indicator of climate change in the western part of the Qilian Mountains, located on the northeastern edge of the Tibetan Plateau. However, information on recent glacier changes in the URSRB is limited. In this study, the changes in ice surface elevation were determined using geodetic methods based on digital elevation models (DEMs) derived from the Shuttle Radar Topography Mission (SRTM) (2000), and from pairs of Third Resources Satellite (ZY-3) of China (taken around 2013). In addition, glacier area changes from 2000–2015, were derived from Landsat TM/ETM+/OLI images. The results suggest that 478 glaciers with an area of 375.1 ± 2.68 km2 remained in the URSRB in 2015. Ice cover diminished by 57.5 ± 2.68 km2 (11.9 ± 0.60%), or 0.79 ± 0.04% a−1 and 35 small glaciers disappeared from 2000 to 2015 in the URSRB. The most pronounced glacier shrinkage occurred during 2004 to 2009. The average ice surface elevation of the URSRB from 1999 to 2013 reduced by about 4.98 ± 0.6 m, which is equal to a mass loss of 0.383 ± 0.046 m·a−1. This reduction indicates that the ice storage loss has accelerated since 1999, compared to a mass loss of 0.21 ± 0.04 m·a−1 around Tuanjiefeng from 1966 to 1999, as reported by Xu et al. (2013). Full article
(This article belongs to the Section Hydrology)
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<p>Location of Upper Reach of Shule River Basin, the blue box in left panel is Tuanjiefeng region.</p> Full article ">Figure 2
<p>The sinusoidal relationship between the elevation difference between ZY-3 digital elevation model (DEM) and Shuttle Radar Topography Mission (SRTM) DEM and aspects in (<b>a</b>) sub-region a; (<b>b</b>) sub-region b; (<b>c</b>) sub-region c; (<b>d</b>) sub-region d; (<b>e</b>) sub-region e of Upper Reach of Shule River Basin</p> Full article ">Figure 3
<p>Histogram of the elevation difference between ZY-3 DEM and SRTM DEM before co-registration correction of (<b>a</b>) sub-region <span class="html-italic">a</span>; (<b>b</b>) sub-region <span class="html-italic">b</span>; (<b>c</b>) sub-region <span class="html-italic">c</span>; (<b>d</b>) sub-region <span class="html-italic">d</span>; (<b>e</b>) sub-region <span class="html-italic">e</span>; and after co-registration correction of (<b>f</b>) sub-region <span class="html-italic">a</span>; (<b>g</b>) sub-region <span class="html-italic">b</span>; (<b>h</b>) sub-region <span class="html-italic">c</span>; (<b>i</b>) sub-region <span class="html-italic">d</span>; (<b>j</b>) sub-region <span class="html-italic">e</span> of Upper Reach of Shule River Basin</p> Full article ">Figure 4
<p>Variations in the glacier area in the Upper Reach of Shule River Basin from Chinese Glacier Inventory (CGI) (1970), 2000 to 2015.</p> Full article ">Figure 5
<p>Box-whisker plot of different glacier area classes in Upper Reach of Shule River Basin; the red total is the number of glaciers in this class; the periods in the columns from left to right are 2000/2001, 2004/2005, 2008/2009, 2012/2013, and 2014/2015, respectively. The top and bottom of the whisker represent the maximum and minimum glacier area in each class, respectively; the top, median, and bottom of the box represent the 75%, 50%, and 25% quantiles of the glacier area in the class, respectively.</p> Full article ">Figure 6
<p>The glacier area changes in different subregions in the Upper Reach of Shule River Basin.</p> Full article ">Figure 7
<p>Glacier ice surface elevation changes from 2000 to 2015 in sub-region <span class="html-italic">a, b</span>, <span class="html-italic">c</span>, <span class="html-italic">d</span>, and <span class="html-italic">e</span> in the Upper Reach of Shule River Basin.</p> Full article ">Figure 8
<p>Annual average air temperature (°C), average air temperature in melt season (°C), and annual precipitation (mm) in the URSRB during 1971 to 2013, where <span class="html-italic">k</span> is the slope of the linear trend, and <span class="html-italic">p</span> is the significance level of the <span class="html-italic">t</span> test.</p> Full article ">
18 pages, 5046 KiB  
Article
Variability of Short-Term Diel Water Temperature Amplitudes in a Mountain Lake
by Adam Choiński and Agnieszka Strzelczak
Water 2018, 10(6), 795; https://doi.org/10.3390/w10060795 - 15 Jun 2018
Cited by 4 | Viewed by 3840
Abstract
This paper presents an analysis of the variability of short-term water temperature amplitudes in Lake Morskie Oko, situated in the Tatra Mountains National Park, which makes the human impact on the lake very limited. The objective of the study was to determine to [...] Read more.
This paper presents an analysis of the variability of short-term water temperature amplitudes in Lake Morskie Oko, situated in the Tatra Mountains National Park, which makes the human impact on the lake very limited. The objective of the study was to determine to what extent an increase in depth contributes to suppressing daily water temperature amplitudes. It was shown, among other things, that water temperature amplitudes were the lowest in the period of occurrence of the ice cover, higher in the period of occurrence of other (than ice cover) ice phenomena, and the highest in the case of their lack. The analysis of profiles of water temperature amplitudes (in the case of lack of ice phenomena) resulted in determination of their six types. A strong correlation was observed in which the effect of mean daily air temperature and the effect of wind on water level amplitudes are considerably lower during the occurrence of ice phenomena in comparison to the period when the water surface is free from such phenomena. It was demonstrated that the near-bottom waters in Lake Morskie Oko are very stable in terms of temperature. The short transition period from ice cover to free water surface was determined to be very important, because it constitutes a threshold in the effect of air temperature and wind on changes in thermal dynamics of water (in this case expressed in amplitude values). Finally, proposals are presented for future expansion of the scope of research on water temperature amplitudes. This work is important, because the amplitudes were investigated not only at the surface of the lake, but also at its bottom, and also during the ice cover period, when the lake was isolated from the atmospheric influences. This study may contribute to better understanding of the lake water temperature responses to climate change and thus to more accurate prediction these patterns in lake globally. Moreover, understanding of changes in water temperature is closely related to the variability of its heat resources, and these in the future may be used on a large scale. In the case of a mountain lake such as Morskie Oko, the heat of water may be used, for example, for heating tourist shelters. Full article
(This article belongs to the Section Hydrology)
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<p>Changes in water temperature amplitudes in time at a depth of 1 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 2
<p>Changes in water temperature amplitudes in time at a depth of 5 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 3
<p>Changes in water temperature amplitudes in time at a depth of 10 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 4
<p>Changes in water temperature amplitudes in time at a depth of 20 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 5
<p>Changes in water temperature amplitudes in time at a depth of 50 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 6
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 1. The dates for each line are described in detail in the text above.</p> Full article ">Figure 7
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 2. The dates for each line are described in detail in the text above.</p> Full article ">Figure 8
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 3. The dates for each line are described in detail in the text above.</p> Full article ">Figure 9
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 4. The dates for each line are described in detail in the text above.</p> Full article ">Figure 10
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 5. The dates for each line are described in detail in the text above.</p> Full article ">Figure 11
<p>Correlation between water temperature amplitude and depth in Lake Morskie Oko. Temperature profile Type 6. The dates for each line are described in detail in the text above.</p> Full article ">Figure 12
<p>Changes in mean daily air temperatures and water temperature amplitudes in time at a depth of 1 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">Figure 13
<p>Changes in mean daily wind velocity and water temperature amplitudes in time at a depth of 1 m. a—presence of ice cover; b—occurrence of ice phenomena; c—lack of ice cover/ice phenomena.</p> Full article ">
18 pages, 3937 KiB  
Article
Navigating the Water-Energy Governance Landscape and Climate Change Adaptation Strategies in the Northern Patagonia Region of Argentina
by Laura Forni, Marisa Escobar, Pablo Cello, Marta Marizza, Gustavo Nadal, Leonidas Girardin, Fernando Losano, Lisandro Bucciarelli, Charles Young and David Purkey
Water 2018, 10(6), 794; https://doi.org/10.3390/w10060794 - 15 Jun 2018
Cited by 9 | Viewed by 6588
Abstract
Water scientists often find themselves interacting with decision-makers with varying levels of technical background. The sustainable management of water resources is complex by nature, and future conditions are highly uncertain, requiring modeling approaches capable of accommodating a variety of parameters and scenarios. Technical [...] Read more.
Water scientists often find themselves interacting with decision-makers with varying levels of technical background. The sustainable management of water resources is complex by nature, and future conditions are highly uncertain, requiring modeling approaches capable of accommodating a variety of parameters and scenarios. Technical findings from these analyses need to be positioned and conducted within the governance institutions to ensure decision-makers utilize them. This paper examines the water resource challenges for a large basin in northern Patagonia, Argentina and utilizes the Robust Decision Support (RDS) framework to evaluate trade-offs and strategies in a participatory process that included researchers and decision-makers. Integrated water resources models using simulation modeling and decision space visualization show significant climate change impacts, which are augmented with irrigated agriculture expansion and increasing hydropower production. Full article
(This article belongs to the Collection Water Policy Collection)
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<p>The Limay, Neuquén and Negro River basins.</p> Full article ">Figure 2
<p>Visualization Process from [<a href="#B32-water-10-00794" class="html-bibr">32</a>].</p> Full article ">Figure 3
<p>WEAP schematic of the Comahue region basins modeled in this study.</p> Full article ">Figure 4
<p>Impacts of climate change on snow pack during the first month of the water year.</p> Full article ">Figure 5
<p>Current management in short term: The color dimension of the visualization is designed so shades of green means the performance metrics fails 10% (or less) of the time. Shades of red show the performance metric fails between 11% and 100% of the time (darker shades of red when close to 100% and lighter shades of red when closer to 11%) [<a href="#B34-water-10-00794" class="html-bibr">34</a>].</p> Full article ">Figure 6
<p>Current management in long term: The color dimension of the visualization is designed so shades of green means the performance metrics fails 10% (or less) of the time. Shades of red show the performance metric fails between 11% and 100% of the time (darker shades of red when close to 100% and lighter shades of red when closer to 11%) [<a href="#B34-water-10-00794" class="html-bibr">34</a>].</p> Full article ">Figure 7
<p>Impact of strategies on the performance metrics [<a href="#B34-water-10-00794" class="html-bibr">34</a>].</p> Full article ">
21 pages, 1766 KiB  
Article
Efficiency Evaluation of Water Consumption in a Chinese Province-Level Region Based on Data Envelopment Analysis
by Ping Hu, Na Chen, Yongjun Li and Qiwei Xie
Water 2018, 10(6), 793; https://doi.org/10.3390/w10060793 - 15 Jun 2018
Cited by 24 | Viewed by 5281
Abstract
Due to the large volume of sewage in China, the efficiency of water consumption evaluated by the traditional model may be inaccurate. This paper evaluates the water consumption efficiency more scientifically. First, this paper uses the CCR model to evaluate the resource efficiency [...] Read more.
Due to the large volume of sewage in China, the efficiency of water consumption evaluated by the traditional model may be inaccurate. This paper evaluates the water consumption efficiency more scientifically. First, this paper uses the CCR model to evaluate the resource efficiency and environmental efficiency separately. The latter is generally lower than the former, which means the issue of water pollution is more serious than the problem of water resource consumption. Then, the water consumption efficiency is integrally evaluated by an eco-inefficiency model which focuses on undesirable outputs. The results are in good agreement with the results of the CCR model: (1) Only Beijing, Tianjin, and Shanghai are eco-efficient in terms of water consumption, water consumption efficiency in the southeastern coastal areas is higher than in the Midwest, and the overall water environment is bad; (2) China needs to focus on reducing industrial wastewater; (3) the output of water consumption has a lot of room for improvement; and (4) the output improvement schemes of all provinces have some similarities and are related to many features. So, this paper has made a clustering analysis of the improvement schemes and given detailed suggestions for improving the eco-efficiency of water consumption in China according to the clustering result. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>Water consumption in provincial regions.</p> Full article ">Figure 2
<p>Output of water consumption.</p> Full article ">Figure 3
<p>Relationship between environmental efficiency and resource efficiency.</p> Full article ">Figure 4
<p>Eco-efficiencies of 31 provinces in China (A darker color indicates a lower eco-efficiency).</p> Full article ">Figure 5
<p>Result of average linkage clustering.</p> Full article ">Figure 6
<p>The proportion of output improvement.</p> Full article ">Figure 7
<p>The spatial distribution of the clustering result.</p> Full article ">
27 pages, 5681 KiB  
Article
Horizontal Circulation Patterns in a Large Shallow Lake: Taihu Lake, China
by Sien Liu, Qinghua Ye, Shiqiang Wu and Marcel J. F. Stive
Water 2018, 10(6), 792; https://doi.org/10.3390/w10060792 - 15 Jun 2018
Cited by 23 | Viewed by 6291
Abstract
Wind induced hydrodynamic circulations play significant roles in the transport and mixing process of pollutants and nutrients in large shallow lakes, but they have been usually overlooked, while environmental, biological, and ecological aspects of eutrophication problems get the most focus. Herein we use [...] Read more.
Wind induced hydrodynamic circulations play significant roles in the transport and mixing process of pollutants and nutrients in large shallow lakes, but they have been usually overlooked, while environmental, biological, and ecological aspects of eutrophication problems get the most focus. Herein we use a three-dimensional model, driven by steady/unsteady wind, river discharge, rainfall, evaporation to investigate the spatially heterogeneous, large-scale hydrodynamic circulations and their role in transporting and mixing mechanisms in Taihu Lake. Wind direction and velocity determines the overall hydrodynamic circulation structure, i.e. direction, intensity, and position. A relative stable hydrodynamic circulation pattern has been formed shortly with steady wind (~2 days). Vertical profiles of horizontal velocities are linearly correlated to the relative shallowness of water depth. Volume exchange between subbasins, influenced by wind speed and initial water level, differs due to the complex topography and irregular shape. With unsteady wind, these findings are still valid to a high degree. Vertical variations in hydrodynamic circulation are important in explaining the surface accumulation of algae scums in Meiliang Bay in summers. Vorticity of velocity field, a key indicator of hydrodynamic circulation, is determined by wind direction, bathymetry gradient, and water depth. The maximum change of velocity vorticity happens when wind direction is perpendicular to bathymetry gradient. Furthermore, Lagrangian-based tracer transport is used to estimate emergency pollution leakage impacts, and also to evaluate operational management measurements, such as, the large-scale water transfer. The conclusion is that the large-scale water transfer does not affect the hydrodynamic circulation and volume exchanges between subbasins significantly, but succeeds to transport and then mix the fresh, clean Yangtze River water to a majority area of Taihu Lake. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>(<b>a</b>) Eight subzones, boundary discharge locations, cross-sections and positions of 5 monitoring stations, namely, 1. Wangting Station; 2. Dapukou Station; 3. Jiapu Station; 4. Xiaomeikou Station; 5. Xishan Station. (<b>b</b>) Grid and depth used in numerical model, depth unit: m.</p> Full article ">Figure 2
<p>Monthly discharge data (bars, positive values represent inflow discharge) and average daily water level (blue line) of Taihu Lake in 2008.</p> Full article ">Figure 3
<p>In situ measured and modeled water level for five monitoring stations in 2008, water levels are based on Wusong Datum. Measured levels are shown with points, and model results are shown with solid lines.</p> Full article ">Figure 4
<p>Depth-averaged circulation gyres for northwest and southeast wind.</p> Full article ">Figure 5
<p>Volume exchange between subbasins, with constant wind unit of numbers shown in the figure in m<sup>3</sup>/s.</p> Full article ">Figure 6
<p>Volume exchange rates between each subbasin with southeast wind. Unit of numbers shown in the figure is m<sup>3</sup>/s; (<b>a</b>) 5 m/s southeast wind scenario; (<b>b</b>) 5 m/s southeast wind and 1 m initial water level scenario; (<b>c</b>) 10 m/s southeast wind scenario; (<b>d</b>) 10 m/s southeast wind and 1 m initial water level scenario.</p> Full article ">Figure 7
<p>Surface and bottom horizontal hydrodynamic circulation pattern with southeast wind, with (<b>a</b>) the surface layer velocity field, (<b>b</b>) the bottom layer velocity field.</p> Full article ">Figure 8
<p>Comparison of surface and bottom hydrodynamic circulation pattern of Meiliang Bay of model result of case 2 with 5 m/s constant southeast wind. The blue vector indicate flow pattern on the surface layer and the red vector indicate the bottom layer. The x, y coordinates (m) are based on Beijing 1954 coordinate system.</p> Full article ">Figure 9
<p>Surface and bottom layer flux with constant wind. The hollow arrows represent surface flux, while solid arrows are the bottom flux. (<b>a</b>) Model result with constant 5 m/s southeast wind, (<b>b</b>) model result with constant 10 m/s southeast wind, (<b>c</b>) model result with constant 5 m/s northwest wind, (<b>d</b>) model result with constant 10 m/s northwest wind. Unit of numbers in the figure is m<sup>3</sup>/s.</p> Full article ">Figure 10
<p>Time series of wind vectors of year 2008, with north wind pointing to the top, length of each arrow refers to the wind speed.</p> Full article ">Figure 11
<p>Time series of surface and bottom flux of reference scenario (case 1), the red line refers to bottom flux, and the blue line refers to the surface flux, and the unit in the figure is m<sup>3</sup>/s.</p> Full article ">Figure 12
<p>Total discharges and corresponding wind vector of southwest1 cross-section in April 2008. The blue vector represents the wind record and the red line represents the total flux through southwest1 cross-section. The length of the blue wind vector is the magnitude of wind speed. The position and the arrow representing the positive flux are illustrated in the upper right subplot.</p> Full article ">Figure 13
<p>(<b>a</b>) Depth contours of Taihu Lake, unit: m; (<b>b</b>) Depth-averaged vorticity with constant northwest wind.</p> Full article ">Figure 14
<p>Numerical model results of velocity and vorticity with 2 m constant depth and constant northwest wind. The vectors represent velocity and colored patches represent vorticity. (<b>a</b>) Surface layer; (<b>b</b>) Bottom layer; (<b>c</b>) Vorticity of depth-averaged velocity.</p> Full article ">Figure 15
<p>Time series of tracer concentration images, with the model driven by constant 5 m/s southeast wind. Unit in the figure is kg/m<sup>3</sup>.</p> Full article ">Figure 16
<p>Time series of tracer concentration images, with the model driven by constant 5 m/s northwest wind. Unit in the figure is kg/m<sup>3</sup>.</p> Full article ">Figure 17
<p>Important spots for pollution (tracer) release experiment analysis.</p> Full article ">Figure 18
<p>Tracer concentration at location A with different tracer release spot, the upper subplot shows tracer concentration with constant 5 m/s southeast wind condition, while the lower subplot describes the northwest wind condition. To make the plot clearer, data are extracted per 3 days from the model results.</p> Full article ">Figure 19
<p>Total discharge of Gonghu Bay and Dongtaihu Bay. The red lines refer to the reference case, while blue line represents scenarios without water transfer.</p> Full article ">Figure 20
<p>Time series of depth-averaged tracer concentration images, unit in map is kg/m<sup>3</sup>. To conclude, the water transfer project in Taihu Lake did not significantly stimulate the hydrodynamic circulation or volume exchange between subbasins. However, tracer scenarios illustrate the project did succeed in redistributing the clearer transferred water throughout the lake, especially in some semi-closed subbasins, like Meiliang Bay and Dongtaihu Bay.</p> Full article ">Figure A1
<p>Vertical cross-section layout for volume exchange calculation.</p> Full article ">
21 pages, 2411 KiB  
Article
Assessing Environmental Flow Targets Using Pre-Settlement Land Cover: A SWAT Modeling Application
by Sean J. Zeiger and Jason A. Hubbart
Water 2018, 10(6), 791; https://doi.org/10.3390/w10060791 - 15 Jun 2018
Cited by 12 | Viewed by 4564
Abstract
Determining environmental flow requirements to sustain aquatic ecosystem health remains a challenge. The purpose of this research was to quantify the extent of current flow alterations relative to baseline hydrologic conditions of a simulated historic flow regime prior to anthropogenic flow disturbance (i.e., [...] Read more.
Determining environmental flow requirements to sustain aquatic ecosystem health remains a challenge. The purpose of this research was to quantify the extent of current flow alterations relative to baseline hydrologic conditions of a simulated historic flow regime prior to anthropogenic flow disturbance (i.e., pre-settlement flows). Results allowed assessment of the efficacy of environmental flow targets based on pre-settlement land cover in a contemporary mixed-land-use catchment (i.e., urban, agricultural, and forested). Pre-settlement flows were simulated using the Soil and Water Assessment Tool (SWAT). Pre-settlement land cover, based on soil physical characteristics, was used to simulate pre-settlement flows with the SWAT model. Environmental flow targets were calculated for each flow element of a historic flow regime (magnitude, frequency, duration, timing, and rate of change). Urban (20% of watershed area) and agricultural development (42% of watershed area) were correlated to decreased median daily stream flow by 0.8 m3 s−1 (percent difference = −115%), increased maximum daily flow by 22 m3 s−1 (percent difference = 13%), and a 34% increase in daily flow variability. High flow frequency increased by 45–76% following development. Results highlight a need for consideration of environmental flow targets appropriate for watersheds already modified by existing land use, and point to a need for long-term, broad-scale, and persistent efforts to develop achievable environmental flow recommendations, particularly in rapidly urbanizing mixed-land-use watersheds. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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Full article ">Figure 1
<p>Nested-scale experimental watershed study design, including five gauging sites located in Hinkson Creek watershed, MO, USA. The 2011 National Land Cover Dataset (NLCD 2011) was used to calculate the land use and land cover shown.</p> Full article ">Figure 2
<p>Scree plot shows the threshold where broken-stick modeled data exceeded observed eigenvalues (at a value of 6.5), indicating that the appropriate number of principal components to consider was six in this study.</p> Full article ">Figure 3
<p>Simulated (dashed line) and observed (solid line) daily average flow duration at five gauging sites in Hinkson Creek, MO, USA.</p> Full article ">Figure 4
<p>Biplot showing scores generated from simulated pre-settlement and observed developed flow data at five gauging sites in Hinkson Creek, MO, USA. Loadings of hydrologic indices with the greatest absolute loadings for nine flow elements are also shown. PC1 and PC2 are principal component axes. Definitions of hydrologic indices are shown in <a href="#water-10-00791-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 5
<p>Percent differences between simulated pre-settlement and observed developed hydrologic indices at five gauging sites in Hinkson Creek, MO, USA. Definitions of hydrologic indices are shown in <a href="#water-10-00791-t002" class="html-table">Table 2</a>.</p> Full article ">
15 pages, 3113 KiB  
Article
Adaptation Strategies of the Hydrosocial Cycles in the Mediterranean Region
by Ana Arahuetes, María Hernández and Antonio M. Rico
Water 2018, 10(6), 790; https://doi.org/10.3390/w10060790 - 15 Jun 2018
Cited by 15 | Viewed by 4771
Abstract
The Spanish Mediterranean region has been affected by several factors over the years (climatic conditions of aridity, high water demands, rapid and intense urban and population growth, climate change), that have generated a negative water balance whereby water resources are unable to meet [...] Read more.
The Spanish Mediterranean region has been affected by several factors over the years (climatic conditions of aridity, high water demands, rapid and intense urban and population growth, climate change), that have generated a negative water balance whereby water resources are unable to meet the demand. Diversifying supply sources by resorting to new resources has been a necessity that has stimulated the expansion and integration of non-conventional water sources (desalination and reuse of reclaimed water) and sustainable solutions. The aim of this paper is to evaluate the adaptation strategies that have been developed in Alicante, Benidorm and Torrevieja in order to adjust their hydrosocial cycles to development and future scenarios. The theoretical analysis developed in this paper is corroborated by the study of the hydrosocial cycle evolution of three cities in the southeast of Spain, and the adaptive measures that the different stakeholders involved in the cycle have developed in each of them. The input and output of the systems are accounted for with information provided by the management companies in each of the phases (urban consumption; treated, reused and desalinated volumes), which highlight how the diversification of resources and the incorporation of non-conventional resources have been essential for adaptation. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on the Hydrological Cycle (ChyCle-2017))
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<p>Location of the cases studied.</p> Full article ">Figure 2
<p>Model to create urban flow diagrams. Compiled by the author.</p> Full article ">Figure 3
<p>Flow diagram of the city of Alicante in 2013. Compiled by the author.</p> Full article ">Figure 4
<p>Flow diagram of the city of Benidorm in 2013. Compiled by the author.</p> Full article ">Figure 5
<p>Flow diagram of the city of Torrevieja in 2013. Compiled by the author.</p> Full article ">Figure 6
<p>Origin and uses of rainwater and grey water [<a href="#B47-water-10-00790" class="html-bibr">47</a>,<a href="#B49-water-10-00790" class="html-bibr">49</a>].</p> Full article ">Figure 7
<p>Origin of the different sources of the MCT.</p> Full article ">Figure 8
<p>Diagram of the distribution, management and use systems linked to case studies in 2017. Compiled by the author.</p> Full article ">
19 pages, 6997 KiB  
Article
Spatio-Temporal Analysis of Meteorological Elements in the North China District of China during 1960–2015
by Jinsong Ti, Yuhao Yang, Xiaogang Yin, Jing Liang, Liangliang Pu, Yulin Jiang, Xinya Wen and Fu Chen
Water 2018, 10(6), 789; https://doi.org/10.3390/w10060789 - 15 Jun 2018
Cited by 22 | Viewed by 4451
Abstract
The North China District (NCD) is one of the main grain production regions in China. The double cropping system of irrigation has been leading to the groundwater table decline at the speed of 1–2 m per year. Climate change leads to uncertainty surrounding [...] Read more.
The North China District (NCD) is one of the main grain production regions in China. The double cropping system of irrigation has been leading to the groundwater table decline at the speed of 1–2 m per year. Climate change leads to uncertainty surrounding the future of the NCD agricultural system, which will have great effects on crop yields and crop water demands. In this research, the Meteorological dataset from 54 weather station sites over the period 1960–2015 were collected to quantify the long-term spatial and temporal trends of meteorological data, including daily minimum temperature (Tmin), maximum temperature (Tmax), precipitation, solar radiation, reference evapotranspiration (ET0), and aridity index (AI). The results show that the long-term wheat and maize growing season and annual average air temperatures (Tmin and Tmax) showed strong north to south increasing trends throughout the NCD. The average annual precipitation was 632.9 mm across the NCD, more than 70% of which was concentrated in the maize growing season. The regional average annual ET0 was 1026.1 mm, which was 531.2 and 497.4 mm for the wheat and maize growing season, respectively. The regional precipitation decreased from northwest to southeast in each growing season and annual timescale. The funnel areas have lower precipitation and higher ET0 than the regional average, which may lead to the mining of the groundwater funnel area. The regional average annual AI is 0.63, which lies in the humid class. For temporal analysis, the regional average trends in annual Tmin, Tmax, solar radiation, ET0, precipitation, and AI were 0.37 °C/10a, 0.15 °C/10a, −0.28 MJ/day/m2/10a, −2.98 mm/10a, −12.04 mm/10a, and 0.005/10a, respectively. The increasing trend of temperature and the decreasing trend of solar radiation may have a negative effect on the regional food security. The funnel area AI showed a significant increasing trend for the winter wheat growing season and a decreasing trend for maize, which indicated that more irrigation will be needed for the maize growing season and the winter fallow policy may lead to the increasing trend precipitation being wasted. Analyzing the growing season and the annual meteorological elements of the spatiotemporal trends can help us better understand the influence of climate change on the natural resources and agricultural development in both the past and the future, and will provide us with invaluable information for the modification of cropping patterns to protect the regional and national water and food security. Full article
(This article belongs to the Section Water Use and Scarcity)
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<p>Location of North China District and the weather station site.</p> Full article ">Figure 2
<p>The spatial distribution of (<b>a</b>–<b>c</b>) long-term average minimum air temperature (T<sub>min</sub>) and (<b>d</b>–<b>f</b>) maximum air temperature (T<sub>max</sub>) on the winter-wheat growing season, summer-maize growing season, and annual basis.</p> Full article ">Figure 3
<p>The spatial distribution and statistical significance of the trends in (<b>a</b>–<b>c</b>) T<sub>min</sub> and (<b>d</b>–<b>f</b>) T<sub>max</sub> on the winter-wheat growing-season, the summer-maize growing-season, and the annual basis.</p> Full article ">Figure 4
<p>The spatial distribution of the long-term average daily solar radiation (R) for (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">Figure 5
<p>The spatial distribution and statistical significance of the trends in the daily solar radiation (R) on (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) annual basis.</p> Full article ">Figure 6
<p>The spatial distribution of the long-term average precipitation (P) for (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">Figure 7
<p>The spatial distribution and statistical significance of the trends in the precipitation (P) of the (<b>a</b>) winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">Figure 8
<p>The spatial distribution of the long-term average reference evapotranspiration (ET<sub>0</sub>) on (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">Figure 9
<p>The spatial distribution and statistical significance of the trends in evapotranspiration (ET<sub>0</sub>) on (<b>a</b>) the winter-wheat growing season, (<b>b</b>) the summer-maize growing season, and (<b>c</b>) the annual basis.</p> Full article ">Figure 10
<p>The spatial distribution of the long-term average aridity index on (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">Figure 11
<p>The spatial distribution and statistical significance of the trends in the aridity index on (<b>a</b>) the winter-wheat growing season; (<b>b</b>) the summer-maize growing season and (<b>c</b>) the annual basis.</p> Full article ">
19 pages, 7431 KiB  
Article
Drought Prediction for Areas with Sparse Monitoring Networks: A Case Study for Fiji
by Jinyoung Rhee and Hongwei Yang
Water 2018, 10(6), 788; https://doi.org/10.3390/w10060788 - 14 Jun 2018
Cited by 14 | Viewed by 6720
Abstract
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized [...] Read more.
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized Precipitation Index for the case study area, Fiji. In order to overcome the limitation of a sparse monitoring network, both in-situ data and bias-corrected dynamic downscaling of historical climate data from the Weather Research Forecasting (WRF) model were used as reference data. Performance measures of the mean absolute error as well as classification accuracy were used. The WRF outputs reflect the topography of the area. Hybrid models showed better performance than simply bias corrected forecasts in most cases. Especially, the model based on Extra-Trees trained using the WRF model outputs performed the best in most cases. Full article
(This article belongs to the Special Issue Statistical Analysis and Stochastic Modelling of Hydrological Extremes)
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<p>Topography of Fiji’s main islands (color shades are in units of meters).</p> Full article ">Figure 2
<p>Location of (<b>a</b>) the rainfall gauges; and (<b>b</b>) the centroids of the Weather Research and Forecasting (WRF) model outputs.</p> Full article ">Figure 3
<p>Flow diagram of the drought prediction model.</p> Full article ">Figure 4
<p>Training performance (<b>a</b>) Total MAE; (<b>b</b>) Drought MAE; (<b>c</b>) Total Accuracy; and (<b>d</b>) Drought Accuracy of SPI6 predictions from simply bias-corrected precipitation forecast (FCST_ONLY), Extra-Trees (ERT) and Adaboost trained using 80% of the WRF model outputs (ERT_WRF and Adaboost_WRF), and ERT and Adaboost trained using 100% of in-situ data (ERT_INSITU and Adaboost_INSITU).</p> Full article ">Figure 5
<p>Scatter plots of reference SPI6 vs. 1-month lead SPI6 predictions for training based on (<b>a</b>) FCST_ONLY; (<b>b</b>) ERT_WRF; (<b>c</b>) Adaboost_WRF; (<b>d</b>) ERT_INSITU; and (<b>e</b>) Adaboost_INSITU. Reference SPI6 are based on 80% of the WRF model outputs from (<b>a</b>) to (<b>c</b>) and 100% of in-situ data for (<b>d</b>,<b>e</b>).</p> Full article ">Figure 6
<p>Test performance (<b>a</b>) Total MAE; (<b>b</b>) Drought MAE; (<b>c</b>) Total Accuracy; and (<b>d</b>) Drought Accuracy of SPI6 predictions from simply bias-corrected precipitation forecast (FCST_ONLY), ERT and Adaboost trained using 80% of the WRF model outputs (ERT_WRF and Adaboost_WRF), and ERT and Adaboost trained using 100% of in-situ data (ERT_INSITU and Adaboost_INSITU). Test was performed using the 20% remaining WRF model outputs.</p> Full article ">Figure 7
<p>Scatter plots of reference SPI6 vs. 1-month lead SPI6 predictions for testing based on (<b>a</b>) FCST_ONLY; (<b>b</b>) ERT_WRF; (<b>c</b>) Adaboost_WRF; (<b>d</b>) ERT_INSITU; and (<b>e</b>) Adaboost_INSITU. Reference SPI6 are based on 20% of the WRF model outputs.</p> Full article ">Figure 8
<p>Scatter plots of reference SPI6 vs. 3-month lead SPI6 predictions for testing based on (<b>a</b>) FCST_ONLY; (<b>b</b>) ERT_WRF; (<b>c</b>) Adaboost_WRF; (<b>d</b>) ERT_INSITU; and (<b>e</b>) Adaboost_INSITU. Reference SPI6 are based on 20% of the WRF model outputs.</p> Full article ">Figure 9
<p>Spatial distribution maps of 1-month lead SPI6 predictions for March 2010 and June 2010 and WRF-based SPI6.</p> Full article ">Figure 9 Cont.
<p>Spatial distribution maps of 1-month lead SPI6 predictions for March 2010 and June 2010 and WRF-based SPI6.</p> Full article ">Figure 10
<p>Relative importance scores of input variables to machine learning models for (<b>a</b>) ERT_WRF; (<b>b</b>) Adaboost_WRF; (<b>c</b>) ERT_INSITU; and (<b>d</b>) Adaboost_INSITU.</p> Full article ">
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