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Water, Volume 9, Issue 5 (May 2017) – 63 articles

Cover Story (view full-size image): Global warming causes rapid shrinking of mountain glaciers and the formation of numerous new lakes. Such new lakes can be amplifiers of natural hazards to downstream populations, but also constitute tourist attractions, offer new potential for hydropower, and may be of interest for water management. Using GIS-based analysis and modeling techniques, a systematic inventory of glacier-bed overdeepenings as sites of possible future lake formation was compiled for the still glacierized parts of the Peruvian Andes. View this paper
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1293 KiB  
Article
Understanding the Role of Groundwater in a Remote Transboundary Lake (Hulun Lake, China)
by Hongbin Gao, M. Cathryn Ryan, Changyou Li and Biao Sun
Water 2017, 9(5), 363; https://doi.org/10.3390/w9050363 - 22 May 2017
Cited by 22 | Viewed by 6437
Abstract
Hulun Lake, located in a remote, semi-arid area in the northeast part of Inner Mongolia, China, shares a transboundary basin with Mongolia and supports a unique wetland ecosystem that includes many endangered species. Decadal scale decreases in the lake stage and increased salinity [...] Read more.
Hulun Lake, located in a remote, semi-arid area in the northeast part of Inner Mongolia, China, shares a transboundary basin with Mongolia and supports a unique wetland ecosystem that includes many endangered species. Decadal scale decreases in the lake stage and increased salinity make an understanding of the lake’s water and salt sources critical for appropriate design of strategies to protect and manage the lake. Multiple tracers (chloride, and δ18O and δ2H in water) in samples collected from lake water, rivers, and nearby water wells were used in conjunction with an annual water balance based on historic data to better understand the lake’s major water and salt sources. The average annual water balance was conducted for two time periods: 1981–2000 and 2001–2013. The contribution of river discharge to the annual lake input decreased by half (from 64% to 31%) between the two time periods, while the volumetric contribution of groundwater discharge increased four-fold (from about 11% to about 50% of the total lake input). Significant evaporation was apparent in the stable isotope composition of the present-day lake water, however, evaporation alone could not account for the high lake water chloride concentrations. Limited domestic well water sampling, a regional salinity survey, and saline soils suggest that high chloride groundwater concentrations exist in the region south of the lake. The chloride mass balance suggested that groundwater currently contributes more than 90% of the annual chloride loading to the lake, which is likely four times greater than the earlier period (1981–2000) with lower groundwater input. The use of water and chloride mass balances combined with water isotope analyses could be applied to other watersheds where hydrologic information is scarce. Full article
(This article belongs to the Special Issue Isotopes in Hydrology and Hydrogeology)
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<p>(<b>a</b>) Study area in country-scale map, with a green star indicating the location of the International Atomic Energy Agency’s Global Network of Isotopes in Precipitation (Qiqihar station); (<b>b</b>) Hulun Lake basin showing two major rivers (Kelulun River and Wuerxun River) that flow into Hulun Lake, and the Xinkai River, which intermittently provides an outlet for Hulun Lake to the Hailaer River; (<b>c</b>) River, domestic water well, and lake sampling site locations. The arrow near Hailaer River shows the flow direction of Hailaer River. The Xinkai River is an intermittent connection between Hulun Lake and the Hailaer River.</p>
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<p>Dual isotope diagram of δ<sup>18</sup>O and δ<sup>2</sup>H in lake, river, and domestic water well samples. The local meteoric water line (LMWL) is shown for the International Atomic Energy Agency’s Global Network of Isotopes in Precipitation, Qiqihar station [<a href="#B42-water-09-00363" class="html-bibr">42</a>].</p>
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<p>A cross plot of δ<sup>18</sup>O values and chloride concentrations in lakes, rivers and wells. Zone I and Zone II are divided based on the significant difference in chloride concentrations in western and southern wells, respectively.</p>
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<p>δ<sup>18</sup>O vs. chloride concentrations including the minimum, average, and maximum values for total input into Hulun Lake (filled black symbols; <a href="#water-09-00363-t003" class="html-table">Table 3</a>), measured Hulun Lake values, and average values for each of the main three inputs (precipitation, river flow, and groundwater) that are thought to contribute to the combined input (with solid arrows indicating likely inputs, and the dashed arrow represents potential sources). The dashed lines represent δ<sup>18</sup>O and chloride concentration evolution with increasing evaporation modeled by <span class="html-italic">E/I</span> ratios as described in <a href="#sec2dot4-water-09-00363" class="html-sec">Section 2.4</a> (with T = 0.3 °C, and humidity = 60%).</p>
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<p>The spatial distribution of salinity (g/L) in domestic water well samples around Hulun Lake. Unpublished data (collected in 2008), College of Water Resources and Civil Engineering, Inner Mongolia Agricultural University.</p>
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5949 KiB  
Article
Thermal and Physical Investigations into Lake Deepening Processes on Spillway Lake, Ngozumpa Glacier, Nepal
by Ulyana Nadia Horodyskyj
Water 2017, 9(5), 362; https://doi.org/10.3390/w9050362 - 22 May 2017
Cited by 2 | Viewed by 6426
Abstract
This paper investigates physical processes in the four sub-basins of Ngozumpa glacier’s terminal Spillway Lake for the period 2012–2014 in order to characterize lake deepening and mass transfer processes. Quantifying the growth and deepening of this terminal lake is important given its close [...] Read more.
This paper investigates physical processes in the four sub-basins of Ngozumpa glacier’s terminal Spillway Lake for the period 2012–2014 in order to characterize lake deepening and mass transfer processes. Quantifying the growth and deepening of this terminal lake is important given its close vicinity to Sherpa villages down-valley. To this end, the following are examined: annual, daily and hourly temperature variations in the water column, vertical turbidity variations and water level changes and map lake floor sediment properties and lake floor structure using open water side-scan sonar transects. Roughness and hardness maps from sonar returns reveal lake floor substrates ranging from mud, to rocky debris and, in places, bare ice. Heat conduction equations using annual lake bottom temperatures and sediment properties are used to calculate bottom ice melt rates (lake floor deepening) for 0.01 to 1-m debris thicknesses. In areas of rapid deepening, where low mean bottom temperatures prevail, thin debris cover or bare ice is present. This finding is consistent with previously reported localized regions of lake deepening and is useful in predicting future deepening. Full article
(This article belongs to the Special Issue Global Warming Impacts on Mountain Glaciers and Communities)
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<p>Overview map of the Khumbu region (April 2013 Landsat) showing Tsho Rolpa Lake (NW-flowing Trakarding glacier) in the Rowaling Valley; Spillway Lake (SE-flowing Ngozumpa glacier) in the Gokyo Valley; and Imja Lake (W-flowing Imja glacier) in the Khumbu Valley.</p>
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<p>Ngozumpa glacier’s terminal moraine and western outlet stream. Boulders along the outlet are a testament to previous floods in the area.</p>
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<p>The evolution of Spillway Lake’s sub-basins since 2001 (<b>A</b>) and depth map from the original 2009/2010 survey work (<b>B</b>). Adapted from [<a href="#B4-water-09-00362" class="html-bibr">4</a>], with permission from © Elsevier (<span class="html-italic">Geomorphology</span>).</p>
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<p>2016 GeoEye high-resolution image of Ngozumpa glacier (<b>A</b>) with Spillway Lake close-up (<b>B</b>), showing temperature buoy locations for this study in the sub-basins (yellow: northwest; green: northeast; red: main; blue: southwest), weather station (white square) and glacier outlet channel (yellow arrow).</p>
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<p>Vertical temperature and turbidity variations in the NW (<b>A</b>,<b>B</b>), NE (<b>C</b>,<b>D</b>) and Main sub-basin (<b>E</b>,<b>F</b>). The blue line is indicative of morning averages, while the black dotted line represents afternoon averages.</p>
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<p>Annual temperatures at the surface (in blue), middle (in green), and bottom (in red) for each sub-basin from 01 June 2013 to 01 June 2014. (<b>A</b>) NW buoy; (<b>B</b>) NE buoy; (<b>C</b>) MB (Main basin) buoy; and (<b>D</b>) SW buoy. The arrows in each show the arrival of Cyclone Phailin (October 2013).</p>
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<p>Seasonal temperature variations in the Main sub-basin buoy location for the summer/monsoon (<b>A</b>), fall (<b>B</b>), winter (<b>C</b>) and spring (<b>D</b>). Surface in blue; middle in green; bottom in red.</p>
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<p>Seasonal temperature variations in the SW sub-basin buoy location for the summer/monsoon (<b>A</b>), fall (<b>B</b>), winter (<b>C</b>) and spring (<b>D</b>). Surface in blue; middle in green; bottom in red.</p>
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<p>Air temperatures (<b>A</b>) and solar radiation (<b>C</b>) during May 2014. Temperatures do not reach their peak until later in the month. Solar radiation remains high (&gt;1000 W/m<sup>2</sup>) during the month, but the 26 May snowstorm drops this to &lt;400 W/m<sup>2</sup>. Zoom-ins of the NE buoy location (<b>B</b>) and the Main buoy location (<b>D</b>) for May 2014 show anomalous bottom water temperatures on 23 May.</p>
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<p>Surface (blue), middle (green) and bottom (red) water temperatures for NW (<b>A</b>) and SW (<b>B</b>) buoy locations. Pressure transducer data reveal a steady rise in water level at the SW site during the spring thaw, with a more chaotic gain seen in NW.</p>
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<p>Surface (blue), middle (green) and bottom (red) water temperatures for NW (<b>A</b>) and SW (<b>B</b>) buoy locations. Pressure transducer data reveal a steady rise in water level at the SW site during the spring thaw, with a more chaotic gain seen in NW.</p>
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<p>Ice melt (meters) for the sub-basins during the summer (blue), fall (orange), winter (black) and spring (green), using 1 cm of debris thickness.</p>
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<p>Air temperatures (<b>A</b>) and surface (blue), bottom (red) water temperatures and debris (black) temperatures (<b>B</b>) were measured in the Main sub-basin from June–October 2014.</p>
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<p>Air temperatures (<b>A</b>) and surface (blue), bottom (red) water temperatures and debris (black) temperatures (<b>B</b>) were measured in the Main sub-basin from June–October 2014.</p>
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<p>Sample sonar image from the Main sub-basin of Spillway Lake, showing the primary echo (E1 layer), which measures roughness (the signal can be mirrored back or take a multi-path), and the secondary (peak Sv layer) echo, which measures hardness based on the material’s acoustic absorption. The former shows sonar returns corresponding with color-coded dots. The darker, the rougher, in a relative scale.</p>
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<p>NE sub-basin roughness and hardness maps, generated from E1 and peak Sv acoustic backscatter values. Scaled smooth (<b>A</b>), rough (<b>B</b>), soft (<b>C</b>) and hard (<b>D</b>) are shown, with boxes overlain to show smooth-hard returns, interpreted to be bare ice.</p>
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<p>Main sub-basin roughness and hardness maps, generated from E1 and peak Sv acoustic backscatter values. Smooth (<b>A</b>), rough (<b>B</b>), soft (<b>C</b>) and hard (<b>D</b>) are shown, with boxes overlain to show smooth-hard returns (A,D), interpreted to be bare ice.</p>
Full article ">Figure 15 Cont.
<p>Main sub-basin roughness and hardness maps, generated from E1 and peak Sv acoustic backscatter values. Smooth (<b>A</b>), rough (<b>B</b>), soft (<b>C</b>) and hard (<b>D</b>) are shown, with boxes overlain to show smooth-hard returns (A,D), interpreted to be bare ice.</p>
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1929 KiB  
Article
Upscaling Stem to Community-Level Transpiration for Two Sand-Fixing Plants: Salix gordejevii and Caragana microphylla
by Limin Duan, Yang Lv, Xue Yan, Tingxi Liu and Xixi Wang
Water 2017, 9(5), 361; https://doi.org/10.3390/w9050361 - 22 May 2017
Cited by 7 | Viewed by 4810
Abstract
The information on transpiration is vital for sustaining fragile ecosystem in arid/semiarid environment, including the Horqin Sandy Land (HSL) located in northeast China. However, such information is scarce in existing literature. The objectives of this study were to: (1) measure sap flow of [...] Read more.
The information on transpiration is vital for sustaining fragile ecosystem in arid/semiarid environment, including the Horqin Sandy Land (HSL) located in northeast China. However, such information is scarce in existing literature. The objectives of this study were to: (1) measure sap flow of selected individual stems of two sand-fixing plants, namely Salix gordejevii and Caragana microphylla, in HSL; and (2) upscale the measured stem-level sap flow for estimating the community-level transpiration. The measurements were done from 1 May to 30 September 2015 (i.e., during the growing season). The upscaling function was developed to have one dependent variable, namely sap flow rate, and two independent variables, namely stem cross-sectional area of Salix gordejevii and leaf area of Caragana microphylla. The results indicated that during the growing season, the total actual transpiration of the Salix gordejevii and Caragana microphylla communities was found to be 287 ± 31 and 197 ± 24 mm, respectively, implying that the Salix gordejevii community might consume 1.5 times more water than the Caragana microphylla community. For this same growing season, based on the Penman–Monteith equation, the total actual evapotranspiration for these two communities was estimated to be 323 and 229 mm, respectively. The daily transpiration from the upscaling function was well correlated with the daily evapotranspiration by the Penman–Monteith equation (coefficient of determination R2 ≥ 0.67), indicating the applicability of this upscaling function, a useful tool for managing and restoring sand-fixing vegetations. Full article
(This article belongs to the Special Issue Water-Soil-Vegetation Dynamic Interactions in Changing Climate)
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<p>Map showing the location, boundary, and geomorphic features of the Agula Ecohydrologic Laboratory Field. The dots signify the locations where the stem-level transpiration was measured in this study.</p>
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<p>Plots showing the basal diameter frequency distribution of the: (<b>a</b>) <span class="html-italic">Salix gordejevii</span> community; and (<b>b</b>) <span class="html-italic">Caragana microphylla</span> community.</p>
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<p>Relationships (<b>a</b>) betweensap flow and the stem cross-sectional area and (<b>b</b>) betweensap flow and the leaf area of gauged stems for both shrubs.</p>
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<p>Plots showing daily stem-level transpiration rate, <span class="html-italic">J<sub>sn</sub></span>, for the: (<b>a</b>) <span class="html-italic">Salix gordejevii</span>; and (<b>b</b>) <span class="html-italic">Caragana microphylla</span>. For a given day, the point represents the mean of the measurements for the measured stems, while the vertical bar represents one standard deviation.</p>
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<p>Plots showing the measured leaf area (LA) of the: (<b>a</b>) <span class="html-italic">Salix gordejevii</span>; and (<b>b</b>) <span class="html-italic">Caragana microphylla</span>. The vertical bar signifies one standard deviation. Shrub-A and Shrub-B are large shrubs with a patch canopy area of larger than 4.0 m<sup>2</sup>, Shrub-C and Shrub-D are medium shrubs with a patch canopy area between 4.0 and 2.5 m<sup>2</sup>, and Shrub-E and Shrub-F are small shrubs with a patch canopy area of less than 2.5 m<sup>2</sup>.</p>
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<p>The upscaled community-level transpiration rate (<span class="html-italic">T<sub>c</sub></span>) and the actual evapotranspiration rate (<span class="html-italic">ET<sub>c</sub></span>) estimated by the Penman–Monteith equation for the <span class="html-italic">Salix gordejevii</span> survey plot. The vertical bar signifies one standard deviation.</p>
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<p>The upscaled community-level transpiration rate (<span class="html-italic">T<sub>c</sub></span>) and the actual evapotranspiration rate (<span class="html-italic">ET<sub>c</sub></span>) estimated by the Penman–Monteith equation for the <span class="html-italic">Caragana microphylla</span> survey plot. The vertical bar signifies one standard deviation.</p>
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<p>Plots showing the upscaled (<span class="html-italic">T<sub>c</sub></span>) versus Penman–Monteith estimated (<span class="html-italic">ET<sub>c</sub></span>) for the: (<b>a</b>) <span class="html-italic">Salix gordejevii</span>; and (<b>b</b>) <span class="html-italic">Caragana microphylla</span> survey plots.</p>
Full article ">
6267 KiB  
Article
Rooftop Rainwater Harvesting for Mombasa: Scenario Development with Image Classification and Water Resources Simulation
by Robert O. Ojwang, Jörg Dietrich, Prajna Kasargodu Anebagilu, Matthias Beyer and Franz Rottensteiner
Water 2017, 9(5), 359; https://doi.org/10.3390/w9050359 - 20 May 2017
Cited by 16 | Viewed by 13171
Abstract
Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand. This article demonstrates the combination of satellite image analysis and modelling as tools for the development of an urban rainwater harvesting policy. For developing a sustainable remedy policy, [...] Read more.
Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand. This article demonstrates the combination of satellite image analysis and modelling as tools for the development of an urban rainwater harvesting policy. For developing a sustainable remedy policy, rooftop rainwater harvesting (RRWH) strategies were implemented into the water supply and demand model WEAP (Water Evaluation and Planning System). Roof areas were detected using supervised image classification. Future population growth, improved living standards, and climate change predictions until 2035 were combined with four management strategies. Image classification techniques were able to detect roof areas with acceptable accuracy. The simulated annual yield of RRWH ranged from 2.3 to 23 million cubic meters (MCM) depending on the extent of the roof area. Apart from potential RRWH, additional sources of water are required for full demand coverage. Full article
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<p>Map of Mombasa City showing the four main zones in the study area.</p>
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<p>Water demand projections for Mombasa City [<a href="#B34-water-09-00359" class="html-bibr">34</a>].</p>
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<p>Methodological framework and workflow.</p>
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<p>Planned areas (<b>a</b>) and unplanned areas (<b>b</b>) (informal settlements).</p>
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<p>Conceptual model for Mombasa city (not drawn to scale).</p>
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<p>Comparison between manually digitized and automatic classification for a portion of the classified area (only roofing materials shown).</p>
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<p>Annual Water demand projections in Million Cubic Metres (MCM) for different scenarios. Abbreviations: better living standards (BLS), high population growth (HPG), low population growth (LPG), efficient water use (EWU).</p>
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<p>Additional water supply delivered under different RRWH scenarios for Mombasa City.</p>
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<p>Average monthly demand coverage of RRWH combined with the existing system (2014–2035).</p>
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<p>Unmet demand for Mombasa City for different management strategies (see <a href="#water-09-00359-t011" class="html-table">Table 11</a> for acronym definitions).</p>
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<p>Demand coverage under different scenario combinations for Mombasa City.</p>
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<p>Effect of climate change on the RRWH_4 and RRWH_5 scenarios.</p>
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3490 KiB  
Article
Exploring Streamwater Mixing Dynamics via Handheld Thermal Infrared Imagery
by Marta Antonelli, Julian Klaus, Keith Smettem, Adriaan J. Teuling and Laurent Pfister
Water 2017, 9(5), 358; https://doi.org/10.3390/w9050358 - 19 May 2017
Cited by 4 | Viewed by 6162
Abstract
Stream confluences are important hotspots of aquatic ecological processes. Water mixing dynamics at stream confluences influence physio-chemical characteristics of the stream as well as sediment mobilisation and pollutant dispersal. In this study, we investigated the potential for handheld thermal infrared (TIR) imagery to [...] Read more.
Stream confluences are important hotspots of aquatic ecological processes. Water mixing dynamics at stream confluences influence physio-chemical characteristics of the stream as well as sediment mobilisation and pollutant dispersal. In this study, we investigated the potential for handheld thermal infrared (TIR) imagery to provide rapid information on stream water mixing dynamics at small scales. In-situ visualisation of water mixing patterns can help reduce analytical errors related to stream water sampling locations and improve our understanding of how confluences and tributaries influence aquatic ecological communities. We compared TIR-inferred stream temperature distributions with water electrical conductivity and temperature (measured with a submerged probe) data from cross-channel transects. We show that the use of a portable TIR camera can enhance the visualisation of mixing dynamics taking place at stream confluences, identify the location of the mixing front between two different water sources and the degree of mixing. Interpretation of handheld TIR observations also provided information on how stream morphology and discharge can influence mixing dynamics in small streams. Overall, this study shows that TIR imagery is a valuable support technique for eco-hydrological investigation at small stream confluences. Full article
(This article belongs to the Special Issue New Developments in Methods for Hydrological Process Understanding)
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<p>Location (map) and overview (pictures) of the study sites. <b>Top</b>: location within Luxemburg and the Attert basin. <b>Middle</b>: overview of the natural stream confluence (NAT site). <b>Bottom</b>: overview of the urban stream confluence (URB site). Location of crossing transect A is displayed on both pictures (NAT site: folding meter ruler; URB site: bold white line). The pictures were collected on the 28 September 2016 at the NAT site and the 2 February 2017 at the URB site (pictures: M. Antonelli).</p>
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<p>Cross-sectional characteristics. (<b>A</b>) Examples of TIR images collected at two cross-sectional transects. Note that the TIR images cover a smaller domain for illustrative purposes; when possible, we covered the widest transect width with a single TIR image (pictures: M. Antonelli); (<b>B</b>) Stream depth profiles at the two cross-sectional transects. In the NAT site, the width of the stream at the transects increased with increasing discharge, expanding exclusively on the left side of the stream. The presence of coarse sediment in the stream makes the depth profiles at the same location highly variable. In the URB site, the stream width increased with increasing discharge expanding uniformly on the left and right bank.</p>
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<p>Illustration of the analysis procedure. TIR images (top panel) and cross-sectional temperature distribution as collected in the individual transects. The shown temperature distribution was extracted from the image collected on the 28 September at the NAT site (transect A) (pictures: M. Antonelli).</p>
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<p>Example of stream conductivity and temperature. Upper and middle panels: stream EC and temperature (measured at the stream bottom—“bottom”—and near the surface—“top”). Lower panel: TIR-inferred temperature distributions. At the NAT site (21 September) we found a good correlation between the TIR distribution and stream EC (R<sup>2</sup> = −0.96) and temperature data (R<sup>2</sup> = 0.96) (on this date, collected only on the bottom because of low water depth). At the URB site (2 February) we found a poor correlation between the TIR distribution and stream EC (bottom: R<sup>2</sup> = 0.49; top: R<sup>2</sup> = 0.70) and temperature data (bottom: R<sup>2</sup> = 0.47; top: R<sup>2</sup> = 0.59). For the temperature data measured near the stream surface (URB site) it was not possible to determine a univocal inflection point.</p>
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<p>Illustration of circular “aura” effect. The “aura” is visible at the edges of the TIR image (NAT—Transect A—17 November) and directly affects the extraction of TIR temperature cross-sectional distributions (as can be seen in the software output).</p>
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<p>Mixing widths for different stream cross-channel transects. The mixing widths were calculated from the curves obtained via data smoothing of TIR-inferred stream temperature and water EC and temperature measured at the bottom and near the surface, and are expressed as percentage of the total transect width. No value fields in the NAT graphs indicate that water depth was too low for obtaining measurements near the water surface.</p>
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6100 KiB  
Article
Shoreline Changes on the Wave-Influenced Senegal River Delta, West Africa: The Roles of Natural Processes and Human Interventions
by Mamadou Sadio, Edward J. Anthony, Amadou Tahirou Diaw, Philippe Dussouillez, Jules T. Fleury, Alioune Kane, Rafael Almar and Elodie Kestenare
Water 2017, 9(5), 357; https://doi.org/10.3390/w9050357 - 19 May 2017
Cited by 42 | Viewed by 10811
Abstract
The Senegal River delta in West Africa, one of the finest examples of “wave-influenced” deltas, is bounded by a spit periodically breached by waves, each breach then acting as a shifting mouth of the Senegal River. Using European Re-Analysis (ERA) hindcast wave data [...] Read more.
The Senegal River delta in West Africa, one of the finest examples of “wave-influenced” deltas, is bounded by a spit periodically breached by waves, each breach then acting as a shifting mouth of the Senegal River. Using European Re-Analysis (ERA) hindcast wave data from 1984 to 2015 generated by the Wave Atmospheric Model (WAM) of the European Centre for Medium-Range Weather Forecasts (ECMWF), we calculated longshore sediment transport rates along the spit. We also analysed spit width, spit migration rates, and changes in the position and width of the river mouth from aerial photographs and satellite images between 1954 and 2015. In 2003, an artificial breach was cut through the spit to prevent river flooding of the historic city of St. Louis. Analysis of past spit growth rates and of the breaching length scale associated with maximum spit elongation, and a reported increase in the frequency of high flood water levels between 1994 and 2003, suggest, together, that an impending natural breach was likely to have occurred close to the time frame of the artificial 2003 breach. Following this breach, the new river mouth was widened rapidly by flood discharge evacuation, but stabilised to its usual hydraulic width of <2 km. In 2012, severe erosion of the residual spit downdrift of the mouth may have been due to a significant drop (~15%) in the longshore sand transport volume and to a lower sediment bypassing fraction across the river mouth. This wave erosion of the residual spit led to rapid exceptional widening of the mouth to ~5 km that has not been compensated by updrift spit elongation. This wider mouth may now be acting as a large depocentre for sand transported alongshore from updrift, and has contributed to an increase in the tidal influence affecting the lower delta. Wave erosion of the residual spit has led to the destruction of villages, tourist facilities and infrastructure. This erosion of the spit has also exposed part of the delta plain directly to waves, and reinforced the saline intrusion within the Senegal delta. Understanding the mechanisms and processes behind these changes is important in planning of future shoreline management and decision-making regarding the articulations between coastal protection offered by the wave-built spit and flooding of the lower delta plain of the Senegal River. Full article
(This article belongs to the Special Issue Sediment Transport in Coastal Waters)
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<p>The coast of West Africa, showing the Senegal (box) and other major river deltas. Much of this coast is wave-dominated, and is characterised by beach-ridge sand barriers and spits.</p>
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<p>The Senegal River delta, a fine example of a wave-dominated delta characterised by the Langue de Barbarie spit and a river-mouth system subject to strong north-south longshore drift.</p>
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<p>Mean wave characteristics (significant wave height (<span class="html-italic">H<sub>s</sub></span>), peak wave period (<span class="html-italic">T</span>), and incident direction (°)) along the Senegal River delta coast from 1984 to 2015 ERA hindcast data. Orange: swell waves, blue: wind waves.</p>
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<p>Gross annual longshore sediment transport (LST) along the Senegal River delta coast from 1984 to 2015. Orange: swell waves, blue: wind waves. Note the significant drop in swell-induced LST between 2009 and 2012, corresponding to a decrease of &gt;35%, and the sharp rise the following year.</p>
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<p>Successive dated locations of the mouth of the Senegal River delta materialised by the distal tip of the Langue de Barbarie spit (<b>left</b>); and spit migration rates in m/year from 1968 to 2004 (<b>right</b>).</p>
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<p>Width of the mouth of the Senegal River delta between 1954 and 2015. Except for the years 1968–1973 and 1987–1988, the width did not exceed 1 km, prior to the 2003 artificial breach. Following this breach, the width of the mouth fluctuated to attain ~1 km in 2008, which corresponds to the average width of the “fluvial” river mouth. A further rapid increase, not related to river-mouth hydraulics (see Discussion), occurred thereafter, peaking in 2013.</p>
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<p>Maximum water levels in the Senegal River channel at St. Louis from 1999 to 2006. Adapted from [<a href="#B34-water-09-00357" class="html-bibr">34</a>].</p>
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<p>Ground photographs showing the initial trench (4 October 2003), dug on the night of 3 October 2003, across the Langue de Barbarie to alleviate flooding of parts of St. Louis. The 5 October 2003 photograph shows the trench considerably widened by river and tidal flow (Photo credit: Service régional de l’Hydraulique, St. Louis du Sénégal).</p>
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<p>Assemblage from Google Earth images showing changes in the Langue de Barbarie spit and Senegal River mouth between March 2003, prior to the October 2003 artificial breach, and 2015. Black: Langue de Barbarie spit and beach sand; dark grey: subaerial lower delta plain potentially subject to river flooding (including St. Louis); light grey: delta plain seasonally flooded by the Senegal River. From 2012 to 2013, rapid wave-induced erosion of the residual spit downdrift of the mouth led to considerable mouth widening, an increase in tidal influence within the lower Senegal delta, and direct wave attack of parts of the delta plain hitherto protected by the residual spit.</p>
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<p>Assemblage from Google Earth images showing changes in the Langue de Barbarie spit and Senegal River mouth between March 2003, prior to the October 2003 artificial breach, and 2015. Black: Langue de Barbarie spit and beach sand; dark grey: subaerial lower delta plain potentially subject to river flooding (including St. Louis); light grey: delta plain seasonally flooded by the Senegal River. From 2012 to 2013, rapid wave-induced erosion of the residual spit downdrift of the mouth led to considerable mouth widening, an increase in tidal influence within the lower Senegal delta, and direct wave attack of parts of the delta plain hitherto protected by the residual spit.</p>
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<p>Significant heights (<span class="html-italic">Hs</span>) of high-energy waves (±1.6 standard deviations around mean <span class="html-italic">Hs</span>) from 1999 to 2015 (<b>top</b>); and number of days per year with high-energy waves along the Senegal River delta coast, derived from ERA hindcast data (<b>bottom</b>). Orange: swell waves (<span class="html-italic">Hs</span> ≥ 2.37 m), blue: wind waves (<span class="html-italic">Hs</span> ≥ 1.36 m). Note the significant drop in high-energy swell waves in 2012 (see also <a href="#water-09-00357-f004" class="html-fig">Figure 4</a>).</p>
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2030 KiB  
Article
The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries
by Rei Itsukushima, Kai Morita and Yukihiro Shimatani
Water 2017, 9(5), 356; https://doi.org/10.3390/w9050356 - 18 May 2017
Cited by 8 | Viewed by 4814
Abstract
River estuaries are important aquatic environments characterized by large environmental gradients in their water quality, riverbed material, and microtopography in the longitudinal and transverse directions. The geography or habitats in river estuaries differ depending on the energy from the tide, waves, and river; [...] Read more.
River estuaries are important aquatic environments characterized by large environmental gradients in their water quality, riverbed material, and microtopography in the longitudinal and transverse directions. The geography or habitats in river estuaries differ depending on the energy from the tide, waves, and river; therefore, the biota inhabiting river estuaries vary depending on the river estuary type. In view of this, for effective conservation in river estuaries, there is a need for information about potential habitats and biota based on objective data about the river estuary type. The objective of this study thus was to classify river estuaries by their molluscan fauna and physical indicators to reveal the relationship between molluscan fauna and the physical environment. The classification results using physical indicators indicated three types of river estuaries (wave energy-dominated group, tide energy-dominated group, and low tide and wave energy group). This classification result was similar to the classification of molluscan fauna. Therefore, it was suggested that molluscan fauna is extremely useful as a variable representing the river estuary environment. From the comparison between molluscan fauna and the physical environment, some rivers were not classified into the same group in the classification of molluscan fauna, despite them having similar physical environments. Some of these rivers with a molluscan fauna that diverged from expectations had undergone channel modification, which is expected to have caused a shift in this fauna group. These results suggest that this approach could be used to identify rivers that have been degraded by human activities. Full article
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<p>Location of the study sites.</p>
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<p>Plotted molluscan species and physical indicators on non-metric multidimensional scaling analysis (nMDS) dimensions.</p>
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<p>nMDS for similarity between the rivers based on molluscan fauna.</p>
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<p>Cluster analysis dendrogram for the environmental variables.</p>
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<p>Comparison of tidal range (<b>a</b>); direct fetch (<b>b</b>); wave exposure (<b>c</b>); form ratio (<b>d</b>); terrain gradient (<b>e</b>) and specific discharge (<b>f</b>) among the three groups.</p>
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<p>Habitat distribution of the Sato River (<b>a</b>) and the Hai River (<b>b</b>).</p>
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<p>Comparison of the cross-sectional profile of the Sato River and the Hai River.</p>
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2480 KiB  
Article
Contamination of Detained Sediment in Sustainable Urban Drainage Systems
by Deonie Allen, Heather Haynes and Scott Arthur
Water 2017, 9(5), 355; https://doi.org/10.3390/w9050355 - 18 May 2017
Cited by 8 | Viewed by 6347
Abstract
Adsorption is a key water pollution remediation measure used to achieve stormwater quality improvement in Sustainable urban Drainage Systems (SuDS). The level of contamination of detained sediment within SuDS assets is not well documented, with published investigations limited to specific contaminant occurrence in [...] Read more.
Adsorption is a key water pollution remediation measure used to achieve stormwater quality improvement in Sustainable urban Drainage Systems (SuDS). The level of contamination of detained sediment within SuDS assets is not well documented, with published investigations limited to specific contaminant occurrence in ponds, wetlands or infiltration devices (bioretention cells) and generally focused on solute or suspended sediment. Guidance on contamination threshold levels and potential deposited sediment contamination information is not included in current UK SuDS design or maintenance guidance, primarily due to a lack of evidence and understanding. There is a need to understand possible deposited sediment contamination levels in SuDS, specifically in relation to sediment removal maintenance activities and potential impact on receiving waterways of conveyed sediment. Thus, the objective of the research presented herein was to identify what major elements and trace metals were observable in (the investigated) SuDS assets detained sediment, the concentration of these major elements and trace metals and whether they met/surpassed ecotoxicity or contaminated land thresholds. The research presented here provides evidence of investigated SuDS sediment major element and trace metal levels to help inform guidance and maintenance needs, and presents a new methodology to identify the general cause (anthropocentric land use) and extent of detained SuDS fine urban sediment contamination through use of a contamination matrix. Full article
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<p>A comparison of deposited sediment contaminant concentrations for the wetland (<b>a</b>), linear wetland (<b>b</b>), short swale (<b>c</b>), long swale (<b>d</b>) and pond (<b>e</b>). Red bars indicated the exceedance of contaminated land thresholds and <span class="html-italic">have no upper limit</span>, blue bars indicate exceedance of ecotoxicity levels and green bars indicate sediment contaminant levels below ecotoxicity thresholds. Where no contaminated land thresholds are presented in guidance documents, the blue and red bars are vacant from the graphs. Field data falling within the red bars indicate exceedance of contaminated land threshold trigger and therefore may be of concern.</p>
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<p>Sediment contaminant concentrations from monitoring points across the SuDS assets. The error bars illustrate sample range for each sample location and contaminant. Sediment contaminants have been presented relative to one of their primary urban sources (e.g., tyre wear) for ease graphical clarity.</p>
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<p>Deposited sediment within the investigated SuDS assets.</p>
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<p>Modal particle size for deposited sediment within the investigated SuDS assets.</p>
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<p>Sediment contamination matrix. The matrix is comprised of three layers of colour indicators, illustrating the alphabetic contamination matrix code (e.g., HHHH, as illustrated to the top right of the matrix).</p>
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3720 KiB  
Article
Soil Moisture Stochastic Model in Pinus tabuliformis Forestland on the Loess Plateau, China
by Yi-Fang Chang, Hua-Xing Bi, Qing-Fu Ren, Hua-Sen Xu, Zhi-Cai Cai, Dan Wang and Wen-Chao Liao
Water 2017, 9(5), 354; https://doi.org/10.3390/w9050354 - 18 May 2017
Cited by 10 | Viewed by 4418
Abstract
As an important restrictive factor of ecological construction on the Loess Plateau, the study of soil moisture dynamics is essential, especially under the impact of climate change on hydrological processes. In this study, the applicability of the Laio soil moisture stochastic model on [...] Read more.
As an important restrictive factor of ecological construction on the Loess Plateau, the study of soil moisture dynamics is essential, especially under the impact of climate change on hydrological processes. In this study, the applicability of the Laio soil moisture stochastic model on a typical plantation Pinus tabuliformis forestland on the Loess Plateau was studied. On the basis of data concerning soil properties, climate, and plants of the typical forestland during the period 2005–2015 in the Chinese National Ecosystem Research Network (Ji County Station) in Ji County, Shanxi, model results were acquired and compared with observed soil moisture from 2005 to 2015 in the study area. The genetic algorithm method was used to optimize model parameters in the calibration process. In the calibration and validation periods, the relative error between numerical characteristics of simulated and observed soil moisture values was mostly within 10%, and model evaluation index J was close to 1, indicating that the Laio model had good applicability in the study area. When calibrating the model, it was recommended to use soil moisture data with a sampling interval of no more than 10 days so as to reduce the loss of soil moisture fluctuation information. In the study area, the Laio model was strongly sensitive to variations of input parameters, including maximum evapotranspiration rate Emax, average rainfall depth α, and average rainfall frequency λ, which should be paid more attention for stable and reliable simulation results. This study offers a method to obtain soil moisture data at ungauged sites. Results from this study provide guidance for Laio model application on the Loess Plateau. Full article
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<p>Setting of soil moisture sampling.</p>
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<p>Setting of roots sampling.</p>
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<p>Monthly average soil moisture in <span class="html-italic">Pinus tabuliformis</span> Plantation in 2005–2015 growing seasons.</p>
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<p>PDF (<b>a</b>) and CDF (<b>b</b>) of soil moisture in the calibration period. (<b>a</b>) PDF of soil moisture; (<b>b</b>) CDF of soil moisture.</p>
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<p>Daily average soil moisture in the calibration period.</p>
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<p>Probability distribution of observed and simulated <span class="html-italic">θ</span> in calibration period.</p>
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<p>PDF (<b>a</b>) and CDF (<b>b</b>) of the soil moisture during the validation period. (<b>a</b>) PDF of soil moisture; (<b>b</b>) CDF of soil moisture.</p>
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<p>Daily average soil moisture in the validation period.</p>
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<p>Probability distribution of observed and simulated <span class="html-italic">θ</span> in validation period.</p>
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<p>PDF of soil moisture at different sampling intervals. (<b>a</b>) Calibration period; (<b>b</b>) Validation period.</p>
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<p>CDF of soil moisture at different sampling intervals. (<b>a</b>) Calibration period; (<b>b</b>) Validation period.</p>
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<p>Soil moisture at different sampling intervals. (<b>a</b>) 10-day sampling interval in the calibration period; (<b>b</b>) 10-day sampling interval in the validation period; (<b>c</b>) 30-day sampling interval in the calibration period; (<b>d</b>) 30-day sampling interval in the validation period.</p>
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<p>Probability distribution of the observed and simulated <span class="html-italic">θ</span> at different sampling intervals. (<b>a</b>) 10-day sampling interval in the calibration period; (<b>b</b>) 10-day sampling interval in the validation period; (<b>c</b>) 30-day sampling interval in the calibration period; (<b>d</b>) 30-day sampling interval in the validation period.</p>
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<p>Probability distribution of the observed and simulated <span class="html-italic">θ</span> at different sampling intervals. (<b>a</b>) 10-day sampling interval in the calibration period; (<b>b</b>) 10-day sampling interval in the validation period; (<b>c</b>) 30-day sampling interval in the calibration period; (<b>d</b>) 30-day sampling interval in the validation period.</p>
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<p>Influence of the strongly-sensitive parameters on the Laio model results. (<b>a</b>) Influence of <span class="html-italic">E<sub>max</sub></span> on model results; (<b>b</b>) Influence of <span class="html-italic">α</span> on model results; (<b>c</b>) Influence of <span class="html-italic">λ</span> on model results.</p>
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5924 KiB  
Article
Variations in Spectral Absorption Properties of Phytoplankton, Non-algal Particles and Chromophoric Dissolved Organic Matter in Lake Qiandaohu
by Liangliang Shi, Zhihua Mao, Jiaping Wu, Mingliang Liu, Yiwei Zhang and Zheng Wang
Water 2017, 9(5), 352; https://doi.org/10.3390/w9050352 - 18 May 2017
Cited by 13 | Viewed by 7023
Abstract
Light absorption by phytoplankton, non-algal particles (NAP) and chromophoric dissolved organic matter (CDOM) was investigated at 90 sites of a clear, deep artificial lake (Lake Qiandaohu) to study natural variability of absorption coefficients. Our study shows that CDOM absorption is a [...] Read more.
Light absorption by phytoplankton, non-algal particles (NAP) and chromophoric dissolved organic matter (CDOM) was investigated at 90 sites of a clear, deep artificial lake (Lake Qiandaohu) to study natural variability of absorption coefficients. Our study shows that CDOM absorption is a major contributor to the total absorption signal in Lake Qiandaohu during all seasons, except autumn when it has an equivalent contribution as total particle absorption. The exponential slope of CDOM absorption varies within a narrow range around a mean value of 0.0164 nm−1 ( s d = 0.00176 nm−1). Our study finds some evidence for thIS autochthonous production of CDOM in winter and spring. Absorption by phytoplankton, and therefore its contribution to total absorption, is generally greatest in spring, suggesting that phytoplankton growth in Lake Qiandaohu occurs predominantly in the spring. Phytoplankton absorption in freshwater lakes generally has a direct relationship with chlorophyll-a concentration, similar to the one established for open ocean waters. The NAP absorption, whose relative contribution to total absorption is highest in summer, has a spectral shape that can be well fitted by an exponential function with an average slope of 0.0065 nm−1 ( s d = 0.00076 nm−1). There is significant spatial variability present in the summer of Lake Qiandaohu, especially in the northwestern and southwestern extremes where the optical properties of the water column are strongly affected by the presence of allochthonous matter. Variations in the properties of the particle absorption spectra with depths provides evidence that the water column was vertically inhomogeneous and can be monitored with an optical measurement program. Moreover, the optical inhomogeneity in winter is less obvious. Our study will support the parameterization of the Bio-optical model for Lake Qiandaohu from in situ or remotely sensing aquatic color signals. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Modeling in Lakes)
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<p>Map of Lake Qiandaohu and the locations of the seasonal sampling sites.</p>
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<p>Total particle spectral absorption coefficients, <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>p</mi> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> versus season.</p>
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<p>Three typical total particle absorption spectra in Lake Qiandaohu, demonstrating corresponding absorption characteristics for NAP and phytoplankton. (<b>a</b>) Phytoplankton are the dominant; (<b>b</b>) Neither phytoplankton nor NAP are the dominant (<b>c</b>) NAP are the dominant.</p>
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<p>(<b>A</b>–<b>E</b>) Total particle absorption spectra in different regions of the summer season in Lake Qiandaohu; and (<b>F</b>) the average total particle absorption spectra from each area.</p>
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<p>Seasonal variability of the total particle absorption spectra at different depths for sampling sites JK, XJS and DB in Lake Qiandaohu (see <a href="#water-09-00352-f001" class="html-fig">Figure 1</a>).</p>
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<p>Seasonal variation of the CDOM absorption spectra. The different colored lines in each panel represent different sampling sites.</p>
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<p>(<b>a</b>) Average CDOM absorption spectra of each season in Lake Qiandaohu and (<b>b</b>) frequency distribution of the slopes, <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics> </math>, of CDOM absorption spectra. The Gaussian curve is superimposed to illustrate the normal distribution corresponding to the average value of <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics> </math> (0.0164 nm<sup>−1</sup>), with standard deviation (0.00176 nm<sup>−1</sup>).</p>
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<p>(<b>A</b>–<b>E</b>) CDOM absorption spectra in the different areas of the summer season in Lake Qiandaohu; and (<b>F</b>) the average CDOM absorption spectra for each area.</p>
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<p>Scatterplot of <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mn>440</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> against <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics> </math> in different seasons.</p>
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<p>Scatterplot of <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>p</mi> <mi>h</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mn>440</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> against <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mn>440</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> and the corresponding least-squares fitted line in different seasons.</p>
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<p>Phytoplankton absorption spectra in Lake Qiandaohu for each season. The curves with different colors in each panel represent different sampling sites.</p>
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<p>Correlations between <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>p</mi> <mi>h</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mn>440</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> and Chl<span class="html-italic">-a</span> concentration (<b>a</b>–<b>d</b>); and between <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>p</mi> <mi>h</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mn>675</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math> and Chl<span class="html-italic">-a</span> concentration (<b>e</b>–<b>h</b>) for each season.</p>
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<p>NAP absorption spectra in each season. The curves with different colors in each panel represent the different sampling sites.</p>
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<p>(<b>A</b>–<b>E</b>) NAP absorption spectra in different regions of the summer season in Lake Qiandaohu; and (<b>F</b>) the average NAP absorption spectra of each area.</p>
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<p>Fractional contribution of absorption coefficients of pure water [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>w</mi> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>], CDOM [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>C</mi> <mi>D</mi> <mi>O</mi> <mi>M</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>], total particles [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>p</mi> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>], phytoplankton [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>p</mi> <mi>h</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>], and NAP [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>N</mi> <mi>A</mi> <mi>P</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>] to the total absorption [<math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>t</mi> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>] in the four seasons.</p>
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<p>Ternary plots for all samples used in this study at specific wavelengths showing the relative contributions of phytoplankton, NAP and CDOM. The relative proportion of a given absorption component <span class="html-italic">x</span> (scaled in 0 and 1) for a given sample is calculated as <span class="html-italic">x</span>/(<span class="html-italic">x</span> + <span class="html-italic">y</span> + <span class="html-italic">z</span>) where <span class="html-italic">y</span> and <span class="html-italic">z</span> are the two other components. The plot is triangular in shape because the three components are constrained to sum to 1.</p>
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2087 KiB  
Article
A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty
by Jing Liu, Yongping Li, Guohe Huang and Yurui Fan
Water 2017, 9(5), 351; https://doi.org/10.3390/w9050351 - 18 May 2017
Cited by 2 | Viewed by 5734
Abstract
In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse [...] Read more.
In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation. Full article
(This article belongs to the Special Issue Modeling of Water Systems)
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<p>Overall flow chat of the methodology.</p>
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<p>Study area. Note: GF, Gufu chemical plant; XL, Xinglong phosphorus mining company; XH, Xinghe phosphorus mining company; XC, Xingchang phosphorus mining company; BSH, Baishahe chemical plant; PYK, Pingyikou chemical plant; LCP, Liucaopo chemical plant; GP, Geping phosphorus mining company; JJW, Jiangjiawan phosphorus mining company; SJS, Shenjiashan phosphorus mining company; XJLY, Xiangjinlianying chemical plant.</p>
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<p>Benefits and costs under different risk levels. Note: (<b>a</b>) lower-bound targeted benefit and system benefit, (<b>b</b>) upper-bound targeted benefit and system benefit, (<b>c</b>) lower-bound regular benefit and penalty, (<b>d</b>) upper-bound regular benefit and penalty; unit: 10<sup>6</sup> RMB¥.</p>
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<p>Benefit corresponding to each scenario and risk level. (<b>a</b>) Lower bound and (<b>b</b>) Upper bound.</p>
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<p>Cumulative risk curve ((<b>a</b>) Lower bound and (<b>b</b>) Upper bound).</p>
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<p>Proportion of benefit under different scenarios. (<b>a</b>) Lower bound under low scenario; (<b>b</b>) Upper bound under low scenario; (<b>c</b>) Lower bound under low-medium scenario; (<b>d</b>) Upper bound under low-medium scenario; (<b>e</b>) Lower bound under medium scenario; (<b>f</b>) Upper bound under medium scenario; (<b>g</b>) Lower bound under medium-high scenario; (<b>h</b>) Upper bound under medium-high scenario; (<b>i</b>) Lower bound under high scenario and (<b>j</b>) Upper bound under high scenario.</p>
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<p>Actual production levels of industrial activities. Note: (<b>a</b>) lower-bound production level of chemical plant; (<b>b</b>) upper-bound production level of chemical plant; (<b>c</b>) lower-bound production level of phosphorus mining company; (<b>d</b>) upper-bound production level of phosphorus mining company; (<b>e</b>) lower-bound water supply and (<b>f</b>) upper-bound water supply.</p>
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<p>Excess Pollutant Discharge. (<b>a</b>) BOD discharge; (<b>b</b>) TP discharge and (<b>c</b>) TN discharge.</p>
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4650 KiB  
Letter
Reversed Currents in Charged Liquid Bridges
by Klaus Morawetz
Water 2017, 9(5), 353; https://doi.org/10.3390/w9050353 - 17 May 2017
Cited by 2 | Viewed by 4386
Abstract
The velocity profile in a water bridge is reanalyzed. Assuming hypothetically that the bulk charge has a radial distribution, a surface potential is formed that is analogous to the Zeta potential. The Navier–Stokes equation is solved, neglecting the convective term; then, analytically and [...] Read more.
The velocity profile in a water bridge is reanalyzed. Assuming hypothetically that the bulk charge has a radial distribution, a surface potential is formed that is analogous to the Zeta potential. The Navier–Stokes equation is solved, neglecting the convective term; then, analytically and for special field and potential ranges, a sign change of the total mass flow is reported caused by the radial charge distribution. Full article
(This article belongs to the Special Issue Electrohydrodynamic Liquid Bridges and Electrified Water)
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<p>The total mass flow (<a href="#FD20-water-09-00353" class="html-disp-formula">20</a>) vs. the dimensionless radius of the bridge. The parameter <math display="inline"> <semantics> <mrow> <mn>8</mn> <mi>ζ</mi> <mo>/</mo> <msub> <mi>ζ</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> = <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>12</mn> </mrow> </semantics> </math> from left to right. The numbered points represent the regions for which the velocity profile is given in <a href="#water-09-00353-f003" class="html-fig">Figure 3</a>.</p>
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<p>The critical parameter <math display="inline"> <semantics> <msub> <mrow> <mo>(</mo> <mi>κ</mi> <mi>R</mi> <mo>)</mo> </mrow> <mn>0</mn> </msub> </semantics> </math> where the mass flow changes the sign vs. the <math display="inline"> <semantics> <mi>ζ</mi> </semantics> </math> potential. The shaded area is the region where the flow reverses the sign.</p>
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<p>The radial velocity profile for the three numbered points of <a href="#water-09-00353-f001" class="html-fig">Figure 1</a>.</p>
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1446 KiB  
Article
Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures
by Marie Parrens, Ahmad Al Bitar, Frédéric Frappart, Fabrice Papa, Stephane Calmant, Jean-François Crétaux, Jean-Pierre Wigneron and Yann Kerr
Water 2017, 9(5), 350; https://doi.org/10.3390/w9050350 - 17 May 2017
Cited by 34 | Viewed by 8123
Abstract
Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. [...] Read more.
Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained. Full article
(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)
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<p>Map of the Amazon Basin with the main rivers and floodplains.</p>
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<p>(<b>a</b>) Elevation (in meters) of the Amazon Basin from the SRTM data rescaled in the EASE v2.0 grid; spatial distribution of the water surface from: (<b>b</b>) ESA CCI; (<b>c</b>) IGBP; (<b>d</b>) Globe Cover; (<b>e</b>) average inundation extent from GIEMS from 1993–2007 over the Amazon Basin; and (<b>f</b>) average inundation extent from Surface Water Microwave Product Series (SWAMPS) from 2010–20over the Amazon Basin.</p>
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<p>(<b>a</b>) Pixel representation with the two contributions: forest and water; (<b>b</b>) location of the “water”, the “forest” and the mixed pixels.</p>
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<p>Time series of TB at H polarization (left) and V polarization (right) at two incidence angles (32<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> and 47<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>): the “water”, “forest” and “mixed” pixels.</p>
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<p>Average in time of the SMOS water fraction during 2010–2015 over the full Amazon Basin. Both H and V polarization and the four incidence angles (32<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>, 37<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>, 42<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>, 47<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>) are considered.</p>
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<p>Spatial average of the SWAF over the full Amazon Basin in H polarization (lines) and V polarization (dashed lines) for the four incidence angles: 32<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> (blue), 37<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> (red), 42<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> (green) and 47<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> (black).</p>
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<p>Monthly average of the SWAF product from 2010–2015 at V-polarization and at 32<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math>± 5<math display="inline"> <semantics> <msup> <mrow/> <mrow> <mo>∘</mo> </mrow> </msup> </semantics> </math> incidence angle.</p>
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<p>Histogram of water fraction for the IGBP map (red), GlobeCover map (black), the ESA CCI (blue) and SWAF (yellow columns) for eight SMOS TB configurations (32–47 angle bins and H/V configurations).</p>
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<p>Bias (reference static maps, SWAF) and RMSE values computed between each reference maps (GlobeCover, IGBP, ESA CCI, GIEMS) and SWAF for the eight configurations.</p>
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<p>For each SMOS configuration, correlation values against the SWAF water surface extent and the water level measured by Jason-2 during 2010–2012. The color dot represents the correlation value. Gray color dots show no significant results (<span class="html-italic">p</span>-value &gt; 0.05).</p>
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<p>For each SMOS configuration, the sum of the correlation value (<span class="html-italic">r</span>) obtained in <a href="#water-09-00350-f008" class="html-fig">Figure 8</a>. Only significant stations for all of the SMOS configuration are used for the computation.</p>
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<p>Temporal correlation values between the SWAMPS and SWAF products from January 2010–March 2013 for each SMOS configuration.</p>
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<p>Monthly normalized anomalies of precipitation (TRMM data), in situ discharge at Obidos and the SWAF at H-pol (top) and V-pol (bottom) with the four incidence angles considered in this study. The precipitation and SWAF anomalies were computed over the entire Amazon Basin.</p>
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1739 KiB  
Article
Using δ15N and δ18O Signatures to Evaluate Nitrate Sources and Transformations in Four Inflowing Rivers, North of Taihu Lake
by Da Li, Xia Jiang and Binghui Zheng
Water 2017, 9(5), 345; https://doi.org/10.3390/w9050345 - 17 May 2017
Cited by 13 | Viewed by 5546
Abstract
Taihu Lake is the third largest freshwater lake in China. Due to rapid economic development and excessive nutrient discharges, there is serious eutrophication in the northern part of the lake. Nitrogen (N) is one of the key factors for eutrophication in Taihu Lake, [...] Read more.
Taihu Lake is the third largest freshwater lake in China. Due to rapid economic development and excessive nutrient discharges, there is serious eutrophication in the northern part of the lake. Nitrogen (N) is one of the key factors for eutrophication in Taihu Lake, which mainly comes from the rivers around the lake. Samples from four inflowing rivers were analysed for δ15N and δ18O isotopes in December 2013 to identify the different sources of nitrogen in the northern part of Taihu Lake. The results indicated that the water quality in Taihu Lake was clearly influenced by the water quality of the inflowing rivers and nitrate (NO3-N) was the main component of the soluble inorganic nitrogen in water. The soil organic N represented more than 70% of the total NO3-N loads in the Zhihugang. Domestic sewage was the major NO3-N source in the Liangxi river, with a contribution of greater than 50%. Soil organic N and domestic sewage, with contributions of more than 30% and 35% respectively, were the major NO3-N sources in the Lihe river and Daxigang river. Denitrification might be responsible for the shifting δ15N-NO3 and δ18O-NO3 values in the Daxigang river, and a mixing process may play a major role in N transformations in the Lihe river in winter. The results of this study will be useful as reference values for reducing NO3 pollution in the inflowing rivers in the north of Taihu Lake. Full article
(This article belongs to the Special Issue Isotopes in Hydrology and Hydrogeology)
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<p>Total Nitrogen (TN) input and output throughout the years in Taihu Lake [<a href="#B2-water-09-00345" class="html-bibr">2</a>,<a href="#B4-water-09-00345" class="html-bibr">4</a>].</p>
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<p>Location of the rivers studied (Zhihugang, Liangxi, Lihe, and Daxigang rivers) and their sampling sites.</p>
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<p>Relationships between NO<sub>3</sub><sup>−</sup>-N and δ<sup>15</sup>N-NO<sub>3</sub><sup>−</sup> in the Zhihugang, Liangxi, Lihe, and Daxigang rivers.</p>
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<p>Relationships between Cl<sup>−</sup> and NO<sub>3</sub><sup>−</sup>-N concentrations in the Zhihugang, Liangxi, Lihe, and Daxigang rivers.</p>
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<p>Relationships between δ<sup>15</sup>N and δ<sup>18</sup>O of NO<sub>3</sub><sup>−</sup>-N in the Zhihugang, Liangxi, Lihe, and Daxigang rivers. The isotopic compositions of various sources in the diagram were modified after [<a href="#B15-water-09-00345" class="html-bibr">15</a>,<a href="#B16-water-09-00345" class="html-bibr">16</a>,<a href="#B53-water-09-00345" class="html-bibr">53</a>,<a href="#B67-water-09-00345" class="html-bibr">67</a>].</p>
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<p>Spatial variations of (<b>a</b>) NH4<sup>+</sup>-N; (<b>b</b>) NO<sub>3</sub>—N; and (<b>c</b>) TN in the Zhihugang, Liangxi, Lihe and Daxigang rivers.</p>
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1280 KiB  
Article
Impacts of Accumulated Particulate Organic Matter on Oxygen Consumption and Organic Micro-Pollutant Elimination in Bank Filtration and Soil Aquifer Treatment
by Josefine Filter, Martin Jekel and Aki Sebastian Ruhl
Water 2017, 9(5), 349; https://doi.org/10.3390/w9050349 - 16 May 2017
Cited by 24 | Viewed by 6106
Abstract
Bank filtration (BF) and soil aquifer treatment (SAT) are efficient natural technologies in potable water reuse systems. The removal of many organic micro-pollutants (OMPs) depends on redox-conditions in the subsoil, especially on the availability of molecular oxygen. Due to microbial transformation of particulate [...] Read more.
Bank filtration (BF) and soil aquifer treatment (SAT) are efficient natural technologies in potable water reuse systems. The removal of many organic micro-pollutants (OMPs) depends on redox-conditions in the subsoil, especially on the availability of molecular oxygen. Due to microbial transformation of particulate and dissolved organic constituents, oxygen can be consumed within short flow distances and induce anoxic and anaerobic conditions. The effect of accumulated particulate organic carbon (POC) on the fate of OMPs in BF and SAT systems is not fully understood. Long-term column experiments with natural sediment cores from the bank of Lake Tegel and from a SAT basin were conducted to investigate the impact of accumulated POC on dissolved organic carbon (DOC) release, on oxygen consumption, on mobilization of iron and manganese, and on the elimination of the organic indicator OMPs. The cores were fed with aerated tap water spiked with OMPs to exclude external POC inputs. Complete oxygen consumption within the first infiltration decimeter in lake sediments caused mobilization of iron, manganese, and DOC. Redox-sensitive OMPs like diclofenac, sulfamethoxazole, formylaminoantipyrine, and gabapentin were eliminated by more than 50% in all sediment cores, but slightly higher residual concentrations were measured in effluents from lake sediments, indicating a negative impact of a high oxygen consumption on OMP removal. Full article
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<p>(<b>a</b>) Photograph of the profile in the soil aquifer treatment (SAT) basin with a replaced top layer, residuals of the colmation layer, and the underlying filter sand with schematic positions where cores C1, C2, S1, and S2 were withdrawn; (<b>b</b>) schematic illustration and approximate dimensions of the experimental setup.</p>
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<p>Dissolved oxygen concentrations of the influent tap water and of the effluents of the cores.</p>
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<p>Iron and manganese concentrations in the influent tap water and in the effluents of the SAT and BF cores.</p>
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<p>Dissolved organic carbon (DOC) concentrations and ultraviolet light absorption at 254 nm wavelength (UV<sub>254</sub>) in influent and column effluents.</p>
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<p>Liquid chromatography and continuous organic carbon (LC-OCD) (<b>a</b>) and liquid chromatography with UV detection (LC-UVD) (<b>b</b>) chromatograms of the influent tap water and the core effluents after 76 days of operation.</p>
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<p>Concentrations of sulfamethoxazole, formylaminoantipyrine, diclofenac, gabapentin, benzotriazole, and carbamazepine adjusted in the influent tap water and in the respective effluents. The boxes represent the 25th and the 75th percentiles as well as the median value, with whiskers indicating the lower (10th percentile) and the upper (90th percentile) adjacent values. The empty dots represent the outside values and the red dots the mean values (n ≥ 11).</p>
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1685 KiB  
Article
Estimation of Active Stream Network Length in a Hilly Headwater Catchment Using Recession Flow Analysis
by Wei Li, Ke Zhang, Yuqiao Long and Li Feng
Water 2017, 9(5), 348; https://doi.org/10.3390/w9050348 - 16 May 2017
Cited by 7 | Viewed by 4686
Abstract
Varying active stream network lengths (ASNL) is a common phenomenon, especially in hilly headwater catchment. However, direct observations of ASNL are difficult to perform in mountainous catchments. Regarding the correlation between active stream networks and stream recession flow characteristics, we developed a new [...] Read more.
Varying active stream network lengths (ASNL) is a common phenomenon, especially in hilly headwater catchment. However, direct observations of ASNL are difficult to perform in mountainous catchments. Regarding the correlation between active stream networks and stream recession flow characteristics, we developed a new method to estimate the ASNL, under different wetness conditions, of a catchment by using streamflow recession analysis as defined by Brutsaert and Nieber in 1977. In our study basin, the Sagehen Creek catchment, we found that aquifer depth is related to a dimensionless parameter defined by Brutsaert in 1994 to represent the characteristic slope magnitude for a catchment. The results show that the estimated ASNL ranges between 9.8 and 43.9 km which is consistent with direct observations of dynamic stream length, ranging from 12.4 to 32.5 km in this catchment. We also found that the variation of catchment parameters between different recession events determines the upper boundary characteristic of recession flow plot on a log–log scale. Full article
(This article belongs to the Special Issue Hillslope and Watershed Hydrology)
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<p>Map of the Sagehen Creek catchment. The stream network is extracted using the flow accumulation threshold of 300 with GIS. The dash lines illustrate the order-one streams. Each order-one stream is associated with a hillslope contribution area (gray zone). The continuous black lines illustrate the order-two and order-three streams.</p>
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<p>The calculated breadth (L) based on Equation (8) with different ASNL (B).</p>
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<p>Plot of log(−dQ/dt) vs. log(Q) derived from the observed streamflow for 51 individual recession events selected over a period of 16 years. The straight line with a slope of 1 illustrates the upper boundary of the plot. This upper boundary is discussed in <a href="#sec5dot5-water-09-00348" class="html-sec">Section 5.5</a>.</p>
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<p>Separated plots of dQ/dt vs. Q, based on the range of Q<sub>min</sub> on a log scale: (<b>a</b>) log(Q<sub>min</sub>) &lt; 3.9, (<b>b</b>) 3.9 ≤ log(Q<sub>min</sub>) &lt; 4.1, (<b>c</b>) 4.1 ≤ log(Q<sub>min</sub>) &lt; 4.3, (<b>d</b>) 4.3 ≤ log(Q<sub>min</sub>) &lt; 4.5, and (<b>e</b>) log(Q<sub>min</sub>) ≥ 4.5. Gray dots illustrate the recession curves in each range. Two straight dash lines illustrate the upper and lower envelopes. Two black curves are obtained from the solution Equation (2) with the condition of D equal to 4 m (right curve), and 27 m (left curve).</p>
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<p>B vs. D curves calculated using Equation (6). The five black curves are related to five ranges, respectively. The gray zone illustrates the possible range of active stream network. The dash line in the gray zone illustrates the estimated ASNL according to the assumptions stated in <a href="#sec4-water-09-00348" class="html-sec">Section 4</a>.</p>
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<p>Sensitivity analysis of estimated ASNL as influenced by variations in k and f: (<b>a</b>) the error bars illustrate the influence of k with a range of 0.026–0.39 m/d; (<b>b</b>) the error bars illustrate the influence of f with a range of 0.08–0.15.</p>
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1014 KiB  
Article
Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain
by Patricia Jimeno-Sáez, Javier Senent-Aparicio, Julio Pérez-Sánchez, David Pulido-Velazquez and José María Cecilia
Water 2017, 9(5), 347; https://doi.org/10.3390/w9050347 - 15 May 2017
Cited by 36 | Viewed by 8399
Abstract
The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the [...] Read more.
The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the consequences derived from flash floods, especially in Mediterranean countries. In this study, empirical methods to estimate the IPF based on maximum mean daily flow (MMDF), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) have been compared. These methods have been applied in 14 different streamflow gauge stations covering the diversity of flashiness conditions found in Peninsular Spain. Root-mean-square error (RMSE), and coefficient of determination (R2) have been used as evaluation criteria. The results show that: (1) the Fuller equation and its regionalization is more accurate and has lower error compared with other empirical methods; and (2) ANFIS has demonstrated a superior ability to estimate IPF compared to any empirical formula. Full article
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<p>Location of the selected basins.</p>
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<p>Structure of feedforward multilayer perceptron network (MLP) network used in this research.</p>
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<p>Scatter plots of observed instantaneous peak flow (IPF) (m<sup>3</sup>/s) and the estimated IPF (m<sup>3</sup>/s) obtained with ANN versus ANFIS for basins (test phase). The letters on the top left of each subfigure show the basin codes (see <a href="#water-09-00347-t001" class="html-table">Table 1</a>).</p>
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4489 KiB  
Article
Summer Season Water Temperature Modeling under the Climate Change: Case Study for Fourchue River, Quebec, Canada
by Jaewon Kwak, André St-Hilaire, Fateh Chebana and Gilho Kim
Water 2017, 9(5), 346; https://doi.org/10.3390/w9050346 - 14 May 2017
Cited by 22 | Viewed by 7598
Abstract
It is accepted that human-induced climate change is unavoidable and it will have effects on physical, chemical, and biological properties of aquatic habitats. This will be especially important for cold water fishes such as trout. The objective of this study is to simulate [...] Read more.
It is accepted that human-induced climate change is unavoidable and it will have effects on physical, chemical, and biological properties of aquatic habitats. This will be especially important for cold water fishes such as trout. The objective of this study is to simulate water temperature for future periods under the climate change situations. Future water temperature in the Fourchue River (St-Alexandre-de-Kamouraska, QC, Canada) were simulated by the CEQUEAU hydrological and water temperature model, using meteorological inputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Circulation Models (GCMs) with Representative Concentration Pathway (RCP) 2.6, 4.5 and 8.5 climate change scenarios. The result of the study indicated that water temperature in June will increase 0.2–0.7 °C and that in September, median water temperature could decrease by 0.2–1.1 °C. The rise in summer water temperature may be favorable to brook trout (Salvelinus fontinalis) growth, but several days over the Upper Incipient Lethal Temperature (UILT) are also likely to occur. Therefore, flow regulation procedures, including cold water releases from the Morin dam may have to be considered for the Fourchue River. Full article
(This article belongs to the Special Issue Ecological Responses of Lakes to Climate Change)
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<p>(<b>A</b>) Study area; (<b>B</b>) drainage basin showing location of reservoir; and (<b>C</b>) land use map showing hydrological units (whole squares) and arrows indicate water routing used for CEQUEAU.</p>
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<p>Schematic description of CEQUEAU model: (<b>a</b>) production function; and (<b>b</b>) routing function [<a href="#B39-water-09-00346" class="html-bibr">39</a>].</p>
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<p>Box plot of air temperature with 20 historical years and the four years with temperature measurements.</p>
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<p>Observed and simulated flows with CEQUEAU hydrological model: (<b>a</b>) calibration (June 2011 to September 2013); and (<b>b</b>) validation periods (October 2013 to September 2014). <a href="#water-09-00346-f005" class="html-fig">Figure 5</a> shows observed and simulated daily water temperatures for the calibration and validation periods. Again, the model provided good results, with Nash coefficients above 0.9, RMSEs below 0.81. A systematic bias is observed during the calibration period and is exacerbated during the validation period, reaching −1.1 °C. This bias indicates that underestimation of the warmer temperatures in July and August by the model is likely.</p>
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<p>Observed and simulated water temperature during summer season with CEQUEAU hydrological model: (<b>a</b>) calibration (June to September 2011 to 2013); and (<b>b</b>) validation periods (June to September 2014).</p>
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<p>Two-variable Kolmogorov–Smirnov test (95% significance level) for RCP 2.6 for last 20 years (1994 to 2014): (*) indicated that the GCMs result identified as different distribution with observed.</p>
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<p>Boxplot of monthly median air temperature from 2020 to 2096 years for RCP 8.5 with 12 climate models; bold red line indicates monthly median air temperature for the observed period (2011 to 2014) and other results for RCP 2.6 (<a href="#app1-water-09-00346" class="html-app">Figure S2</a>) and RCP 4.5 (<a href="#app1-water-09-00346" class="html-app">Figure S3</a>) are in <a href="#app1-water-09-00346" class="html-app">Supplementary Materials</a>.</p>
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<p>Simulated result of runoff and water temperature based on MPI-ESM-LR climate model with RCP 2.6.</p>
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<p>Boxplot of daily water temperature (2016 to 2096 years) in summer season: (<b>a</b>) RCP 2.6 (9 climate models); (<b>b</b>) RCP 4.5 (12 climate models); and (<b>c</b>) RCP 8.5 (12 climate models) climate scenarios and bold red line indicate monthly median water temperature for observed period (2011 to 2014).</p>
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<p>Boxplot of number of the day over UILT of the brook trout for each RCP climate scenarios; RCP 2.6, RCP 4.5 and 8.5.</p>
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5126 KiB  
Article
Local Climate Change and the Impacts on Hydrological Processes in an Arid Alpine Catchment in Karakoram
by Jiao Liu, Min Luo, Tie Liu, Anming Bao, Philippe De Maeyer, Xianwei Feng and Xi Chen
Water 2017, 9(5), 344; https://doi.org/10.3390/w9050344 - 12 May 2017
Cited by 17 | Viewed by 5667
Abstract
Climate change and the impacts on hydrological processes in Karakoram region are highly important to the available water resources in downstream oases. In this study, a modified quantile perturbation method (QPM), which was improved by considering the frequency changes in different precipitation intensity [...] Read more.
Climate change and the impacts on hydrological processes in Karakoram region are highly important to the available water resources in downstream oases. In this study, a modified quantile perturbation method (QPM), which was improved by considering the frequency changes in different precipitation intensity ranges, and the Delta method were used to extract signals of change in precipitation and temperature, respectively. Using a historical period (1986–2005) for reference, an average ensemble of 18 available Global Circulation Models (GCMs) indicated that the annual precipitation will increase by 2.9–4.4% under Representative Concentration Pathway 4.5 (RCP4.5) and by 2.8–7.9% in RCP8.5 in different future periods (2020–2039, 2040–2059, 2060–2079 and 2080–2099) due to an increased intensity of extreme precipitation events in winter. Compared with the historical period, the average ensemble also indicated that temperature in future periods will increase by 0.31–0.38 °C/10a under RCP4.5 and by 0.34–0.58 °C/10a under RCP8.5. Through coupling with a well-calibrated MIKE SHE model, the simulations suggested that, under the climate change scenarios, increasing evaporation dissipation will lead to decreased snow storage in the higher altitude mountain region and likewise with regard to available water in the downstream region. Snow storage will vary among elevation bands, e.g., the permanent snowpack area below 5600 m will completely vanish over the period 2060–2079, and snow storage in 5600–6400 m will be reduced dramatically; however, little or no change will occur in the region above 6400 m. Warming could cause stronger spring and early summer stream runoff and reduced late summer flow due to a change in the temporal distribution of snowmelt. Furthermore, both the frequency and intensity of flooding will be enhanced. All the changes in hydrological processes are stronger under RCP8.5 than those under RCP4.5. In Karakoram region, the transformations among different forms of water resources alter the distributions of hydrologic components under future climate scenarios, and more studies are needed on the transient water resources system and the worsening of flood threats in the study area. Full article
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<p>The locations of the Yarkant River basin, meteorological and hydrological stations.</p>
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<p>Monthly precipitation, pan evaporation and temperature values at the Tashkurgan station and discharge at the Kaqun station.</p>
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<p>The mean frequency changes of the rainy days in each month of the future periods (FPs) relative to those of the history period (HP) determined by the 18 Global Circulation Models (GCMs).</p>
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<p>The mean quantile perturbations of the precipitation intensity in each month of the future periods (FPs) relative to those of the history period (HP) determined by the 18 GCMs.</p>
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<p>The amount of monthly precipitation change rates generated in the future periods (FPs) with respect to those observed in the history period (HP).</p>
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<p>The mean monthly changes of temperature of the future periods (FPs) compared to those of the history period (HP) determined by the 18 GCMs.</p>
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<p>The amount of monthly snowmelt simulated by the MIKE SHE model in the history period (HP) and future periods (FPs).</p>
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<p>The spatial distribution of the snowpack simulated by the MIKE SHE model on 31 August of the last year in each period.</p>
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<p>The distributions of permanent snow storage simulated by the MIKE SHE model in the different elevation bands of the catchment on 31 August of the last year in each period.</p>
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<p>Average monthly discharge simulated by the MIKE SHE model in each period at the Kaqun station.</p>
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<p>Exceedance probabilities of the simulated discharge simulated by the MIKE SHE model at the Kaqun station in each period.</p>
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1154 KiB  
Article
Cost-Benefit Analysis of the Managed Aquifer Recharge System for Irrigation under Climate Change Conditions in Southern Spain
by Carmen Rupérez-Moreno, Julio Pérez-Sánchez, Javier Senent-Aparicio, Pilar Flores-Asenjo and Carmen Paz-Aparicio
Water 2017, 9(5), 343; https://doi.org/10.3390/w9050343 - 12 May 2017
Cited by 22 | Viewed by 8236
Abstract
Droughts and climate change in regions with profitable irrigated agriculture will impact groundwater resources with associated direct and indirect impacts. In the integrated water resource management (IWRM), managed aquifer recharge (MAR) offers efficient solutions to protect, conserve, and ensure survival of aquifers and [...] Read more.
Droughts and climate change in regions with profitable irrigated agriculture will impact groundwater resources with associated direct and indirect impacts. In the integrated water resource management (IWRM), managed aquifer recharge (MAR) offers efficient solutions to protect, conserve, and ensure survival of aquifers and associated ecosystems, as the Water Framework Directive requires. The purpose of this paper is to analyse the socio-economic feasibility of the MAR system in the overexploited Boquerón aquifer in Hellín (Albacete, Spain) under climate change and varying irrigation demand conditions. To assess, in monetary terms, the profitability of the MAR system, a cost-benefit analysis (CBA) has been carried out. The results for the period 2020–2050 showed that the most favourable situations would be scenarios involving artificial recharge, in which future irrigation demand remains at the present level or falls below 10% of the current irrigation surface, as these scenarios generated an internal rate of return of between 53% and 57%. Additionally, the regeneration of the habitat will take between 5 and 9 years. Thus, the IWRM with artificial recharge will guarantee the sustainability of irrigation of the agricultural lands of Hellín and will achieve water balance even in severe climate change conditions. Full article
(This article belongs to the Special Issue Water Economics and Policy)
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<p>Location of Boquerón aquifer in the Hellín municipality and agricultural area.</p>
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<p>Evolution in water table and aquifer recharge: (<b>a</b>) without variation in irrigation demand; (<b>b</b>) with a fall in irrigation demand of 10%; (<b>c</b>) with an increase in irritation demand of 10%.</p>
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2266 KiB  
Article
The Water Footprint of Heavy Oil Extraction in Colombia: A Case Study
by Luis Gabriel Carmona, Kai Whiting and Angeles Carrasco
Water 2017, 9(5), 340; https://doi.org/10.3390/w9050340 - 12 May 2017
Cited by 7 | Viewed by 9166
Abstract
This paper is a Colombian case study that calculates the total water footprint (blue, green, and grey) for heavy crude production (11.5 average API gravity) occurring in three fields, located in the Magdalena watershed. In this case study, the highest direct blue footprint [...] Read more.
This paper is a Colombian case study that calculates the total water footprint (blue, green, and grey) for heavy crude production (11.5 average API gravity) occurring in three fields, located in the Magdalena watershed. In this case study, the highest direct blue footprint registers 0.19 m3/barrel and is heavily influenced by cyclic steam stimulation practices. This value could be reduced if the water coming out of the production well was to be cleaned with highly advanced wastewater treatment technologies. The highest grey water footprint, at 0.06 m3/barrel, is minimal and could be reduced with conventional wastewater treatment technologies and rigorous maintenance procedures. The green water footprint is negligible and cannot be reduced for legal reasons. The indirect blue water footprint is also considerable at 0.19–0.22 m3/barrel and could be reduced if electricity was produced onsite instead of purchased. In addition, the paper identifies methodological flaws in the Colombian National Water Study (2014), which wrongly calculated the direct blue water footprint, leading to a 5 to 32-fold sub-estimation. It also ignored the grey, with important implications for water resource policy and management. To rectify the situation, future National Surveys should follow the procedure published here. Full article
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<p>Watersheds distribution in Colombia. Note: Basins with the letter M belong to the Magdalena catchment. Basins with “MM” indicate that they come under the Middle Magdalena sub-area.</p>
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<p>Product water footprint assessment stages.</p>
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<p>System scope, core processes and direct water footprint flows. CSS: cyclic steam stimulation; SIAR: Produced wastewater treatment plant.</p>
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<p>Water footprint of a produced barrel in m<sup>3</sup>. SR: secondary recovery; CSS: cyclic steam simulation).</p>
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<p>Blue water footprint evolution 2012–2015.</p>
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<p>Water footprint and water consumption comparisons for onshore conventional crude oil production. Note: TS: this study. SR: secondary recovery. EOR: enhanced oil recovery.</p>
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<p>The contribution of energy and materials to the indirect blue water footprint in CSS 1 vs. the disaggregated carbon emissions of the same field.</p>
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2528 KiB  
Article
Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling
by Tao Chen, Liliang Ren, Fei Yuan, Xiaoli Yang, Shanhu Jiang, Tiantian Tang, Yi Liu, Chongxu Zhao and Liming Zhang
Water 2017, 9(5), 342; https://doi.org/10.3390/w9050342 - 11 May 2017
Cited by 91 | Viewed by 10100
Abstract
The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall [...] Read more.
The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall interpolation at annual, daily, and hourly time scales to provide a comprehensive evaluation. An improved regression-based scheme is proposed using principal component regression with residual correction (PCRR) and is compared with inverse distance weighting (IDW) and multiple linear regression (MLR) interpolation methods. In this study, the meso-scale catchment of the Fuhe River in southeastern China was selected as a typical region. Furthermore, a hydrological model HEC-HMS was used to calculate streamflow and to evaluate the impact of rainfall interpolation methods on the results of the hydrological model. Results show that the PCRR method performed better than the other methods tested in the study and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. Simulated streamflow showed different characteristics based on the mean, maximum, minimum, and peak flows. The results simulated by PCRR exhibited the lowest streamflow error and highest correlation with measured values at the daily time scale. The application of the PCRR method is found to be promising because it considers multicollinearity among variables. Full article
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<p>Location of the study area and the geographic distribution of hydrometeorological stations.</p>
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<p>Scatterplots of observed versus predicted values for all interpolation methods of annual precipitation.</p>
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<p>Statistical error of rainfall between interpolation methods and meteorological station rainfall measurements in the basin.</p>
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<p>Spatial distribution of all interpolation methods for annual precipitation.</p>
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<p>Differences in monthly rainfall for the Xinxie catchment using different interpolations: (<b>a</b>) PCRR-IDW and (<b>b</b>) PCRR-MLR.</p>
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<p>Modeled and measured runoff during May to June 2010 in Xinxie catchment for principal component regression with residual correction (PCRR), inverse distance weighting (IDW), and MLR rainfall interpolation.</p>
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<p>Modeled and measured runoff of two flood events in Xinxie catchment for PCRR, IDW, and MLR rainfall interpolation.</p>
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2142 KiB  
Article
Mechanism of Nitrogen Removal from Aqueous Solutions Using Natural Scoria
by Tianzi Dong, Yuling Zhang, Xiaosi Su, Zhiyu Chen and Chaoqun Si
Water 2017, 9(5), 341; https://doi.org/10.3390/w9050341 - 11 May 2017
Cited by 5 | Viewed by 4868
Abstract
The efficiencies and mechanisms of nitrogen removal from groundwater by scoria were studied. When NH4+-N concentration was 0.5–10 mg/L, the removal was 96–89%. When NO2-N concentration was 0.1–5 mg/L, the removal was 93–85%. When NO3 [...] Read more.
The efficiencies and mechanisms of nitrogen removal from groundwater by scoria were studied. When NH4+-N concentration was 0.5–10 mg/L, the removal was 96–89%. When NO2-N concentration was 0.1–5 mg/L, the removal was 93–85%. When NO3-N concentration was 30–150 mg/L, the removal was 85–70%. Additionally, van der Waals forces had a positive impact on the adsorption, which promoted NH4+-N adsorption. Ion exchange and dissolution did not exist. Functional groups of N-H, C-H, and C-N changed after adsorption. Overall, this study indicates that scoria is an ecologically friendly and safe material that can be utilized for groundwater purification to treat nitrogen-contaminated water. Full article
(This article belongs to the Special Issue Groundwater Monitoring and Remediation)
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<p>Scoria adsorbent.</p>
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<p>Nitrogen removal in different concentrations.</p>
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<p>Nitrogen removal in different concentrations.</p>
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<p>Weight percentage and accumulated weight percentage of different particle sizes.</p>
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<p>Weight percentage and accumulated weight percentage of different particle sizes.</p>
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<p>Effect of specific surface area on nitrogen removal.</p>
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<p>Zero point of charge (PZC) of scoria.</p>
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<p>Fourier transform infrared (FT-IR) spectrum of scoria before and after NH<sub>4</sub><sup>+</sup>-N removal.</p>
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<p>FT-IR spectrum of scoria before and after NO<sub>2</sub><sup>−</sup>-N removal.</p>
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<p>FT-IR spectra of scoria before and after NO<sub>3</sub><sup>−</sup>-N removal.</p>
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1641 KiB  
Article
Water Bridging Dynamics of Polymerase Chain Reaction in the Gauge Theory Paradigm of Quantum Fields
by L. Montagnier, J. Aïssa, A. Capolupo, T. J. A. Craddock, P. Kurian, C. Lavallee, A. Polcari, P. Romano, A. Tedeschi and G. Vitiello
Water 2017, 9(5), 339; https://doi.org/10.3390/w9050339 - 11 May 2017
Cited by 15 | Viewed by 13401
Abstract
We discuss the role of water bridging the DNA-enzyme interaction by resorting to recent results showing that London dispersion forces between delocalized electrons of base pairs of DNA are responsible for the formation of dipole modes that can be recognized by Taq polymerase. [...] Read more.
We discuss the role of water bridging the DNA-enzyme interaction by resorting to recent results showing that London dispersion forces between delocalized electrons of base pairs of DNA are responsible for the formation of dipole modes that can be recognized by Taq polymerase. We describe the dynamic origin of the high efficiency and precise targeting of Taq activity in PCR. The spatiotemporal distribution of interaction couplings, frequencies, amplitudes, and phase modulations comprise a pattern of fields which constitutes the electromagnetic image of DNA in the surrounding water, which is what the polymerase enzyme actually recognizes in the DNA water environment. The experimental realization of PCR amplification, achieved through replacement of the DNA template by the treatment of pure water with electromagnetic signals recorded from viral and bacterial DNA solutions, is found consistent with the gauge theory paradigm of quantum fields. Full article
(This article belongs to the Special Issue Electrohydrodynamic Liquid Bridges and Electrified Water)
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<p>Mediating wave fields in subatomic and biological physics. (<b>A</b>) In quantum field theory electron-electron interactions are mediated by photons propagating between vertex 1 and vertex 2; (<b>B</b>) Analogously, long-range correlations between DNA and enzymes are mediated by dipole waves in the water matrix. These renderings are schematic, neither drawn to scale nor representative of the actual orientations of water molecules. Adapted from Ref. [<a href="#B1-water-09-00339" class="html-bibr">1</a>].</p>
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<p>Logarithmic plot of power vs frequency of EMS emitted from water solution of DNA HIV-1 long terminal repeat (LTR 194 base pairs). The decimal dilutions of original concentration of 2 ng/mL HIV-1 emitting EMS are between <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> </semantics> </math> [<a href="#B3-water-09-00339" class="html-bibr">3</a>,<a href="#B4-water-09-00339" class="html-bibr">4</a>,<a href="#B5-water-09-00339" class="html-bibr">5</a>,<a href="#B6-water-09-00339" class="html-bibr">6</a>]. The signal analysis is limited to the frequency range between about 100 and 2000 Hz. The signal in the frequencies below ≈40 Hz and above ≈2000 Hz (dotted lines) does not show self-similar behavior.</p>
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<p>Collective dipole oscillations in aromatic amino acid networks of <span class="html-italic">Taq</span> DNA polymerase. Collective normal-mode solutions to networks of aromatic induced dipoles in <span class="html-italic">Taq</span> are within the energy range of the collective dipole modes of DNA bounded by its relevant protein clamps. In the diagonalized form of the coupled harmonic oscillator Hamiltonian, the number operator <math display="inline"> <semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mo>=</mo> <msubsup> <mi>a</mi> <mrow> <mi>s</mi> </mrow> <mo>†</mo> </msubsup> <msub> <mi>a</mi> <mi>s</mi> </msub> </mrow> </semantics> </math> acts on these states to produce a zero eigenvalue, analogously to Equation (<a href="#FD2-water-09-00339" class="html-disp-formula">2</a>) for DNA. They are identified with zero-point modes of the ground state of the enzyme aromatic network. Data for the collective modes (arranged along the abscissa axis according to increasing energy) are presented in units of eV on the ordinate axis. Adapted from Ref. [<a href="#B1-water-09-00339" class="html-bibr">1</a>].</p>
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9969 KiB  
Article
Dynamics of Suspended Sediments during a Dry Season and Their Consequences on Metal Transportation in a Coral Reef Lagoon Impacted by Mining Activities, New Caledonia
by Jean-Michel Fernandez, Jean-Dominique Meunier, Sylvain Ouillon, Benjamin Moreton, Pascal Douillet and Olivier Grauby
Water 2017, 9(5), 338; https://doi.org/10.3390/w9050338 - 10 May 2017
Cited by 12 | Viewed by 5880
Abstract
Coral reef lagoons of New Caledonia form the second longest barrier reef in the world. The island of New Caledonia is also one of the main producers of nickel (Ni) worldwide. Therefore, understanding the fate of metals in its lagoon waters generated from [...] Read more.
Coral reef lagoons of New Caledonia form the second longest barrier reef in the world. The island of New Caledonia is also one of the main producers of nickel (Ni) worldwide. Therefore, understanding the fate of metals in its lagoon waters generated from mining production is essential to improving the management of the mining activities and to preserve the ecosystems. In this paper, the vertical fluxes of suspended particulate matter (SPM) and metals were quantified in three bays during a dry season. The vertical particulate flux (on average 37.70 ± 14.60 g·m2·d−1) showed fractions rich in fine particles. In Boulari Bay (moderately impacted by the mining activities), fluxes were mostly influenced by winds and SPM loads. In the highly impacted bay of St Vincent and in the weakly impacted bay of Dumbéa, tide cycles clearly constrained the SPM and metal dynamics. Metals were associated with clay and iron minerals transported by rivers and lagoonal minerals, such as carbonates, and possibly neoformed clay as suggested by an unusually Ni-rich serpentine. Particle aggregation phenomena led to a reduction in the metal concentrations in the SPM, as identified by the decline in the metal distribution constants (Kd). Full article
(This article belongs to the Special Issue Sediment Transport in Coastal Waters)
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<p>Map location of the study area in the west coast of New-Caledonia: Boulari Bay, influenced by a medium-scale mine activities until 1981; Dumbéa Bay, halted mining activity in order to maintain the water supply of Nouméa (the peninsula between Dumbéa Bay and Boulari Bay); St Vincent Bay, affected by intense opencast mining activities.</p>
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<p>Wind and current speeds and directions for Boulari, Dumbéa and St Vincent bays during the study period (21 November to 14 December 2005).</p>
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<p>Median diameter (D50), Junge parameter (<span class="html-italic">s</span>), flux and turbidity for Boulari, Dumbéa and St Vincent bays over the study period (21 November to 14 December 2005), 3 m above the seabed.</p>
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<p>Time variation of the Ca and the 8 metals analysed in SPM trapped during study period from 21 November to 14 December, 2005 in each sampling site: (<b>a</b>) Boulari Bay; (<b>b</b>) Dumbéa Bay and (<b>c</b>) St Vincent Bay.</p>
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<p>Time variation of the Ca and the 8 metals analysed in SPM trapped during study period from 21 November to 14 December, 2005 in each sampling site: (<b>a</b>) Boulari Bay; (<b>b</b>) Dumbéa Bay and (<b>c</b>) St Vincent Bay.</p>
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<p>X-ray diffractograms of suspended particulate matter showing the main minerals found in the three study sites (Sm = smectite; T = talc; Se = serpentine; Go = goethite; Ar = aragonite; Q = quartz; Ca = Calcite).</p>
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<p>Images and composition determined by transmitted electron microscopy of some particles collected during the study. The chemical formulae are given in <a href="#water-09-00338-t011" class="html-table">Table 11</a>.</p>
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<p>Particulate Ni flux, distribution constant (<span class="html-italic">K<sub>d</sub></span>) of Ni and dissolved concentration of Ni for Boulari, Dumbéa and St Vincent bays over the study period (21 November to 14 December 2005), 3 m above the seabed.</p>
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8006 KiB  
Article
The Winter Environmental Continuum of Two Watersheds
by Benoit Turcotte and Brian Morse
Water 2017, 9(5), 337; https://doi.org/10.3390/w9050337 - 9 May 2017
Cited by 10 | Viewed by 4970
Abstract
This paper examines the winter ecosystemic behavior of two distinct watersheds. In cold-temperate regions, the hydrological signal and environmental parameters can fluctuate dramatically over short periods of time, causing major impacts to aquatic habitats. This paper presents the results of the 2011–2012 winter [...] Read more.
This paper examines the winter ecosystemic behavior of two distinct watersheds. In cold-temperate regions, the hydrological signal and environmental parameters can fluctuate dramatically over short periods of time, causing major impacts to aquatic habitats. This paper presents the results of the 2011–2012 winter field campaign in streams and rivers near Quebec City, QC, Canada. The objective was to quantify water quantity and quality parameters and their environmental connectivity from headwater creeks above to the larger rivers below over the entire freeze-up, mid-winter and breakup periods with a view toward exploring the watershed continuum. The paper presents how aquatic pulses (water level, discharge, temperature, conductivity, dissolved oxygen and turbidity, measured at seven sites on an hourly basis along channels of different sizes and orders) evolve through the aquatic environment. Ice conditions and the areal ice coverage were also evaluated (on a daily time step along each instrumented channel). Some findings of the investigation revealed that water temperatures remained well above 0 °C during winter in headwater channels, that dissolved oxygen levels during winter were relatively high, but with severe depletions prior to and during breakup in specific settings, that high conductivity spikes occurred during runoff events, that annual turbidity extremes were measured in the presence of ice and that dynamic ice cover breakup events have the potential to generate direct or indirect mortality among aquatic species and to dislodge the largest rocks in the channel. The authors believe that the environmental impact of a number of winter fluvial processes needs to be further investigated, and the relative significance of the winter period in the annual environmental cycle should be given additional attention. Full article
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<p>(<b>A</b>) Geographic location of the Montmorency (M) and Etchemin (E) watersheds located on both sides of the St. Lawrence River in Quebec City; (<b>B</b>) Research channels in the M watershed with instrumented sites identified by white circles; (<b>C</b>) Research channels in the E watershed with instrumented sites identified by white circles. Channel colors are representative of their approximate Strahler order. White triangles represent discharge estimation sites, and white circles indicate environmental parameter monitoring sites.</p>
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<p>(<b>A</b>) Automated Canon 20D digital camera in adapted pelican case at Site M2; (<b>B</b>) aquatic view of the YSI 6600 V2 in its PVC tube at Site M3; (<b>C</b>) retrieving and downloading the YSI 6600 V2 at Site E4 in February 2012; and (<b>D</b>) retrieving and downloading the YSI 6600 V2 prior to breakup at Site E3.</p>
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<p>Larvae and fish adopting the YSI 6600 V2 basket as a wintering habitat at Site E2.</p>
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<p>Hourly data (air temperature (T<sub>air</sub>), discharge (Q), ice coverage (Ic), water temperature (T<sub>w</sub>), specific conductivity (Sp.C), dissolved oxygen (DO) and turbidity (Turb)) from 1 November 2011 to 29 April 2012 at Sites M1, M2, M3 and M4.</p>
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<p>Hourly turbidity (Turb) and water depth (Y) data from Sites M2 and M3 (<b>A</b>) from 8 March to 11 March and (<b>B</b>) from 20 March to 23 March. The delay between turbidity and local Q peaks vary between 1 and 25 h.</p>
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<p>Hourly data (air temperature (T<sub>air</sub>), discharge (Q), ice coverage (I<sub>c</sub>), water temperature (T<sub>w</sub>), specific conductivity (Sp.C), dissolved oxygen (DO) and turbidity (Turb)) from 1 November 2011 to 29 April 2012 at Sites E2, E3 and E4. The red diamonds in the DO graph represent punctual measurements at Site E2 with a portable instrument.</p>
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<p>(<b>A</b>) Measured water depth (Y) and estimated discharge (Q) at breakup on the third order channel E3 showing the signature of an ice jam; (<b>B</b>) ice jam (photograph taken on 21 March 2012) and (<b>C</b>) new gravel bar formed where the jam toe had been momentarily located (photograph taken on 1 August 2012).</p>
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<p>Data and power function interpolations between open water and ice-affected turbidity-Q relationships at Sites E4, E3 and E2. Q is made dimensionless by using the annual average Q at all sites.</p>
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<p>2013 field data and interpolated relationship between suspended solids (mg/L) and turbidity (NTU) at Sites E3 and E4.</p>
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<p>(<b>A</b>) Dead crayfish (8 cm in length) on a sandy bar along a secondary channel of the Montmorency River after the 15 April 2014 breakup and (<b>B</b>) dead fish (5–10 cm in length) found on a gravel bar of the Ste. Anne River after a snow slush consolidation event on 21 December 2012.</p>
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<p>Supercooling events measured during freeze-up (<b>left</b>), prior to breakup (<b>middle</b>) and after breakup (<b>right</b>) in the Ste. Anne River watershed located northwest of Quebec City, QC, Canada, during winter 2014–2015.</p>
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<p>(<b>A</b>) Stones and mud found on an ice floe deposited in the floodplain after the 2014 breakup in the Montmorency River; (<b>B</b>) 300-mm stone trapped in thermal ice deposited on the floodplain after the 2014 breakup in the Montmorency River; and (<b>C</b>) 1.5-m boulder pushed by an ice run (1 April 2016) in the Ste. Anne River (left and right photographs respectively taken before and after winter at a similar discharge).</p>
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14925 KiB  
Article
Compiling an Inventory of Glacier-Bed Overdeepenings and Potential New Lakes in De-Glaciating Areas of the Peruvian Andes: Approach, First Results, and Perspectives for Adaptation to Climate Change
by Daniel Colonia, Judith Torres, Wilfried Haeberli, Simone Schauwecker, Eliane Braendle, Claudia Giraldez and Alejo Cochachin
Water 2017, 9(5), 336; https://doi.org/10.3390/w9050336 - 9 May 2017
Cited by 50 | Viewed by 9678
Abstract
Global warming causes rapid shrinking of mountain glaciers. New lakes can, thus, form in the future where overdeepenings in the beds of still-existing glaciers are becoming exposed. Such new lakes can be amplifiers of natural hazards to downstream populations, but also constitute tourist [...] Read more.
Global warming causes rapid shrinking of mountain glaciers. New lakes can, thus, form in the future where overdeepenings in the beds of still-existing glaciers are becoming exposed. Such new lakes can be amplifiers of natural hazards to downstream populations, but also constitute tourist attractions, offer new potential for hydropower, and may be of interest for water management. Identification of sites where future lakes will possibly form is, therefore, an essential step to initiate early planning of measures for risk reduction and sustainable use as part of adaptation strategies with respect to impacts from climate change. In order to establish a corresponding knowledge base, a systematic inventory of glacier-bed overdeepenings and possible future lakes was compiled for the still glacierized parts of the Peruvian Andes using the 2003–2010 glacier outlines from the national glacier inventory and the SRTM DEM from the year 2000. The resulting inventory contains 201 sites with overdeepened glacier beds >1 ha (104 m2) where notable future lakes could form, representing a total volume of about 260 million m3. A rough classification was assigned for the most likely formation time of the possible new lakes. Such inventory information sets the stage for analyzing sustainable use and hazard/risk for specific basins or regions. Full article
(This article belongs to the Special Issue Global Warming Impacts on Mountain Glaciers and Communities)
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<p>Initial lake formation at Glaciar Artesonraju, Cordillera Blanca. The now already visible large pond may connect to the main lake, which is modeled to develop further up-glacier during the coming years to decades in the marked bed overdeepening underneath the flat glacier tongue (cf. Figure 5). Photography by D. Colonia, February 2016.</p>
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<p>Study area with the glacier-covered Cordilleras of Peru in South America, between 7°32′1′′–16°48′52′′ south and 68°56′54′′–78°27′25′′ west, situated in the Northern, Central and Southern Andes, hydrologically located in the Pacific, Atlantic, and Titicaca catchments.</p>
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<p>Morphological criteria (numbers 1, 2, 3) that indicate the existence of glacier-bed overdeepenings: Glaciar Rajupaquinan, Cordillera Blanca. Images from Google Earth.</p>
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<p>Comparison of results from step 1 (surface slope &lt; 10°) and step 2 (three morphological criteria) with step 3 (GlabTop modeling of bed topographies) for a region in the Cordillera Blanca (<b>top</b>) and one in the Cordillera Vilcanota (<b>bottom</b>). Glacier outlines are from [<a href="#B15-water-09-00336" class="html-bibr">15</a>] and coordinates from UTM zones 18s (Blanca) and 19s (Vilcanota).</p>
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<p>Comparison of results from step 1 (surface slope &lt; 10°) and step 2 (three morphological criteria) with step 3 (GlabTop modeling of bed topographies) for a region in the Cordillera Blanca (<b>top</b>) and one in the Cordillera Vilcanota (<b>bottom</b>). Glacier outlines are from [<a href="#B15-water-09-00336" class="html-bibr">15</a>] and coordinates from UTM zones 18s (Blanca) and 19s (Vilcanota).</p>
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<p>Identification of an overdeepening in the bed of Glaciar Artesonraju (Cordillera Blanca) with possible lake formation: ice-thickness distribution (<b>top</b>, maximum calculated ice thickness is 186 m) and glacier-bed overdeepening (<b>bottom</b>), both from the GlabTop model. The pond now already visible in nature (<a href="#water-09-00336-f001" class="html-fig">Figure 1</a>) at the western glacier margin may be connected (arrow) at the orographic right (northern) side of the flat glacier tongue with a larger lake probably forming at a later stage. Image SPOT 5 of 2003.</p>
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<p>Identification of an overdeepening in the bed of Glaciar Artesonraju (Cordillera Blanca) with possible lake formation: ice-thickness distribution (<b>top</b>, maximum calculated ice thickness is 186 m) and glacier-bed overdeepening (<b>bottom</b>), both from the GlabTop model. The pond now already visible in nature (<a href="#water-09-00336-f001" class="html-fig">Figure 1</a>) at the western glacier margin may be connected (arrow) at the orographic right (northern) side of the flat glacier tongue with a larger lake probably forming at a later stage. Image SPOT 5 of 2003.</p>
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<p>Glaciar Yerupajá 3, Cordillera Huayhuash, in 2007 with two possible future lakes; Google Earth image with longitudinal topographic profile through the two possible lakes (both approximately 400 m in length) in case of continued glacier retreat (<b>top</b>). Glaciar Shullcon 3, Cordillera Central, in 2009 with two possible future lakes; Google Earth image and longitudinal topographic profile through the two possible lakes (both approximately 500 m in length) in case of continued glacier retreat (<b>bottom</b>).</p>
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<p>Glaciar Yerupajá 3, Cordillera Huayhuash, in 2007 with two possible future lakes; Google Earth image with longitudinal topographic profile through the two possible lakes (both approximately 400 m in length) in case of continued glacier retreat (<b>top</b>). Glaciar Shullcon 3, Cordillera Central, in 2009 with two possible future lakes; Google Earth image and longitudinal topographic profile through the two possible lakes (both approximately 500 m in length) in case of continued glacier retreat (<b>bottom</b>).</p>
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<p>Calculating the time of initial lake formation at Glaciar Artesonraju; P<sub>g</sub>: the lowest point of the glacier where H = H<sub>min</sub> in the glacier inventory, P<sub>fl</sub>: the point of starting lake formation, ∆L: the length between P<sub>g</sub> and P<sub>fl</sub>, and ∆H: the elevation difference between P<sub>g</sub> and P<sub>fl</sub>. Google Earth image of 2003.</p>
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<p>Time classes of possible future lake formation in the catchment of Laguna Parón, Cordillera Blanca, showing two lakes with formation imminent or underway and two lakes with probable formation during the first half of the century. The calculation was done using an acceleration scenario and extrapolated altitude changes. Glacier outlines are from [<a href="#B15-water-09-00336" class="html-bibr">15</a>].</p>
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<p>Estimation of glacier response time at Glaciar Artesonraju; H<sub>max</sub>, H<sub>min</sub> = maximum, minimum elevation, ∆H = elevation range (H<sub>max</sub> − H<sub>min</sub>), ELA = estimated equilibrium line altitude.</p>
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<p>Calculated glacier response times in the catchment of the Laguna Parón. Piramide 2 (like 4989946-30 in the Marañon catchment) is a debris-covered glacier tongue essentially decoupled from the upper glacier parts. Calculated response times in such cases concern disintegrating and downwasting ice. They primarily indicate generally long but still unknown response times rather than specific values. Glacier outlines are from [<a href="#B15-water-09-00336" class="html-bibr">15</a>].</p>
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<p>Areas and numbers of possible future lakes in the Cordilleras of Peru. A number of mountain ranges were identified where no further lake formation appears likely because the glaciers there are located on steep slopes and have small areas.</p>
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<p>Estimated volumes of possible future lakes from the GlabTop model run for 11 cordilleras.</p>
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<p>Model run of an outburst flood from a possible future lake (about 380,000 m<sup>3</sup>; upper right corner of the image) using the modified single flow (MSF) approach. The color scheme indicates probability levels of pixels to be affected. See text for discussion.</p>
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7080 KiB  
Article
Seasonal Variation in Flocculation Potential of River Water: Roles of the Organic Matter Pool
by Byung Joon Lee, Jin Hur and Erik A. Toorman
Water 2017, 9(5), 335; https://doi.org/10.3390/w9050335 - 8 May 2017
Cited by 31 | Viewed by 7914
Abstract
Organic matter in the water environment can enhance either flocculation or stabilization and, thus, controls the fate and transportation of cohesive sediments and causes seasonal variation in the turbidity of river water, determining floc morphology and settling velocity. The aim of this study [...] Read more.
Organic matter in the water environment can enhance either flocculation or stabilization and, thus, controls the fate and transportation of cohesive sediments and causes seasonal variation in the turbidity of river water, determining floc morphology and settling velocity. The aim of this study was to elucidate the way that biological factors change the organic matter composition and enhances either flocculation or stabilization in different seasons. Jar test experiments were performed using a mixture of standard kaolinite and the filtered river water samples collected (bi-)weekly or monthly from April to December 2015 upstream a constructed weir in Nakdong River, to estimate the flocculation potential of the seasonal river water samples. Chlorophyll-a concentration, algae number concentration, and the fluorescence characteristics of organic matter were used to represent the biological factors. Our results revealed that flocculation potential depended not only on the algal population dynamics, but also the origins (or chemical composition) of organic matter in the river water. Extracellular polymeric substances (EPS), as algal organic matter, enhanced flocculation, while humic substances (HS), as terrestrial organic matter, enhanced stabilization, rather than flocculation. Since flocculation potential reached its maximum around the peaks of algal population, algae-produced EPS likely enhanced flocculation by binding sediment particles in the flocs. This observation supports previous findings of seasonal variation in EPS production and EPS-mediated flocculation. However, when HS was transported from the surrounding basin by a heavy rainfall event, cohesive sediments tended to be rather stabilized. Supplementary flocculation potential tests, which were performed with artificial water containing refined EPS and HS, also showed the opposing effects of EPS and HS. Full article
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Figure 1

Figure 1
<p>Map of the study site, including a section of the Nakdong River from the Goryeong weir to the Dalsung weir. The points on the map represent the weirs, the water quantity and quality monitoring stations, and the sampling sites.</p>
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<p>(<b>a</b>) A raw image; and (<b>b</b>) a processed image of a fixed floc sample on an agar plate.</p>
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<p>Seasonal variation in the physical and biogeochemical indicators of (<b>a</b>) rainfall intensity, flow rate, and temperature; (<b>b</b>) chlorophyll-a concentration and blue-green algae concentration; (<b>c</b>) dissolved organic carbon (DOC); and (<b>d</b>) relative fractions of the organic matter components.</p>
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<p>Typical excitation-emission matrices for the river water samples. The respective abbreviations of Em and Ex represent the excitation and emission wavelengths of the FEEM-PARAFAC analysis.</p>
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<p>Typical results from the flocculation potential tests with (<b>a</b>) a low flocculation potential (measured on 30 July 2015); and (<b>b</b>) a high flocculation potential (on 6 August 2015). The top panel of each figure shows the floc diameters (D<sub>Floc</sub>), and the bottom panel shows the normalized ratios of the residual solid concentration (C/Co) and the suspended solid concentration (SSC).</p>
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<p>(<b>a</b>) Seasonal variation in the biochemical drivers and the flocculation potential via (<b>b</b>) floc diameter (D<sub>Floc</sub>), and (<b>c</b>) the normalized ratio of the residual solid concentration (C/Co).</p>
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<p>Typical results from the flocculation potential tests, measured (<b>a</b>) before the algal bloom on 5 June 2015; (<b>b</b>) at the peak of algal bloom on 24 June 2015; (<b>c</b>) after the peak of algal bloom on 7 July 2015; and (<b>d</b>) after the heavy rainfall on 16 July 2015.</p>
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<p>Typical results from the flocculation potential tests, measured (<b>a</b>) before the algal bloom on 5 June 2015; (<b>b</b>) at the peak of algal bloom on 24 June 2015; (<b>c</b>) after the peak of algal bloom on 7 July 2015; and (<b>d</b>) after the heavy rainfall on 16 July 2015.</p>
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<p>Results from the supplementary flocculation potential tests with artificial water containing 10 mg/L of refined extracellular polymeric substances (EPS) and 0, 0.5, and 2.0 mg/L of humic substances (HS). (<b>a</b>,<b>b</b>) show floc diameters (D<sub>Floc</sub>) and the normalized ratios of the residual solid concentration (C/Co), respectively, at increasing kaolinite concentrations.</p>
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<p>Schematic diagram illustrating the conceptual model of extracellular polymeric substances (EPS)-mediated flocculation and humic substance (HS)-mediated stabilization in the mixture of EPS and HS.</p>
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2284 KiB  
Article
Uncertainty of Hydrological Drought Characteristics with Copula Functions and Probability Distributions: A Case Study of Weihe River, China
by Panpan Zhao, Haishen Lü, Guobin Fu, Yonghua Zhu, Jianbin Su and Jianqun Wang
Water 2017, 9(5), 334; https://doi.org/10.3390/w9050334 - 8 May 2017
Cited by 36 | Viewed by 5466
Abstract
This study investigates the sensitivity and uncertainty of hydrological droughts frequencies and severity in the Weihe Basin, China during 1960–2012, by using six commonly used univariate probability distributions and three Archimedean copulas to fit the marginal and joint distributions of drought characteristics. The [...] Read more.
This study investigates the sensitivity and uncertainty of hydrological droughts frequencies and severity in the Weihe Basin, China during 1960–2012, by using six commonly used univariate probability distributions and three Archimedean copulas to fit the marginal and joint distributions of drought characteristics. The Anderson-Darling method is used for testing the goodness-of-fit of the univariate model, and the Akaike information criterion (AIC) is applied to select the best distribution and copula functions. The results demonstrate that there is a very strong correlation between drought duration and drought severity in three stations. The drought return period varies depending on the selected marginal distributions and copula functions and, with an increase of the return period, the differences become larger. In addition, the estimated return periods (both co-occurrence and joint) from the best-fitted copulas are the closet to those from empirical distribution. Therefore, it is critical to select the appropriate marginal distribution and copula function to model the hydrological drought frequency and severity. The results of this study can not only help drought investigation to select a suitable probability distribution and copulas function, but are also useful for regional water resource management. However, a few limitations remain in this study, such as the assumption of stationary of runoff series. Full article
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<p>The study basin map.</p>
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<p>Threshold level method with a variable threshold to define the drought duration and drought severity.</p>
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<p>Drought events in three stations during 1960–2012.</p>
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<p>The number events of different drought (<b>a</b>) durations and (<b>b</b>) severities at three stations.</p>
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<p>Marginal distribution modeling of drought duration and drought severity. (<b>a</b>): LJC drought duration; (<b>b</b>): XY drought duration; (<b>c</b>): HX drought duration; (<b>d</b>): LJC drought severity; (<b>e</b>): XY drought severity; (<b>f</b>): HX drought severity.</p>
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<p>Probability-probability (PP) plot of the joint distributions of duration-severity at three stations. (<b>a</b>): LJC; (<b>b</b>): XY; (<b>c</b>): HX.</p>
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<p>Co-occurrence return period and joint return period of the best-selected copula. (<b>a</b>): LJC drought duration; (<b>b</b>): XY drought duration; (<b>c</b>): HX drought duration; (<b>d</b>): LJC drought severity; (<b>e</b>): XY drought severity; (<b>f</b>): HX drought severity.</p>
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<p>The return period of drought duration and severity based on different marginal distributions at three stations. (<b>a</b>): LJC drought duration; (<b>b</b>): XY drought duration; (<b>c</b>): HX drought duration; (<b>d</b>): LJC drought severity; (<b>e</b>): XY drought severity; (<b>f</b>): HX drought severity.</p>
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<p>Sensitivity of the co-occurrence return period and joint return period of three drought events at Huaxian station and the selection of the univariate distribution and the copulas. (<b>a</b>): co-occurrence return period of drought event 1; (<b>b</b>): co-occurrence return period of drought event 2; (<b>c</b>): co-occurrence return period of drought event 3; (<b>d</b>): joint return period of drought event 1; (<b>e</b>): joint return period of drought event 2; (<b>f</b>): joint return period of drought event 3.</p>
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7010 KiB  
Article
Evaluation of Spatio-Temporal Patterns of Remotely Sensed Evapotranspiration to Infer Information about Hydrological Behaviour in a Data-Scarce Region
by Frank Joseph Wambura, Ottfried Dietrich and Gunnar Lischeid
Water 2017, 9(5), 333; https://doi.org/10.3390/w9050333 - 8 May 2017
Cited by 18 | Viewed by 6917
Abstract
Information about the hydrological behaviour of a river basin prior to setting up, calibrating and validating a distributed hydrological model requires extensive datasets that are hardly available for many parts of the world due to insufficient monitoring networks. In this study, the focus [...] Read more.
Information about the hydrological behaviour of a river basin prior to setting up, calibrating and validating a distributed hydrological model requires extensive datasets that are hardly available for many parts of the world due to insufficient monitoring networks. In this study, the focus was on prevailing spatio-temporal patterns of remotely sensed evapotranspiration (ET) that enabled conclusions to be drawn about the hydrological behaviour and spatial peculiarities of a river basin at rather high spatial resolution. The prevailing spatio-temporal patterns of ET were identified using a principal component analysis of a time series of 644 images of MODIS ET covering the Wami River basin (Tanzania) between the years 2000 and 2013. The time series of the loadings on the principal components were analysed for seasonality and significant long-term trends. The spatial patterns of principal component scores were tested for significant correlation with elevations and slopes, and for differences between different soil texture and land use classes. The results inferred that the temporal and spatial patterns of ET were related to those of preceding rainfalls. At the end of the dry season, high ET was maintained only in areas of shallow groundwater and in cloud forest nature reserves. A region of clear reduction of ET in the long-term was related to massive land use change. The results also confirmed that most soil texture and land use classes differed significantly. Moreover, ET was exceptionally high in natural forests and loam soil, and very low in bushland and sandy-loam soil. Clearly, this approach has shown great potential of publicly available remote sensing data in providing a sound basis for water resources management as well as for distributed hydrological models in data-scarce river basins at lower latitudes. Full article
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<p>The Wami River basin (elevation, rainfall stations and grid points from Shuttle Radar Topography Mission (SRTM), Tanzania Meteorological Agency (TMA) and Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) databases, respectively).</p>
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<p>Topograhic slopes (derived from STRM-90m), soil texture classes of the year 2003 [<a href="#B36-water-09-00333" class="html-bibr">36</a>] and land use classes of the year 1997 [<a href="#B37-water-09-00333" class="html-bibr">37</a>].</p>
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<p>Average annual MODerate resolution Imaging Spectroradiometer evapotranspiration (MODIS ET) (2000–2013).</p>
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<p>Time series of the loadings on the first principal component (PC1) (<b>a</b>). Map of the scores of PC1 and natural forest areas (<b>b</b>). Quartiles of the scores of PC1 for different soil texture (<b>c</b>) and land use (<b>d</b>) classes, similar score distributions at the 5% level of significance are marked with the same small letter.</p>
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<p>Time series of the loadings on the second principal component (PC2) (<b>a</b>). Map of the scores of PC2, natural forest areas and depths to static water levels (DSWL) measured below the ground (<b>b</b>). Quartiles of the scores of PC2 for different soil texture (<b>c</b>) and land use (<b>d</b>) classes, similar score distributions at the 5% level of significance are marked with the same small letter.</p>
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<p>Time series of the loadings on the third principal component (PC3) (<b>a</b>). Map of the scores of PC3 and natural forest areas (<b>b</b>). Quartiles of the scores of PC3 for different soil texture (<b>c</b>) and land use (<b>d</b>) classes, similar score distributions at the 5% level of significance are marked with the same small letter.</p>
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<p>Time series of the loadings on the fourth principal component (PC4) (<b>a</b>). Map of the scores of PC4 (<b>b</b>). Quartiles of the scores of PC4 for different soil texture (<b>c</b>) and land use (<b>d</b>) classes, similar score distributions at the 5% level of significance are marked with the same small letter.</p>
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<p>Time series of the loadings on the fifth principal component (PC5) (<b>a</b>). Map of the scores of PC5 and natural forest areas (<b>b</b>). Quartiles of the scores of PC5 for different soil texture (<b>c</b>) and land use (<b>d</b>) classes, similar score distributions at the 5% level of significance are marked with the same small letter.</p>
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<p>Satellite imageries for the years 2001 (<b>a</b>) and 2012 (<b>b</b>) mapped as red, green, and blue (RGB) (648, 555, and 470 nm). The deep and light green colours represent dense and sparse forests respectively, other colours represent scattered vegetation (not discernible at 500 m resolution).</p>
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