[go: up one dir, main page]

Next Issue
Volume 5, December
Previous Issue
Volume 5, June
 
 
water-logo

Journal Browser

Journal Browser

Water, Volume 5, Issue 3 (September 2013) – 31 articles , Pages 852-1456

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
1215 KiB  
Article
Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets
by Mohammad Zounemat-Kermani and Miklas Scholz
Water 2013, 5(3), 1441-1456; https://doi.org/10.3390/w5031441 - 23 Sep 2013
Cited by 20 | Viewed by 7040
Abstract
An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning [...] Read more.
An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN) and multiple linear regression (MLR) models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash–Sutcliffe efficiency, the correlation coefficient and the Bias. Full article
Show Figures

Figure 1

Figure 1
<p>Typical representation of a low-level outlet works.</p>
Full article ">Figure 2
<p>(<b>a</b>) Schematic of lab-scale low-level outlet works; and (<b>b</b>) experimental set-up after [<a href="#B4-water-05-01441" class="html-bibr">4</a>].</p>
Full article ">Figure 3
<p>Schematic representation of the (<b>a</b>) adaptive neural-based fuzzy inference system; and the (<b>b</b>) feed-forward Levenberg-Marquardt artificial neural network structure.</p>
Full article ">Figure 4
<p>Cluster centres using the centre of gravity method in (<b>a</b>) input 2 [gate opening (<span class="html-italic">O</span>)] and input 4 [water discharge (<span class="html-italic">Q<sub>w</sub></span>)]; and in (<b>b</b>) input 3 [head of water (<span class="html-italic">H</span>)] and input 4 (<span class="html-italic">Q<sub>w</sub></span>).</p>
Full article ">Figure 5
<p>Comparing the results of Campbell and Kalinske-Robertson’s empirical models <span class="html-italic">versus</span> the observations (1st fold).</p>
Full article ">Figure 6
<p>Comparing the results of adaptive neural-based fuzzy inference system (ANFIS) and multiple linear regression (MLR) models <span class="html-italic">versus</span> the observations (testing set of 1st fold).</p>
Full article ">Figure 7
<p>Illustration of the power relationship between the Froude number (<span class="html-italic">Fr</span>) and the aeration coefficient (<span class="html-italic">β</span>) of the applied experimental data.</p>
Full article ">
116 KiB  
Correction
Rex, J., et al. Mountain Pine Beetles, Salvage Logging, and Hydrologic Change: Predicting Wet Ground Areas. Water 2013, 5, 443–461
by John Rex, Stéphane Dubé and Vanessa Foord
Water 2013, 5(3), 1440; https://doi.org/10.3390/w5031440 - 18 Sep 2013
Viewed by 5398
Abstract
The authors wish to acknowledge a funding agency in their recently published paper [1] and make the following correction. [...] Full article
(This article belongs to the Special Issue Ecological Watershed Management)
1301 KiB  
Article
Groundwater Risk Assessment Model (GRAM): Groundwater Risk Assessment Model for Wellfield Protection
by Nara Somaratne, Hajrudin Zulfic, Glyn Ashman, Hayley Vial, Brooke Swaffer and Jacqueline Frizenschaf
Water 2013, 5(3), 1419-1439; https://doi.org/10.3390/w5031419 - 18 Sep 2013
Cited by 22 | Viewed by 9900
Abstract
A groundwater risk assessment was carried out for 30 potable water supply systems under a framework of protecting drinking water quality across South Australia. A semi-quantitative Groundwater Risk Assessment Model (GRAM) was developed based on a “multi-barrier” approach using likelihood of release, contaminant [...] Read more.
A groundwater risk assessment was carried out for 30 potable water supply systems under a framework of protecting drinking water quality across South Australia. A semi-quantitative Groundwater Risk Assessment Model (GRAM) was developed based on a “multi-barrier” approach using likelihood of release, contaminant pathway and consequence equation. Groundwater vulnerability and well integrity have been incorporated to the pathway component of the risk equation. The land use of the study basins varies from protected water reserves to heavily stocked grazing lands. Based on the risk assessment, 15 systems were considered as low risk, four as medium and 11 systems as at high risk. The GRAM risk levels were comparable with indicator bacteria—total coliform—detection. Most high risk systems were the result of poor well construction and casing corrosion rather than the land use. We carried out risk management actions, including changes to well designs and well operational practices, design to increase time of residence and setting the production zone below identified low permeable zones to provide additional barriers to contaminants. The highlight of the risk management element is the well integrity testing using down hole geophysical methods and camera views of the casing condition. Full article
Show Figures

Figure 1

Figure 1
<p>Conceptual model of the groundwater risk assessment model (GRAM).</p>
Full article ">Figure 2
<p>Generic risk matrix.</p>
Full article ">Figure 3
<p>Groundwater supply systems.</p>
Full article ">Figure 4
<p>Frequency of coliform detection in relation to aquifer type and geological strata. Sample size is given in brackets.</p>
Full article ">Figure 5
<p>Frequency of coliform detection in relation to well construction. Sample size is given in brackets.</p>
Full article ">Figure 6
<p>Coliform detection in Trench 1, Bore 485 and western spear points.</p>
Full article ">Figure 7
<p><span class="html-italic">E. coli</span> detection in Trench 1, Bore 485 and western spear points.</p>
Full article ">Figure 8
<p>Ammonia in confined aquifers.</p>
Full article ">Figure 9
<p>GRAM likelihood <span class="html-italic">vs.</span> actual coliform detection.</p>
Full article ">Figure 10
<p>Layout of the Kingston South East (SE) wellfield.</p>
Full article ">Figure 11
<p>Salinity in Kingston SE water supply wells.</p>
Full article ">Figure 12
<p>Coffin Bay TWS 3 salinity.</p>
Full article ">Figure 13
<p>Mount Burr town water supply wells and waste water treatment plant (WWTP).</p>
Full article ">Figure 14
<p>Millicent water supply wells.</p>
Full article ">
250 KiB  
Article
Water Pollution Control Legislation in Israel: Understanding Implementation Processes from an Actor-Centered Approach
by Sharon Hophmayer-Tokich
Water 2013, 5(3), 1393-1418; https://doi.org/10.3390/w5031393 - 18 Sep 2013
Cited by 1 | Viewed by 6016
Abstract
In the State of Israel, advanced legislation for the management of scarce water resources, including legislation to prevent water pollution, were put in place in the early stages of the State’s formation. Despite that, on-going uncontrolled pollution has deteriorated the quality of water [...] Read more.
In the State of Israel, advanced legislation for the management of scarce water resources, including legislation to prevent water pollution, were put in place in the early stages of the State’s formation. Despite that, on-going uncontrolled pollution has deteriorated the quality of water sources for decades, with the main source of pollution being untreated or partially treated domestic wastewater. This has been mainly the result of lack of enforcement of the existing laws. During the 1990s and onwards, a shift to forceful enforcement has been observed and wastewater treatment substantially improved. The paper analyzes the implementation processes of the pollution control legislations (the lack-of and the shift to forceful enforcement) based on an actor-centered approach, using the contextual interaction theory. Full article
412 KiB  
Article
Experimental Analysis of a Vertical Drop Shaft
by Roberta Padulano, Giuseppe Del Giudice and Armando Carravetta
Water 2013, 5(3), 1380-1392; https://doi.org/10.3390/w5031380 - 12 Sep 2013
Cited by 11 | Viewed by 6577
Abstract
An experimental campaign is undertaken in order to investigate the hydraulic features of a vertical drop shaft, also considering the influence of a venting system consisting of a coaxial vertical pipe, projecting within the drop shaft with different plunging rates. Three different flow [...] Read more.
An experimental campaign is undertaken in order to investigate the hydraulic features of a vertical drop shaft, also considering the influence of a venting system consisting of a coaxial vertical pipe, projecting within the drop shaft with different plunging rates. Three different flow regimes are observed: a “weir flow” for very low head values, where the flow profile is subject to the atmospheric pressure; a “full flow” for high head values, where water flows in a pressurized regime along the whole shaft; and a “transitional flow” for intermediate water head values. Weir flow and full flow can be experimentally investigated under steady-state conditions, whereas transitional flow is a pulsating condition, alternately switching from full flow to weir flow. Considering some significant geometric parameters, a head-discharge relation is sought both for the non-vented and for the vented configurations, by means of an energy balance equation, with specific assumptions about intake losses. Full article
Show Figures

Figure 1

Figure 1
<p>Schematization of the area of interest.</p>
Full article ">Figure 2
<p>Diversion structure views: (<b>a</b>) cross-section and (<b>b</b>) plan view.</p>
Full article ">Figure 3
<p>(<b>a</b>) Plan view of the experimental setup and (<b>b</b>) lateral view of the filling tank.</p>
Full article ">Figure 4
<p>(<b>a</b>) Experimental data and (<b>b</b>) focus on full flow data.</p>
Full article ">Figure 5
<p>Pressure distribution along the drop shaft wall (experiment 48).</p>
Full article ">Figure 6
<p>Local loss parameter, <span class="html-italic">ξ</span>, <span class="html-italic">vs</span>. water head, <span class="html-italic">h</span>.</p>
Full article ">Figure 7
<p>Dimensionless results [Equations (<a href="#FD5-water-05-01380" class="html-disp-formula">5</a>) and (<a href="#FD6-water-05-01380" class="html-disp-formula">6</a>)].</p>
Full article ">
335 KiB  
Article
Polydimethylsiloxane Rods for the Passive Sampling of Pesticides in Surface Waters
by Azziz Assoumani, Christelle Margoum, Yannick Lassalle, Bernard Herbreteau, Karine Faure, Marina Coquery and Jérôme Randon
Water 2013, 5(3), 1366-1379; https://doi.org/10.3390/w5031366 - 11 Sep 2013
Cited by 3 | Viewed by 7566
Abstract
In this work, the low cost synthesis of polydimethylsiloxane (PDMS) rods is described, and the performances of this new passive sampling device (in laboratory and in situ) are compared to the passive stir bar sorptive extraction (SBSE) for the monitoring of pesticides [...] Read more.
In this work, the low cost synthesis of polydimethylsiloxane (PDMS) rods is described, and the performances of this new passive sampling device (in laboratory and in situ) are compared to the passive stir bar sorptive extraction (SBSE) for the monitoring of pesticides from different classes (herbicides, insecticides and fungicides) in surface waters. The influence of synthesis parameters of PDMS rods (i.e., heating temperature, heating time and relative amount of curing agent) were assessed regarding their efficiency for the extraction of the target pesticides through a Hadamard’s experimental design. This allowed the determination of the effect of the three parameters on the sorption of pesticides within four experiments. Thus, specific conditions were selected for the synthesis of the PDMS rods (heating at 80 °C for 2 h with 10% of curing agent). Laboratory experiments led to similar to lower extraction recovery in the PDMS rods in comparison with passive SBSE, depending on the pesticide. The in situ application demonstrated the efficiency of the PDMS rods for the passive sampling of the target pesticides in river water, although lower amounts of pesticides were recovered in comparison with passive SBSE. So, these very low cost PDMS rods could be used as an alternative to passive SBSE for large-scale monitoring campaigns. Full article
(This article belongs to the Special Issue Analytical Chemistry of Water)
Show Figures

Figure 1

Figure 1
<p>Recovery for the extraction of the target pesticides by the four types of synthesized polydimethylsiloxane (PDMS) rods and Twisters, at 700 rpm for 3 h. Numbers in brackets on the <span class="html-italic">x</span> axis are the octanol-water partitioning coefficients of the target pesticides (log<span class="html-italic">K</span><sub>ow</sub>), sorted by increasing values. Errors bars represent ± standard deviation (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 2
<p>Sorption isotherms of (<b>a</b>) chlorpyrifos-methyl and (<b>b</b>) spiroxamine on PDMS rods (phase volume = 180 μL) and Twisters (phase volume = 126 μL). Extraction at 700 rpm for 5 h.</p>
Full article ">Figure 3
<p>Comparison of the amounts of pesticides accumulated in Twisters and PDMS rods exposed in river water for two weeks. “LQ” is the quantification limit in ng for both devices. “nq” stands for non-quantified, “I” stands for the intermediary sampling site and “D” stands for the downstream sampling site. Numbers in brackets on the <span class="html-italic">x</span> axis are the octanol-water partitioning coefficients of the quantified pesticides (log<span class="html-italic">K</span><sub>ow</sub>), sorted by increasing values. Errors bars represent ± standard deviation (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 4
<p>(<b>a</b>) Sorption kinetics of procymidon (at 5 μg L<sup>−1</sup>); (<b>b</b>) diflufenican (at 0.5 μg L<sup>−1</sup>); (<b>c</b>) fenitrothion (at 5 μg L<sup>−1</sup>); (<b>d</b>) 3,4-dichloroaniline (at 2.5 μg L<sup>−1</sup>); (<b>e</b>) chlorpyrifos-methyl (at 2 μg L<sup>−1</sup>); and (<b>f</b>) spiroxamine (at 0.125 μg L<sup>−1</sup>) for PDMS_5 rods and Twisters. Extraction at 700 rpm for maximum 5 h.</p>
Full article ">
321 KiB  
Review
Pharmaceuticals in the Built and Natural Water Environment of the United States
by Randhir P. Deo and Rolf U. Halden
Water 2013, 5(3), 1346-1365; https://doi.org/10.3390/w5031346 - 11 Sep 2013
Cited by 55 | Viewed by 15191
Abstract
The known occurrence of pharmaceuticals in the built and natural water environment, including in drinking water supplies, continues to raise concerns over inadvertent exposures and associated potential health risks in humans and aquatic organisms. At the same time, the number and concentrations of [...] Read more.
The known occurrence of pharmaceuticals in the built and natural water environment, including in drinking water supplies, continues to raise concerns over inadvertent exposures and associated potential health risks in humans and aquatic organisms. At the same time, the number and concentrations of new and existing pharmaceuticals in the water environment are destined to increase further in the future as a result of increased consumption of pharmaceuticals by a growing and aging population and ongoing measures to decrease per-capita water consumption. This review examines the occurrence and movement of pharmaceuticals in the built and natural water environment, with special emphasis on contamination of the drinking water supply, and opportunities for sustainable pollution control. We surveyed peer-reviewed publications dealing with quantitative measurements of pharmaceuticals in U.S. drinking water, surface water, groundwater, raw and treated wastewater as well as municipal biosolids. Pharmaceuticals have been observed to reenter the built water environment contained in raw drinking water, and they remain detectable in finished drinking water at concentrations in the ng/L to ?g/L range. The greatest promises for minimizing pharmaceutical contamination include source control (for example, inputs from intentional flushing of medications for safe disposal, and sewer overflows), and improving efficiency of treatment facilities. Full article
(This article belongs to the Special Issue Sustainable Wastewater Treatment and Pollution Control)
Show Figures

Figure 1

Figure 1
<p>Schematic showing inputs of pharmaceuticals to, and the interconnectivity of, the natural and built water environment.</p>
Full article ">Figure 2
<p>Number of pharmaceuticals detected in various water matrices of the built water environment. Each pharmaceutical is represented only once and shown in the category reflecting its respective maximum concentration reported in a given aquatic compartment.</p>
Full article ">Figure 3
<p>(<b>A</b>) Number of pharmaceuticals detected in sewage sludge. Only maximum concentrations of the pharmaceuticals were included and categorized into different concentration ranges; (<b>B</b>) Identity and maximum concentrations of pharmaceuticals detected in sewage sludge at concentrations exceeding 10,000 μg/kg dry weight.</p>
Full article ">
7400 KiB  
Review
Hydrogeological Characteristics of Hellenic Aqueducts-Like Qanats
by Konstantinos S. Voudouris, Yiannis Christodoulakos, Emmanouil Steiakakis and Andreas N. Angelakis
Water 2013, 5(3), 1326-1345; https://doi.org/10.3390/w5031326 - 11 Sep 2013
Cited by 33 | Viewed by 14882
Abstract
In ancient Hellas, water management began in the early Minoan Era (ca. 3200–1100 BC) and was related to the geomorphology, the geology, the topography, and the local climatic, hydrological, and socio-political conditions. Historical and archaeological evidences show that ancient Greeks had developed [...] Read more.
In ancient Hellas, water management began in the early Minoan Era (ca. 3200–1100 BC) and was related to the geomorphology, the geology, the topography, and the local climatic, hydrological, and socio-political conditions. Historical and archaeological evidences show that ancient Greeks had developed even qanat-related technologies since the Classical times. During democratic periods, the focus of water management was on sustainable small scale, safe, and cost effective management practices, and institutional arrangements, whereas in oligarchic periods, emphasis was on the construction of large-scale hydraulic projects, including aqueducts and/or qanats, mostly related to the public sectors. Aqueducts-like qanats are gently sloping, artificially constructed underground galleries, which bring groundwater from the mountainous area to the lowlands, where water is used, sometimes several kilometers away. It is worth noticing that no large-scale lifting techniques were available, and water was transferred from the source (usually a spring) by aqueducts (qanats) from a higher elevation to a lower level by gravity. Historically, the aqueduct-like qanat technology was developed by Persians in the middle of 1st Millennium BC, and spread towards the Arabian Peninsula and Egypt. The expansion of Islam led to diffusion of qanats in Mediterranean countries (e.g., Spain, Italy, and Cyprus). Much of the population of Iran and other arid countries in North Africa and in Asia depend on water supply by aqueducts-like qanats, even today. This technology is characterized by its durability and sustainability, although an aqueduct-like qanat is expensive, both in construction and maintenance. It is pointed out that, the technique of tunneling was used during the Classical period in ancient Hellas. Since the well known tunnel at the island of Samos, Hellas, was designed and constructed by Eupalinos (ca. 530 BC), several underground tunnels (with and without well-like vertical shafts) in order to convey water from one location to another one located in a lower level were implemented in this country. Several aqueducts (qanat) paradigms (e.g., in Athens, on islands of Crete and Rhodes, and in the area of Serres in north country), which are in use even today, are presented and discussed. Overall, it seems that water-related problems of modern societies are not very different from those during antiquity. Full article
Show Figures

Figure 1

Figure 1
<p>A typical cross-section of a qanat [<a href="#B8-water-05-01326" class="html-bibr">8</a>].</p>
Full article ">Figure 2
<p>Areas and places of aqueduct-like qanats evidence in European countries [<a href="#B13-water-05-01326" class="html-bibr">13</a>].</p>
Full article ">Figure 3
<p>The most significant ancient aqueducts of Athens [<a href="#B16-water-05-01326" class="html-bibr">16</a>]. (<b>1</b>) Hymettos; (<b>2</b>) Long Walls; (<b>3</b>) Acharnian branch: <b>3.1</b> and <b>3.2</b> hypothetical end sections; (<b>4</b>) Hadrianic; and (<b>5</b>) Late Roman: <b>5.1</b> Kifissia branch, <b>5.2</b> Herakleion branch, <b>5.3</b> and <b>5.4</b> hypothetical end sections.</p>
Full article ">Figure 4
<p>General view of ancient Polyrrhenia (with permission of D. Tzortzakis, archive of the 25th Ephorate of Antiquities, Chania, Hellas).</p>
Full article ">Figure 5
<p>Mouth of subterranean cistern in a Hellenistic house in the ancient town of Polyrrhenia.</p>
Full article ">Figure 6
<p>Exit of the tunnel; and channel of aqueduct 1 [<a href="#B20-water-05-01326" class="html-bibr">20</a>].</p>
Full article ">Figure 7
<p>Rock-cut cistern at the end of aqueduct 1 [<a href="#B20-water-05-01326" class="html-bibr">20</a>].</p>
Full article ">Figure 8
<p>Part of the tunnel of aqueduct 1 [<a href="#B20-water-05-01326" class="html-bibr">20</a>].</p>
Full article ">Figure 9
<p>Tunnel with channel along the semicircular tower of the Hellenistic wall [<a href="#B20-water-05-01326" class="html-bibr">20</a>].</p>
Full article ">Figure 10
<p>Fountain of the early 1960s in which the water from aqueduct 2 ends up to [<a href="#B20-water-05-01326" class="html-bibr">20</a>].</p>
Full article ">Figure 11
<p>Simplified cross section illustrating different geological formations and the aqueduct tunnels in Polyrrhenia.</p>
Full article ">Figure 12
<p>Wells at the soil surface as appear today.</p>
Full article ">Figure 13
<p>A general view of the entrance in a well (shaft).</p>
Full article ">Figure 14
<p>Schematic cross section of the tunnel Jeni-su in Rhodes with a series of vertical wells [<a href="#B23-water-05-01326" class="html-bibr">23</a>].</p>
Full article ">Figure 15
<p>Sites of aqueducts-like qanats evidence in Phyllida region, North Hellas (with permission of G. Andreou).</p>
Full article ">Figure 16
<p>Plan (no scale) of an aqueduct-like qanat in the Phyllida area, Serres, Hellas [<a href="#B25-water-05-01326" class="html-bibr">25</a>].</p>
Full article ">Figure 17
<p>The tunnel of the aqueduct-like qanat in Nea Zichni constructed in the 18th century.</p>
Full article ">
427 KiB  
Article
Buffer Capacity, Ecosystem Feedbacks, and Seawater Chemistry under Global Change
by Christopher P. Jury, Florence I.M. Thomas, Marlin J. Atkinson and Robert J. Toonen
Water 2013, 5(3), 1303-1325; https://doi.org/10.3390/w5031303 - 6 Sep 2013
Cited by 48 | Viewed by 12518
Abstract
Ocean acidification (OA) results in reduced seawater pH and aragonite saturation state (?arag), but also reduced seawater buffer capacity. As buffer capacity decreases, diel variation in seawater chemistry increases. However, a variety of ecosystem feedbacks can modulate changes in both average [...] Read more.
Ocean acidification (OA) results in reduced seawater pH and aragonite saturation state (?arag), but also reduced seawater buffer capacity. As buffer capacity decreases, diel variation in seawater chemistry increases. However, a variety of ecosystem feedbacks can modulate changes in both average seawater chemistry and diel seawater chemistry variation. Here we model these effects for a coastal, reef flat ecosystem. We show that an increase in offshore pCO2 and temperature (to 900 µatm and + 3 °C) can increase diel pH variation by as much as a factor of 2.5 and can increase diel pCO2 variation by a factor of 4.6, depending on ecosystem feedbacks and seawater residence time. Importantly, these effects are different between day and night. With increasing seawater residence time and increasing feedback intensity, daytime seawater chemistry becomes more similar to present-day conditions while nighttime seawater chemistry becomes less similar to present-day conditions. Recent studies suggest that carbonate chemistry variation itself, independent of the average chemistry conditions, can have important effects on marine organisms and ecosystem processes. Better constraining ecosystem feedbacks under global change will improve projections of coastal water chemistry, but this study shows the importance of considering changes in both average carbonate chemistry and diel chemistry variation for organisms and ecosystems. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>–<b>d</b>) Plots of modeled changes in seawater chemistry; (<b>e</b>) net ecosystem calcification; and (<b>f</b>) net ecosystem production in comparison to measurements made on the reef flat by Shamberger <span class="html-italic">et al.</span> [<a href="#B10-water-05-01303" class="html-bibr">10</a>], shown in grey. pH is reported here on the Total hydrogen ion scale (pH<sub>T</sub>). Model output is for the calcification and dissolution + photosynthesis and respiration feedback scenario (GD + PR) under present-day seawater conditions (results from other modeled feedback scenarios under present-day conditions are very similar). Thick, black line <b>(a</b>–<b>d)</b> shows offshore, seawater chemistry conditions. Thin black line (<b>e</b>,<b>f</b>) shows zero on the y-axis.</p>
Full article ">Figure 2
<p>Box plots showing modeled seawater chemistry on the reef flat. Within each plot boxes are arranged by offshore pCO<sub>2</sub>: the five boxes on the left side of each plot correspond to 400 µatm; the five boxes in the center of each plot correspond to 600 µatm; the five boxes to the right side of each plot correspond to 900 µatm. Thick, black lines in each plot show offshore, seawater chemistry conditions: solid black line = 400 µatm; dashed black line = 600 µatm; dotted black line = 900 µatm. Thin, black line <b>(g</b>–<b>i)</b> shows Ω<sub>arag</sub> = 1.</p>
Full article ">Figure 3
<p>Plots showing seawater chemistry anomalies relative to present-day conditions on the reef flat under each model scenario. Data are hourly average maximum, median, and minimum. Thick, black lines in each plot show the magnitude of the anomaly relative to offshore, seawater chemistry conditions (<span class="html-italic">i.e.</span>, the “expected” anomaly): solid black line = 600 µatm; dashed black line = 900 µatm.</p>
Full article ">Figure 4
<p>(<b>a</b>–<b>c</b>)Plots showing integral, daily net community calcification; (<b>d</b>–<b>f</b>) net ecosystem calcification; and (<b>g</b>–<b>i</b>) net community dissolution for the reef flat and the offshore reef edge as a function of offshore pCO<sub>2</sub> for all model scenarios. Thin black line (<b>a</b>–<b>f</b>) shows zero net calcification. Note difference in scale for net community dissolution and that for the offshore reef edge, rates of net community dissolution are directly overlapping for the two modeled scenarios (<b>g-i</b>).</p>
Full article ">
431 KiB  
Article
Setting Target Measurement Uncertainty in Water Analysis
by Ricardo J.N. Bettencourt Da Silva
Water 2013, 5(3), 1279-1302; https://doi.org/10.3390/w5031279 - 3 Sep 2013
Cited by 24 | Viewed by 8498
Abstract
Water is the most frequently and thoroughly characterised product due to the impact of the chemical composition of water of different sources or destinations on public health and on economy. The adequacy of water characterisation relies on measurement quality, which is a function [...] Read more.
Water is the most frequently and thoroughly characterised product due to the impact of the chemical composition of water of different sources or destinations on public health and on economy. The adequacy of water characterisation relies on measurement quality, which is a function of measurement traceability and uncertainty. In some analytical fields, target values of measurement performance parameters are set to ensure that the measurements quality is fit for the intended use. Nevertheless, frequently, these performance parameters do not allow the control of the magnitude of relevant components of measurement uncertainty. This work discusses the need for assessing fitness of the measurement for its intended use through the magnitude of uncertainty associated to the measurement value. The way this evaluation should be performed, when no guidelines are available, is also suggested. Target values of relevant performance parameters, results of interlaboratory tests, or the magnitude of trends of the measured quantity, are some types of information useful to define the maximum admissible uncertainty, i.e., target uncertainty. The information and algorithms used to define the target uncertainty are presented from the most suitable to the less likely to produce consensual values. Calculations are illustrated with application examples of different analytical fields. In this work, the way in which variability of uncertainty evaluation is taken into account when comparing estimated with target uncertainty is also discussed. Full article
(This article belongs to the Special Issue Analytical Chemistry of Water)
Show Figures

Figure 1

Figure 1
<p>Expected trend of the variation of the absolute <span class="html-italic">U</span> and relative <span class="html-italic">U’</span> expanded measurement uncertainty with the quantity value, <span class="html-italic">q</span>: (<b>a</b>) slight increase and (<b>b</b>) decrease.</p>
Full article ">Figure 2
<p>Measurement error, <span class="html-italic">E</span>, results from a combination of a random, ε, and the systematic component Δ (<span class="html-italic">E</span> = ε + Δ) [<a href="#B4-water-05-01279" class="html-bibr">4</a>]. Assuming random errors are normally distributed, the standard deviation σ can be used to define predictive models of ε.</p>
Full article ">Figure 3
<p>The mean error <span class="html-italic"><span class="html-overline">E</span></span> of <span class="html-italic">n</span> measurements comes from the combination of a random, ε<span class="html-italic"><sub>m</sub></span>, and the systematic component Δ. The ε<span class="html-italic"><sub>m</sub></span> is a random error resulting from measurements dispersion quantified by the standard deviation of the mean ( <span class="html-fig-inline" id="water-05-01279-i028"> <img alt="Water 05 01279 i028" src="/water/water-05-01279/article_deploy/html/images/water-05-01279-i028.png"/></span>) (where σ is the standard deviation of individual measurements, <a href="#water-05-01279-f002" class="html-fig">Figure 2</a>).</p>
Full article ">Figure 4
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” threshold quantity, <span class="html-italic">q<sup>tr</sup></span>, with a 95% chance of correctly indicating the non-compliance of the product, considering a maximum target quantity value, (<span class="html-italic">q<sup>tq</sup></span>), by producing a measured quantity, <span class="html-italic">q</span>, above the guard band (<span class="html-italic">q<sup>tg</sup></span> + <span class="html-italic">t<sub>1</sub>u</span>). Compliance assessment takes measurement uncertainty into account. Above <span class="html-italic">q<sup>tr</sup></span>, the chance of indicating the non-compliance is larger than 95%.</p>
Full article ">Figure 5
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” threshold quantity, <span class="html-italic">q<sup>tr</sup></span>, with a 95% chance of correctly indicating the non-compliance of the product, considering a maximum target quantity value, (<span class="html-italic">q<sup>tg</sup></span>), by producing a measured quantity, <span class="html-italic">q</span>, above <span class="html-italic">q<sup>tg</sup></span>. Compliance assessment does not take measurement uncertainty into account. Above <span class="html-italic">q<sup>tr</sup></span>, the chance of indicating the non-compliance is larger than 95%.</p>
Full article ">Figure 6
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” threshold quantity, <span class="html-italic">q<sup>tr</sup></span>, with a 95% chance of correctly indicating the non-compliance of the product, considering a minimum target quantity value, (<span class="html-italic">q<sup>tq</sup></span>), by producing a measured quantity, <span class="html-italic">q</span>, below the guard band (<span class="html-italic">q<sup>tg</sup></span> − <span class="html-italic">t<sub>1</sub>u</span>). Compliance assessment takes measurement uncertainty into account. Below <span class="html-italic">q<sup>tr</sup></span>, the chance of indicating the non-compliance is larger than 95%.</p>
Full article ">Figure 7
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” threshold quantity, <span class="html-italic">q<sup>tr</sup></span>, with a 95% chance of correctly indicating the non-compliance of the product, considering a minimum target quantity value, (<span class="html-italic">q<sup>tg</sup></span>), by producing a measured quantity, <span class="html-italic">q</span>, below <span class="html-italic">q<sup>tg</sup></span>. Compliance assessment does not take measurement uncertainty into account. Below <span class="html-italic">q<sup>tr</sup></span>, the chance of indicating the non-compliance is larger than 95%.</p>
Full article ">Figure 8
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” quantity equivalent to <span class="html-italic">q<sup>tg</sup></span>, with a 5% chance of producing a measured quantity value above a maximum quantity, <span class="html-italic">q<sup>tr</sup></span>.</p>
Full article ">Figure 9
<p>The standard uncertainty <span class="html-italic">u</span> that supports measurements of a “true” quantity equivalent to <span class="html-italic">q<sup>tg</sup></span>, with a 5% chance of producing a measured quantity value below a minimum quantity, <span class="html-italic">q<sup>tr</sup></span>.</p>
Full article ">Figure 10
<p>Expected trends of the variation of (<b>a</b>) absolute (<span class="html-italic">U</span>) and (<b>b</b>) relative (<span class="html-italic">U’</span>) measurement uncertainty with the quantity value (<span class="html-italic">q</span>). The <span class="html-italic">x</span> is the quantity value for which target absolute measurement uncertainty is defined.</p>
Full article ">Figure 11
<p>(<b>a</b>) The absolute <span class="html-italic">U<sup>tg</sup></span> and (<b>b</b>) relative <span class="html-italic">U’<sup>tg</sup></span> target measurement uncertainties defined at a quantity value can be applied half a magnitude order below and above this value, respectively. Where <span class="html-fig-inline" id="water-05-01279-i043"> <img alt="Water 05 01279 i043" src="/water/water-05-01279/article_deploy/html/images/water-05-01279-i043.png"/></span> represents the expected trend of <span class="html-italic">U</span> or <span class="html-italic">U’</span> with the quality value, <span class="html-italic">q</span>.</p>
Full article ">
488 KiB  
Article
Recovery of N and P from Urine by Struvite Precipitation Followed by Combined Stripping with Digester Sludge Liquid at Full Scale
by Nicolás Morales, Marc Anton Boehler, Sandra Buettner, Christoph Liebi and Hansruedi Siegrist
Water 2013, 5(3), 1262-1278; https://doi.org/10.3390/w5031262 - 29 Aug 2013
Cited by 80 | Viewed by 15306
Abstract
A novel ammonia stripping method, including a CO2 pre-stripper was used to treat a mix of supernatant liquor from an anaerobic digester and urine in order to recycle nitrogen as ammonium sulfate at full-scale in the WWTP Kloten/Opfikon. Waste streams were not [...] Read more.
A novel ammonia stripping method, including a CO2 pre-stripper was used to treat a mix of supernatant liquor from an anaerobic digester and urine in order to recycle nitrogen as ammonium sulfate at full-scale in the WWTP Kloten/Opfikon. Waste streams were not generated, since the ammonia was recovered as a marketable nitrogen fertilizer, turning a waste product into a valuable product. The efficiency of this system was increased by means of the addition of pre-treated urine collected separately at EAWAG building. The separation step was performed by the use of water free urinals and urine diversion flush toilets. An increase of 10% in the liquid flux with the addition of the urine translated into a 40% increase of the ammonia concentration in the inlet of the stripping unit. The achievement of these percentages generated a proportional increase in the fertilizer production. The urine pre-treatment was carried out by adding magnesium to produce a precipitate of struvite. The first experiments with the combined treatment showed the feasibility of the combination of the separation and pre-treatment steps. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Flow-chart of the co-treatment of sludge liquid and treated urine.</p>
Full article ">Figure 2
<p>(<b>a</b>) Evolution of phosphorus concentration (─) and pH (···) values in the reactor in the experiments: Tank 1 (▲), Tank 2 (△), Tank 3 (<b>□</b>), Tank 4 (×) and Tank 5 (〇); (<b>b</b>) Crystal size distribution of particles in Tank1, before the addition of magnesium (─) and at the end of the experiment (···).</p>
Full article ">Figure 3
<p>Scanning Electron Microscopy images of crystals in the reactor.</p>
Full article ">Figure 4
<p>Total nitrogen concentration (■) in mg N/L and volumetric flow ( <span class="html-fig-inline" id="water-05-01262-i005"> <img alt="Water 05 01262 i005" src="/water/water-05-01262/article_deploy/html/images/water-05-01262-i005.png"/></span>) in (m<sup>3</sup>/h) in the urine, liquid sludge, liquid sludge after the urine addition, effluent in the experiment I, and effluent for the removal efficiency reference (89%) [<a href="#B26-water-05-01262" class="html-bibr">26</a>].</p>
Full article ">
1065 KiB  
Article
Impacts of Hydrologic Change on Sandbar Nesting Availability for Riverine Turtles in Eastern Minnesota, USA
by Christian F. Lenhart, Jason R. Naber and John L. Nieber
Water 2013, 5(3), 1243-1261; https://doi.org/10.3390/w5031243 - 28 Aug 2013
Cited by 15 | Viewed by 8005
Abstract
There have been significant increases in stream flow in many rivers of the Upper Midwestern United States since 1980. Increased summer flows may negatively impact ecological processes, including aquatic organisms’ life cycles. The smooth softshell (Apalone mutica) and wood turtle ( [...] Read more.
There have been significant increases in stream flow in many rivers of the Upper Midwestern United States since 1980. Increased summer flows may negatively impact ecological processes, including aquatic organisms’ life cycles. The smooth softshell (Apalone mutica) and wood turtle (Glyptemys insculpta) are threatened by alteration of stream flow regime and other changes to river ecosystems in the Upper Midwest. We hypothesized that prolonged duration of high summer flows would reduce time available for nesting. We assessed hydrologic change using the Indicators of Hydrologic Alteration program and stream gauge data, characterized physical properties of sandbars, surveyed turtle nesting sites and assessed historical channel change using aerial photos in GIS on five Upper Midwest rivers. A river stage-sandbar area relationship was developed to determine the effect of prolonged summer flow duration on turtle nesting opportunity for the 1940–2009 time period. Suitable water levels have declined since 1980 in the agricultural watersheds of southern Minnesota likely delaying hatching and reducing survival, particularly for aquatic turtles such as A. mutica. In contrast to the agricultural watersheds, there was no significant change in the northern forested rivers’ stream flow and sandbar availability during the nesting season. Management to reduce summer stream flow in agricultural watersheds and protection of known nest sites could benefit threatened aquatic turtle populations. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Figure 1

Figure 1
<p>Location of study reaches on five Minnesota Rivers. From north to south the rivers are: the St. Louis, Kettle, Cannon, Minnesota, and Root Rivers.</p>
Full article ">Figure 2
<p>Sandbar emergence from stream flow in the lower Minnesota River. The area of sandbar exposed in the study reach of the river (<span class="html-italic">y</span>-axis) at different stream discharge points indicated on the <span class="html-italic">x</span>-axis. This analysis helped to determine the incipient point of sandbar exposure, which ranged from 57 to 143 m<sup>3</sup>/s. To the lower right at flows &gt;100 m<sup>3</sup>/s, the sandbars are submerged, while they become nearly fully exposed at about 30 m<sup>3</sup>/s. The bankfull flow in this reach is approximately 567 m<sup>3</sup>/s.</p>
Full article ">Figure 3
<p>Date of earliest sandbar emergence that would allow for nesting to occur, based on sandbar—discharge relationships developed for each river. Sandbar emergence is required for nesting, particularly for turtles that nest near the water or below the top of the stream bank, such as <span class="html-italic">A. mutica</span>. As sandbar exposure is delayed, it may become too late for successful turtle nesting or at least may add stress on declining turtle populations.</p>
Full article ">Figure 4
<p>Cross-section of a sandbar on the Kettle River from the turtle survey. Sandbars on the Kettle River were above the mean annual flow level in many cases. At this survey location, turtle nest sites were found high on the sandbar, well above the mean annual flow level.</p>
Full article ">Figure 5
<p>Cross-section of a sandbar on the Minnesota River from the turtle survey. Most of the sandbar surface area was below the mean annual flow level. No turtle nests were found in this location. The elevation of the sandbar may need to be built up higher through deposition to allow for nesting of smooth softshell turtles above the mean annual flow level.</p>
Full article ">
612 KiB  
Article
Spatial Heterogeneity of Soil Moisture and the Scale Variability of Its Influencing Factors: A Case Study in the Loess Plateau of China
by Qiang Feng, Wenwu Zhao, Yang Qiu, Mingyue Zhao and Lina Zhong
Water 2013, 5(3), 1226-1242; https://doi.org/10.3390/w5031226 - 16 Aug 2013
Cited by 33 | Viewed by 8721
Abstract
Soil moisture is an important factor for vegetation restoration and ecosystem sustainability in the Loess Plateau of China. The strong spatial heterogeneity of soil moisture is controlled by many environmental factors, including topography and land use. Moreover, the spatial patterns and soil hydrological [...] Read more.
Soil moisture is an important factor for vegetation restoration and ecosystem sustainability in the Loess Plateau of China. The strong spatial heterogeneity of soil moisture is controlled by many environmental factors, including topography and land use. Moreover, the spatial patterns and soil hydrological processes depend on the scale of the site being investigated, which creates a challenge for soil moisture forecasts. This study was conducted at two scales: watershed and small watershed. The goal of the study was to investigate the spatial variability in soil moisture and the scale effect of its controlling factors, as well as to provide references for soil moisture forecasting and studies of scale transformation. We took samples at 76 sites in the Ansai watershed and at 34 sites in a typical small watershed within the Ansai watershed in August. Next, we measured the soil moisture in five equal layers from a depth of 0–100 cm and recorded the land use type, location on the hill slope, slope, aspect, elevation and vegetation cover at the sampling sites. The results indicated that soil moisture was negatively correlated with relative elevation, slope and vegetation cover. As depth increased, the correlations among slope, aspect and soil moisture increased. At the small watershed and watershed scales, the soil moisture was highest in cultivated land, followed by wild grassland and lowest in garden plots, woodland and shrubland. The soil moisture was distributed similarly with respect to the location on the hill slope at both scales: upper slope < middle-upper slope < middle slope < middle-lower slope < lower slope. The deep layer soil moisture value of the slope top was high, being close to the soil moisture in the lower slope. Therefore, wild grassland or low-density woodland should be prioritized for farmland recovery in the Ansai watershed, and the locations on the hill slope, slope and elevation should be combined to configure different mosaic patterns. For example, low-density woodland or wild grassland would be appropriate for sites with low soil moisture content, such as upper slope, high elevation and steep slope sites. A stepwise regression analysis indicated that the dominant factor controlling the spatial variability of soil moisture values varied at different scales. At the small watershed scale, the order of significance for the influence of environmental factors on soil moisture values was as follows: land use type, slope, relative elevation and vegetation cover. The order of significance at the watershed scale was also determined: location on the hill slope, vegetation cover, slope, relative elevation and sine of the aspect. This result indicated that the influence of different environmental factors on soil moisture variability was dependent on the scale. The forecasting capability of regression models for soil moisture decreases from the small watershed scale to the watershed scale. This study could provide a reference for relevant scale transformation studies and offer guidance for water resource management and vegetation restoration approaches on the Loess Plateau. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Figure 1

Figure 1
<p>Study area and location of the sampling points.</p>
Full article ">Figure 2
<p>Difference in soil moisture value for different land use types.</p>
Full article ">Figure 3
<p>Differences in soil moisture value at different locations on the hill slopes.</p>
Full article ">
1721 KiB  
Article
Pump as Turbine (PAT) Design in Water Distribution Network by System Effectiveness
by Armando Carravetta, Giuseppe Del Giudice, Oreste Fecarotta and Helena M. Ramos
Water 2013, 5(3), 1211-1225; https://doi.org/10.3390/w5031211 - 12 Aug 2013
Cited by 81 | Viewed by 11031
Abstract
Water distribution networks face several problems related to leakages, where the pressure control strategy is a common practice for water loss management. Small-scale hydropower schemes, where pumps as turbines replace pressure reducing valves, can be considered an interesting technical solution, which ensures both [...] Read more.
Water distribution networks face several problems related to leakages, where the pressure control strategy is a common practice for water loss management. Small-scale hydropower schemes, where pumps as turbines replace pressure reducing valves, can be considered an interesting technical solution, which ensures both economic convenience and system flexibility. Due to the water networks’ variable operating conditions, a new methodology to model the effectiveness of pumps as turbines was developed based on the efficiency and the mechanical reliability of the hydropower device and the flexibility of the plant. System effectiveness is proposed as the objective function in the optimization procedure and applied to a real system, enabling one to emphasize that the hydraulic regulation mode of the plant is better than the electric regulation mode for American Petroleum Industry (API) manufacturing standards of pumps. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Measured pressure head (<math display="inline"> <msub> <mi>H</mi> <mi>u</mi> </msub> </math>) and flow rate (<span class="html-italic">Q</span>) values; and (<b>b</b>) daily patterns of net-head (<span class="html-italic">H</span>), flow rate (<span class="html-italic">Q</span>) and available power (<span class="html-italic">P</span>) with a given backpressure value (<math display="inline"> <mrow> <msub> <mi>H</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>30</mn> </mrow> </math> m).</p>
Full article ">Figure 2
<p>Pump as turbine (PAT) operating conditions in (<b>a</b>) hydraulic; or (<b>b</b>) electrical regulation.</p>
Full article ">Figure 3
<p><math display="inline"> <msub> <mi>η</mi> <mi>p</mi> </msub> </math> values resulting from the Variable Operating Strategy (VOS): (<b>a</b>) HR mode; and (<b>b</b>) ER mode.</p>
Full article ">Figure 4
<p><math display="inline"> <msub> <mi>ϕ</mi> <mi>p</mi> </msub> </math> values resulting from VOS: (<b>a</b>) HR mode; and (<b>b</b>) ER mode.</p>
Full article ">Figure 5
<p>Plot of <math display="inline"> <mrow> <mi>μ</mi> <mo>(</mo> <mi>Q</mi> <mo>/</mo> <msub> <mi>Q</mi> <mi>B</mi> </msub> <mo>)</mo> </mrow> </math> and <math display="inline"> <mrow> <mi>h</mi> <mo>(</mo> <mi>Q</mi> <mo>/</mo> <msub> <mi>Q</mi> <mi>B</mi> </msub> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 6
<p>Load <span class="html-italic">L</span> variation during the period, <span class="html-italic">T</span>.</p>
Full article ">Figure 7
<p><math display="inline"> <msub> <mi>μ</mi> <mi>p</mi> </msub> </math> values resulting from VOS: (<b>a</b>) HR mode and (<b>b</b>) ER mode.</p>
Full article ">Figure 8
<p><span class="html-italic">E</span> values resulting from VOS: (<b>a</b>) HR mode and (<b>b</b>) ER mode.</p>
Full article ">
3940 KiB  
Article
Development of Web GIS-Based VFSMOD System with Three Modules for Effective Vegetative Filter Strip Design
by Youn Shik Park, Bernie A. Engel, Yongchul Shin, Joongdae Choi, Nam-Won Kim, Seong-Joon Kim, Dong Soo Kong and Kyoung Jae Lim
Water 2013, 5(3), 1194-1210; https://doi.org/10.3390/w5031194 - 7 Aug 2013
Cited by 13 | Viewed by 9612
Abstract
In recent years, Non-Point Source Pollution has been rising as a significant environmental issue. The sediment-laden water problem is causing serious impacts on river ecosystems not only in South Korea but also in most countries. The vegetative filter strip (VFS) has been thought [...] Read more.
In recent years, Non-Point Source Pollution has been rising as a significant environmental issue. The sediment-laden water problem is causing serious impacts on river ecosystems not only in South Korea but also in most countries. The vegetative filter strip (VFS) has been thought to be one of the most effective methods to reduce the transport of sediment to down-gradient area. However, the effective width of the VFS first needs to be determined before VFS installation in the field. To provide an easy-to-use interface with a scientific VFS modeling engine, the Web GIS-based VFSMOD system was developed in this study. The Web GIS-based VFSMOD uses the UH and VFSM executable programs from the VFSMOD-w model as core engines to simulate rainfall-runoff and sediment trapping. To provide soil information for a point of interest, the Google Map interface to the MapServer soil database system was developed using the Google Map API, Javascript, Perl/CGI, and Oracle DB programming. Three modules of the Web GIS-based VFSMOD system were developed for various VFS designs under single storm, multiple storm, and long-term period scenarios. These modules in the Web GIS-based VFSMOD system were applied to the study watershed in South Korea and these were proven as efficient tools for the VFS design for various purposes. Full article
Show Figures

Figure 1

Figure 1
<p>Single Storm Event Analysis module (SSEA), Multiple Storm Events Analysis (MSEA) module, and HUFF &amp; SCS UH-based VFS design module in the Web GIS-based VFSMOD system [<a href="#B9-water-05-01194" class="html-bibr">9</a>].</p>
Full article ">Figure 2
<p>Overview of Web GIS-based VFSMOD system.</p>
Full article ">Figure 3
<p>Single Storm Event Analysis (SSEA) module in Web GIS-based VFSMOD system.</p>
Full article ">Figure 4
<p>HTML reference tables for various input parameters.</p>
Full article ">Figure 5
<p>Google Map interface for extracting various soil input parameters; (<b>a</b>) South Korea; (<b>b</b>) AL, IN, IL, KY, MI, MS, TN, and WI, USA.</p>
Full article ">Figure 6
<p>Multiple Storm Events Analysis (MSEA) module in the Web GIS-based VFSMOD system.</p>
Full article ">Figure 7
<p>HUFF &amp; SCS UH-based VFS design module using Huff method.</p>
Full article ">Figure 8
<p>Time-Variant USLE C factor DB of 30 major crops.</p>
Full article ">Figure 9
<p>Tabular and graphical output of Web GIS-based VFSMOD system (<b>a</b>) Output interface of Single Storm Event Analysis (SSEA) module; (<b>b</b>) Output interface of Multiple Storm Event Analysis (SSEA) module; (<b>c</b>) Output interface of HUFF &amp; SCS UH-based VFS design module.</p>
Full article ">Figure 10
<p>Location of Su-dong study watershed in South Korea.</p>
Full article ">Figure 11
<p>Single Storm Event Analysis (SSEA) output of the Web GIS-based VFSMOD system (8 m VFS width against one-hour duration–100 year Return Period). (<b>a</b>) Total rainfall on filter, total runoff from source area, total runoff out from the Filter, total infiltration in the filter under the given rainfall and VFS width condition; (<b>b</b>) Mass sediment input to filter, mass sediment output from filter, mass sediment retained in filter under the given rainfall and VFS width condition.</p>
Full article ">Figure 12
<p>Trapping efficiency under various design storm event and filter strip width conditions.</p>
Full article ">Figure 13
<p>Trapping efficiency under three filter strip width conditions.</p>
Full article ">
3686 KiB  
Article
Quantitative Assessment of Water Use Efficiency in Urban and Domestic Buildings
by Thorsten Schuetze and Vicente Santiago-Fandiño
Water 2013, 5(3), 1172-1193; https://doi.org/10.3390/w5031172 - 6 Aug 2013
Cited by 26 | Viewed by 13154
Abstract
This paper discusses the potential of water savings at property, household and urban levels, through the application of environmentally sound technologies (ESTs), as well as their quantification using the software Wise Water. Household centered measures are identified that allow for significant reduction of [...] Read more.
This paper discusses the potential of water savings at property, household and urban levels, through the application of environmentally sound technologies (ESTs), as well as their quantification using the software Wise Water. Household centered measures are identified that allow for significant reduction of drinking water consumption with comparatively small effort, and without limitation of comfort. Furthermore, a method for the estimation of water recycling, for rainwater harvesting and for the utilization potential as locally available renewable freshwater is presented. Based on this study, the average drinking water consumption in urban households of industrialized countries could be reduced by approximately one third, without significant investment costs, either within the framework of new constructions or by the remodeling of water and sanitation systems in residential buildings. By using a secondary water quality, the drinking water demand could even be reduced by 50%. In the case of an area-wide application, the overall fresh water demand of cities and the exploitation of fresh water resources could be significantly reduced. Due to the comparability of the domestic water use of the investigated households, the findings are internationally transferable, for example to countries in Europe, Asia, and also the USA. Full article
Show Figures

Figure 1

Figure 1
<p>Illustration of different waterless toilet types (<b>left</b>), and a water-saving toilet with options for design and operation (<b>right</b>).</p>
Full article ">Figure 2
<p>Examples of a water-saving tap with a ‘water brake’ and two flow rates (<b>left</b>); and a water-saving showerhead with a retrofitting device, which can be inserted in the fitting between the showerhead and hose (<b>right</b>).</p>
Full article ">Figure 3
<p>Water consumption of conventional household appliances (<b>top</b>), compared to that of water saving household appliances (<b>bottom</b>).</p>
Full article ">Figure 4
<p>Section of a building with centralized potable water supply and secondary water supply, with service water (<b>left</b>). Recycling of greywater from bathrooms on building level (<b>right</b>).</p>
Full article ">Figure 5
<p>User Interface of the Microsoft Excel based software tool “Wise Water”.</p>
Full article ">Figure 6
<p>User interface of the “Base Water Consumption” sheet in “Wise Water”. In the blue and yellow marked cells, the user can change default data for “Base Water Consumption at Household Level”, “Water Consumption at Property Level”, and “Details of Property Area and Rainfall”.</p>
Full article ">Figure 7
<p>User interface of the “Water Consumption after environmentally sound technologies (EST)” sheet in “Wise Water”. By means of pull down menus in the blue marked cells, the user can choose from different options for reducing “Water Consumption at Household Level”, “Managing Water Consumption through Recycling at Property Level”, and “Controlling Water Losses at Property Level”.</p>
Full article ">Figure 8
<p>User interface of the “Water Consumption after EST” sheet in “Wise Water”. The user can choose to “Apply Rainwater Harvesting”. Water Consumption Pattern after Application of EST’s is displayed for household and property levels.</p>
Full article ">Figure 9
<p>“Summary” sheet in “Wise Water” with summary of water consumption pattern and graphics displaying change in the water consumption after application of ESTs, and results of EST applications.</p>
Full article ">Figure 10
<p>Diagrams on the “Summary” sheet in “Wise Water” illustrating the service water demand at property level, total service water demand after application of rainwater harvesting as well as water distribution with and without ESTs.</p>
Full article ">Figure 11
<p>“City Level rainwater harvesting (RWH)” sheet in “Wise Water”. The user can enter the size of different area types in the blue marked cells. Diagrams illustrate the contribution of different areas to the rainwater harvesting as well as the amounts of rainwater that can be used for storage and recharge.</p>
Full article ">
612 KiB  
Review
Food for Thought: A Critical Overview of Current Practical and Conceptual Challenges in Trace Element Analysis in Natural Waters
by Montserrat Filella
Water 2013, 5(3), 1152-1171; https://doi.org/10.3390/w5031152 - 30 Jul 2013
Cited by 12 | Viewed by 8556
Abstract
The practical and conceptual challenges faced by the analysis of trace elements present in natural waters are not merely, as is often thought, an endless race towards lower detection limits or to the development of techniques allowing the determination of any possible chemical [...] Read more.
The practical and conceptual challenges faced by the analysis of trace elements present in natural waters are not merely, as is often thought, an endless race towards lower detection limits or to the development of techniques allowing the determination of any possible chemical species formed by all chemical elements. Rather, as discussed in this paper, they include the development of (i) robust, cheap, and reliable methods that could also be used by laypeople (the experience gained in the development of field kits for As is discussed as an example from which similar developments for other elements may be drawn); (ii) more environmentally-friendly methods (the current guiding criteria probably being too simplistic); and (iii) methods making it possible to follow diel concentration changes and sharp concentration variations caused by the probable increase of heavy rainfall events. This paper also claims that neither the measurement of total concentrations (reliable methods are lacking for many elements of the periodic table of trace elements, as illustrated through the cases of Bi, Te, and Sb), nor chemical speciation analysis, are as mature as often thought. In particular, chemical speciation studies demand the development of a better, comprehensive conceptual framework. A trial is carried out to lay the basis of such a framework. Full article
(This article belongs to the Special Issue Analytical Chemistry of Water)
Show Figures

Figure 1

Figure 1
<p>Situation of analytical chemistry in the context of environmental chemistry. Analytical chemistry links the macroscopic and global processes with those occurring at the molecular and microscopic level under both undisturbed systems and those subject to contamination systems. Adapted from [<a href="#B1-water-05-01152" class="html-bibr">1</a>] with permission.</p>
Full article ">Figure 2
<p>Published tellurium concentrations for surface seawater as a function of their year of publication. The insight shows the same data with a different scale in the <span class="html-italic">y</span>-axis. Data from [<a href="#B11-water-05-01152" class="html-bibr">11</a>,<a href="#B12-water-05-01152" class="html-bibr">12</a>,<a href="#B15-water-05-01152" class="html-bibr">15</a>,<a href="#B16-water-05-01152" class="html-bibr">16</a>,<a href="#B17-water-05-01152" class="html-bibr">17</a>,<a href="#B18-water-05-01152" class="html-bibr">18</a>,<a href="#B19-water-05-01152" class="html-bibr">19</a>,<a href="#B20-water-05-01152" class="html-bibr">20</a>,<a href="#B21-water-05-01152" class="html-bibr">21</a>,<a href="#B22-water-05-01152" class="html-bibr">22</a>].</p>
Full article ">Figure 3
<p>Published antimony concentrations for surface seawater as a function of their year of publication. (<b>a</b>) Figure 1 in [<a href="#B14-water-05-01152" class="html-bibr">14</a>]; (<b>b</b>) New values published after 2002 added in red, data from [<a href="#B23-water-05-01152" class="html-bibr">23</a>,<a href="#B24-water-05-01152" class="html-bibr">24</a>,<a href="#B25-water-05-01152" class="html-bibr">25</a>,<a href="#B26-water-05-01152" class="html-bibr">26</a>,<a href="#B27-water-05-01152" class="html-bibr">27</a>,<a href="#B28-water-05-01152" class="html-bibr">28</a>,<a href="#B29-water-05-01152" class="html-bibr">29</a>,<a href="#B30-water-05-01152" class="html-bibr">30</a>,<a href="#B31-water-05-01152" class="html-bibr">31</a>,<a href="#B32-water-05-01152" class="html-bibr">32</a>,<a href="#B33-water-05-01152" class="html-bibr">33</a>,<a href="#B34-water-05-01152" class="html-bibr">34</a>,<a href="#B35-water-05-01152" class="html-bibr">35</a>,<a href="#B36-water-05-01152" class="html-bibr">36</a>,<a href="#B37-water-05-01152" class="html-bibr">37</a>,<a href="#B38-water-05-01152" class="html-bibr">38</a>,<a href="#B39-water-05-01152" class="html-bibr">39</a>,<a href="#B40-water-05-01152" class="html-bibr">40</a>].</p>
Full article ">Figure 4
<p>(<b>a</b>) Schematic representation of the interactions of a metal with different types of aquatic systems constituents, reproduced with permission from [<a href="#B49-water-05-01152" class="html-bibr">49</a>]; (<b>b</b>) Proposed new schema that takes into account aspects discussed in the text.</p>
Full article ">Figure 5
<p>Schematic representation of different approaches used to study the speciation of trace elements in water.</p>
Full article ">
382 KiB  
Article
Preliminary Study on the Effect of Wastewater Storage in Septic Tank on E. coli Concentration in Summer
by Dominique Appling, Mussie Y. Habteselassie, David Radcliffe and James K. Bradshaw
Water 2013, 5(3), 1141-1151; https://doi.org/10.3390/w5031141 - 26 Jul 2013
Cited by 19 | Viewed by 9558
Abstract
On-site wastewater treatment systems (OWTS) work by first storing the wastewater in a septic tank before releasing it to soils for treatment that is generally effective and sustainable. However, it is not clear how the abundance of E. coli changes during its passage [...] Read more.
On-site wastewater treatment systems (OWTS) work by first storing the wastewater in a septic tank before releasing it to soils for treatment that is generally effective and sustainable. However, it is not clear how the abundance of E. coli changes during its passage through the tank. In this study, which was conducted under the UGA young Scholar Program in summer of 2010, we examined the change in wastewater quality parameters during the passage of the wastewater through the tank and after its release into soil. We collected wastewater samples at the inlet and outlet of an experimental septic tank in addition to obtaining water samples from lysimeters below trenches where the drainpipes were buried. We report that E. coli concentration was higher by 100-fold in the septic tank effluent than influent wastewater samples, indicating the growth of E. coli inside the tank under typical Georgian summer weather. This is contrary to the assumption that E. coli cells do not grow outside their host and suggests that the microbial load of the wastewater is potentially enhanced during its storage in the tank. Electrical conductivity, pH and nitrogen were similar between the influent and effluent wastewater samples. E. coli and total coliform concentrations were mainly below detection in lysimeter samples, indicating the effectiveness of the soil in treating the wastewater. Full article
(This article belongs to the Special Issue Sustainable Wastewater Treatment and Pollution Control)
Show Figures

Figure 1

Figure 1
<p>pH (<b>A</b>) and electrical conductivity (EC); (<b>B</b>) values of wastewater samples from septic tank inlet (SIN) and outlet (SOUT), in addition to water samples from lysimeters installed 15 cm below the drainfield trenches (T1, T2 and T3). Wastewater and water samples were collected on June 3 (week 1), June 10 (week 2) and June 17 (week 3).</p>
Full article ">Figure 2
<p>(<b>A</b>) <span class="html-italic">E. coli</span>; and total coliform (<b>B</b>) counts of wastewater samples from septic tank inlet (SIN) and outlet (SOUT), in addition to water samples from lysimeters installed 15 cm below drainfield trenches (T1, T2 and T3). Wastewater and water samples were collected on June 3 (week 1), June 10 (week 2) and June 17 (week 3).</p>
Full article ">Figure 3
<p>Minimum (min), average (mean) and maximum (max) daily (<b>A</b>) air temperature; and (<b>B</b>) relatively humidity for the month of June 2010 for the study site. Wastewater and water samples were collected on June 3 (week 1), June 10 (week 2) and June 17 (week 3).</p>
Full article ">
15387 KiB  
Article
Diagnosing Atmospheric Influences on the Interannual 18O/16O Variations in Western U.S. Precipitation
by Nikolaus H. Buenning, Lowell Stott, Lisa Kanner and Kei Yoshimura
Water 2013, 5(3), 1116-1140; https://doi.org/10.3390/w5031116 - 25 Jul 2013
Cited by 17 | Viewed by 8563
Abstract
Many climate proxies in geological archives are dependent on the isotopic content of precipitation (?18Op), which over sub-annual timescales has been linked to temperature, condensation height, atmospheric circulation, and post-condensation exchanges in the western U.S. However, many proxies [...] Read more.
Many climate proxies in geological archives are dependent on the isotopic content of precipitation (?18Op), which over sub-annual timescales has been linked to temperature, condensation height, atmospheric circulation, and post-condensation exchanges in the western U.S. However, many proxies do not resolve temporal changes finer than interannual-scales. This study explores causes of the interannual variations in ?18Op within the western U.S. Simulations with the Isotope-incorporated Global Spectral Model (IsoGSM) revealed an amplifying influence of post-condensation exchanges (i.e., raindrop evaporation and vapor equilibration) on interannual ?18Op variations throughout the western U.S. Mid-latitude and subtropical vapor tagging simulations showed that the influence of moisture advection on ?18Op was relatively strong in the Pacific Northwest, but weak over the rest of the western U.S. The vapor tags correlated well with interannual variations in the 18O/16O composition of vapor, an indication that isotopes in vapor trace atmospheric circulation. However, vertical-tagging simulations revealed a strong influence of condensation height on ?18Op in California. In the interior of the western U.S., a strong temperature effect was found only after annual mean temperatures were weighted by monthly precipitation totals. These multiple influences on ?18Op complicate interpretations of western U.S. climate proxies that are derived from isotopes in precipitation. Full article
(This article belongs to the Special Issue Environmental Tracers)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Spatial distribution of variance of <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> for the control simulation; and (<b>b</b>)–(<b>h</b>) 7 model experiments. Variance is calculated from interannual time series at each grid cell. Contour intervals are 0.25‰.</p>
Full article ">Figure 2
<p>(<b>a</b>) Variance difference between the control simulation (CTRL) simulation and the simulation that removed isotope effects from post-condensation exchanges (NORNEV); (<b>b</b>) The same variance difference divided by the variance of the CTRL simulation; (<b>c</b>) Correlation between interannual <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> variations from the CTRL simulation and the difference in <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> between the CTRL simulation and NORNEV simulation. Open purple rectangles in panel (c) show the boxed regions for <a href="#water-05-01116-f003" class="html-fig">Figure 3</a>.</p>
Full article ">Figure 3
<p>Regional average interannual time series of <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> for the CTRL and NORNEV simulations. The panels display the (<b>a</b>) Pacific Northwest; (<b>b</b>) the interior of the western U.S.; (<b>c</b>) and central/southern California.</p>
Full article ">Figure 4
<p>(<b>a</b>)–(<b>g</b>) Correlation between interannual <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> variations and the fraction of precipitation from each of the vertical tags. Correlations with the bottommost tag is shown in panel (<b>a</b>) (tag 1); and correlations with higher level tags are shown in panels (<b>b</b>)–(<b>g</b>). Contour intervals are 0.1. Absolute values of <span class="html-italic">r</span> above 0.263 are significant above the 95% confidence level.</p>
Full article ">Figure 5
<p>(<b>a</b>) Multiple correlation between tags 2–6 (<span class="html-italic">p<sub>tag</sub></span>/<span class="html-italic">p<sub>total</sub></span>) and <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> from the CTRL simulation; and (<b>b</b>) the NORNEV simulation.</p>
Full article ">Figure 6
<p>(<b>a</b>–<b>g</b>) The contribution of each vertical tag to the total precipitation. Closed contour intervals are 0.01.</p>
Full article ">Figure 7
<p>(<b>a</b>–<b>g</b>) The isotopic composition of vapor (<span class="html-italic">δ</span><sup>18</sup>O<sub>v</sub>) corresponding with each of the vertical tags. Mean values are calculated by weighting vertical <span class="html-italic">δ</span><sup>18</sup>O<sub>v</sub> from the CTRL simulation by the vertical distribution of the given tag. The bottommost tag is shown in panel a (tag 1), and higher level tags are shown in panels (<b>b</b>)–(<b>g</b>). Contour intervals are 1‰.</p>
Full article ">Figure 8
<p>Difference between the isotopic composition of vapor between vertical tags 3 and 5 (<a href="#water-05-01116-f007" class="html-fig">Figure 7</a>e and <a href="#water-05-01116-f007" class="html-fig">Figure 7</a>c). More negative values indicate a steeper veritcal gradient in <span class="html-italic">δ</span><sup>18</sup>O values of vapor (<span class="html-italic">δ</span><sup>18</sup>O<sub>v</sub>). Closed contour intervals are 0.5‰.</p>
Full article ">Figure 9
<p>Interannual variance of fraction of precipitation from (<b>a</b>) vertical tag 3; and (<b>b</b>) vertical tag 5.</p>
Full article ">Figure 10
<p>(<b>a</b>) Correlation between interannual <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> variations and the fraction of precipitation from middle latitude tags; and (<b>b</b>) subtropical tags; Panels (<b>c</b>) and (<b>d</b>) show the same correlations, as (<b>a</b>) and (<b>b</b>) (respectively), but with the influence of post-condensation exchanges and condensation height removed. Contour intervals are 0.1. Absolute values of <span class="html-italic">r</span> above 0.263 are significant above the 95% confidence level.</p>
Full article ">Figure 11
<p>(<b>a</b>) Correlation between interannual <span class="html-italic">δ</span><sup>18</sup>O<sub>PW</sub> variations and the fraction of precipitable water from middle latitude tags; and (<b>b</b>) subtropical tags. Contour intervals are 0.1. Absolute values of <span class="html-italic">r</span> above 0.263 are significant above the 95% confidence level.</p>
Full article ">Figure 12
<p>(<b>a</b>,<b>b</b>) Correlation between <span class="html-italic">δ</span><sup>18</sup>O<sub>p</sub> and annual mean temperature; and (<b>c</b>,<b>d</b>) surface level specific humidity (bottom panels). Right panel annual means of temperature and specific humidity were weighted by monthly precipitation. Absolute values of <span class="html-italic">r</span> above 0.263 are significant above the 95% confidence level.</p>
Full article ">
645 KiB  
Review
Synergistic Water-Treatment Reactors Using a TiO2-Modified Ti-Mesh Filter
by Tsuyoshi Ochiai, Ken Masuko, Shoko Tago, Ryuichi Nakano, Kazuya Nakata, Masayuki Hara, Yasuhiro Nojima, Tomonori Suzuki, Masahiko Ikekita, Yuko Morito and Akira Fujishima
Water 2013, 5(3), 1101-1115; https://doi.org/10.3390/w5031101 - 22 Jul 2013
Cited by 16 | Viewed by 13539
Abstract
The recent applications of a TiO2-modified Ti-mesh filter (TMiP™) for water purification are summarized with newly collected data including biological assays as well as sewage water treatment. The water purification reactors consist of the combination of a TMiP, a UV lamp, [...] Read more.
The recent applications of a TiO2-modified Ti-mesh filter (TMiP™) for water purification are summarized with newly collected data including biological assays as well as sewage water treatment. The water purification reactors consist of the combination of a TMiP, a UV lamp, an excimer VUV lamp, and an ozonation unit. The water purification abilities of the reactor were evaluated by decomposition of organic contaminants, inactivation of waterborne pathogens, and treatment efficiency for sewage water. The UV-C/TMiP/O3 reactor disinfected E. coli in aqueous suspension in approximately 1 min completely, and also decreased the number of E. coli in sewage water in 15 min dramatically. The observed rate constants of 7.5 L/min and 1.3 L/min were calculated by pseudo-first-order kinetic analysis respectively. Although organic substances in sewage water were supposed to prevent the UV-C/TMiP/O3 reactor from purifying water, the reactor reduced E. coli in sewage water continuously. On the other hand, although much higher efficiencies for decomposition of organic pollutants in water were achieved in the excimer/TMiP reactor, the disinfection activity of the reactor for waterborne pathogens was not as effective as the other reactors. The difference of efficiency between organic pollutants and waterborne pathogens in the excimer/TMiP reactor may be due to the size, the structure, and the decomposition mechanism of the organic pollutants and waterborne pathogens. These results show that a suitable system assisted by synergy of photocatalysts and other technologies such as ozonation has a huge potential as a practical wastewater purification system. Full article
(This article belongs to the Special Issue Sustainable Wastewater Treatment and Pollution Control)
Show Figures

Figure 1

Figure 1
<p>Fabrication method of TiO<sub>2</sub> nanoparticles modified titanium mesh (TMiP™). Reproduced with permission from Ochiai <span class="html-italic">et al</span>. [<a href="#B10-water-05-01101" class="html-bibr">10</a>], Catalysis Science and Technology; published by RSC Publishing, 2011.</p>
Full article ">Figure 2
<p>Schematic illustrations of (<b>a</b>) water-purification reactor and (<b>b</b>) water-purification system. Reproduced with permission from Ochiai <span class="html-italic">et al</span>. [<a href="#B10-water-05-01101" class="html-bibr">10</a>,<a href="#B14-water-05-01101" class="html-bibr">14</a>,<a href="#B16-water-05-01101" class="html-bibr">16</a>], Catalysis Science and Technology; published by RSC Publishing, 2011.</p>
Full article ">Figure 3
<p>Methylene blue (MB) decolorization by the water-purification reactors. Crosses: blank; open triangles: BLB/TMiP reactor; solid diamonds: BLB/TMiP/air reactor. Reproduced with permission from Ochiai <span class="html-italic">et al</span>. [<a href="#B10-water-05-01101" class="html-bibr">10</a>], Catalysis Science and Technology; published by RSC Publishing, 2011.</p>
Full article ">Figure 4
<p>Phenol decomposition with exponential curve fitting for the water-purification reactors. Open triangles: BLB/TMiP reactor; open squares: O<sub>3</sub> bubbling alone (0.5–1.0 mg/L); solid triangles: BLB/TMiP/O<sub>3</sub> reactor (0.5–1.0 mg/L); asterisks: excimer alone; open diamonds: excimer/TMiP reactor. Reproduced with permission from Ochiai <span class="html-italic">et al</span>. [<a href="#B16-water-05-01101" class="html-bibr">16</a>], Chemical Engineering Journal; published by Elsevier, 2013.</p>
Full article ">Figure 5
<p>Time courses of log number of (<b>a</b>) <span class="html-italic">E. coli</span>; (<b>b</b>) <span class="html-italic">L. pneumophila</span>; (<b>c</b>) Qβ; and (<b>d</b>) FCV in the water purification system. Open squares: O<sub>3</sub> bubbling alone (0.5–1.0 mg/L); solid triangles: BLB/TMiP/O<sub>3</sub> reactor (0.5–1.0 mg/L); open diamonds: excimer/TMiP reactor.</p>
Full article ">Figure 6
<p>Time course of log survival rate of (<b>a</b>) <span class="html-italic">E. coli</span>; and (<b>b</b>) Qβ in the water purification system. open circles: UV-C/TMiP reactor; open squares: O<sub>3</sub> alone (0.5–1.0 mg/L); solid squares: O<sub>3</sub> alone (10 mg/L); solid circles: UV-C/TMiP/O<sub>3</sub> reactor (10 mg/L). Reproduced with permission from Ochiai <span class="html-italic">et al</span>. [<a href="#B14-water-05-01101" class="html-bibr">14</a>], Catalysis Science and Technology; published by RSC Publishing, 2011.</p>
Full article ">Figure 7
<p>Phenol decomposition by the water-purification system. open circles: UV-C/TMiP reactor; solid circles: UV-C/TMiP/O<sub>3</sub> reactor (10 mg/L). Reproduced with permission from Ochiai <span class="html-italic">et al.</span> [<a href="#B16-water-05-01101" class="html-bibr">16</a>], Chemical Engineering Journal; published by Elsevier, 2013.</p>
Full article ">Figure 8
<p>Time course of (<b>a</b>) SS and (<b>b</b>) PtCo Color of the sewage water in the water-purification system. solid squares: O<sub>3</sub> bubbling (10 mg/L); solid circles: UV-C/TMiP/O<sub>3</sub> reactor (10 mg/L).</p>
Full article ">Figure 9
<p>Time course of survival rate of (<b>a</b>) SPC; (<b>b</b>) TC; and (<b>c</b>) <span class="html-italic">E. coli</span> in the water purification system. Solid squares: O<sub>3</sub> bubbling (10 mg/L); solid circles: UV-C/TMiP/O<sub>3</sub> reactor (10 mg/L).</p>
Full article ">
1235 KiB  
Article
An Agent Based Model of Household Water Use
by Lilli Linkola, Clinton J. Andrews and Thorsten Schuetze
Water 2013, 5(3), 1082-1100; https://doi.org/10.3390/w5031082 - 17 Jul 2013
Cited by 46 | Viewed by 12453
Abstract
Households consume a significant fraction of total potable water production. Strategies to improve the efficiency of water use tend to emphasize technological interventions to reduce or shift water demand. Behavioral water use reduction strategies can also play an important role, but a flexible [...] Read more.
Households consume a significant fraction of total potable water production. Strategies to improve the efficiency of water use tend to emphasize technological interventions to reduce or shift water demand. Behavioral water use reduction strategies can also play an important role, but a flexible framework for exploring the “what-ifs” has not been available. This paper introduces such a framework, presenting an agent-based model of household water-consuming behavior. The model simulates hourly water-using activities of household members within a rich technological and behavioral context, calibrated with appropriate data. Illustrative experiments compare the resulting water usage of U.S. and Dutch households and their associated water-using technologies, different household types (singles, families with children, and retired couples), different water metering regimes, and educational campaigns. All else equal, Dutch and metered households use less water. Retired households use more water because they are more often at home. Water-saving educational campaigns are effective for the part of the population that is receptive. Important interactions among these factors, both technological and behavioral, highlight the value of this framework for integrated analysis of the human-technology-water system. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The three main levels influencing the water consumption of a building: (1) Indoors are situated the core elements of the domestic water system, water related technologies and activities inside the building; (2) The building type defines the frame, the building itself as well as its equipment and use; (3) The World includes the context of water use. These are all factors affecting the water technologies and water use behavior in a specific environment and society.</p>
Full article ">Figure 2
<p>System structure: The units of enquiry in the model are individual agents. Agents acquire information from the environment which affects agent state. Agents behave according to rules. Agents form households, the behavioral system to be observed. The household state is the result of the agent actions; we call it the aggregate state. Agent actions are triggered by their habits.</p>
Full article ">Figure 3
<p>Agent action and general model processes scheduling during one time step of the model, which is one model tick. Agent actions and their order are listed in the grey box.</p>
Full article ">Figure 4
<p>Graphical representation of the agent decision-making process in the model to execute each of the water use habits: clean body, get clean clothes, wash dishes, get sustenance and eliminate. An agent perceives its needs and desires, plans which actions to take, checks the household locus of control and intention to save water, and finally decides and executes.</p>
Full article ">Figure 5
<p>Distribution of water usage per agent per day by household type and country. The middle box is the 50% percentile, the bottom and top of the box are the 25% and 75% per-centiles of observations. Each boxplots represents 1200 model runs and 12 households worth of data, except for U.S. single which represents 12 households worth of data but has three times more 3600 model runs.</p>
Full article ">Figure 6
<p>Distribution of total water usage in m<sup>3</sup> per year per water fixture and per household type.</p>
Full article ">Figure 7
<p>Distribution of fixture and appliance use frequency per year per water fixture and per household type. The differences in frequencies of use are the consequence of differences in agent behavior.</p>
Full article ">Figure 8
<p>A density plot of a one-person household in USA home mean total water consumption per day and how it is affected by water metering, ongoing water saving campaigns and the household level of care for the environment. Parameter sweeps are done by following model parameters: CareForEnvironment, WaterMeter and WaterSavingCampaigns. The agent care for environment is the lowest when it is 0 and highest when it is 5. The number of samples for each specific parameter setting is N = 900.</p>
Full article ">Figure 9
<p>A scatter plot of agent bathing frequency (shower or bath) and water consumption in L/agent/day in these activities. The agents of different household types are distinguished with colors. Each single point represents the outcome of one model run. There are 21,000 observations for both family and couple and 64,800 observations for single.</p>
Full article ">
561 KiB  
Review
Intelligent Metering for Urban Water: A Review
by Thomas Boyle, Damien Giurco, Pierre Mukheibir, Ariane Liu, Candice Moy, Stuart White and Rodney Stewart
Water 2013, 5(3), 1052-1081; https://doi.org/10.3390/w5031052 - 11 Jul 2013
Cited by 177 | Viewed by 23030
Abstract
This paper reviews the drivers, development and global deployment of intelligent water metering in the urban context. Recognising that intelligent metering (or smart metering) has the potential to revolutionise customer engagement and management of urban water by utilities, this paper provides a summary [...] Read more.
This paper reviews the drivers, development and global deployment of intelligent water metering in the urban context. Recognising that intelligent metering (or smart metering) has the potential to revolutionise customer engagement and management of urban water by utilities, this paper provides a summary of the knowledge-base for researchers and industry practitioners to ensure that the technology fosters sustainable urban water management. To date, roll-outs of intelligent metering have been driven by the desire for increased data regarding time of use and end-use (such as use by shower, toilet, garden, etc.) as well as by the ability of the technology to reduce labour costs for meter reading. Technology development in the water sector generally lags that seen in the electricity sector. In the coming decade, the deployment of intelligent water metering will transition from being predominantly “pilot or demonstration scale” with the occasional city-wide roll-out, to broader mainstream implementation. This means that issues which have hitherto received little focus must now be addressed, namely: the role of real-time data in customer engagement and demand management; data ownership, sharing and privacy; technical data management and infrastructure security, utility workforce skills; and costs and benefits of implementation. Full article
Show Figures

Figure 1

Figure 1
<p>Displacement meters [<a href="#B54-water-05-01052" class="html-bibr">54</a>].</p>
Full article ">Figure 2
<p>Conceptualising intelligent metering as Automated Meter Reading (AMR), Advanced Metering Infrastructure (AMI) and within an intelligent urban water network (IUWN).</p>
Full article ">Figure 3
<p>Locating rationale for IM.</p>
Full article ">
9880 KiB  
Article
Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery
by Andreas Schmitt and Brian Brisco
Water 2013, 5(3), 1036-1051; https://doi.org/10.3390/w5031036 - 11 Jul 2013
Cited by 88 | Viewed by 9370
Abstract
One fundamental task in wetland monitoring is the regular mapping of (temporarily) flooded areas especially beneath vegetation. Due to the independence of weather and illumination conditions, Synthetic Aperture Radar (SAR) sensors could provide a suitable data base. Using polarimetric modes enables the identification [...] Read more.
One fundamental task in wetland monitoring is the regular mapping of (temporarily) flooded areas especially beneath vegetation. Due to the independence of weather and illumination conditions, Synthetic Aperture Radar (SAR) sensors could provide a suitable data base. Using polarimetric modes enables the identification of flooded vegetation by means of the typical double-bounce scattering. In this paper three decomposition techniques—Cloude-Pottier, Freeman-Durden, and Normalized Kennaugh elements—are compared to each other in terms of identifying the flooding extent as well as its temporal change. The image comparison along the time series is performed with the help of the Curvelet-based Change Detection Method. The results indicate that the decomposition algorithm has a strong impact on the robustness and reliability of the change detection. The Normalized Kennaugh elements turn out to be the optimal representation for Curvelet-based change detection processing. Furthermore, the co-polarized channels (same transmit and receive polarization in horizontal (HH) and vertical (VV) direction respectively) appear to be sufficient for wetland monitoring so that dual-co-polarized imaging modes could be an alternative to conventional quad-polarized acquisitions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
Show Figures

Figure 1

Figure 1
<p>Location map (EOWEB<sup>®</sup>) and optical image (©2009 Microsoft Corporation, Redmond, WA, USA; Imagery © Harris Corp, Melbourne, FL, USA; Earthstar Geographics LLC, San Diego, CA, USA; NAVTEQ, Chicago, IL, USA) of the study area near Gagetown, New Brunswick, Canada.</p>
Full article ">Figure 2
<p>Quicklooks for different polarimetric decompositions of the first image dating from 27 April 2010. (<b>a</b>) Total intensity equal to Kennaugh <span class="html-italic">K</span><sub>0</sub>; (<b>b</b>) Cloude-Pottier RGB: Mean Alpha, Entropy, Anisotropy; (<b>c</b>) Freeman-Durden RGB: Double-bounce, Volume, Surface; and (<b>d</b>) Kennaugh RGB: −<span class="html-italic">k</span><sub>2</sub>, −<span class="html-italic">k</span><sub>1</sub>, −<span class="html-italic">k</span><sub>3</sub>.</p>
Full article ">Figure 3
<p>Cloude-Pottier entropy changes between sequent acquisitions (without unit): (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 4
<p>Cloude-Pottier anisotropy changes between sequent acquisitions (without unit): (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 5
<p>Cloude-Pottier mean alpha changes between sequent acquisitions in degrees: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 6
<p>Cloude-Pottier dominant alpha changes between sequent acquisitions in degrees: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 7
<p>Change in double-bounce scattering intensity according to the Freeman-Durden decomposition between sequent acquisitions scaled to decibel values: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 8
<p>Change in surface scattering intensity according to the Freeman-Durden decomposition between sequent acquisitions scaled to decibel values: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 9
<p>Change in volume scattering intensity according to the Freeman-Durden decomposition between sequent acquisitions scaled to decibel values: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 10
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>0</sub> (total intensity) between sequent acquisitions scaled in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 11
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>1</sub> (parallel absorption) between sequent acquisitionsscaled in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 12
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>2</sub> (diagonal absorption) between sequent acquisitions scaled in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 13
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>3</sub> (circular absorption) between sequent acquisitionsscaled in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 14
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>4</sub> (Parallel Diattenuation) between sequent acquisitions scaled in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">Figure 15
<p>Change in Kennaugh Element <span class="html-italic">k</span><sub>7</sub> (Parallel Retardance) in decibel: (<b>a</b>) April to May; (<b>b</b>) May to June; (<b>c</b>) June to July; and (<b>d</b>) July to August.</p>
Full article ">
457 KiB  
Article
Prospects of Source-Separation-Based Sanitation Concepts: A Model-Based Study
by Taina Tervahauta, Trang Hoang, Lucía Hernández, Grietje Zeeman and Cees Buisman
Water 2013, 5(3), 1006-1035; https://doi.org/10.3390/w5031006 - 8 Jul 2013
Cited by 43 | Viewed by 11462
Abstract
Separation of different domestic wastewater streams and targeted on-site treatment for resource recovery has been recognized as one of the most promising sanitation concepts to re-establish the balance in carbon, nutrient and water cycles. In this study a model was developed based on [...] Read more.
Separation of different domestic wastewater streams and targeted on-site treatment for resource recovery has been recognized as one of the most promising sanitation concepts to re-establish the balance in carbon, nutrient and water cycles. In this study a model was developed based on literature data to compare energy and water balance, nutrient recovery, chemical use, effluent quality and land area requirement in four different sanitation concepts: (1) centralized; (2) centralized with source-separation of urine; (3) source-separation of black water, kitchen refuse and grey water; and (4) source-separation of urine, feces, kitchen refuse and grey water. The highest primary energy consumption of 914 MJ/capita(cap)/year was attained within the centralized sanitation concept, and the lowest primary energy consumption of 437 MJ/cap/year was attained within source-separation of urine, feces, kitchen refuse and grey water. Grey water bio-flocculation and subsequent grey water sludge co-digestion decreased the primary energy consumption, but was not energetically favorable to couple with grey water effluent reuse. Source-separation of urine improved the energy balance, nutrient recovery and effluent quality, but required larger land area and higher chemical use in the centralized concept. Full article
(This article belongs to the Special Issue Sustainable Wastewater Treatment and Pollution Control)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sanitation Concepts (<b>1</b>–<b>4</b>) included in the model with wastewater streams and corresponding treatment systems (AS = activated sludge process; SBR = sequencing batch reactor, MBR = membrane bioreactor; A-trap = A-stage of AB-process; TF = trickling filter; UASB = up-flow anaerobic sludge blanket reactor; OLAND = oxygen limited anaerobic nitrification denitrification).</p>
Full article ">Figure 2
<p>Total primary energy consumption in sanitation concepts with different grey water treatment configurations.</p>
Full article ">Figure 3
<p>Nutrient recovery in Concepts 2–4 with different grey water treatment configurations.</p>
Full article ">Figure 4
<p>Total primary energy consumption in sanitation concepts with and without indirect energy gain from water saving and reuse, and nutrient recovery.</p>
Full article ">Figure 5
<p>Chemical use in Concepts 1–4 with different grey water treatment configurations.</p>
Full article ">
1202 KiB  
Article
Past, Present, and Future Nutrient Quality of a Small Southeastern River: A Pre-Dam Assessment
by Jonathan M. Miller and Paul M. Stewart
Water 2013, 5(3), 988-1005; https://doi.org/10.3390/w5030988 - 8 Jul 2013
Cited by 2 | Viewed by 7480
Abstract
Riverine dams alter both the physical environment and water chemistry, thus affecting species assemblages within these environments. In the United States, dam construction is on the decline and there is a growing trend for dam removal. The Choctawhatchee, Pea, and Yellow Rivers Watershed [...] Read more.
Riverine dams alter both the physical environment and water chemistry, thus affecting species assemblages within these environments. In the United States, dam construction is on the decline and there is a growing trend for dam removal. The Choctawhatchee, Pea, and Yellow Rivers Watershed Management Authority had initiated the permitting process for placing a reservoir dam on the Little Choctawhatchee River (LCR), a tributary to the Choctawhatchee River. The purpose of the proposed reservoir was water supply, and while the permit application has been suspended, history shows that this or related projects are likely to arise in the future. This study collected data on nutrient quality seasonally (four times) from 12 sites in the LCR watershed from October 2007 to June 2008 in order to determine pre-dam conditions and to compare these data to historical and regional information. Historical and current nutrient concentrations were elevated throughout the watershed, in most cases above suggested criteria, and indicated that water quality of the river was and continues to be nutrient rich. A future reservoir at recent levels of water quality will likely be highly eutrophic, and anthropogenic influences will further stress this ecosystem and its water quality as the urban region expands. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Little Choctawhatchee River near Dothan in southeast Alabama.</p>
Full article ">Figure 2
<p>The Little Choctawhatchee River watershed demonstrating general land use and the twelve stream sampling sites near Dothan in southeast Alabama, USA.</p>
Full article ">Figure 3
<p>Box plot of total phosphorus (TP) concentrations (as P) in mg/L by site (4 samples/site) in the Little Choctawhatchee River watershed from October 2007 to June 2008. Reference line indicates U.S. EPA suggested criteria for TP as P (0.0365 mg/L as P).</p>
Full article ">Figure 4
<p>Box plot of orthophosphate (OP) concentrations in mg/L by site (4 samples/site) in the Little Choctawhatchee River watershed from October 2007 to June 2008. Reference line indicates U.S. EPA suggested criteria for TP as P (0.0365 mg/L as P).</p>
Full article ">Figure 5
<p>Box plot of nitrate (NO<sub>3</sub>) concentrations in mg/L by site (4 samples/site) in the Little Choctawhatchee River watershed from October 2007 to June 2008. Reference line indicates U.S. EPA suggested criteria for TN as N (0.69 mg/L as P).</p>
Full article ">Figure 6
<p>Photograph of Site 11 (Beaver Creek on Honeysuckle Rd.) during September 2008.</p>
Full article ">
2776 KiB  
Review
Minoan and Etruscan Hydro-Technologies
by Andreas N. Angelakis, Giovanni De Feo, Pietro Laureano and Anastasia Zourou
Water 2013, 5(3), 972-987; https://doi.org/10.3390/w5030972 - 8 Jul 2013
Cited by 24 | Viewed by 20997
Abstract
The aim of this study is to present water and wastewater technologies used during the Minoan (ca. 3200–1100 BC) and Etruscan (ca. 800–100 BC) civilizations. The basic technologies considered are: water harvesting and distribution systems, cisterns, groundwater and wells as well [...] Read more.
The aim of this study is to present water and wastewater technologies used during the Minoan (ca. 3200–1100 BC) and Etruscan (ca. 800–100 BC) civilizations. The basic technologies considered are: water harvesting and distribution systems, cisterns, groundwater and wells as well as drainage and sewerage systems. Minoan water collection and distribution systems primarily consisted of cisterns and pipes. The Etruscans’ hydro-technology also consisted of cisterns and pipes but was developed for urban areas and included distinctions between public and private water use. The long-term sustainability of Minoan cisterns is evidenced by the fact that this technique is still practiced today in rural areas of Crete. In addition to cisterns, wells have been used in Crete since Neolithic times, and enjoyed wide-spread use during the Etruscan era. All the Minoan palaces applied strategies to dispose of water and wastewater with open terracotta or stone masonry-conduits, and stone masonry sewers; while, the drainage and sewerage systems developed by the Etruscans were based both on a coordinated and comprehensive planning of the slopes of drainage channels on the sides of streets as well as on a massive use of drainage tunnels. Full article
Show Figures

Figure 1

Figure 1
<p>Minoan water distribution projects: pipes of rectangular shape from Myrtos-Pyrgos (<b>a</b>) and closed terracotta pipes at Knossos palace (<b>b</b>).</p>
Full article ">Figure 2
<p>Etruscan archaeological site of Marzabotto, Central Italy: general view (<b>a</b>) and collection and distribution system originally found at a depth of about 4.50 m (<b>b</b>).</p>
Full article ">Figure 3
<p>Minoan cisterns: at Myrtos-Pyrgos in the S.E. Crete (<b>a</b>) and at Tylissos Houses (<b>b</b>).</p>
Full article ">Figure 4
<p>Plant of the cistern in <span class="html-italic">Via Cesare Caporali</span>, Perugia, central Italy (<b>a</b>) [<a href="#B27-water-05-00972" class="html-bibr">27</a>]; and Tunnels and galleries, more than 30 cisterns, Etruscan, Roman and Medieval and about 500 wells of several ages in the city of Todi, Umbria (<b>b</b>).</p>
Full article ">Figure 5
<p>Minoan well used for water supply at Palaikastro city, eastern Crete (<b>a</b>) and Well at the location <span class="html-italic">Campetti</span>, Etruscan City of Veio north of Rome (<b>b</b>).</p>
Full article ">Figure 6
<p>Minoan central sewerage and drainage systems: at palace of Phaistos (<b>a</b>) and at palace of Knossos (<b>b</b>).</p>
Full article ">Figure 7
<p>Sections of the “<span class="html-italic">Fontana della Rua</span>” tunnel in Todi, Umbria region [<a href="#B42-water-05-00972" class="html-bibr">42</a>].</p>
Full article ">
4265 KiB  
Article
Assessing Watershed-Wildfire Risks on National Forest System Lands in the Rocky Mountain Region of the United States
by Matthew P. Thompson, Joe Scott, Paul G. Langowski, Julie W. Gilbertson-Day, Jessica R. Haas and Elise M. Bowne
Water 2013, 5(3), 945-971; https://doi.org/10.3390/w5030945 - 2 Jul 2013
Cited by 40 | Viewed by 10738
Abstract
Wildfires can cause significant negative impacts to water quality with resultant consequences for the environment and human health and safety, as well as incurring substantial rehabilitation and water treatment costs. In this paper we will illustrate how state-of-the-art wildfire simulation modeling and geospatial [...] Read more.
Wildfires can cause significant negative impacts to water quality with resultant consequences for the environment and human health and safety, as well as incurring substantial rehabilitation and water treatment costs. In this paper we will illustrate how state-of-the-art wildfire simulation modeling and geospatial risk assessment methods can be brought to bear to identify and prioritize at-risk watersheds for risk mitigation treatments, in both pre-fire and post-fire planning contexts. Risk assessment results can be particularly useful for prioritizing management of hazardous fuels to lessen the severity and likely impacts of future wildfires, where budgetary and other constraints limit the amount of area that can be treated. Specifically we generate spatially resolved estimates of wildfire likelihood and intensity, and couple that information with spatial data on watershed location and watershed erosion potential to quantify watershed exposure and risk. For a case study location we focus on National Forest System lands in the Rocky Mountain Region of the United States. The Region houses numerous watersheds that are critically important to drinking water supplies and that have been impacted or threatened by large wildfires in recent years. Assessment results are the culmination of a broader multi-year science-management partnership intended to have direct bearing on wildfire management decision processes in the Region. Our results suggest substantial variation in the exposure of and likely effects to highly valued watersheds throughout the Region, which carry significant implications for prioritization. In particular we identified the San Juan National Forest as having the highest concentration of at-risk highly valued watersheds, as well as the greatest amount of risk that can be mitigated via hazardous fuel reduction treatments. To conclude we describe future opportunities and challenges for management of wildfire-watershed interactions. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Figure 1

Figure 1
<p>Conceptual model of primary components of wildfire risk and risk mitigation assessment framework.</p>
Full article ">Figure 2
<p>National Forests and Grasslands in the Forest Service’s Rocky Mountain Region (CO = Colorado; KS = Kansas; NE = Nebraska; SD = South Dakota; WY = Wyoming).</p>
Full article ">Figure 3
<p>Fire modeling area (upper right box), with National Forest (NF) and Grassland (NG) boundaries and Fire Planning Unit (FPU) boundaries identified. The location and number of high value watersheds within each NF/NG are also identified (left three panels). Note that in the inset map state boundaries are not shown to allow for clearer presentation of FPU boundaries.</p>
Full article ">Figure 4
<p>Annual Burn Probability (<span class="html-italic">BP</span>) values mapped across the assessed high value watersheds [See Equation (1)]. The top 20 high risk watershed boundaries are highlighted. CO = Colorado; KS = Kansas; NE = Nebraska; SD = South Dakota; WY = Wyoming.</p>
Full article ">Figure 5
<p>Erosion Potential categories mapped across the assessed high value watersheds, with the top 20 high risk watershed boundaries identified. CO = Colorado; KS = Kansas; NE = Nebraska; SD = South Dakota; WY = Wyoming.</p>
Full article ">Figure 6
<p>Expected net value change (loss) mapped for the high value watersheds, with the top 20 high risk watersheds identified [See Equation (3)]. CO = Colorado; KS = Kansas; NE = Nebraska; SD = South Dakota; WY = Wyoming.</p>
Full article ">Figure 7
<p>Histogram of expected loss by watershed, presenting the total count of watersheds according to expected loss bins.</p>
Full article ">Figure 8
<p>Comparison of conditional probabilities by flame length class (FLC), for the bottom 48 low risk watersheds (1st column in <a href="#water-05-00945-f006" class="html-fig">Figure 6</a>) and the top 20 high risk watersheds (4th–10th column in <a href="#water-05-00945-f006" class="html-fig">Figure 6</a>; <a href="#water-05-00945-t002" class="html-table">Table 2</a>).</p>
Full article ">Figure 9
<p>Expected loss summarized at the forest-level, and filtered according to spatial treatment opportunities [See Equation (3)]. RX Fire = Prescribed fire; NF = National Forest.</p>
Full article ">Figure 10
<p>Comparison of unadjusted (top) and modified (bottom) FPA burn probability results. For display purposes fire modeling results are aggregated into three flame length probability (FLP) classes, and mapped across two FPU boundaries [See Equations (1) and (2)].</p>
Full article ">
2158 KiB  
Article
Real-Time Forecast of Hydrologically Sensitive Areas in the Salmon Creek Watershed, New York State, Using an Online Prediction Tool
by Helen E. Dahlke, Zachary M. Easton, Daniel R. Fuka, M. Todd Walter and Tammo S. Steenhuis
Water 2013, 5(3), 917-944; https://doi.org/10.3390/w5030917 - 2 Jul 2013
Cited by 10 | Viewed by 10018
Abstract
In the northeastern United States (U.S.), watersheds and ecosystems are impacted by nonpoint source pollution (NPS) from agricultural activity. Where agricultural fields coincide with runoff-producing areas—so called hydrologically sensitive areas (HSA)—there is a potential risk of NPS contaminant transport to streams during rainfall [...] Read more.
In the northeastern United States (U.S.), watersheds and ecosystems are impacted by nonpoint source pollution (NPS) from agricultural activity. Where agricultural fields coincide with runoff-producing areas—so called hydrologically sensitive areas (HSA)—there is a potential risk of NPS contaminant transport to streams during rainfall events. Although improvements have been made, water management practices implemented to reduce NPS pollution generally do not account for the highly variable, spatiotemporal dynamics of HSAs and the associated dynamics in NPS pollution risks. This paper presents a prototype for a web-based HSA prediction tool developed for the Salmon Creek watershed in upstate New York to assist producers and planners in quickly identifying areas at high risk of generating storm runoff. These predictions can be used to prioritize potentially polluting activities to parts of the landscape with low risks of generating storm runoff. The tool uses real-time measured data and 24–48 h weather forecasts so that locations and the timing of storm runoff generation are accurately predicted based on present-day and future moisture conditions. Analysis of HSA predictions in Salmon Creek show that 71% of the largest storm events between 2006 and 2009 were correctly predicted based on 48 h forecasted weather data. Real-time forecast of HSAs represents an important paradigm shift for the management of NPS in the northeastern U.S. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Integrated system components of the hydrologically sensitive area (HSA) prediction tool.</p>
Full article ">Figure 2
<p>Conceptual diagram of the water balance model, which estimates the soil water content <span class="html-italic">θ<sub>j</sub></span> in each wetness class <span class="html-italic">j</span> and the saturation excess runoff, <span class="html-italic">Q</span>, at the watershed outlet using precipitation, <span class="html-italic">P</span>, and potential evapotranspiration <span class="html-italic">E<sub>p</sub></span> in each time step (t). <span class="html-italic">E<sub>a</sub></span> is the actual evapotranspiration, <span class="html-italic">θ<sub>fc</sub></span> and <span class="html-italic">θ<sub>wp</sub></span> are the soil water content at field capacity and at wilting point respectively, <span class="html-italic">σ<sub>e</sub></span><sub>,<span class="html-italic">j</span></sub> is the maximum effective soil storage of wetness class <span class="html-italic">j</span>, <span class="html-italic">P<sub>e</sub></span> is the effective precipitation (<span class="html-italic">P<sub>e</sub></span> =<span class="html-italic">P</span> − <span class="html-italic">E<sub>p</sub></span>), <span class="html-italic">Perc</span> is a percolation coefficient determining the amount of water, <span class="html-italic">p</span>, percolating to the bedrock reservoir, <span class="html-italic">R<sub>s</sub></span> and <span class="html-italic">a</span> is a recession coefficient controlling the rate at which baseflow, <span class="html-italic">BF</span>, is contributed from the bedrock reservoir to the watershed’s streamflow, <span class="html-italic">R</span>.</p>
Full article ">Figure 3
<p>Daily updated status report showing the forecasted rainfall amounts, the probability of precipitation for each projected 24 h period, and the expected percent area of the watershed that could saturate or generate runoff.</p>
Full article ">Figure 4
<p>Start page and user interface of the HSA prediction tool.</p>
Full article ">Figure 5
<p>User interface of the HSA prediction tool. Red areas show HSA predicted with the semi-distributed water balance model. A daily update of forecasted weather conditions and HSA dynamics in Salmon Creek watershed is given in the top, right frame.</p>
Full article ">Figure 6
<p>Location and characteristics of Salmon Creek watershed.</p>
Full article ">Figure 7
<p>Precipitation (bars) and daily streamflow predicted by the water balance model plotted against the measured streamflow at the catchment outlet of Salmon Creek for the time period 22 July 2006–31 December 2009. The inset shows the linear 1:1 relationship. The coefficient of determination (<span class="html-italic">R</span><sup>2</sup>) and Nash-Sutcliffe efficiency (<span class="html-italic">E</span>) are calculated based on the entire modeling period. <a href="#water-05-00917-t003" class="html-table">Table 3</a> summarizes uncertainty statistics for the calibration and validation period.</p>
Full article ">Figure 8
<p>Correlation of saturated fractional areas (<span class="html-italic">A<sub>f</sub></span>-values) predicted using observed weather data (<span class="html-italic">A<sub>f</sub></span><sub>,<span class="html-italic">t</span></sub>) versus <span class="html-italic">A<sub>f</sub></span>-values predicted with 24 h (<span class="html-italic">A<sub>f</sub></span><sub>,24 h</sub>) forecasted (<b>a</b>,<b>b</b>) and 48 h (<span class="html-italic">A<sub>f</sub></span><sub>,48 h</sub>) forecasted weather data (<b>c</b>,<b>d</b>). Plots (<b>a</b>) and (<b>c</b>) compare <span class="html-italic">A<sub>f</sub></span>-values on a daily basis for the entire record period. Plots (<b>b</b>) and (<b>d</b>) show the correlations for storm events with observed discharges greater than 5 mm·day<sup>−1</sup>. Grey lines indicate the 1:1 relationship and black lines show fits of a simple linear regression.</p>
Full article ">Figure 9
<p>Comparison of modeled fractional saturated areas (<b>a</b>,<b>b</b>), predicted <span class="html-italic">vs.</span> observed precipitation (<b>c</b>,<b>d</b>), and modeled wetness classes (<b>e</b>,<b>f</b>) for the 10 largest storm events observed in Salmon Creek watershed between July 2006 and December 2009. Plots (<b>a</b>) and (<b>b</b>) compare <span class="html-italic">A<sub>f</sub></span>,<span class="html-italic"><sub>t</sub></span>-values computed with the VSA water balance model based on observed weather data versus <span class="html-italic">A<sub>f</sub></span><sub>,24 h</sub>, and A<span class="html-italic"><sub>f</sub></span><sub>,48 h</sub> values modeled using 24 h and 48 h GFS MOS projected meteorological data for each event. <span class="html-italic">P<sub>obs</sub></span>, <span class="html-italic">P<sub>pred</sub></span><sub>,24 h</sub> and <span class="html-italic">P<sub>pred</sub></span><sub>,48 h</sub> are the observed and 24 h, 48 h forecasted total daily precipitation amounts for these events. Plots (<b>e</b>) and (<b>f</b>) compare the wetness class predicted with the VSA water balance model using observed (wetness class,<span class="html-italic"><sub>t</sub></span>) and 24 h (wetness class,<sub>24 h</sub>) and 48 h (wetness class,<sub>48 h</sub>) forecasted meteorological data.</p>
Full article ">Figure 10
<p>Monthly probability of saturation for Salmon Creek watershed. For each month the factional watershed area is shown that saturates or generates runoff in more than 50% (red areas), 25% (yellow areas) and 10% (green areas) of the rainfall events.</p>
Full article ">
2073 KiB  
Article
A Preliminary Investigation of Wastewater Treatment Efficiency and Economic Cost of Subsurface Flow Oyster-Shell-Bedded Constructed Wetland Systems
by Rita S.W. Yam, Chia-Chuan Hsu, Tsang-Jung Chang and Wen-Lian Chang
Water 2013, 5(3), 893-916; https://doi.org/10.3390/w5030893 - 28 Jun 2013
Cited by 10 | Viewed by 8684
Abstract
We conducted a preliminary investigation of wastewater treatment efficiency and economic cost of the oyster-shell-bedded constructed wetlands (CWs) compared to the conventional gravel-bedded CW based on field monitoring data of water quality and numerical modeling. Four study subsurface (SSF) CWs were built to [...] Read more.
We conducted a preliminary investigation of wastewater treatment efficiency and economic cost of the oyster-shell-bedded constructed wetlands (CWs) compared to the conventional gravel-bedded CW based on field monitoring data of water quality and numerical modeling. Four study subsurface (SSF) CWs were built to receive wastewater from Taipei, Taiwan. Among these sites, two are vertical wetlands, filled with bagged- (VA) and scattered- (VB) oyster shells, and the other two horizontal wetlands were filled with scattered-oyster shells (HA) and gravels (HB). The BOD, NO3?, DO and SS treatment efficiency of VA and VB were higher than HA and HB. However, VA was determined as the best option of CW design due to its highest cost-effectiveness in term of BOD removal (only 6.56 US$/kg) as compared to VB, HA and HB (10.88–25.01 US$/kg). The results confirmed that oyster shells were an effective adsorption medium in CWs. Hydraulic design and arrangement of oyster shells could be important in determining their treatment efficiency and cost-effectiveness. A dynamic model was developed to simulate substance transmissions in different treatment processes in the CWS using AQUASIM 2.1 based on the water quality data. Feasible ranges of biomedical parameters involved were determined for characterizing the importance of different biochemical treatment processes in SSF CWs. Future work will involve extending the experimental period to confirm the treatment efficiency of the oyster-shell-bedded CW systems in long-term operation and provide more field data for the simulated model instead of the literature values. Full article
(This article belongs to the Special Issue Sustainable Wastewater Treatment and Pollution Control)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Configuration; and (<b>b</b>) arrangement plan of the constructed wetlands (CWs) [<a href="#B16-water-05-00893" class="html-bibr">16</a>]. VA, VB, HA and HB were built with bagged oyster shells, scattered oyster shells, scattered oyster shells and gravels as adsorption media respectively.</p>
Full article ">Figure 2
<p>Waste removal quantity (g/m<sup>3</sup>/day) of (<b>a</b>) oxygen demand (BOD); (<b>b</b>) dissolved oxygen (DO); (<b>c</b>) total phosphorous (TP); (<b>d</b>) suspended solids (SS); (<b>e</b>) NH<sub>4</sub><sup>+</sup>; and (<b>f</b>) NO<sub>3</sub><sup>−</sup> in the four study SSF wetlands (HA = black circles; HB = grey circles; VA = inverted grey triangle; VB = white triangle).</p>
Full article ">Figure 3
<p>Simulated BOD, DO, TP, SS, NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup> outflow results and measured data in (<b>a</b>) HA; (<b>b</b>) HB; (<b>c</b>) VA; and (<b>d</b>) VB wetlands.</p>
Full article ">
914 KiB  
Article
The Impact of “Man-Made Hydrological Drought” on Plant Species Abundance in the Low-Flow Channel Downstream from the Matawin Dam, Quebec
by Ali Assani, Émilie Simard, Édith Gravel, Ghassen Ibrahim and Stéphane Campeau
Water 2013, 5(3), 875-892; https://doi.org/10.3390/w5030875 - 28 Jun 2013
Cited by 7 | Viewed by 7245
Abstract
The interannual variability of streamflow affects the composition and species richness of vegetation in low-flow channels and alluvial plains. Although climate conditions in 2003 and 2004 were nearly identical, large differences in streamflow were observed downstream from the Matawin dam. These differences resulted [...] Read more.
The interannual variability of streamflow affects the composition and species richness of vegetation in low-flow channels and alluvial plains. Although climate conditions in 2003 and 2004 were nearly identical, large differences in streamflow were observed downstream from the Matawin dam. These differences resulted in numerous days without flow (no water release) during the growing period (May to August) in 2003, leading to man-made hydrological drought. While this drought had no effect on abiotic variables (grain-size distribution and nutrient concentrations in sediments), a significant decrease in the number of terrestrial species was observed in 2004 (year without drought) relative to 2003 (drought year) on three sand bars studied. This decrease is interpreted to result from prolonged submergence of the sites in 2004. Principal component analysis highlighted the effect of individual sites (first principal component) and of the interannual variability of streamflow (second component) on the number of species. The study suggests that, from a flow management standpoint, it is advisable to release enough water downstream from the dam during the growing season to prevent low-flow channel colonization by invasive terrestrial species. Full article
(This article belongs to the Special Issue Ecological Watershed Management)
Show Figures

Figure 1

Figure 1
<p>Location of bars sampled upstream (UD) and downstream (DD) from the Taureau reservoir (Matawin dam).</p>
Full article ">Figure 2
<p>Coefficient of monthly discharge (%) upstream (black bars) and downstream (grey bars) of the dam (1931–2004).</p>
Full article ">Figure 3
<p>(<b>a</b>) Comparison of mean monthly temperatures measured at the Saint-Michel-des-Saints station in 2003 (blue curve) and 2004 (red curve); (<b>b</b>) Comparison of total monthly precipitation measured at the Saint-Michel-des-Saints station in 2003 (blue curve) and 2004 (red curve). SF = total snowfall in fall and winter.</p>
Full article ">Figure 4
<p>Comparison of daily flows in 2003 (blue curve) and 2004 (red curve) during the growing season, upstream (<b>a</b>); and downstream (<b>b</b>) from the Matawin dam.</p>
Full article ">Figure 5
<p>Location of sites upstream (UD) and downstream (DD) from the dam in the space defined by the first two significant components.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop