Water2015, 7(8), 4247-4255; doi:10.3390/w7084247 (registering DOI) - published 3 August 2015 Show/Hide Abstract
Abstract: Free acidity of aqueous solutions was initially defined in 1909 by Søren Peter Lauritz Sørensen as pH = −lgcH+ (c/mol·dm−3 or m/mol·kg−1 of the free hydrogen ions in solution, H+) soon (1910) was changed to pH = paH+ = −lgaH+, integrating the new concepts of activity, aiand activity coefficient γi, for the ionic species i under concern, H+ in this case; it is ai = −lg(miγi). Since individual ions do not exist alone in solution, primary pH values cannot be assigned solely by experimental measurements, requiring extra thermodynamic model assumptions for the activity coefficient, γH+, which has put pH in a unique situation of not being fully traceable to the International System of Units (SI). Also the concept of activity is often not felt to be as perceptible as that of concentration which may present difficulties, namely with the interpretation of data. pH measurements on unknown samples rely on calibration of the measuring setup with adequate reference pH buffers. In this work, the assignment of pH values to buffers closely matching the samples, e.g., seawater, is revisited. An approach is presented to assess the quantity pmH+ = −lgmH+ profiting from the fact that, contrary to single ion activity coefficients, mean activity coefficients, can be assessed based on experimentally assessed quantities alone, γExp ±, thus ensuring traceability to the mole, the SI base unit for amount of substance. Compatibility between γExp ± and mean activity coefficient calculated by means of Pitzer model equations, γPtz ±, validates the model for its intended use.
Water2015, 7(8), 4232-4246; doi:10.3390/w7084232 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.
Water2015, 7(8), 4200-4231; doi:10.3390/w7084200 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: The complex relationships within the water-energy-food security nexus tend to be place-specific, increasing the importance of identifying transferable principles to facilitate implementation of a nexus approach. This paper aims to contribute transferable principles by using global model data and concepts to illustrate and analyze the water history of Central Asia. This approach builds on extensive literature about Central Asia and global change as well as recent advances in global water modeling. Decadal water availability and sectorial water consumption time series are presented for the whole 20th century, along with monthly changes in discharge attributable to human influences. Concepts from resilience and socio-ecological system theory are used to interpret the results and identify five principles relevant to managing the transboundary nexus: (1) the subsystems included/excluded from the nexus are case-specific and should be consciously scrutinized; (2) consensus is needed on what boundaries can acceptably be crossed within the nexus; (3) there is a need to understand how reducing trade-offs will modify system dependencies; (4) global stakeholders have both a responsibility and right to contribute to the shaping of the nexus; (5) combining data with global and local perspectives can help to enhance transferability and understanding of shared problems in our globalized world.
Water2015, 7(8), 4175-4199; doi:10.3390/w7084175 (registering DOI) - published 31 July 2015 Show/Hide Abstract
Abstract: Groundwater-fed lakes in northeastern Germany are characterized by significant lake level changes, but for only a few lakes are in situ water level measurements available. In this study, we test the potential of RapidEye satellite images for indirectly reconstructing lake level changes. The lake levels are derived by intersecting water-land borders with a high-resolution digital elevation model (DEM). Based on Lake Fürstenseer (LF), we define requirements and limitations of the method. Water-land borders were extracted automatically from the 37 RapidEye images available for the period between 2009 and 2014. Otsu’s threshold was used for the NIR band and for the normalized difference water index (NDWI). The results were validated with in situ gauging, contour lines from the DEM, and in situ Differential Global Positioning System (DGPS) measurements of the shoreline. Using an ideal shoreline subset, the lake levels could be reconstructed with decimeter accuracy using the NIR water-land border, but the levels were systematically underestimated by 0–20 cm. The accuracy of the reconstructed lake level retrieval strongly depends on the precision of the water-land border retrieval, on the accuracy of the DEM, and on the lake level itself. A clear shift of the water-land border with increasing lake level is also essential for the unambiguous reconstruction of different levels. This shift needs to be several times larger than the pixel size. The biggest challenges for lake level reconstruction are the presence of vegetation at the shorelines, the quality of the topographic data in the underwater area, the slope of the shoreline, and shadows in combination with low solar angles.
Water2015, 7(8), 4161-4174; doi:10.3390/w7084161 (registering DOI) - published 30 July 2015 Show/Hide Abstract
Abstract: The purpose of this study was to construct a scheme that makes it possible to compare the relationship between water usage, satisfaction, and physical properties in three countries. The physical properties of the shower were measured using physical properties testing apparatus of water-saving standard or scheme for showerheads issued in several water-saving countries and data for users satisfaction evaluation was acquired through bathing experiments. In this paper, we analyzed the result from Taiwanese and Vietnamese individuals to compare them to of Japanese subjects analyzed in the previous study. We compared the physical properties of showers assessed low in satisfaction by Taiwanese, Vietnamese and Japanese subjects. It was assumed that spray pattern tends to decrease satisfaction when the water volume ratio within 100 mm and 150 mm of a measuring device is located a 450 mm distance from the showerhead is low, and that, because all three countries showed the same value, it was imagined that there were no differences in the water volume ratio of high-satisfaction showerheads among three countries. On the other hand, the values of Spray Force-per-Hole, Temperature Drop, and Spray Angle were different among three countries. We speculated that these differences are affected respectively by ethnic differences in pain tolerance, thermoregulatory response and bathing habit.
Water2015, 7(8), 4144-4160; doi:10.3390/w7084144 - published 28 July 2015 Show/Hide Abstract
Abstract: There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. The proposed model was compared with the original ANN model without data preprocessing and a nonlinear perturbation model (NLPM) based on ANN, i.e., the NLPM-ANN model. Eight watersheds were selected for calibrating and testing these models. Comparative study shows that the learning and training ability of ANN models can be improved by SSA and NLPM techniques significantly, and the performance of the SSA-ANN model is much better than the NLPM-ANN model, with high foresting accuracy. The SSA-ANN1 model, which only considers rainfall as model input, was compared with the SSA-ANN2 model, which considers both rainfall and previous runoff as model inputs. It is shown that the Nash-Sutcliffe criterion of the SSA-ANN2 model is much higher than that of the SSA-ANN1 model, which means that the proper selection of previous runoff data as rainfall-runoff model inputs can significantly improve model performance since they usually are highly auto-correlated.