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Water Quality: A Component of the Water-Energy-Food Nexus

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A special issue of Water (ISSN 2073-4441).

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 52730

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Prof. Dr. Xiaohong Chen

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Guest Editor

Center for Water Resources & Environment, Sun Yat-sen University, Guangzhou, China
Interests: hydrological modeling and forecasting; hydrological variations under changing environment; water resources allocation and regulation; water resources management; water quality modeling

Special Issue Information

Dear Colleagues,

There is increasing policy and scientific emphasis on the water–energy–food nexus as a framework for analyzing human–environment systems at global and local scales and proposing more sustainable pathways to a secure future. However, consideration of water quality in the nexus is often a secondary consideration. The aim of this Special Issue is to evaluate the role of water quality in the water–energy–food nexus and to discuss approaches to water quality assessment within the nexus framework through original researches, with discussion of case-studies and analyses at different scales. This issue mainly covers the following topics:

(1) Case-studies examining water quality and quantity in the water-energy-food nexus.

(2) Regional/global scale analyses and models of water quality/quantity in the water-energy-food nexus.

(3) Monitoring and assessment of water quality/quantity in the water–energy–food nexus—current approaches and novel techniques.

(4) Water quality/quantity management approaches informed by the water–energy–food nexus.

Prof. Xiaohong Chen
Guest Editor

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Keywords

Water quality
water–energy–food nexus
human–environment systems
sustainability

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Published Papers (10 papers)

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Research

19 pages, 5179 KiB

Open AccessArticle

Quantitative Evaluation Method for Landscape Color of Water with Suspended Sediment

by Mao Ye, Ran Li, Weimin Tu, Jialing Liao and Xunchi Pu

Water 2018, 10(8), 1042; https://doi.org/10.3390/w10081042 - 6 Aug 2018

Cited by 5 | Viewed by 3739

Abstract

Landscape water is an important part of natural landscape, and a reasonable assessment of water landscape color is the basis for scientifically evaluating the quality of water landscape. To evaluate water landscape color with different concentrations of sediment objectively and quantitatively, a method of evaluating water landscape color based on hyperspectral technology is proposed to calculate water landscape color. The color spectrum calculation model of the water landscape color was constructed using the Commission Internationale de L’Eclairage spectrum three stimulus system (CIE-XYZ) calculation method and the response relationship among water reflectance, water depth, and sediment concentration. Under the conditions of eliminating as many external factors as possible, using a hyperspectral instrument to measure the reflectance of sediment and water, the response relationship between water depth and sediment concentration and water reflectance is calculated. Water depth and sediment concentration, which did not appear previously, were verified by experiments that proved the reliability of the water landscape color spectrum calculation model. By using different absolute value of chromatic coordinates in the international CIE-XYZ calculation method, a formula for determining the difference in sediment concentration for water landscape color was defined, and the quantitative evaluation method of landscape color of sand-laden water was established. In this research, we found that the predicted water landscape color, quantified by the color spectrum calculation model, is basically consistent with the actual color of landscape water and is basically in line with actual observation about significant difference assessment, which demonstrated the accuracy and reliability of the model. Hence, this research provides a scientific basis for the establishment of other water quality factors to evaluate water color, which makes it possible to quantify the color of the water landscape based on the establishment the color spectrum calculation model. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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20 pages, 6589 KiB

Open AccessArticle

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

by Peng Wang, Xiaoyan Bai, Xiaoqing Wu, Haijun Yu, Yanru Hao and Bill X. Hu

Water 2018, 10(8), 1019; https://doi.org/10.3390/w10081019 - 1 Aug 2018

Cited by 19 | Viewed by 4266

Abstract

Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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14 pages, 2556 KiB

Open AccessArticle

Distribution Characteristics of Phosphorus in the Yarlung Zangbo River Basin

by Shuqing Nan, Jia Li, Linglei Zhang, Ruidong An, Xunchi Pu and Wendian Huang

Water 2018, 10(7), 913; https://doi.org/10.3390/w10070913 - 11 Jul 2018

Cited by 9 | Viewed by 3257

Abstract

Phosphorus is an important limiting biogenic material. The special topography and climate of the Yarlung Zangbo River Basin generate unique distribution and transport characteristics of total phosphorus (TP). A survey of TP concentration, precipitation, runoff, sediment content, suspended load discharge, and relevant data was carried out for the Yarlung Zangbo River Basin in the last ten years. In combination with the regional geography and social economies, the basic spatial-temporal characteristics of P-water-sediment were analyzed by using the correlation and time series analysis methods. Furthermore, the transport characteristics of P and the main control factors were also studied. The results show that the TP concentration in this basin displays the characteristics of interannual cyclical variation and annual phasic variation, and the peak value appears in the wet season. Among the Yarlung Zangbo River, Nyangqu River, Lhasa River, and Nyang River, the TP concentration is the highest in the Nyangqu River, exceeding 0.4 mg/L several times in the wet season. In this basin, the distribution patterns of the TP concentration are similar to those of the rainfall, runoff, suspended load discharge, and sediment concentration. The coupling property of the TP concentration is the strongest with the suspended load discharge among the meteorological and hydrological parameters. The spatial variation of dissolved P in the wet and dry seasons in 2016 responded to the distribution patterns of the population density, environmental factors, farming, and animal husbandry. This indicates that the TP in the water is mainly from non-point sources and is affected by agricultural, geographical, and ecological factors. The transport of TP is mainly controlled by the suspended load discharge due to precipitation. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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14 pages, 1965 KiB

Open AccessFeature PaperArticle

Response of Sediment Load to Hydrological Change in the Upstream Part of the Lancang-Mekong River over the Past 50 Years

by Yanhu He, Zihan Gui, Chengjia Su, Xiaohong Chen, Dongwei Chen, Kairong Lin and Xiaoyan Bai

Water 2018, 10(7), 888; https://doi.org/10.3390/w10070888 - 3 Jul 2018

Cited by 13 | Viewed by 4472

Abstract

Sediment load and its response to the variations of the hydrological elements are important for the healthy utilization of a river. In this study, the response of sediment load to hydrological change was explored in the upstream part of the Lancang-Mekong River, a major transboundary river originating from the Tibetan Plateau and running through China, over the past 50 years. A sediment rating curve for the Jiuzhou Station was developed based on the available SSC-Q (suspended sediment concentration (SSC) and flow) data and trends in annual precipitation, runoff, peak flow (PF), low flow (LF), maximum water level (MWL), and sediment load were analyzed from 1957 to 2006. The correlation analysis method and Random Forest (RF) were adopted to qualitatively and quantitatively quantify the contribution of each hydrological element to the sediment load change. Results indicated that both the runoff and sediment load showed a significantly upward trend, especially after 1979, at the 95% confidence level. The sediment load had significantly positive correlations with runoff, PF, and MWL at the 99% confidence level, respectively. In particular, the sediment load had the largest significant positive correlation with runoff since 1980. Runoff had the largest variable importance to the sediment load change, followed by PF, MWL, precipitation, and LF. The increasing trend in the sediment load was mainly attributed to the increase of runoff in the upstream part of the Lancang-Mekong River since the mid-1980s. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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19 pages, 5551 KiB

Open AccessArticle

Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network

by Siyu Chen, Guohua Fang, Xianfeng Huang and Yuhong Zhang

Water 2018, 10(6), 806; https://doi.org/10.3390/w10060806 - 18 Jun 2018

Cited by 59 | Viewed by 8460

Abstract

Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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11 pages, 806 KiB

Open AccessArticle

Research on Fuzzy Cooperative Game Model of Allocation of Pollution Discharge Rights

by Xiaoyu Huang, Xiaohong Chen and Ping Huang

Water 2018, 10(5), 662; https://doi.org/10.3390/w10050662 - 19 May 2018

Cited by 17 | Viewed by 3969

Abstract

The allocation of pollution rights is significant to the economic development of a region, which determines the industrial structure of the region in another way. This study established an allocation model based on fuzzy coalition game theory. Formation of fuzzy coalitions between many producers in a region and reallocation of pollution discharge rights in the region through these coalitions was used to increase the total production value of the region while total pollution discharge amount is constant. At the same time, the fuzzy Shapley value method was used to allocate benefits obtained from the cooperation to the participants in various coalitions. This model was validated by its application in the case of three production bases near the Shizi channel in Dongguan city for reallocation of pollution discharge rights. Results showed that this model could increase the coalition benefits of the three production bases in this region, which observed increases of 4.28%, 7.74%, and 13.98%, respectively. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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17 pages, 3225 KiB

Open AccessFeature PaperArticle

Quantitative Evaluation of the Impact of Climate Change and Human Activity on Runoff Change in the Dongjiang River Basin, China

by Yuliang Zhou, Chengguang Lai, Zhaoli Wang, Xiaohong Chen, Zhaoyang Zeng, Jiachao Chen and Xiaoyan Bai

Water 2018, 10(5), 571; https://doi.org/10.3390/w10050571 - 27 Apr 2018

Cited by 48 | Viewed by 6198

Abstract

Climate change and human activity are typically regarded as the two most important factors affecting runoff. Quantitative evaluation of the impact of climate change and human activity on runoff is important for the protection, planning, and management of water resources. This study assesses the contributions of climate change and human activity to runoff change in the Dongjiang River basin from 1960 to 2005 by using linear regression, the Soil and Water Assessment Tool (SWAT) hydrologic model, and the climate elasticity method. Results indicate that the annual temperature in the basin significantly increased, whereas the pan evaporation in the basin significantly decreased (95%). The natural period ranged from 1960 to 1990, and the affected period ranged from 1991 to 2005. The percentage of urban area during the natural period, which was 1.94, increased to 4.79 during the affected period. SWAT modeling of the Dongjiang River basin exhibited a reasonable and reliable performance. The impacts induced by human activity on runoff change were as follows: 39% in the upstream area, 13% in the midstream area, 77% in the downstream area, and 42% in the entire basin. The impacts of human activity on runoff change were greater in the downstream area than in either upstream and midstream areas. However, the contribution of climate change (58%) is slightly larger than that of human activity (42%) in the whole basin. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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15 pages, 7647 KiB

Open AccessArticle

Surface Water Quality Evaluation Based on a Game Theory-Based Cloud Model

by Bing Yang, Chengguang Lai, Xiaohong Chen, Xiaoqing Wu and Yanhu He

Water 2018, 10(4), 510; https://doi.org/10.3390/w10040510 - 20 Apr 2018

Cited by 22 | Viewed by 7220

Abstract

Water quality evaluation is an essential measure to analyze water quality. However, excessive randomness and fuzziness affect the process of evaluation, thus reducing the accuracy of evaluation. Therefore, this study proposed a cloud model for evaluating the water quality to alleviate this problem. Analytic hierarchy process and entropy theory were used to calculate the subjective weight and objective weight, respectively, and then they were coupled as a combination weight (CW) via game theory. The proposed game theory-based cloud model (GCM) was then applied to the Qixinggang section of the Beijiang River. The results show that the CW ranks fecal coliform as the most important factor, followed by total nitrogen and total phosphorus, while biochemical oxygen demand and fluoride were considered least important. There were 19 months (31.67%) at grade I, 39 months (65.00%) at grade II, and one month at grade IV and grade V during 2010–2014. A total of 52 months (86.6%) of GCM were identical to the comprehensive evaluation result (CER). The obtained water quality grades of GCM are close to the grades of the analytic hierarchy process weight (AHPW) due to the weight coefficient of AHPW set to 0.7487. Generally, one or two grade gaps exist among the results of the three groups of weights, suggesting that the index weight is not particularly sensitive to the cloud model. The evaluated accuracy of water quality can be improved by modifying the quantitative boundaries. This study could provide a reference for water quality evaluation, prevention, and improvement of water quality assessment and other applications. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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18 pages, 2159 KiB

Open AccessArticle

Trends and Consumption Structures of China’s Blue and Grey Water Footprint

by Huixiao Wang and Yaxue Yang

Water 2018, 10(4), 494; https://doi.org/10.3390/w10040494 - 17 Apr 2018

Cited by 15 | Viewed by 4652

Abstract

Water footprint has become a common method to study the water resources utilization in recent years. By using input–output analysis and dilution theory, the internal water footprint, blue water footprint and grey water footprint of China from 2002 to 2012 were estimated, and the consumption structure of water footprint and virtual water trade were analyzed. The results show: (1) From 2002 to 2012, the average annual internal water footprint was 3.83 trillion m³ in China, of which the blue water footprint was 0.25 trillion m³, and the grey water footprint was 3.58 trillion m³ (with Grade III water standard accounting); both the internal water footprint and grey water footprint experienced decreasing trends from 2002 to 2012, except for a dramatic increase in 2010; (2) Average annual virtual blue water footprint was the greatest in agriculture (39.2%), while tertiary industry (27.5%) and food and tobacco processing (23.7%) were the top two highest for average annual virtual grey water footprint; (3) Virtual blue water footprint in most sectors showed increasing trends due to the increase of final demand, while virtual grey water footprint in most sectors showed decreasing trends due to the decreases of total return water coefficients and conversion coefficients of virtual grey water footprint; (4) For water resources, China was self-reliant: the water used for producing the products and services to meet domestic consumption was taken domestically; meanwhile, China exported virtual water to other countries, which aggravated the water stress in China. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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16 pages, 5471 KiB

Open AccessArticle

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

by Ziyi Yin, Benyou Jia, Shiqiang Wu, Jiangyu Dai and Deshan Tang

Water 2018, 10(4), 385; https://doi.org/10.3390/w10040385 - 26 Mar 2018

Cited by 33 | Viewed by 5136

Abstract

Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions. Full article

(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)

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