scholarly journals Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3250
Author(s):  
Fei Zhang ◽  
Ngai Weng Chan ◽  
Changjiang Liu ◽  
Xiaoping Wang ◽  
Jingchao Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Body ◽  
Water Quality Index ◽  
Quality Index ◽  
Lake Basin ◽  
Ebinur Lake ◽  
Remote Sensing Techniques ◽  
The Relationship ◽  
Ebinur Lake Basin

Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

scholarly journals Application of multi hydrochemical indices for spatial groundwater quality assessment: Ziway Lake Basin of the Ethiopian Rift Valley

2021 ◽  
Author(s):  
Abraham Mechal ◽  
Hassen Shube ◽  
Tewodros Rango ◽  
Kristine Walraevens ◽  
Steffen Birk
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Rift Valley ◽  
Quality Index ◽  
Mean Value ◽  
World Health ◽  
Ethiopian Rift ◽  
Lake Basin ◽  
Climate Effect ◽  
Drinking And Irrigation

Abstract The Ethiopian Rift Valley (ERV), which is characterized by arid and semi-arid climate, groundwater is the most important water resource used for drinking and irrigation purposes. However, in the region people are suffering from severe water scarcity exacerbated by climate effect. Besides water availability, endemic water quality issues are critical and affect the suitability of the water and human health risks. The present study evaluates the suitability of groundwater for drinking and agricultural purposes in the Ziway Lake Basin (ZLB) of the ERV. Groundwater used for drinking contains multiple inorganic contaminants in levels that surpass the World Health Organization recommended limits. The most frequent of these violations were for Na+, K+, HCO3-, F- and few samples for Mn, As, U, Pb and Mo. The modeled Drinking Water Quality Index (DWQI) values of the groundwater show wide variation ranging from 12.7 (Excellent category) to 714 (Unsuitable category) with mean value of 94. Likewise, Irrigation Water Quality Index (IWQI) computed by considering EC, SAR, Na%, RSC and PI of the groundwater vary from 13.2 to 520 with mean value of 106. Both DWQI and IWQI value suggest that groundwater is generally of Excellent quality for drinking and irrigation use in the headwater regions of the ZLB and progressively becomes extremely Unsuitable towards the rift floor. The exceptionally high DWQI values to the west of Lake Ziway is mainly associated with the co-occurrence of multiple toxic elements from a groundwater from the Quaternary sediments and rhyolitic volcanic aquifers.

scholarly journals Assessment of Water Quality of Okomu Wetland Using Water Quality Index

2020 ◽  
Vol 4 (2) ◽  
pp. 450-457
Author(s):  
S. I. Ehiorobo ◽  
A. E. Ogbeibu
Keyword(s):  
Water Quality ◽  
Water Body ◽  
Water Quality Index ◽  
Quality Index ◽  
Anthropogenic Activities ◽  
Physicochemical Characteristics ◽  
Poor Quality ◽  
Quality Data ◽  
Water Quality Data

The water quality of the Okomu Wetland was evaluated using the Water Quality Index (WQI) technique which provides a number that expresses overall water quality of a water body or water sample at a particular time. Sampling of physicochemical parameters spanned two years covering the wet and dry seasons and the water quality data were obtained from 10 sampling locations; Ponds 36, 52, 54, 61, 64, 90, 94, Arhakhuan Stream, Okomu River (Agekpukpu) and Okomu River (Iron bridge) all within the Okomu National Park. Parameters such as Total Dissolved Solids (TDS), Turbidity, pH, Electrical conductivity (EC), Chlorine (Cl), Nitrate (NO3), Sulphate (SO4), Sodium (Na), Magnesium (Mg), (Iron) Fe, Chromium (Cr), Zinc (Zn), Copper (Cu), Manganese (Mn), Lead (Pb), and Nikel (Ni) were used to compute WQI and the values obtained for the wetland ranged between 34.36 and 167.28. The Index shows that pond 36, 52 and 54 are unfit for drinking with values between 103.86 and 167.28; ponds 61 and 64 are of the very poor quality category with WQI values of 95.19 and 92.44 respectively, Pond 90, pond 94, Arhakhuan Stream and Okomu River (Agekpukpu) are of poor quality and WQI values between and 53.58 and 73.15. Whereas, the Okomu River (Iron bridge) is within the good water quality (34.36) category. The Okomu River by Iron bridge is of good quality rating while other sampled points were of poor, very poor or unfit for drinking though these water bodies are mostly free from anthropogenic activities because of the conservative status of the study area. A major source of pollution within the wetland is surface runoff. The water quality of the wetland may not be suitable for man’s consumption especially pond water which are majorly impacted by runoff, yet very important for the survival and sustenance of the forest animals and plants. The water quality index (WQI) interprets physicochemical characteristics of water by providing a value which expresses the overall water quality and thus, reveals possible pollution problems of a water body. It turns complex water quality data into information that is easily understandable and usable by scientists, researchers and the general public.

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