Long-term spatiotemporal changes of surface water and its influencing factors in the mainstream of Han River, China

Zhang, Wen; Zhang, Zhen; Wei, Xinting; Hu, Yijie; Li, Yuanxi; Meng, Lingkui

doi:10.1016/j.ejrh.2022.101009

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…The Mann-Kendall method is used to test the significance of the areal change in each water body type during the study period; this method does not require the samples to obey a certain regular distribution and can prevent interference resulting from a few outliers. It is a nonparametric statistical test method that has been widely used in climate ecology research and other related fields [42,43]. The calculation formulas are as follows:…”

Section: Trend Slope Analysis Methodsmentioning

confidence: 99%

Multiwater Index Synergistic Monitoring of Typical Wetland Water Bodies in the Arid Regions of West-Central Ningxia over 30 Years

Pang

Wang

Hou

et al. 2022

Water

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The Shapotou National Nature Reserve in the Ningxia Hui Autonomous Region is a typical arid region in China. There is an exceptionally serious problem of surface water resource conservation, and dynamic monitoring of surface water with the help of water indices can help to elucidate its change patterns and impact mechanisms. Here, we analysed the characteristics of interannual variation in surface water area in the study area from 1992–2021. The correlation coefficients of the surface water area in the previous year and the contemporaneous water bodies of the Yellow River with the total surface water area (TSWA) were calculated. The results show the following: ① In terms of the classification accuracy of the two methods, water indices and support vector machine classification, water indices are more suitable for water body extraction in the study area. In particular, the three water indices, NDWI, MNDWI and AWEIsh, were more effective, with average overall accuracies of 90.38%, 90.33% and 90.36% over the 30-year period, respectively. ② From the TSWA extraction results from the last 30 years, the TSWA showed an increasing trend with an increase of 368.28 hm2. Among the areas, Tenggeli Lake contributed the most to the increase in TSWA. ③ The highest correlation between the TSWA and the previous year’s TSWA was 0.89, indicating that the better way to protect the water body is to maintain water surface stability year-round. The surface water area of the Yellow River and TSWA also showed a strong correlation, indicating that the rational use of Yellow River water is also an important direction for the future conservation of water resources in the study area.

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Section: Trend Slope Analysis Methodsmentioning

confidence: 99%

Multiwater Index Synergistic Monitoring of Typical Wetland Water Bodies in the Arid Regions of West-Central Ningxia over 30 Years

Pang

Wang

Hou

et al. 2022

Water

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“…That means when MNDWI cannot extract the effective water body, it can be extracted through the joint calculation of EVI and MNDWI. Similarly, when This study jointly used three water-body indices (the MNDWI [8], NDVI [30], and EVI [31]) to extract water-body information from Landsat_Collection 1_T1_SR remote sensing imagery. Although some studies have shown that the water-body extraction performance of MNDWI is higher than that of NDWI and EVI, MNDWI is not 100% effective, especially in alpine regions with snow cover.…”

Section: Extracting Water Bodies From Satellite-based Databasesmentioning

confidence: 99%

“…Previous studies mainly used four water-body indices, namely MNDWI, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) to extract water bodies from remote-sensing images. For example, Zhang et al [8] analyzed the long-term spatiotemporal dynamics of surface water in the Hanjiang River based on the MNDWI. Deng et al [9] analyzed the seasonal changes in reservoir water storage in two basins of the upper Yellow River based on the NDWI.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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“…Surface water, especially that from rivers, is the main source of fresh water and is important for ecology, social well-being, and economic development [ 1 , 2 ]. According to a report by the United Nations Environment Program (UNEP) [ 3 ], in some countries, surface water comprises up to 90% of the population’s main drinking water sources.…”

Section: Introductionmentioning

confidence: 99%

Improving Water Quality Index Prediction Using Regression Learning Models

Hoque

Aziz

Alelyani

et al. 2022

IJERPH

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Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

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Long-term spatiotemporal changes of surface water and its influencing factors in the mainstream of Han River, China

Cited by 6 publications

References 55 publications

Multiwater Index Synergistic Monitoring of Typical Wetland Water Bodies in the Arid Regions of West-Central Ningxia over 30 Years

Multiwater Index Synergistic Monitoring of Typical Wetland Water Bodies in the Arid Regions of West-Central Ningxia over 30 Years

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

Improving Water Quality Index Prediction Using Regression Learning Models

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