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Spatio-Temporal Variation Characteristics of Water Quality and Its Response to Climate: A Case Study in Yihe River Basin

J. Y. Ren1,2,3, H. M. Liu2,3,4, S. Y. Ding2,3 *, and Z. H. Cao2,3,5

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, China-Canada Center for Energy, Environment and Ecology Research, UR-BNU, School of Environment, Beijing Normal University, Beijing 100875, China
  2. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China
  3. College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
  4. Civil and Economic Law College, Henan University of Economics and Law, Zhengzhou 450000, China
  5. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

*Corresponding author. Tel.: 0371-23881850; Fax: 0371-23881850. E-mail address: (S. Y. Ding).


Spatial-temporal patterns of river water quality, the identification of pollution sources and contaminated areas are crucial to water environment protection and sustainable development of the river basin. In this study, spatial-temporal characteristics of river water quality in the Yihe river basin were investigated through multivariate analysis methods, including principal component analysis (PCA), cluster analysis (CA), discriminant analysis (DA), and one-way ANOVA. The water quality indicators (Hydrogen ion concentration (pH), electric conductivity (EC), dissolved oxygen (DO), turbidity, chemical oxygen demand (COD), total phosphorus (TP), and ammonia nitrogen (NH4+-N)) were investigated at 17 sampling sites in three periods (i.e., high-, mean-, low flow period) during 2016 ~ 2017. The results show that: (1) PCA served to extract and recognize the most significant indicators affecting water quality in the Yihe river basin, i.e., pH, EC, COD, and NH4+-N. (2) CA divided the Yihe river basin into three groups with similar water quality features, namely the upper, middle, and lower reaches. (3) DA demonstrated strong dimensionality reduction ability with the accuracy of clustering was 94.1%, and only a few indicators (i.e., DO, EC, turbidity, NH4+-N, and TP) could reflect the spatial variations in water quality. (4) One-way ANOVA indicated that the water quality was the worst in the lower reach of Yihe river basin during the mean-flow period, followed by which in the upper and middle reaches during the high-flow period. (5) The spatiotemporal characteristics of water quality were mainly restrained by human factors (e.g., the construction of highway and agricultural activities), climate change (e.g., precipitation and temperature), and natural environments (e.g., topography).

Keywords: river water quality, spatial-temporal characteristics, multivariate statistical analysis, Yihe River Basin, climate change

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