Response of Non-point Source Pollution Loads to Land Use Change under Different Precipitation Scenarios from a Future Perspective

Xiaoyan Bai, Wen Shen, Peng Wang (), Xiaohong Chen and Yanhu He
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Xiaoyan Bai: Guangdong University of Technology
Wen Shen: Guangdong University of Technology
Peng Wang: Guangdong University of Technology
Xiaohong Chen: Sun Yat-sen University
Yanhu He: Guangdong University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 13, No 4, 3987-4002

Abstract: Abstract To optimize the management of non-point source (NPS) pollution in a changing environment, the cellular automata (CA) and Hydrological Simulation Program-Fortran (HSPF) models were used to study the response mechanism of NPS pollution loads to land use change and different precipitation scenarios. Taking the Dongjiang River Basin as a case study, the land use situation and its spatial distribution patterns in 2020, 2030 and 2050 were predicted by the logistic regression-based CA model. The trends of the NPS pollution loads under different land use and precipitation scenarios were quantitatively evaluated. The results show that the total accuracy of the land use change simulated by the CA model was 81%. Both the HSPF model and the CA model were highly applicable to this basin. Precipitation is proven to be the main driving force of NPS pollution. From 2020 to 2050, the annual load, average monthly load, maximum and minimum monthly load of the BOD and TP show an upward trend. TN shows a slight downward trend, which is related to the reduction in cultivated land area and the use of nitrogen fertilizer. In view of the future trend of NPS pollution, the basin should continue to control TN pollution and focus on strengthening BOD and TP control to achieve high-quality management of the water environment.

Keywords: HSPF model; Cellular automata; Non-point source pollution load; Land use change; Dongjiang River basin (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11269-020-02626-0

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