Abstract
Dongliao River Basin (DLRB) is facing serious deterioration of water quality impacted by anthropogenic activities. As China attaches increasing importance to environmental protection, many regions are trying to reduce water pollution through land use changes. The long-term variations of the nonpoint source (NPS) pollution affected by land use changes in the DLRB have not been previously assessed. In this study, the contributions of land use/land cover (LULC) changes in 1980–2015 to NPS pollution were evaluated by comparing simulations under paired land use scenarios using the Soil and Water Assessment Tool (SWAT). The historical trend and ecological protection scenarios in 2025 were established based on CA–Markov model, and pollution loads in both scenarios were forecasted. Results show that the expansion of dryland and urban areas and the decline in forest and grassland coverage were the major contributors to the increase in NPS pollution in the DLRB. The expansion of paddy field resulted in an increase in actual total phosphorus (TP) but a decrease in total nitrogen (TN). In the historical trend scenario, dryland would decrease by 4.57%. TN and TP loads were 1.40% and 1.45% lower than those in 2015, respectively. In the ecological protection scenario, TN and TP loads were 3.37% and 6.11% lower than those in 2015, respectively, due to the decreased area of dryland by 7.22%. Pollutions in the riverside and southeast areas of the basin would be reduced in 2025. This finding shows that NPS pollution is controlled under the current policy.
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Acknowledgements
This research is jointly supported by National Key Research and Development Plan (2018YFC1800404), Science Foundation of Jilin province (20150101116JC), the National Natural Science Foundation of China (Grant No.41807155), the Project funded by China Postdoctoral Science Foundation (Grant No.2017 M621211). We also gratefully acknowledge to the 111 Project (No. B16020).
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Gong, X., Bian, J., Wang, Y. et al. Evaluating and Predicting the Effects of Land Use Changes on Water Quality Using SWAT and CA–Markov Models. Water Resour Manage 33, 4923–4938 (2019). https://doi.org/10.1007/s11269-019-02427-0
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DOI: https://doi.org/10.1007/s11269-019-02427-0