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Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China

Author

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  • Yang, Shengtian
  • Dong, Guotao
  • Zheng, Donghai
  • Xiao, Honglin
  • Gao, Yunfei
  • Lang, Yang

Abstract

Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003–2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination (R2) during the calibration and validation period. The high values of Nash–Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO3–N, NH4+–N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas.

Suggested Citation

  • Yang, Shengtian & Dong, Guotao & Zheng, Donghai & Xiao, Honglin & Gao, Yunfei & Lang, Yang, 2011. "Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China," Ecological Modelling, Elsevier, vol. 222(20), pages 3701-3717.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:20:p:3701-3717
    DOI: 10.1016/j.ecolmodel.2011.09.004
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    References listed on IDEAS

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    1. Kang, M.S. & Park, S.W. & Lee, J.J. & Yoo, K.H., 2006. "Applying SWAT for TMDL programs to a small watershed containing rice paddy fields," Agricultural Water Management, Elsevier, vol. 79(1), pages 72-92, January.
    2. Ouyang, Wei & Wang, Xuelei & Hao, Fanghua & Srinivasan, R., 2009. "Temporal-spatial dynamics of vegetation variation on non-point source nutrient pollution," Ecological Modelling, Elsevier, vol. 220(20), pages 2702-2713.
    3. Zhang, H. & Huang, G.H., 2011. "Assessment of non-point source pollution using a spatial multicriteria analysis approach," Ecological Modelling, Elsevier, vol. 222(2), pages 313-321.
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    Cited by:

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    2. Qiang Fu & Yunqiang Zhu & Shengli Huang, 2020. "Regionalization of Agricultural Nonpoint Source Pollution over China with a Combination of Qualitative and Quantitative Method," Sustainability, MDPI, vol. 12(1), pages 1-16, January.
    3. Zhang, XiaoHong & Pan, HengYu & Cao, Jun & Li, JinRong, 2015. "Energy consumption of China’s crop production system and the related emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 111-125.
    4. Alam, Md Jahangir & Dutta, Dushmanta, 2012. "A process-based and distributed model for nutrient dynamics in river basin: Development, testing and applications," Ecological Modelling, Elsevier, vol. 247(C), pages 112-124.

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