Environmental Science and Pollution Research

, Volume 26, Issue 10, pp 10363–10373 | Cite as

Effect of irrigation amount and fertilization on agriculture non-point source pollution in the paddy field

  • Huiliang Wang
  • Peng He
  • Chenyang Shen
  • Zening WuEmail author
Research Article


It is the key point to reveal the effect of irrigation water and fertilization conditions on the agriculture non-point pollution in the paddy field. In this study, the estimation model of agricultural non-point source pollution loads at field scale was established on the basis of agricultural drainage irrigation model and combined with pollutant concentration predication model. Based on the estimation model of agricultural non-point source pollution in the field and experimental data, the load of agricultural non-point source pollution in different irrigate amount and fertilization schedule in paddy field was calculated. The results showed that the variation of field drainage varies greatly under different irrigation conditions, and there is an “inflection point” between the irrigation water amount and field drainage amount. The non-point pollution load increased with the increase of irrigation water and showed a significant power correlation. Under the different irrigation condition, the increase amplitude of non-point pollution load with the increase of irrigation water was different. When the irrigation water is smaller, the non-point pollution load increase relatively less, and when the irrigation water increased to inflection point, the non-point pollution load will increase considerably. In addition, there was a positive correlation between the fertilization and non-point pollution load. The non-point pollution load had obvious difference in different fertilization schedule even with same fertilization level, in which the fertilizer pollution load increased the most in the period of turning green to tillering. The results provide some basis for the field control and management of agricultural non-point source pollution.


Agricultural non-point source pollution Pollution load intensity Irrigation Fertilizers Fertilization schedule 



The authors would like to express their sincere gratitude to the anonymous reviewers for their constructive comments and the Editor of the journal. Their detailed suggestions have resulted in an improved manuscript.

Funding information

Funding was supported by the National Natural Science Foundation of China (No. 51509223 and No. 51879242).


  1. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development1. JAWRA J Am Water Resour As 34(1):73–89CrossRefGoogle Scholar
  2. Azzellino A, Salvetti R, Vismara R (2006) Combined use of the EPA-QUAL2E simulation model and factor analysis to assess the source apportionment of point and non point loads of nutrients to surface waters. Sci Total Environ 371(1–3):214–222CrossRefGoogle Scholar
  3. Bicknell BR, Imhoff JC, Kittle JJL, Jobes THD, JrAS (2011) Hydrological simulation program – Fortran (HSPF): User’s manual for release 12. U.S. Environmental Protection Agency, Athens, GaGoogle Scholar
  4. Bryan BA (2011) Designing a policy mix and sequence for mitigating agricultural non-point source pollution in a water supply catchment. Water Resour Manag 25(3):875–892CrossRefGoogle Scholar
  5. Chen J, Lu J (2014) Establishment of reference conditions for nutrients in an intensive agricultural watershed, eastern China. Environ Sci Pollut R 21(4):2496–2505CrossRefGoogle Scholar
  6. Du X, Li X, Zhang W, Wang H (2014) Variations in source apportionments of nutrient load among seasons and hydrological years in a semi-arid watershed: GWLF model results. Environ Sci Pollut R 21(10):6506–6515CrossRefGoogle Scholar
  7. Du X, Shrestha NK, Wang J (2019) Integrating organic chemical simulation module into SWAT model with application for PAHs simulation in Athabasca oil sands region, Western Canada. Environ Model Softw 111:432–443CrossRefGoogle Scholar
  8. Haas MB, Guse B, Fohrer N (2017) Assessing the impacts of best management practices on nitrate pollution in an agricultural dominated lowland catchment considering environmental protection versus economic development. J Environ Manag 196:347–364CrossRefGoogle Scholar
  9. Li X, Weller DE, Jordan TE (2010) Watershed model calibration using multi-objective optimization and multi-site averaging. J Hydrol 380(3):277–288CrossRefGoogle Scholar
  10. Li Q, Hu Y, Jia Q et al (2018) Establishment and application of the estimation model for pollutant concentrfation in agriculture drain [C]//IOP conference series: earth and environmental science. IOP Publishing 121(3):032046Google Scholar
  11. Lu H, Xie H (2018) Impact of changes in labor resources and transfers of land use rights on agricultural non-point source pollution in Jiangsu Province, China. J Environ Manag 207:134–140CrossRefGoogle Scholar
  12. Moges MA, Schmitter P, Tilahun SA, Steenhuis TS (2018) Watershed modeling for reducing future non-point source sediment and phosphorus load in the Lake Tana Basin, Ethiopia. J Soils Sediments 18(1):309–322CrossRefGoogle Scholar
  13. Ongley ED, Xiaolan Z, Tao Y (2010) Current status of agricultural and rural non-point source pollution assessment in China. Environ Pollut 158(5):1159–1168CrossRefGoogle Scholar
  14. Pratt B, Chang H (2012) Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales. J Hazard Mater 209:48–58CrossRefGoogle Scholar
  15. Price K, Purucker ST, Kraemer SR, Babendreier JE, Knightes CD (2014) Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrol Process 28(9):3505–3520CrossRefGoogle Scholar
  16. Rinaldo A, Benettin P, Harman CJ, Hrachowitz M, McGuire KJ, Velde Y, Bertuzzo E, Botter G (2015) Storage selection functions: a coherent framework for quantifying how catchments store and release water and solutes. Water Resour Res 51(6):4840–4847CrossRefGoogle Scholar
  17. Shen Z, Hong Q, Yu H, Liu R (2008) Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the three gorges reservoir region, China. Sci Total Environ 405(1):195–205CrossRefGoogle Scholar
  18. Shen Z, Chen L, Hong Q, Xie H, Qiu J, Liu R (2013) Vertical variation of nonpoint source pollutants in the three gorges reservoir region. PLoS One 8(8):e71194CrossRefGoogle Scholar
  19. Skaggs RW, Youssef MA, Chescheir GM (2012) DRAINMOD: model use, calibration, and validation. T ASABE 55(4):1509–1522CrossRefGoogle Scholar
  20. Wang H, Li X, Hao S (2015a) Effects of rainfall data resolution on watershed-scale model performance in predicting runoff. J Water Clim Chang 6(2):227–240CrossRefGoogle Scholar
  21. Wang H, Wu Z, Hu C, Du X (2015b) Water and nonpoint source pollution estimation in the watershed with limited data availability based on hydrological simulation and regression model. Environ Sci Pollut R 22(18):14095–14103CrossRefGoogle Scholar
  22. Wang H, Wu Z, Hu C (2015c) A comprehensive study of the effect of input data on hydrology and non-point source pollution modeling. Water Resour Manag 29(5):1505–1521CrossRefGoogle Scholar
  23. White RE, Dyson JS, Haigh RA, Jury WA, Sposito G (1986) A transfer function model of solute transport through soil: 2. Illustrative applications. Water Resour Res 22(2):248–254CrossRefGoogle Scholar
  24. Woodward SJR, Stenger R, Bidwell VJ (2013) Dynamic analysis of stream flow and water chemistry to infer subsurface water and nitrate fluxes in a lowland dairying catchment. J Hydrol 505:299–311CrossRefGoogle Scholar
  25. Wriedt G, Rode M (2006) Modelling nitrate transport and turnover in a lowland catchment system. J Hydrol 328(1–2):157–176CrossRefGoogle Scholar
  26. Wu L, Gao JE, Ma XY, Li D (2015) Application of modified export coefficient method on the load estimation of non-point source nitrogen and phosphorus pollution of soil and water loss in semiarid regions. Environ Sci Pollut R 22(14):10647–10660CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Huiliang Wang
    • 1
  • Peng He
    • 1
  • Chenyang Shen
    • 1
  • Zening Wu
    • 1
    Email author
  1. 1.College of Water Conservancy & Environmental EngineeringZhengzhou UniversityZhengzhouPeople’s Republic of China

Personalised recommendations