Cooperative identification for critical periods and critical source areas of nonpoint source pollution in a typical watershed in China

  • Shuhe Ruan
  • Yanhua ZhuangEmail author
  • Song HongEmail author
  • Liang Zhang
  • Zhen Wang
  • Xianqiang Tang
  • Weijia Wen
Research Article


Critical periods (CPs) and critical source areas (CSAs) refer to the high-risk periods and areas of nonpoint source (NPS) pollution in a watershed, respectively, and they play a significant role in NPS pollution control. The upstream Daning River Basin is a typical watershed in the Three Gorges Reservoir area. In this study, a Hydrological Simulation Program-Fortran (HSPF) model was used to simulate phosphorus loss in the upstream Daning River Basin. Co-analysis of critical periods and critical source areas (CACC) is a quantitative collaborative analysis method for the identification of CSAs in CPs, and it was used to classify the periods and areas of NPS pollution as CPs, sub-CPs, non-CPs, CSAs, and non-CSAs. The CPs occurred in months 5–7 and accounted for 53.7% of the total phosphorus (TP) loads, and the sub-CPs occurred in months 1, 3, 4, and 8 and accounted for 29.2% of the TP loads. In CSAs, 49.4% of the TP loads occurred in 26.8% of the basin. Furthermore, we proposed the following multilevel priority control measure for NPS pollution in the upstream Daning River Basin: CSAs in CPs (with load-area rate of 1.4), CSAs in sub-CPs (0.7), CSAs in non-CPs (0.4), non-CSAs in CPs (0.3), non-CSAs in sub-CPs (0.2), and non-CSAs in non-CPs (0.1). CSAs in CPs accounted for 25.8% of the TP loads from 19.0% of the areas in only 3 months while 49.4% of the TP loads from similar areas over an entire year. These findings indicated that the CSAs in CPs located in farmland along the Daning, Dongxi, and Houxi Rivers should be prioritized for pollution management measures.


Nonpoint source (NPS) pollution Critical periods (CPs) Critical source areas (CSAs) Hydrological Simulation Program-Fortran (HSPF) Load-time curve Load-area curve 


Funding information

This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA23040403], the CRSRI Open Research Program of China [grant number CKWV2017531/KY], the Hubei Provincial Natural Science Foundation of China [grant numbers 2016CFA058], the Hubei Technological Innovation Special Fund of China [grant number 2018ACA148], and the Youth Innovation Promotion Association, CAS [grant number 2018370 and 2016304].

Conflict of interest

Compliance with ethical standards

The authors declare that they have no conflict of interest.


  1. Abari ME, Majnounian B, Malekian A, Jourgholami M (2017) Effects of forest harvesting on runoff and sediment characteristics in the Hyrcanian forests, northern Iran. Eur J For Res 136:375–386CrossRefGoogle Scholar
  2. Bicknell BR, Imhoff JC, Kittle JL (2001) Hydrological simulation program–FORTRAN (HSPF), User’s manual for version 12.0. EPA, AthensGoogle Scholar
  3. Borah D, Bera M (2013) Watershed-scale hydrologic and nonpoint-source pollution models: review of mathematical bases. Trans ASAE 46:1553–1566CrossRefGoogle Scholar
  4. Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8:559–568CrossRefGoogle Scholar
  5. Chen L, Shen ZY (2014) Method and application of watershed non-point source pollution priority control area identification. China Environmental Press, BeijingGoogle Scholar
  6. Cheng X, Chen L, Sun R, Jing Y (2018) An improved export coefficient model to estimate non-point source phosphorus pollution risks under complex precipitation and terrain conditions. Environ Sci Pollut Res 25:20946–20955CrossRefGoogle Scholar
  7. Davis RL, Zhang H, Schroder JL, Wang JJ, Payton ME, Zazulak A (2005) Soil characteristics and phosphorus level effect on phosphorus loss in runoff. J Environ Qual 34:1640–1650CrossRefGoogle Scholar
  8. Djodjic F, Borling K, Bergdtrom L (2004) Phosphorus leaching in relation to soil type and soil phosphorus content. J Environ Qual 33:678–684CrossRefGoogle Scholar
  9. Gburek W, Sharpley A (1998) Hydrologic controls on phosphorus loss from upland agricultural watersheds. J Environ Qual 27:267–277CrossRefGoogle Scholar
  10. Heathwaite AL, Dils RM (2000) Characterising phosphorus loss in surface and subsurface hydrological pathways. Sci Total Environ 251:523–538CrossRefGoogle Scholar
  11. Liu ZA, Yang J, Yang Z, Zou J (2012) Effects of rainfall and fertilizer types on nitrogen and phosphorus concentrations in surface runoff from subtropical tea fields in Zhejiang, China. Nutr Cycl Agroecosyst 93:297–307CrossRefGoogle Scholar
  12. Mao XY, Sun KJ, Wang DH, Liao ZW (2005) Controlled-release fertilizer(CRF): a green fertilizer for controlling non-point contamination in agriculture. J Environ Sci 17:181–184Google Scholar
  13. Nelson NO, Shober AL (2012) Evaluation of phosphorus indices after twenty years of science and development. J Environ Qual 41:1703–1710CrossRefGoogle Scholar
  14. Niraula R, Kalin L, Srivastava P, Anderson CJ (2013) Identifying critical source areas of nonpoint source pollution with SWAT and GWLF. Ecol Model 268:123–133CrossRefGoogle Scholar
  15. Pradhanang SM, Briggs RD (2014) Effects of critical source area on sediment yield and streamflow. Water Environ J 28:222–232CrossRefGoogle Scholar
  16. Renwick WH, Vanni MJ, Fisher TJ, Morris EL (2018) Stream nitrogen, phosphorus, and sediment concentrations show contrasting long-term trends associated with agricultural change. J Environ Qual 47:1513–1521CrossRefGoogle Scholar
  17. Ryberg KR (2017) Structural equation model of Total phosphorus loads in the Red River of the North Basin, USA and Canada. J Environ Qual 46:1072–1080CrossRefGoogle Scholar
  18. Sharpley AN, Weld JL, Beegle DB, Kleinman PJA, Gburek WJ, Moore PA, Mullins G (2003) Development of phosphorus indices for nutrient management planning strategies in the United States. J Soil Water Conserv 58:137–152Google Scholar
  19. 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 405:195–205CrossRefGoogle Scholar
  20. Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrological modeling of the Iroquois River watershed using HSPF and SWAT. J Am Water Resour Assoc 41:343–360CrossRefGoogle Scholar
  21. Volf CA, Ontkean GR, Bennett DR, Chanasyk DS, Miller JJ (2007) Phosphorus losses in simulated rainfall runoff from manured soils of Alberta. J Environ Qual 36:730–741CrossRefGoogle Scholar
  22. Wang F, Sun Z, Zheng S, Yu J, Liang X (2018a) An integrated approach to identify critical source areas of agricultural nonpoint-source pollution at the watershed scale. J Environ Qual 47:922–929CrossRefGoogle Scholar
  23. Wang M, Liu J, Liu Y, Li C, Xiao W (2018b) Analysis of nitrogen and phosphorus pollution loads from agricultural non-point sources in the three gorges reservoir of Hubei Province from 1991 to 2014. J Agro-Environ Sci 37:294–301Google Scholar
  24. Wang X, Hao F, Zhang X (2013) Optimization of best management practices for non-point source pollution in Danjiangkou Reservoir Basin. China Environ Sci 33:1335–1343Google Scholar
  25. Wang Y, Bian J, Wang S, Tang J, Ding F (2016) Evaluating SWAT snowmelt parameters and simulating spring snowmelt nonpoint source pollution in the source area of the Liao River. Pol J Environ Stud 25:2177–2185CrossRefGoogle Scholar
  26. Wei L, Wei A, Min Y, Jinfeng M (2016a) Runoff and sediment yield modeling and soil Erosion analysis in Daning River watershed in three gorges reservoir region based on SWAT model. J Soil Water Conserv 30:49–56Google Scholar
  27. Wei P, Ouyang W, Gao X, Hao F, Hao Z, Liu H (2017) Modified control strategies for critical source area of nitrogen (CSAN) in a typical freeze-thaw watershed. J Hydrol 551:518–531CrossRefGoogle Scholar
  28. Wei P, Ouyang W, Hao F, Gao X, Yu Y (2016b) Combined impacts of precipitation and temperature on diffuse phosphorus pollution loading and critical source area identification in a freeze-thaw area. Sci Total Environ 553:607–616CrossRefGoogle Scholar
  29. Wu L, Long T-Y, Cooper WJ (2011) Simulation of spatial and temporal distribution on dissolved non-point source nitrogen and phosphorus load in Jialing River watershed, China. Environ Earth Sci 65:1795–1806CrossRefGoogle Scholar
  30. Wu L, Long T, Li C (2010) The simulation research of dissolved nitrogen and phosphorus non-point source pollution in Xiao-Jiang watershed of three gorges reservoir area. Water Sci Technol 61:1601–1616CrossRefGoogle Scholar
  31. Xiang X, Zhong L, Wang L (2013) Review fo non-point source pollution models. J Shanghai Jiaotong Univ (Agric Sci) 31:53–60Google Scholar
  32. Xu F, Dong G, Wang Q, Liu L, Yu W, Men C, Liu R (2016) Impacts of DEM uncertainties on critical source areas identification for non-point source pollution control based on SWAT model. J Hydrol 540:355–367CrossRefGoogle Scholar
  33. Yang Y, Zhang M, Zheng L, Cheng D, Liu M, Geng Y, Chen J (2013) Controlled-release urea for rice production and its environmental implications. J Plant Nutr 36:781–794CrossRefGoogle Scholar
  34. Zhuang Y, Zhang L, Du Y, Yang W, Wang L, Cai X (2016) Identification of critical source areas for nonpoint source pollution in the Danjiangkou Reservoir Basin, China. Lake Reserv Manag 32:341–352CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Hubei Provincial Engineering Research Center of Non-point Source Pollution ControlInstitute of Geodesy and Geophysics, Chinese Academy of SciencesWuhanPeople’s Republic of China
  2. 2.School of Resource and Environmental SciencesWuhan UniversityWuhanPeople’s Republic of China
  3. 3.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  4. 4.College of Resources and EnvironmentHuazhong Agricultural UniversityWuhanPeople’s Republic of China
  5. 5.Changjiang River Scientific ResearchInstitute of Changjiang Water Resources CommissionWuhanPeople’s Republic of China

Personalised recommendations