Runoff Curve Number for 36 Small Agricultural Plots at Two Different Climatic Conditions in India

  • Mohan Lal
  • S. K. Mishra
  • Ashish Pandey
  • Yogendra Kumar
Conference paper
First Online:
Part of the Water Science and Technology Library book series (WSTL, volume 75)

Abstract

The performance of eight different curve number (CN) estimation methods, viz. storm event mean and median, rank-order mean and median, log-normal frequency, S-probability (SP), geometric mean and least square fit, was evaluated using rainfall–runoff data measured on 36 small agricultural plots located at two different climatic conditions in India. The least square fit method was observed to estimate significantly lower CN than other methods except log-normal frequency method. Based on the overall score, the method performance in runoff estimation was as follows: S-probability > geometric mean > storm event mean > rank-order median > rank-order mean > least square fit > storm event median > log-normal frequency. The runoff (or CN) production in the study plots was mainly dependent on soil type as compared to land uses and slope. An inverse relationship between CN and infiltration capacity was found to observe which support the applicability of National Engineering Handbook (Chap.  4) tables where CNs decline with soil type (or infiltration capacity).

Keywords

Agricultural plot Curve number Climatic India Runoff 
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Notes

Acknowledgements

This research (site 1) was supported by a grant from the Indian National Committee on Surface Water (INCSW) and Ministry of Water Resources, Govt. of India, New Delhi under the Research and Development project (MOW-627-WRD) on “Experimental Verification of SCS Runoff Curve Numbers for Selected Soils and Land Uses.” Special thanks to the Mondal et al. [27] for providing the 40 rainfall–runoff data.

References

  1. 1.
    SCS: ‘Hydrology’ National Engineering Handbook, Supplement A, Section 4, Soil Conservation Service, USDA, Washington, DC (1972)Google Scholar
  2. 2.
    Rallison, R.E.: Origin and evaluation of the SCS runoff equation. In: Proceedings of Irrigation and Drainage Symposia on Watershed Management, vol. 2, pp. 912–924. ASCE, New York (1980)Google Scholar
  3. 3.
    Arnold, J.G., Williams, J.R., Srinivasan, R., King, K.W.: SWAT: soil and water assessment tool. USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX (1996)Google Scholar
  4. 4.
    Young, R.A., Onstad, C.A., Bosch, D.D., Anderson, W.P.: AGNPS: a nonpoint-source pollution model for evaluating agricultural watersheds. J. Soil Water Conserv. 44(2), 168–173 (1989)Google Scholar
  5. 5.
    Knisel, W.G.: CREAMS: a field-scale model for chemical, runoff and erosion from agricultural management systems. Conservation Research Report No. 26, South East Area, US Department of Agriculture, Washington, DC (1980)Google Scholar
  6. 6.
    Sharpley, A.N., Williams, J.R.: EPIC-Erosion/productivity impact calculator: 1. Model determination. US Department of Agriculture. Tech. Bull., No. 1768 (1990)Google Scholar
  7. 7.
    Gao, G.Y., Fu, B.J., Lu, Y.H., Liu, Y., Wang, S., Zhou, J.: Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China. Hydrol. Earth Syst. Sci. 16, 2347–2364 (2012)CrossRefGoogle Scholar
  8. 8.
    Mishra, S.K., Tyagi, J.V., Singh, V.P., Singh, R.: SCS-CN based modelling of sediment yield. J. Hydrol. 324, 301–322 (2006)CrossRefGoogle Scholar
  9. 9.
    Tyagi, J.V., Mishra, S.K., Singh, R., Singh, V.P.: SCS-CN based time-distributed sediment yield model. J. Hydrol. 352, 388–403 (2008)CrossRefGoogle Scholar
  10. 10.
    Ebrahimian, M., Nuruddin, A.A.B., Soom, M.A.B.M., Sood, A.M., Neng, L.J.: Runoff estimation in steep slope watershed with standard and slope-adjusted curve number methods. Pol. J. Environ. Stud. 21(5), 1191–1202 (2012)Google Scholar
  11. 11.
    Ajmal, M., Moon, G., Ahn, J.: Kim T Quantifying excess storm water using SCS-CN–based rainfall runoff models and different curve number determination methods. J. Irrig. Drain. Eng. 141(3), 04014058 (2015)CrossRefGoogle Scholar
  12. 12.
    McCuen, R.H.: Approach to confidence interval estimation for curve numbers. J. Hydrol. Eng. 7:1(43), 43–48 (2002). doi: 10.1061/(ASCE)1084-0699(2002
  13. 13.
    Schneider, L.E., McCuen, R.H.: Statistical guidelines for curve number generation. J. Irrig. Drain. Eng. 131(3), 282–290 (2005)CrossRefGoogle Scholar
  14. 14.
    Hawkins, R.H.: The importance of accurate curve numbers in the estimation of storm runoff. Water Resour. Bull. 11(5), 887–891 (1975)Google Scholar
  15. 15.
    Hawkins, R.H., Ward, T.J., Woodward, D.E., Van Mullen, J.A. (eds.): Curve Number Hydrology: State of the Practice. ASCE, Reston, VA (2009)Google Scholar
  16. 16.
    Tedela, N.H., McCutcheon, S.C., Rasmussen, T.C., Hawkins, R.H., Swank, W.T., Campbell, J.L., Adams, M.B., Jackson, C.R., Tollner, E.W.: Runoff curve number for 10 small forested watersheds in the mountains of the Eastern United States. J. Hydrol. Eng. 17, 1188–1198 (2012)CrossRefGoogle Scholar
  17. 17.
    Hawkins, R.H., Jiang, R., Woodward, D.E., Hjelmfelt, A.T., Van Mullem, J.A., Quan, Q.D.: Runoff curve number method: examination of the initial abstraction ratio. In: Proceedings of the Second Federal Interagency Hydrologic Modeling Conference. ASCE Publications, Las Vegas (2002)Google Scholar
  18. 18.
    Hawkins, R.H.: Asymptotic determination of runoff curve numbers from data. J. Irrig. Drain. Eng. 119(2), 334–345 (1993)CrossRefGoogle Scholar
  19. 19.
    Hjelmfelt Jr., A.T.: Investigation of curve number procedure. J. Hydraul. Eng. 117, 725–737 (1991)CrossRefGoogle Scholar
  20. 20.
    Ali, S., Sharda, V.N.: A comparison of curve number based methods for runoff estimation for small watersheds in semi arid region of India. Int. J. Hydrol. Res. 39(3), 191–200 (2008)CrossRefGoogle Scholar
  21. 21.
    D’Asaro, F., Grillone, G.: Empirical investigation of curve number method parameters in the Mediterranean area. J. Hydrol. Eng. 17, 1141–1152 (2012)CrossRefGoogle Scholar
  22. 22.
    D’Asaro, F., Grillone, G., Hawkins, R.H.: Curve number: empirical evaluation and comparison with curve number handbook tables in sicily. J. Hydrol. Eng. 19(12), 04014035 (2014)CrossRefGoogle Scholar
  23. 23.
    Lal, M., Mishra, S.K., Pandey, A.: Physical verification of the effect of land features and antecedent moisture on runoff curve number. Catena 133, 318–327 (2015)CrossRefGoogle Scholar
  24. 24.
    Stewart, D., Canfield, E., Hawkins, R.H.: Curve number determination methods and uncertainty in hydrologic soil groups from semiarid watershed data. J. Hydrol. Eng. 17, 1180–1187 (2012)CrossRefGoogle Scholar
  25. 25.
    Soulis, K.X., Valiantzas, J.D.: SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds—the two-CN system approach. J. Hydrol. Earth Sys. Sci. 16, 1001–1015 (2012)CrossRefGoogle Scholar
  26. 26.
    Tedela, N.H., McCutcheon, S.C., Rasmussen, T.C., Tollner, E.W.: Evaluation and improvement of the curve number method of hydrological analysis on selected forested watersheds of Georgia. Project report submitted to Georgia Water Resources Institute, Supported by the U.S. Geological Survey, p. 40 (2008)Google Scholar
  27. 27.
    Mandal, U.K., Sharma, K.L., Prasad, J.V.N.S., Reddy, B.S., Narsimlu, B., Saikia, U.S., Adake, R.V., Yadaiah, P., Masane, R.N., Venkanna, K., Venkatravamma, K., Satyam, B., Raju, B., Srivastava, N.N.: Nutrient losses by runoff and sediment from an agricultural field in semi-arid tropical India, Indian. J. Dryland Agric. Res. Dev. 27(1), 01–09 (2012)Google Scholar
  28. 28.
    Hawkins, R.H.: Improved prediction of storm runoff in mountain watershed. Irrig. Drain. Div. ASCE 99, 519–523 (1973)Google Scholar
  29. 29.
    Bonta, J.V.: Determination of watershed curve number using derived distributions. J. Irrig. Drain. Div. 123(1), 28–36 (1997)CrossRefGoogle Scholar
  30. 30.
    NRCS: ‘Hydrology’ National Engineering Handbook, Supplement A, Section 4, Soil Conservation Service, USDA, Washington, DC (1997)Google Scholar
  31. 31.
    Mays, L.W.: Water Resources Engineering, 2nd edn. Willey, Arizona. ISBN: 978-0-470-46064-1 (2005)Google Scholar
  32. 32.
    Hawkins, R.H., Hjelmfelt, A.T., Zevenbergen, A.W.: Runoff probability, storm depth, and curve numbers. J. Irrig. Drain. Eng. 111(4), 330–340 (1985)CrossRefGoogle Scholar
  33. 33.
    Hjelmfelt, A.T.: Empirical-investigation of curve number techniques. J. Hydraul. Eng. Div. 106(9), 1471–1476 (1980)Google Scholar
  34. 34.
    Hjelmfelt, A.T., Kramer, K.A., Burwell, R.E.: Curve numbers as random variables. In: Proceedings of International Symposium (1982)Google Scholar
  35. 35.
    Nash, J.E., Sutcliffe, J.E.: Modeling infiltration during steady rain. Water Resour. Res. 9, 384–394 (1970)Google Scholar
  36. 36.
    Legates, D.R., McCabe, G.J.: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydro climatic model validation. Water Resour. Res. 35(1), 233–241 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohan Lal
    • 1
    • 2
  • S. K. Mishra
    • 1
  • Ashish Pandey
    • 1
  • Yogendra Kumar
    • 2
  1. 1.Department of Water Resources Development and ManagementIndian Institute of TechnologyRoorkeeIndia
  2. 2.Irrigation and Drainage Engineering DepartmentGovind Ballabh Pant University of Agriculture and TechnologyPantnagarIndia

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