Skip to main content
Log in

An enhanced SMA based SCS-CN inspired model for watershed runoff prediction

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Incorporation of initial soil moisture (V 0) in the Soil Conservation Service Curve Number (SCS-CN) methodology helps to avoid the sudden jumps in Curve Number (CN) and, in turn, in computed runoff. It invoked the development of an enhanced (yet simple) Soil Moisture Accounting (SMA) procedure-based-SCS-CN inspired model, by incorporating initial moisture (V 0). Its performance is tested using a dataset of 152 small to large watersheds of USDA (total 38,169 storm events), and compared with original SCS-CN method, Mishra and Singh (Acta Geophys Polon 50(3):457–477, 2002), Michel et al. (Water Resour Res 41(2):W02011, 2005) and Singh et al. (Water Resour Manag 29(11): 4111–4127, 2015) model using four statistical indices (RMSE, R 2, PBIAS and NSE) and rank grading system (RGS). The proposed model scores highest (= 691 marks out of maximum 2280 marks) (Rank I) followed by Singh et al. (Water Resour Manag 29(11):4111–4127, 2015) model with 642 marks (Rank II), Michel et al. (Water Resour Res 41(2):W02011, 2005) model with 376 marks (Rank III) and Mishra and Singh model with 362 marks (= Rank IV). The original SCS-CN model, however, performs the poorest of all with 209 marks (Rank V).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aitken AP (1973) Assessing systematic errors in rainfall–runoff models. J Hydrol 20(2):121–136

    Google Scholar 

  • Ajmal M, Moon G, Ahn J, Kim T (2015) Investigation of SCS-CN and its inspired modified models for runoff estimation in South Korean watersheds. J Hydro-Environ Res 9(4):592–603

    Google Scholar 

  • Babu PS, Mishra SK (2012) An improved SCS-CN inspired model. J Hydrol Eng ASCE 17(11):1164–1172

    Google Scholar 

  • Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. Mc-Graw-Hill, New York

    Google Scholar 

  • Coffey ME, Workman SR, Taraba JL, Fogle AW (2004) Statistical procedures for evaluating daily and monthly hydrologic model predictions. Trans ASAE 47:59–68

    Google Scholar 

  • Deshmukh DS, Chaube UC, Hailu AE, Gudeta DA, Kassa MT (2013) Estimation and comparison of curve numbers based on dynamic land use land cover change, observed rainfall-runoff data and land slope. J Hydrol 492:89–101. doi:10.1016/j.jhydrol.2013.04.001

    Google Scholar 

  • EI-Sadek A, Feyen J, Berlamont J (2001) Comparison of models for computing drainage discharge. J Irrig Drain Eng 127:363–369

    Google Scholar 

  • Fentie B, Yu B, Silburn MD, Ciesiolka CAA (2002) Evaluation of eight different methods to predict hill-slope runoff rates for a grazing catchment in Australia. J Hydrol 261:102–114

    Google Scholar 

  • Garen D, Moore DS (2005) Curve number hydrology in water quality modeling: uses, abuses, and future directions. J Am Water Resour Assoc 41(2):377–388

    Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143

    Google Scholar 

  • Hawkins RH (1993) Asymptotic determination of runoff curve numbers from data. J Irrig Drain Eng ASCE 119(2):334–345

    Google Scholar 

  • Hawkins RH, Ward TJ, DE Woodward, Van Mullen JA (2009) Curve number hydrology: state of the practice, vol 106. ASCE, Reston. ISBN 978-0-7844-1044-2

    Google Scholar 

  • Hawkins RH, Ward TJ, Woodward DE, Van Mullem JA (2010) Continuing evolution of rainfall-runoff and the curve number precedent. In: 2nd joint federal interagency conference, Las Vegas, NV

  • Hjelmfelt AT Jr (1991) Investigation of curve number procedure. J Hydrol Eng ASCE 117(6):725–737

    Google Scholar 

  • Jain MK, Mishra SK, Babu PS, Venugopal K, Singh VP (2006) Enhanced runoff curve number model incorporating storm duration and a non-linear Ia-S relation. J Hydrol Eng ASCE 11(6):631–635

    Google Scholar 

  • Marquardt DW (1963) An algorithm for least squares estimation of non-linear parameters. J Soc Ind Appl Math 11:431–441

    Google Scholar 

  • Michel C, Vazken A, Charles P (2005) Soil conservation service curve number method: how to mend among soil moisture accounting procedure? Water Resour Res 41(2):W02011

    Google Scholar 

  • Mishra SK, Singh VP (1999) Another look at the SCS-CN method. J Hydrol Eng ASCE 4(3):257–264

    Google Scholar 

  • Mishra SK, Singh VP (2002) SCS-CN method: part-I: derivation of SCS-CN based models. Acta Geophys Polon 50(3):457–477

    Google Scholar 

  • Mishra SK, Singh VP (2003) Soil conservation service curve number (SCS-CN) methodology. Kluwer Publishers, Dordrecht

    Google Scholar 

  • Mishra SK, Jain MK, Singh VP (2004) Evaluation of SCS-CN based models incorporating antecedent moisture. Water Resour Manag 18(6):567–589

    Google Scholar 

  • Mishra SK, Geetha K, Rastogi AK, Pandey RP (2005) Long-term hydrologic simulation using storage and source area concepts. Hydrol Process 19(14):2845–2861

    Google Scholar 

  • Mishra SK, Sahu RK, Eldho TI, Jain MK (2006) An improved Ia–S relation incorporating antecedent moisture in SCS-CN methodology. Water Resour Manag 20:643–660

    Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part I—A discussion of principles. J Hydrol 10:282–290

    Google Scholar 

  • Sahu RK, Mishra SK, Eldho TI, Jain MK (2007) An advanced soil moisture accounting procedure for SCS curve number method. Hydrol Process 21(21):2827–2881

    Google Scholar 

  • Sahu RK, Mishra SK, Eldho TI (2012) Performance evaluation of modified versions of SCS curve number method for two watersheds of Maharashtra. India ISH J Hydraul Eng 18(1):27–36

    Google Scholar 

  • Santhi C, Arnold JG, Williams JR, Dugas WA, Srinivasan R, Hauck LM (2001) Validation of the SWAT model on a large river basin with point and nonpoint sources. J Am Water Resour Assoc 37(5):1169–1188

    Google Scholar 

  • SCS (1956/1972) National engineering handbook section 4: hydrology, chapter 4. Soil conservation service. USDA, Washington

  • Singh PK, Gaur ML, Mishra SK, Rawat SS (2010) An updated hydrological review on recent advancements in soil conservation service curve-number technique. J Water and Clim Change 1(2):118–134

    Google Scholar 

  • Singh PK, Mishra SK, Berndtsson R, Jain MK, Pandey RP (2015) Development of a modified SMA based MSCS-CN model for runoff estimation. Water Resour Manag 29(11):4111–4127 doi:10.1007/s11269-015-1048-1

    Google Scholar 

  • Soulis KX, Valiantzas JD (2012) SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds-the two-CN system approach. Hydrol Earth Syst Sci 16:1001–1015

    Google Scholar 

  • Soulis KX, Valiantzas JD, Dercas N, Londra PA (2009) Analysis of the runoff generation mechanism for the investigation of the SCS-CN method applicability to a partial area experimental watershed. Hydrol Earth Syst Sci 13:605–615

    Google Scholar 

  • Steenhuis TS, Winchell M, Rossing J, Zollweg JA, Walter MF (1995) SCS runoff equation revisited for variable-source runoff areas. J Irrig Drain Eng ASCE 121(3):234–238

    Google Scholar 

  • Verma S, Verma RK, Mishra SK, Singh A, Jayaraj GK (2017) A revisit of NRCS-CN methodology and application of RS and GIS for surface runoff estimation. Hydrol Sci J 62(12):1891–1930

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Verma.

Appendices

Appendix 1

See Table 4.

Table 4 NSE (%) and RMSE resulted by applications of models in 152 watersheds

Appendix 2

See Table 5.

Table 5 PBIAS and R2 resulted by applications of models in 152 watersheds

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, S., Mishra, S.K., Singh, A. et al. An enhanced SMA based SCS-CN inspired model for watershed runoff prediction. Environ Earth Sci 76, 736 (2017). https://doi.org/10.1007/s12665-017-7062-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-017-7062-2

Keywords

Navigation