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Water Resources Management

, Volume 30, Issue 4, pp 1433–1448 | Cite as

Excess Stormwater Quantification in Ungauged Watersheds Using an Event-Based Modified NRCS Model

  • Muhammad Ajmal
  • Jae-Hyun Ahn
  • Tae-Woong KimEmail author
Article
  • 326 Downloads

Abstract

Quantifying runoff from a storm event is a crucial part of rainfall-runoff model development. The objective of this study is to illustrate inconsistencies in the initial abstraction (I a) and curve number (CN) in the Natural Resources Conservation Service (NRCS) model for ungauged steep slope watersheds. Five alternatives to the NRCS model were employed to estimate stormwater runoff in 39 forest-dominated mountainous watersheds. The change to the parameterization (slope-adjusted CN and I a) leads to more efficient modified NRCS models. The model evaluations based on root mean square error (RMSE), Nash-Sutcliffe coefficient E, coefficient of determination (R 2 ), and percent bias (PB) indicated that our proposed model with modified I a, consistently performed better than the other four models and the original NRCS model, in reproducing the runoff. In addition to the quantitative statistical accuracy measures, the proposed I a modification in the NRCS model showed very encouraging results in the scatter plots of the combined 1799 storm events, compared to other alternatives. This study’s findings support modifications to the CN and the I a in the NRCS model for steep slope ungauged watersheds and suggest additional changes for more accurate runoff estimations.

Keywords

Curve number Initial abstraction Maximum potential retention NRCS model 

Notes

Acknowledgments

This research was supported by a grant from the Construction Technology Innovation Program (11CTIPC06-Development of Korean Advanced Technology for Hydrologic Analysis) funded by the Ministry of Land, Infrastructure, and Transport (MLIT) of Korea. Special thanks to the Hydrological Survey Center (HSC) of Korea for providing measured data of streamflow.

References

  1. Ajmal M, Kim TW (2015) Quantifying excess stormwater using SCS-CN–based rainfall runoff models and different curve number determination methods. J Irrig Drain Eng 141(3):04014058CrossRefGoogle Scholar
  2. Ajmal M, Waseem M, Wi S, Kim TW (2015) Evolution of a parsimonious rainfall-runoff model using soil moisture proxies. J Hydrol 530:623–633CrossRefGoogle Scholar
  3. Baltas EA, Dervos NA, Mimikou MA (2007) Technical note: determination of the SCS initial abstraction ratio in an experimental watershed in Greece. Hydrol Earth Syst Sci 11(6):1825–1829CrossRefGoogle Scholar
  4. Beck HE, De Jeu RAM, Schellekens J, Van Dijk AIJM, Bruijnzeel LA (2009) Improving curve number based storm runoff estimates using soil moisture proxies. J Sel Topics Appl Earth Observ Remote Sens 2(4):250–259CrossRefGoogle Scholar
  5. Blair A, Sanger D, White D, Holland AF, Vandiver L, Bowker C, White S (2014) Quantifying and simulating stormwater runoff in watersheds. Hydrol Process 28(3):559–569CrossRefGoogle Scholar
  6. Bryant RB, Gburek WJ, Veith TL, Hively WD (2006) Perspective on the potential for hydropedology to improve watershed modeling of phosphorous loss. Geoderma 131(3–4):299–8307CrossRefGoogle Scholar
  7. Cao H, Vervoort RW, Dabney SM (2011) Variation of curve number derived from plot runoff data for New South Wales (Australia). Hydrol Process 25(24):3774–3789CrossRefGoogle Scholar
  8. Chaplot VAM, Bissonnais YL (2003) Runoff features for interrill erosion at different rainfall intensities, slope lengths, and gradients in an agricultural loessial hillslope. Soil Sci Soc Am J 67(3):844–851CrossRefGoogle Scholar
  9. 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 492:89–101CrossRefGoogle Scholar
  10. Diaz-Ramirez JN, McAnally WH, Martin JL (2011) Analysis of hydrological processes applying the HSPF model in selected watersheds in Alabama, Mississippi, and Puerto Rico. Appl Eng Agric 27(6):937–954CrossRefGoogle Scholar
  11. Durbude DG, Jain MK, Mishra SK (2011) Long-term hydrologic simulation using SCS-CN-based improved soil moisture accounting procedure. Hydrol Process 25(4):561–579CrossRefGoogle Scholar
  12. Evett SR, Dutt GR (1985) Length and slope effects on runoff from sodium dispersed, compacted earth microcatchments. Soil Sci Soc Am J 49(1):734–738CrossRefGoogle Scholar
  13. Feyereisen GW, Strickland TC, Bosch DD, Truman CC, Sheridan JM, Potter TL (2008) Curve number estimates for conventional and conservation tillage in the Southeast Coastal Plain. J Soil Water Conserv 63(3):120–128CrossRefGoogle Scholar
  14. Geetha K, Mishra SK, Eldho TI, Rastogi AK, Panday RP (2008) SCS-CN-based continuous simulation model for hydrologic forecasting. Water Resour Manag 22(2):165–190CrossRefGoogle Scholar
  15. Grimaldi S, Petroselli A, Romano N (2013) Green-Ampt Curve-Number mixed procedure as an empirical tool for rainfall–runoff modelling in small and ungauged basins. Hydrol Process 27(8):1253–1264CrossRefGoogle Scholar
  16. Hawkins RH (1993) Asymptotic determination of runoff curve number from data. J Irrig Drain Eng 119(2):334–345CrossRefGoogle Scholar
  17. Hawkins RH, Ward TJ, Woodward DE, Van Mullem JA (2009) Curve number hydrology-state of practice. The ASCE/EWRI publication, ISBN 978-0-7844-7257-6Google Scholar
  18. Huang MB, Gallichand J, Wang Z, Goulet M (2006) A modification to the soil conservation service curve number method for steep slopes in the Loess Plateau of China. Hydrol Process 20(3):579–589CrossRefGoogle Scholar
  19. Jain MK, Mishra SK, Suresh Babu P, Venugopal K (2006) On the Ia-S relation of the SCS-CN model. Nord Hydrol 37(3):261–275CrossRefGoogle Scholar
  20. Kim NW, Lee JW, Lee J, Lee JE (2010) SWAT application to estimate design runoff curve number for South Korean conditions. Hydrol Process 24(15):2156–2170Google Scholar
  21. Michel C, Andreassian V, Perrin C (2005) Soil Conservation Service curve number method: how to mend a wrong soil moisture accounting procedure? Water Resour Res 41(2):W02011Google Scholar
  22. Mishra SK, Singh VP (2003) Soil Conservation Service curve number (SCS-CN) methodology. Kluwer Academic Dordrecht, The Netherlands, ISBN 978-94-017-0147-1Google Scholar
  23. Mishra SK, Jain MK, Singh VP (2004) Evaluation of the SCS-CN-based model incorporating antecedent moisture. Water Resour Manag 18(6):567–589CrossRefGoogle Scholar
  24. Mishra SK, Jain MK, Pandey RP, Singh VP (2005) Catchment area-based evaluation of the AMC-dependent SCS-CN-inspired rainfall-runoff models. Hydrol Process 19(14):2701–2718CrossRefGoogle Scholar
  25. 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(5):643–660CrossRefGoogle Scholar
  26. Mishra SK, Jain MK, Babu PS, Venugopal K, Kaliappan S (2008) Comparison of AMC-dependent CN-conversion formulae. Water Resour Manag 22(10):1409–1420CrossRefGoogle Scholar
  27. Mishra SK, Chaudhary A, Shrestha RK, Pandey A, Lal M (2014) Experimental verification of the effect of slope and land use on SCS runoff curve number. Water Resour Manag 28(11):3407–3416CrossRefGoogle Scholar
  28. 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(3):885–900CrossRefGoogle Scholar
  29. Nalbantis I, Efstratiadis A, Rozos E, Kopsiafti M, Koutsoyiannis D (2011) Holistic versus monomeric strategies for hydrological modeling of human-modified hydrosystems. Hydrol Earth Sys Sci 15:743–758CrossRefGoogle Scholar
  30. Patil JP, Sarangi A, Singh OP, Singh AP, Ahmad T (2008) Development of a GIS interface for estimation of runoff from watersheds. Water Resour Manag 22(9):1221–1239CrossRefGoogle Scholar
  31. Philip JR (1991) Hillslope infiltration: planar slopes. Water Resour Res 27(1):109–117CrossRefGoogle Scholar
  32. Ponce VM, Hawkins RH (1996) Runoff curve number: has it reached maturity? J Hydrol Eng 1(1):11–19CrossRefGoogle Scholar
  33. Ritter A, Muñoz-Carpena R (2013) Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480:33–45CrossRefGoogle Scholar
  34. Sahu RK, Mishra SK, Eldho TI (2010) An improved AMC-coupled runoff curve number model. Hydrol Process 24(20):2834–2839CrossRefGoogle Scholar
  35. Sharpley AN, Williams JR (1990) EPIC-Erosion/Productivity Impact Calculator: 1. Model documentation. US Department of Agriculture Tech. Bull., No. 1768Google Scholar
  36. Shaw SB, Walter MT (2009) Improving runoff risk estimates: formulating runoff as a bivariate process using the SCS curve number method. Water Resour Res 45(3):W03404Google Scholar
  37. Shi ZH, Chen LD, Fang NF, Qin DF, Cai CF (2009) Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China. Catena 77(1):1–7CrossRefGoogle Scholar
  38. USDA-FS (US Department of Agriculture, Forest Service) (1968) Rainfall interception by annual grass and chaparral. Berkeley, CA: Pacific Southwest Forest and Range Experiment Station, USDA Forest Service Research Paper PSW-48Google Scholar
  39. USDA-NRCS (US Department of Agriculture, Natural Resources Conservation Service) (2004) ‘Hydrology’ National Engineering Handbook, Supplement A, Section 4. Soil Conservation Service, USDA, Washington, DCGoogle Scholar
  40. Wang X, Liu T, Yang W (2012) Development of a robust runoff-prediction model by fusing the rational equation and a modified SCS-CN method. Hydrol Sci J 57(6):1118–1140CrossRefGoogle Scholar
  41. Woodward DE, Hawkins RH, Jiang R, Hjelmfelt Jr AT, Van Mullem JA, Quan DQ (2003) Runoff curve number model: examination of the initial abstraction ratio, World Water & Environ. Resour. Congress and Related Symposia, EWRI, ASCE, 23–26 June, 2003, Philadelphia, Pennsylvania, USAGoogle Scholar
  42. Woodward DE, Hoeft CC, Hawkins RH, Van Mullem J, Ward TJ (2010) Discussion of “Modifications to SCS-CN method for long-term hydrologic simulation” by K. Geetha, S. K. Mishra, T. I. Eldho, A. K. Rastogi, and R. P. Pandey. J Irrig Drain Eng 136(6):444–446CrossRefGoogle Scholar
  43. Yuan Y, Nie W, McCutcheon SC, Taguas EV (2014) Initial abstraction and curve numbers for semiarid watersheds in Southeastern Arizona. Hydrol Process 28(3):774–783CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringHanyang UniversitySeoulRepublic of Korea
  2. 2.Department of Agricultural EngineeringUniversity of Engineering and TechnologyPeshawarPakistan
  3. 3.Department of Civil EngineeringSeokyeong UniversitySeoulRepublic of Korea
  4. 4.Department of Civil and Environmental EngineeringHanyang UniversityAnsanRepublic of Korea

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