Water Resources Management

, Volume 23, Issue 12, pp 2455–2473 | Cite as

Estimation of the Runoff Curve Number via Direct Rainfall Simulator Measurements in the State of Iowa, USA

Article

Abstract

This study was the first to provide detailed methodological steps to estimate in-situ runoff curve number (CN) for selected agricultural fields in the State of Iowa via rainfall simulators. Representative fields in six counties were chosen to identify the effects of the following variables on runoff CN: rainfall intensity, soil type, soil moisture condition, tillage practice, and residue cover. The study also re-evaluated the range of the existing CN values for the different hydrologic soil groups in Iowa, and revised the equations describing the CN method to consider variables such as residue cover and soil moisture in a more detailed manner than the existing USDA method. The findings of this investigation showed that rainfall simulators are useful instruments for estimating in-situ runoff CN because rainfall intensity was adjustable during an experimental run. Further, the simulators eliminate the need of natural storm events. The range of the estimated CN values in summer agreed well (deviation less than 6%) with the reported CN values. However, the range of the estimated CN values in fall was generally less the reported CN values (deviation of about 40%) due to the high residue levels found in the fields after harvest. The effects of tillage practice and crop type were insignificant compared to residue cover and soil moisture. The study has also shown that the initial abstraction Ia is not linearly proportional to the potential maximum retention S, which agrees with the available literature.

Keywords

Runoff curve number Rainfall intensity Soils Residue cover Tillage practice Soil moisture Non-linear regression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arnold JG, Williams JR, Nicks AD, Summons NB (1990) SWRRB—A basin scale simulation model for soil and water resources management. Texas A&M University Press, College StationGoogle Scholar
  2. Auerswald K, Haider J (1996) Runoff curve numbers for small grain under German cropping conditions. J Environ Manag 47:223–228. doi:10.1006/jema.1996.0048 CrossRefGoogle Scholar
  3. Bales J, Betson RP (1981) The curve number as a hydrologic index. In: Singh VP (ed) Rainfall runoff relationship. Water Resources, Littleton, pp 371–386Google Scholar
  4. Beven K (1983) Surface water hydrology-runoff generation and basin structure. Rev Geophys 21(3):721–730. doi:10.1029/RG021i003p00721 CrossRefGoogle Scholar
  5. Bhuyan SJ, Mankin KR, Koelliker JK (2003) Watershed-scale AMC selection for hydrologic modeling. Trans ASAE 46:237–244Google Scholar
  6. Bondelid TR, McCuen RH, Jackson TJ (1982) Sensitivity of SCS models to curve number variation. Water Resour Bull 18:111–116Google Scholar
  7. Brezonik PL, Bierman VJ, Alexander R, Anderson J, Barko J, Dortch M, Hatch L, Hitchcock GL, Keeney D, Mulla D, Smith V, Walker C, Whitledge T, Wiseman WJ (1999) Effects of reducing nutrient loads to surface waters within the Mississippi River Basin and the Gulf of Mexico: topic 4 report for the integrated assessment on hypoxia in the Gulf of Mexico. In: NOAA Coastal Ocean Program. Decision Analysis Series, no 18. National Oceanic and Atmospheric Admnistration Coastal Ocean Office, Silver Spring, pp 113Google Scholar
  8. Draper N, Smith H (1998) Applied regression analysis, 3rd edn. Wiley, New YorkGoogle Scholar
  9. Frasson RPM (2007) Observational studies of rainfall interception by corn. Master thesis, Department of Civil and Environmental Engineering, The University of IowaGoogle Scholar
  10. Graf WL (1988) Fluvial processes in dryland rivers. Springer, New YorkGoogle Scholar
  11. Hawkins RH (1975) The importance of accurate curve numbers in the estimation of storm runoff. Water Resour Bull 11:887–891Google Scholar
  12. Hawkins RH (1978) Runoff curve numbers with varying site moisture. J Irrig Drain Eng 104(4):389–398Google Scholar
  13. Hawkins RH (1981) Interpretation of source-area variability in rainfall–runoff relationships. In: Singh VP (ed) Rainfall runoff relationship. Water Resources, Littleton, pp 303–324Google Scholar
  14. Hawkins RH (1993) Asymptotic determination of runoff curve numbers from data. J Irrig Drain Eng 119(2):334–345. doi:10.1061/(ASCE)0733-9437(1993)119:2(334) CrossRefGoogle Scholar
  15. Hjelmfelt AT (1991) Investigation of curve number procedure. J Hydraul Eng 117(6):725–737. doi:10.1061/(ASCE)0733-9429(1991)117:6(725) CrossRefGoogle Scholar
  16. Hjelmfelt AT, Kramer LA, Burwell RE (1982) Curve numbers as random variables. In: Proceedings of the international symposium on rainfall–runoff modeling. Water Resources, Littleton, pp 365–373Google Scholar
  17. Jain MK, Mishra SK, Babu PS, Venugopal K, Singh VP (2006) Enhanced runoff curve number model incorporating storm duration and a nonlinear Ia-S relation. J Hydrol Eng 11(6):631–635. doi:10.1061/(ASCE)1084-0699(2006)11:6(631) CrossRefGoogle Scholar
  18. Knisel WG (ed) (1980) CREAMS: a field-scale model for chemical, runoff and erosion from agricultural management systems. Conservation Research Report, No 26. USDA, Washington, DCGoogle Scholar
  19. Linsley RK, Kohler MA, Paulhus JL (1986) Hydrology for engineers, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  20. Littleboy M, Silburn DM, Freebairn DM, Woodruff D, Hammer UL, Leslie JK (1992) Impact of soil erosion on production in cropping land systems: 1. Development and validation of a simulation model. Aust J Soil Res 30:757–774. doi:10.1071/SR9920757 CrossRefGoogle Scholar
  21. Marshall JS, Palmer WK (1948) The distribution of raindrops with size. J Meteorol 5:165–166Google Scholar
  22. McCuen RH (2002) Approach to confidence interval estimation for curve numbers. J Hydrol Eng 7(1):43–48. doi:10.1061/(ASCE)1084-0699(2002)7:1(43) CrossRefGoogle Scholar
  23. McCuen RH (2003) Hydrologic analysis and design, 3rd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  24. Mishra SK, Jain MK, Singh VP (2004) Evaluation of SCS-CN-based model incorporating antecedent moisture. Water Resour Manag 18(6):567–589. doi:10.1007/s11269-004-8765-1 CrossRefGoogle Scholar
  25. Mishra SK, Jain MK, Bhunya PK, Singh VP (2005) Field applicability of the SCS-CN-based Mishra-Singh general model and its variant. Water Resour Manag 19(1):37–62. doi:10.1007/s11269-005-1076-3 CrossRefGoogle Scholar
  26. 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. doi:10.1007/s11269-005-9000-4 CrossRefGoogle Scholar
  27. Mishra SK, Singh VP (1999) Another look at SCS-CN method. J Hydrol Eng 4(3):257–264. doi: 10.1061/(ASCE)1084-0699(1999)4:3(257) CrossRefGoogle Scholar
  28. Mishra SK, Singh VP (2003) Soil conservation service curve number (SCS-CN) methodology. Kluwer Academic, DordrechtGoogle Scholar
  29. Nearing MA, Liu BY, Risse LM, Zhang XC (1996) Curve numbers and Green-Ampt effective hydraulic conductivities. Water Resour Bull 32(1):125–136Google Scholar
  30. Norton LD (2006) A linear variable intensity rainfall simulator for erosion studies. In: Wanielista M, Smoot J (eds) Proc. of 2nd bieannial stormwater management research symp., May 4–5, 2006. Univ. of Central Florida, Orlando, pp 93–103Google Scholar
  31. Papanicolaou AN, Abaci O (2008) Upland erosion modeling in a semi-humid environment via the water erosion prediction project model. J of Irrigation and Drainage Engineering, Ms. No. IRENG-07-5256R1, p 127Google Scholar
  32. Papanicolaou AN, Elhakeem M, Krallis G, Prakash S, Edinger J (2008) Sediment transport modeling review—current and future developments. J Hydraul Eng 134(1):1–14. doi:10.1061/(ASCE)0733-9429(2008)134:1(1) CrossRefGoogle Scholar
  33. Ponce VM (1989) Engineering hydrology, principles and practices. Prentice Hall, Englewood CliffsGoogle Scholar
  34. Ponce VM, Hawkins RH (1996) Runoff curve number: Has it reached maturity? J Hydrol Eng 1(1):11–19. doi:10.1061/(ASCE)1084-0699(1996)1:1(11) CrossRefGoogle Scholar
  35. Rallison RE (1980) Origin and evolution of the SCS runoff equation. In: Proceedings of symposium on watershed management. ASCE, New York, pp 912–924Google Scholar
  36. Rallison RE, Miller N (1981) Past, present, and future SCS runoff procedure. In: Singh VP (ed) Rainfall runoff relationship. Water Resources, Littleton, pp 353–364Google Scholar
  37. Ravisangar V, Dennett KE, Sturm TW, Amirtharajah A (2001) Effect of sediment pH on resuspension of kaolinite sediments. J Environ Eng 127(6):531–538. doi:10.1061/(ASCE)0733-9372(2001)127:6(531) CrossRefGoogle Scholar
  38. Rawls WJ, Onstad CA (1978) Residue and tillage effects on SCS runoff curve number. In: American society of agricultural engineers for winter meeting. Chicago, 18–20 December 1978, p 18Google Scholar
  39. Rawls WJ, Onstad CA, Richardson HH (1980) Residue and tillage effects on SCS runoff curve numbers. Trans Am Soc Agric Eng 23:357–361Google Scholar
  40. Risse LM, Nearing MA, Savabi MR (1994) Determining the green-ampt effective hydraulic conductivity from rainfall–runoff data for the WEPP model. Trans Am Soc Agric Eng 37(2):411–418Google Scholar
  41. Schneider LE, McCuen RH (2005) Statistical guidelines for curve number generation. J Irrig Drain Eng 131(3):282–290. doi:10.1061/(ASCE)0733-9437(2005)131:3(282) CrossRefGoogle Scholar
  42. Shahin M, van Orschot HL, Delange SJ (1993) Statistical analysis in water resources engineering. A. A. Balkema, RotterdamGoogle Scholar
  43. Sharpley AN, Williams JR (1990) EPIC—Erosion/productivity impact calculator: 1. model documentation. USDA-technical bulletin, No 1768. US Government Printing Office, Washington, DCGoogle Scholar
  44. Silveira L, Charbonnier F, Genta JL (2000) The antecedent soil moisture condition of the curve number procedure. Hydrol Sci 45(1):3–12CrossRefGoogle Scholar
  45. SUDAS (2004) Statewide Urban design and specifications, Iowa statewide urban design standards manual. Chapter 3: stormwater management and drainage, Central Iowa Metropolitan Areas and MunicipalitiesGoogle Scholar
  46. USDA (1986) Urban hydrology for small watersheds. Technical release, no 55 (TR-55). Soil Conservation Service, Washington, DCGoogle Scholar
  47. USDA (1993) Soil survey manual. Handbook 18. Soil Conservation Service, Washington, DCGoogle Scholar
  48. Wehmeyer LL (2006) Evaluation of design flood frequency methods for Iowa stream. M.Sc. Thesis, Department of Civil and Environmental Engineering, The University of Iowa, Iowa, USAGoogle Scholar
  49. Williams JR, Nicks AD, Arnold JG (1985) Simulator for water resources in rural basins. J Hydraul Eng 111:970–986CrossRefGoogle Scholar
  50. Young RA, Onstad CA, Bosch DD, Anderson WP (1987) AGNPS, agricultural non-point source pollution model—a watershed analysis tool. USDA Conserv Res Rep 35:1–80Google Scholar
  51. Young RA, Onstad CA, Bosch DD, Anderson WP (1989) AGNPS: a non-point source pollution model for evaluating agricultural watersheds. J Soil Water Conserv 44(2):168–173Google Scholar
  52. Young RA, Onstad CA, Bosch DD, Anderson WP (1994) Agricultural non-point source pollution model. Version 4.03, AGNPS User’s Guide. North Central Soil Conservation Research Laboratory, Morris, MinnesotaGoogle Scholar
  53. Yu B (1998) Theoretical justification of SCS method for runoff estimation. J Irrig Drain Eng 124(6):306–310. doi:10.1061/(ASCE)0733-9437(1998)124:6(306) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.100 Hydraulics Laboratory, IIHR—Hydroscience & Engineering, Department of Civil and Environmental EngineeringThe University of IowaIowa CityUSA

Personalised recommendations