Comparison of Different Methods in Monthly Surface Runoff Estimation Based on the Monthly Curve Number

Document Type : Research

Authors

1 Ph.D. Student, Faculty of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Iran

2 Associate Professor of Faculty of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Iran

3 Assistant Professor of Faculty of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Iran

4 Assistant Professor of Faculty of Natural Resources and Earth Sciences, Shahrekord University, Iran

Abstract

It is a matter of utmost importance to estimate accurately watersheds’ surface runoff in order to manage a region's water resources. Due to the inadequacy and lack of surface runoff monitoring in some watersheds, as well as high error and relatively high data demand of the daily runoff estimation methods, it is necessary to rely on the monthly runoff estimation methods. Different scenarios of combining monthly surface runoff estimation methods (the monthly SCS method, SCS-based runoff coefficient, the integrated curve number (CN), and the runoff coefficient method) with three methods of calculating monthly curve number (based on the leaf area index, on the retention potential, and on the average CN-based reference tables) as input parameters for the surface runoff estimation methods. These estimations were applied and compared across the Araz-Kouseh Watershed, east of the Province of Golestan. Results indicated that the monthly SCS method in combination with all methods of monthly CN has appropriately simulated the surface runoff with the Nash-Sutcliffe (NS) coefficient and bias values of higher than 0.6 and lower than 0.3, respectively. While the SCS-based runoff coefficient method performed poorly (with negative NS values and bias values above 4), particularly in combination with the average CN estimation based on the reference table. The integrated method of curve number and runoff coefficient in combination with all methods of monthly CN estimation indicated relatively acceptable results (with the NS values of about 0.6) during the calibration period; however, for the validation period, the results in combination with some monthly CN calculation methods were less reliable. Therefore, the monthly SCS method was selected as a suitable and robust method for the monthly surface runoff simulation of the Araz-Kouseh Watershed; thus it may be recommended for estimation of the water balance in similar watersheds in the region.

Keywords


Abdollahi K, Bashir I, Verbeiren B, Harouna MR, Van Griensven A, Huysmans M, Batelaan O. 2017. A distributed monthly water balance model: Formulation and application on black volta basin. Environmental Earth Sciences. 76(5):198.
Abramowitz M, Stegun IA. 1965. Handbook of mathematical functions: With formulas, graphs, and mathematical tables. Courier Corporation.
Banasik K, Krajewski A, Sikorska A, Hejduk L. 2014. Curve number estimation for a small urban catchment from recorded rainfall-runoff events. Archives of Environmental Protection. 40(3):75-86.
Barkhordari J, Vartanian T, Khosravi H. 2015. Determination of Yazd-Ardakan watershed water balance by using Thornthwaite–Mather method and application of distributed rainfall-runoff model. Iranian Journal of Range and Desert Research. 22(3):466–480. (In Persian).
Chatterjee C, Jha R, Lohani A, Kumar R, Singh R. 2002. Estimation of scs curve numbers for a basin using rainfall-runoff data. ISH Journal of Hydraulic Engineering. 8(1):40-49.
Dingman SL. 1994. Physical hydrology prentice hall. Inc, New Jersey. 7458.
Eagleson PS. 1978. Climate, soil, and vegetation: 1. Introduction to water balance dynamics. Water Resources Research. 14(5):705-712.
Easton ZM, Fuka DR, Walter MT, Cowan DM, Schneiderman EM, Steenhuis TS. 2008. Re-conceptualizing the soil and water assessment tool (swat) model to predict runoff from variable source areas. Journal of hydrology. 348(3-4):279-291.
Freeze RA. 1972. Role of subsurface flow in generating surface runoff: 1. Base flow contributions to channel flow. Water Resources Research. 8(3):609-623.
Gundalia M, Dholakia M. 2014. Impact of monthly curve number on daily runoff estimation for ozat catchment in india. Open Journal of Modern Hydrology. 4(04):144.
Guswa AJ, Hamel P, Meyer K. 2018. Curve number approach to estimate monthly and annual direct runoff. Journal of Hydrologic Engineering. 23(2):04017060.
Hatami Yazd A, Ghahrman B. 2008. Survey and extrapolation of monthly and annual rainfall – runoff equations to ungauged watersheds. Scientific Journal of Agriculture. 30(4):1–15. (In Persian).
Hawkins R, Woodward D, Jiang R. 2001. Investigation of the runoff curve number abstraction ratio. USDA-NRCS hydraulic engineering workshop, Tucson, Arizona.
Hawkins RH. 1975. The importance of accurate curve numbers in the estimation of storm runoff 1. JAWRA Journal of the American Water Resources Association. 11(5):887-890.
Hawkins RH. 1984. A comparison of predicted and observed runoff curve numbers. American Society of Civil Engineers.
Hawkins RH. 1993. Asymptotic determination of runoff curve numbers from data. Journal of Irrigation and Drainage Engineering. 119(2):334-345.
Hoesein A, Pilgrim D, Titmarsh G, Cordery I. 1989. Assessment of the US Conservation Service method for estimating design floods. New Directions for Surface Water ModelingfPmceeàings of the Baltimore Symposium.
Jarosław C, Batelaan O. 2011. Application of the wetspa distributed hydrological model for catchment with significant contribution of organic soil. Upper biebrza case study. Annals of Warsaw University of Life Sciences-SGGW Land Reclamation. 43(1):25-35.
Kowalik T, Walega A. 2015. Estimation of cn parameter for small agricultural watersheds using asymptotic functions. Water. 7(3):939-955.
Laio F, Porporato A, Ridolfi L, Rodriguez-Iturbe I. 2001. Plants in water-controlled ecosystems: Active role in hydrologic processes and response to water stress: Ii. Probabilistic soil moisture dynamics. Advances in Water Resources. 24(7):707-723.
Lim KJ, Engel BA, Tang Z, Choi J, Kim KS, Muthukrishnan S, Tripathy D. 2005. Automated web gis based hydrograph analysis tool, what 1. JAWRA Journal of the American Water Resources Association. 41(6):1407-1416.
Liu Y, De Smedt F. 2004. Wetspa extension, a gis-based hydrologic model for flood prediction and watershed management. Vrije Universiteit Brussel, Belgium. 1:e108.
Malekian A, Mohseni Saravi M, Mahdavi M. 2005. Applicability of the USDA-NRCS Curve Number Method for Runoff Estimation. Iranian Journal of Natural Resources. 57(4):621–633. (In Persian).
Mishra S, Singh V. 1999b. Behaviour of SCS-CN method in CI∗ a-λ spectrum. Proceedings “Hydrologic Modeling”, International Conference on Water, Environment, Ecology, Socioeconomics, and Health Engineering. 112-117.
Mishra SK, Singh VP. 1999a. Another look at scs-cn method. Journal of Hydrologic Engineering. 4(3):257-264.
Mishra SK, Singh VP. 2002. Scs-cn-based hydrologic simulation package. Water Resources Publications: Littleton, CO.
Mishra SK, Singh VP. 2004. Validity and extension of the scs‐cn method for computing infiltration and rainfall‐excess rates. Hydrological Processes. 18(17):3323-3345.
Mishra SK, Singh VP. 2013. Soil conservation service curve number (scs-cn) methodology. Springer Science & Business Media.
Myneni R, Knyazikhin Y, Park T. 2015. MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006. NASA EOSDIS Land  Processes DAAC. http://doi.org/10.5067/MODIS/MCD15A2H.006
Mostafazadeh R, Mirzaei SH, Nadiri P.2018. Curve Number Determination using Rainfall and Runoff Data and its Variations with Rainfall Components in a Forested Watershed. Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources). 21(4):15–28. (In Persian).
Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models part i—a discussion of principles. Journal of hydrology. 10(3):282-290.
Parisay Z, Sheikh V, Bahremand A, Komaki CB, Abdollahi K. 2019. An approach for estimating monthly curve number based on remotely-sensed modis leaf area index products. Water Resources Management.1-18.
Pistocchi A, Bouraoui F, Bittelli M. 2008. A simplified parameterization of the monthly topsoil water budget. Water Resources Research. 44(12).
Ponce VM, Hawkins RH. 1996. Runoff curve number: Has it reached maturity? Journal of hydrologic engineering. 1(1):11-19.
Richardson CW. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water resources research. 17(1):182-190.
Rodriguez-Iturbe I, Porporato A, Ridolfi L, Isham V, Coxi D. 1999. Probabilistic modelling of water balance at a point: The role of climate, soil and vegetation. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences. 455(1990):3789-3805.
Rostamiyan R, Mosavi SF, Heydarpor M, Afyoni M, Abaspor K. 2009. Application of SWAT2000 model for estimation of runoff and sediment in the Behesht-abad sub-watershed of the North Karon river basin. Journal of Sciences and Technology of Agriculture and Natural Resources, 12(46b):517–531. (In Persian).
Salmani H, Bahremand A, Saber Chenari K, Rostami Khalaj, M. 2015. Evaluation of the Efficiency of AWBM, Sacramento and Tank Rainfall Runoff Model in Runoff Simulation in Arazkoose - Goorganrood Basin, Golestan Porovince. Iranian Journal of EcoHydrology. 1(3):207–221. (In Persian).
Sane I, Saghafan B. 2018. A Review of Conceptual Monthly Water Balance Models. Journal of Water and Sustainable Development. 5(1):101–114. (In Persian).
Satheeshkumar S, Venkateswaran S, Kannan R. 2017. Rainfall–runoff estimation using scs–cn and gis approach in the pappiredipatti watershed of the vaniyar sub basin, south india. Modeling Earth Systems and Environment. 3(1):24.
Sattari M, Joudi A. 2018. Modelling monthly runoff by using data mining methods based on attribute selection algorithms. Journal of Water and Soil Resources Conservation. 7(4):39–54. (In Persian).
Soulis K, Valiantzas J, Dercas N, Londra P. 2009. Analysis of the runoff generation mechanism for the investigation of the scs-cn method applicability to a partial area experimental watershed. Hydrology & Earth System Sciences Discussions. 6(1).
Soulis K, Valiantzas J. 2012. Scs-cn parameter determination using rainfall-runoff data in heterogeneous watersheds–the two-cn system approach. Hydrology and Earth System Sciences. 16(3):1001-1015.
Steenhuis TS, Winchell M, Rossing J, Zollweg JA, Walter MF. 1995. Scs runoff equation revisited for variable-source runoff areas. Journal of Irrigation and Drainage Engineering. 121(3):234-238.
Swamee PK, Ojha CSP. 1990. Pump test analysis of confined aquifer. Journal of Irrigation and Drainage Engineering. 116(1):99-106.
Vaes G. 1999. The influence of rainfall and model simplification on the design of combined sewer systems. PhD thesis, University of Leuven, Belgium.
Woodward DE, Hawkins RH, Hjelmfelt A, Van Mullem J, Quan QD. 2002. Curve number method: Origins, applications and limitations. US Geological Survey Advisory Committee on Water Information–Second Federal Interagency Hydrologic Modeling Conference July.
Zare Garizi A, Talebi A. 2017. Water balance simulation for the Ghare-Sou Watershed, Golestan, using the SWAT model. Journal Management System. 9(30):37–50. (In Persian).
Zelelew DG. 2017. Spatial mapping and testing the applicability of the curve number method for ungauged catchments in northern ethiopia. International Soil and Water Conservation Research. 5(4):293-301.