VOL. 14, NO. 19, OCTOBER 2019 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 3328 ESTIMATION OF RUNOFF FOR BINA RIVER BASIN USING CURVE NUMBER MODEL AND GIS TECHNIQUES Ankit Balvanshi and H. L. Tiwari Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India E-Mail: ankit19balvanshi@rediffmail.com ABSTRACT Rainfall-Runoff computation of any basin plays a vital role in development of the water resources project of any country. Looking on the industrial importance of Bina river basin situated in Central India, this basin was selected for rainfall-runoff modeling by implementing the SCS CN conceptual model with the variation in initial abstraction ratio value, along with the GIS tool. Runoff assessment is carried out using daily rainfall data, gauge-discharge data, meteorological data of Bina river basin, India. A new trial was made to estimate the runoff more precisely by varying the Initial Abstraction Ratio for the Bina river basin. The Bina catchment area of 1120 km 2 has all over hydrologic soil type C & D, which indicates high runoff potential on ground. Results specify a initial abstraction ratio (λ) value of 0.20 gives a better fit to the data and proved to be more precise for use in runoff calculations in comparison to λ = 0.1, 0.15 and 0.25. The model was evaluated on the Nash-Sutcliffe Efficiency criteria and the coefficient of determination (R 2 ) for the years 1997, 1998, 2003 and 2007. The model showed Nash-Sutcliffe efficiency in the range of 0.70 to 0.90 and R 2 values in the range of 0.71 to 0.94. The Composite Curve Number came out to be 77 for the basin. It was concluded that initial abstraction ratio λ = 0.1, 1.15 results in slight over prediction for this catchment while λ = 0.25 slightly under predicts the runoff. This research study indicates that the SCS CN model when employed with GIS tool becomes more useful for the hydrological study of any basin having hydrologic soil group C & D, with customary value of λ = 0.2. Keywords: rainfall, runoff, SCS CN, GIS, initial abstraction ratio. 1. INTRODUCTION Water is an important natural resource which needs preservation, control and management. A rainfall runoff model is helpful in computation of discharge from a basin, Das [1]. Runoff is a sign of the availability of water in any area, thus the water resources can be properly managed by implementing and improving the engineering practices, Zade [2]. Rainfall data, catchment area, soil type, land use, vegetative cover and moisture content of soil are some primary input data required for any kind of model development, Tiwari et al. [3]. The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff volume for a given rainfall event, Mishra et al. [4], Kumar et al. [5]. This method was originally developed by the US Department of Agriculture, Soil Conservation Service for application to hydrologic design activities connected with small agricultural watersheds, Soil Conservation Service [6]. Ever since the development of the curve number method, it has turned out to be an extensively used method for estimating and predicting runoff, Berod et al.[7], Kyoung [8], Mishra et al. [4], Nagarajan et al. [9]. Sarangi [10] employed the ED GIUH and NRCS CN approach for the evaluation of curve numbers for ungauged catchments. It was concluded that the ED GIUH technique was able to estimate runoff more precisely (E = 0.96, R 2 = 0.97) if the storm duration was less than 6 hours while the curve number method is more suitable for rainfall events more than 6 hours (E = 0.84, R 2 =0.89). The researchers nowadays believe that the conventional method of data collection is not as accurate as the RS data, so use of RS data and GIS tool has now become a global trend, Balvanshi and Tiwari [11]. The RS data is very useful in the rainfall runoff modelling and modelling can be done in remote areas also, Ramakrishnan et al. [12], Pradhan et al. [13], Somashekar et al. [14], Dhawale [15], Viji et al. [16]. The more accurate the curve number, the more precise and accurate is the result of the SCS CN model, Balvanshi and Tiwari [12], Joycee and Santhi [17], Moon et al. [18]. Jiang [19] conducted a research study using the CN model with the variation in initial abstraction ratio values (0.2, 0.05). The value of 0.05 was best fitted for watershed. The Runoff estimation using λ = 0.05 was found better than λ= 0.2 in 252 of 307 cases. The initial abstraction ratio (λ) is one of the crucial parameter after the curve number in SCS CN model. In the present research study, Bina river basin is chosen having catchment area about 1120 km 2 . The catchment area of Bina basin mainly has black cotton soils with high shrinking and swelling property and classified under hydrologic soil group C and D. This research study also portrays the effect in efficiency of SCS CN model with variation of values of initial abstraction ratio (λ) 0.10, 0.15, 0.20, 0.25 for the Bina river basin. 2. METHODOLOGY A The basic data required to develop and apply the SCS CN method is the rainfall, soil retention or soil storage, land use pattern, soil group/type. The curve number is actually the main driver of the SCS CN method. The value of curve number depends on the land use/ land cover of any catchment, NEH Section 4 Soil Conservation Service [6]. The final SCS CN model equations for runoff estimation used in calculation are expressed as follows: Q= (P−I a ) 2 (P−I a )+S if P ≥ Ia … (1)