LONG TERM HYDROLOGIC SIMULATION USING SCS-CN METHOD

Abstract

Estimation of monthly runoff is required for planning and management of the water resources projects. The rainfall in monsoon season mainly contributes to annual runoff. The runoff during non-monsoon season however also depends on the base flow, and it decreases because of the excessive pumping of ground water wells. For estimation of runoff, a number of models varying from the simplest empirical relations to the most complex physically based models are available in literature. The Soil Conservation Service Curve Number (SCS-CN) method is one of the simplest and most popular methods available and widely used world over for predicting direct surface runoff from given storm rainfall amount. Of late, the method has also been employed in long term hydrologic simulation. In this study, an SCS-CN-based model is proposed and applied to the 9-year daily data of a watershed (area = 2785 sq. km) of Sutlej River. The Sutlej River Catchment from Rampur to Kasol located in Himachal Pradesh is selected for the study. It is a major river of the Indus System, which originates from Mansarovar Lake in Tibet. The total catchment area of Sutlej River from Rampur to Kasol is about 2785 sq. km. The watershed is hilly and has mostly forest and barren land. The available nine years of data was split into two parts; the first five years data were used for calibration, and the rest four years data for validation. Besides a yearly volumetric analysis, a sensitivity analysis of the four parameters of the proposed model was also carried out. The model performance degraded with the increase in length of data. In yearly simulations as well in calibration and validation, model showed a satisfactory performance. The model simulated the yearly runoff values with significantly low relative errors, further indicating a satisfactory performance. Notably, the yearly runoff volume was not taken as a constraint in parameter optimization and it further supports the model validity and dependability. The least sensitive and most significant parameter So of the SCS-CN model indicated its amenability to field applications employing the NEH-4 CN values or the CN values derived using remote sensing data. Over and above all, the model is simple, has four parameters, and dependable.