The Soil Conservation Services Curve Number (SCS-CN) method presented in this report has been applied to two large catchments in sub-humid regions of India and its performance evaluated. The variation of the curve number is examined and discussed and a critical evaluation of the employment of the single linear reservoir routing technique and linear regression technique presented.
Long-term hydrologic simulation provides a useful and important input to water resources planning and watershed management practices. The SCS-CN method is a widely used event based rainfall-runoff method. In this report, the SCS-CN method is used for simulating daily rainfall-runoff data of two catchments, viz Hemavathi and Ramganga catchments falling in sub-humid regions of India.
The model formulation is based on the conversion of precipitation to rainfall excess using SCS-CN method and it’s routing by single linear reservoir and linear regression techniques. The baseflow that is assumed to be a fraction of the infiltration amount is routed using the lag and route method. The variation of SCS-CN parameter potential maximum retention S is governed by evapotranspiration and antecedent moisture conditions.
This model when applied to Hemavathi has shown efficiency of 75.31 per cent and 82.03 per cent in calibration and validation, respectively, and when applied to Ramganga, these are 58.34 per cent and 67.2 per cent respectively in calibration and validation. The stability of the computed parameters is examined by reversing the data sets of calibration and validation and it is found that a greater length of data will be required to stabilize the model parameters.
The application of single linear reservoir technique to Hemavathi data is found to conserve the mass successfully in Hemavathi application whereas the application of linear regression fails to conserve the mass of Ramganga runoff.
The computed values of the initial abstraction, rainfall excess, infiltration and baseflow are also presented in a tabular form along with their annual and seasonal statistics. Initial abstraction values are found to be much higher in non-monsoon season than in monsoon season.
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