Kshipra Sub Basin - Asian Development Bank

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Support to the National Water Mission NAPCC Appendix 3 Kshipra Sub Basin Figure 50: Kshipra Basin land use Table 40: Land use Categories – Kshipra Basin Land Use Area (ha) % of Watershed Area Wheat 126723 23 Rice 18372 3 Sugarcane 9149 2 Tomato 19495 3 Soyabean 388371 69 189. Soils: Information on the soil profile is required for simulating the hydrological character of the basin. In the absence of high resolution soil data, the FAO global soil map was used for the modelling of the Kshipra basin. The soil is predominantly clay. However, clay loam is also present in the south. The soils map is shown in Figure 51. 85
Support to the National Water Mission NAPCC Appendix 3 Kshipra Sub Basin Figure 51: Kshipra Basin soils 4. SWAT Model Calibration and Performance 190. Model Evaluation Statistics (Dimensionless): Statistical parameters, namely regression coefficients (R 2 ) and Nash Sutcliffe efficiency coefficient (NSE) were used to assess the model efficiency on monthly SWAT hydrologic streamflow predictions. The Nash-Sutcliffe efficiency (NSE) is a normalized statistic that determines the relative magnitude of the residual variance (―noise‖) compared to the measured data variance (―information‖) (Nash and Sutcliffe, 1970 48 ). NSE indicates how well the plot of observed versus simulated data fits the 1:1 line. NSE is computed as: obs th sim th where Yi is the i observation for the constituent being evaluated, Yi is the i simulated value for the constituent being evaluated, Y mean is the mean of observed data for the constituent being evaluated, and n is the total number of observations. NSE ranges between −∞ and 1.0 (1 inclusive), with NSE = 1 being the optimal value. Values between 0.0 and 1.0 are generally viewed as acceptable 48 Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models: Part 1. A discussion of principles. J. Hydrology 10(3): 282-290 86
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Kshipra Sub Basin - Asian Development Bank

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