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Hydrological Modelling of canal command using Remote Sensing and GIS
Results and Discussion
Model Calibration The calibration of the model was done by matching the post-monsoon water table fluctuations at fifteen locations in the command area. The model parameters varied during calibration are saturated hydraulic conductivity, Ks (1.07 cm/h to 2.33 cm/h), drainage constant (2e-6) and exponent (n) in the hydraulic conductivity function (7.1 to 11.5). Fig. 6 shows relationship between observed and predicted ground water elevations. Regression analysis between observed and predicted post monsoon groundwater level yielded a high (0.84) coefficient of determination. To statistically evaluate the calibration performance of the model two other criteria, suggested by ASCE committee (1993), Nash-Sutcliff coefficient and Student's t-test are also carried out. Nash-Sutcliff coefficient is found as 0.775, which is reasonably good and Student's t-test reveals that the model results are acceptable at 1% level of significance. Simulation Results The calibrated model is used to perform simulations with different scenarios to study the effect of using remote sensing data over conventional data with distributed hydrological model. The results are presented below: Effect of spectral soil distribution The first and fourth scenarios are used to study the effect of spectral soil distribution on the water balance simulations with the distributed hydrological model. Figs. 7 and 8 present temporal water content variation at a selected node in head reach for conventional and RS soil, respectively. It is evident that soil moisture content variations are different due to dissimilar spatial variability of soil obtained from different sources. This is obvious because the same node may represent different soil in RS soil map and NBSS soil map. Fig. 9 shows the moisture content variation at tail reach node for the first simulation scenario. Comparison between Figs. 7 and 9 reveal that head reach nodes are having high moisture content throughout the season as compared to the tail reach nodes. This indicates that head reach receives more irrigation water. Similar soil moisture pattern between head reach and tail reach nodes was also observed for the fourth simulation. Furthermore, there are dry patches at head reach as well as tail reach of the command for both simulation scenarios, in spite of heavy rainfall during the season. This implies that additional irrigation is essential for attaining the potential crop yield. The additional water requirements are calculated at head reach and tail reach of the command for both first and fourth scenarios. Due to variation in the soil moisture regime, the additional water requirements differ at head reach and tail reach in both scenarios. The additional irrigation water requirements to bring the soil moisture content of the paddy field to 75 percent of saturation value at head reach and tail reach are found as 55.7 cm and 63.0 cm for the season respectively for first scenario. The same for fourth scenario are found as 41.5 cm and 50.8 cm respectively. It is evident that total additional water requirement is comparatively less in fourth scenario (considering RS soil map) than that in first scenario (considering NBSS soil map) at head reach as well as at tail reach. It is also observed that total irrigation water requirements are more at tail reach than that at head reach with both soil maps. This implies that less water is available to the tail reach, a usual phenomenon in most of the Indian irrigation commands. 
Fig. 7 Soil moisture variation in a node using RS soil (loamy sand) 
Fig. 8 Soil moisture variation in a node using NBSS soil (sandy loam) 
Fig. 9 Soil moisture content variation for tail reach node using NBSS-soil map Effect of spectral land use distribution The third and fourth scenarios are used to study the effect of spectral land use distribution on the water balance simulations with distributed hydrological model. The soil moisture variations are slightly different at the same node in RS land use pattern (categorised in six classes, early paddy, medium paddy, late paddy, fallow, homestead and water bodies) and conventional land use pattern (considering early paddy in whole area except for paved area), at head reach as well as tail reach. This is because a particular node represents different crop transplanting dates in RS land use and conventional land use, i.e., the node representing late paddy in RS land use pattern, represents early paddy in conventional land use pattern. |