In view of the ongoing environmental and ecological changes in the Western Ghats, it is important to understand the environmental parameters pertaining to the sustenance of the region. Rainfall is one such parameter governing the hydrological processes crucial to agriculture planning, afforestation and eco-system management. Therefore, it is essential to understand rainfall distribution and its variation in relevance to such activities. The present study is an attempt to gain in-depth understanding in this direction. The study area comprises of one coastal district and its adjoining areas in Karnataka State. Mean annual rainfall data of 93 rain gauge stations distributed over the study area for a period of 10-50 years are used for the study. In order to assess the variation of rainfall across the ghats, several bands were constructed parallel to the latitudes to facilitate the analysis. The statistical analyses conducted included cluster analysis and analysis of variance. The study revealed that there exist three distinct zones of rainfall regimes in the study area, namely, Coastal zone, Transition zone and Malanad zone. It is observed that, the maximum rainfall occurs on the windward side ahead of the geographical peak. Further, mean monthly rainfall distribution over the zones has been depicted to enable agricultural planning in the study area.
The inverse problem of determining soil hydraulic parameters (saturated hydraulic conductivity and water retention parameters) of border-strip irrigation from irrigation event data is analyzed. The inverse problem is solved using sequential unconstrained minimization technique. The forward problem involves the solution of coupled Saint-Venant's equation governing overland flow and Richard's equation governing subsurface flow. Saint-Venant's equations are solved using the MacCormack scheme-based finite-difference method while Richard's equation is solved using a mass conservative fully implicit finite-difference method. Field experiments are conducted on two border strips to obtain surface and subsurface irrigation data such as irrigation advance, recession, flow depth, and soil moisture content. The soil hydraulic parameters, i.e., saturated hydraulic conductivity and soil retention parameters, are estimated by minimizing the deviations between the model-predicted and field-observed irrigation data. The results indicate that defining the objective function in terms of flow depths results in the optimization algorithm converging to the true values as compared to the use of irrigation advance data. Further, it is observed that underestimating the initial guess results in the least number of iterations for the optimization algorithm to converge to the true values. It is also observed that simultaneous estimation of all three soil hydraulic parameters is not possible even with the inclusion of subsurface moisture content data in the objective function.
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