Comparative Analysis of Spatial Interpolation Techniques for Rainfall Over Hassan District, Karnataka, India

Measured rainfall data from rain gauges, though available as point data, has been a valuable input parameter for analysis in climate studies, soil moisture studies, watershed management and so on. However, data requirements for such studies had surpassed conventional monitoring strategies and moved towards finer resolutions, both in time and space scales. As it is not feasible to locate rain gauges at all locations, values from neighbouring rain gauge stations can be used to estimate the rainfall amounts at unrecorded sites by various techniques and ultimately use it to develop rainfall maps. In this study, 5 years daily precipitation data from January, 2011 to December, 2015 was obtained for Hassan district in Karnataka. The performance of Inverse Distance Weighting (IDW), Spline, Trend and Kriging interpolation techniques, was compared. Thirty eight rain gauge stations (28 for interpolation, 10 for validation) were used in the study. Interpolation was carried out using the Automated Rainfall Mapping Tool, developed by using Python 2.7, PyQT, Wxpython and ArcGIS. Cross validation results are reported in terms RMSE and R2 error values. The interpolation of 5 year annual average rainfall gave best concordance with the actual values for universal kriging with quadratic drift, yielding an RMSE of 132 mm and R2 value of 0.906. Besides, kriging performed well (RMSE= 0.6 to 1.7.mm, R2= 0.91 to 0.96) during rainy months while IDW performed relatively better than the other techniques on consideration of all 60 months. Exceedance probability curves showed that 10% of the total (60) months considered, kriging and spline give R2 of greater than 0.9, while considering only the rainy months, it was noticed that kriging, spline and IDW give R2 values of more than 0.8 about 60% of the total time. Interpolation of daily rainfall revealed high variability in performance of the interpolators for each day, making it difficult to choose one technique as the best amongst others.