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Improving Spatio-Temporal Rainfall Interpolation Using Remote Sensing CCD Data in a Tropical Basin: A Geostatistical Modeling Approach
Abstract
This chapter looks at how interpolated annual and monthly rainfall variation can be improved by developing a geostatistical model that uses remotely-sensed cold cloud duration (CCD) data as a background image for a typical tropical basin, the Rufiji basin in Tanzania. We explored the Kriging model and its variants, and found it to be a good estimator in spatial interpolation mainly due to the inclusion of the non-stationary local mean during estimation. Model parameter sensitivity analysis and residual analysis of errors were used to test model adequacy and performance. They revealed that the parameter values of the variogram namely, the nugget effect, the range, sill value and maximum direction of continuity, as long as they are in acceptable ranges, have low effect on model efficiency and accuracy. Instead interpolation was found to improve when remotely-sensed CCD data was used as a background image as compared to estimation using observed point rainfall data alone. This improvement was revealed in terms of the Nash-Sutcliffe model performance index (R2). Although Kriging model application seems to be data intensive and time consuming in nature, it results in improved spatio-temporal interpolated surfaces so long as interpolated results can be interpreted with confidence and with prudent judgement of the model users.
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