What is the best spatial interpolation technique for evaluating droughts?

Abstract

Drought is a destructive phenomenon that negatively impacts the environment and
socioeconomic aspects. Drought can be evaluated temporarily and spatially based on drought
indices, such as Standardized Precipitation Index (SPI). Spatial evaluation of drought has been
conducted in the literature using many spatial interpolation methods without mentioning the
difference between these methods and their accuracy. This research paper aims to find the
accuracy of using Inverse Distance Weighted (IDW) and Kriging methods in evaluating
drought based on SPI. Monthly precipitation data from 21 stations in Istanbul were used, 16
stations were used for interpolation, and 5 stations were used for validation. The results
showed that the IDW method has more extreme drought values, which means more
conservative regarding drought analysis and monitoring than the Kriging method. However,
IDW and Kriging have approximately the same correlation coefficient (0.6) with observed
values. Also, the correlation between IDW and Kriging was about 0.8. Generally, for drought
analysis and monitoring, IDW is more conservative and gives more extreme drought values.
Subsequently, validating and using the most suitable method and more research about using
interpolation methods in drought are suggested before using any interpolation method.