Changepoint detection in SPI transition probabilities

The Standardized Precipitation Index (SPI) has become a popular measure of drought across the globe. Several techniques have been employed in attempts to estimate the time at which any changes in drought patterns occur using categories defined by SPI values. Log-linear models, odds ratios and multiple confidence intervals have all been used while trying to estimate such changepoints and have met with varying degrees of success, in part due to small cell counts and the large number of inferences that must be carried out. The purpose of this paper is to provide an alternative method for the detection of a changepoint in SPI data, specifically in the matrix of transitions from one drought class to another. A resampling algorithm is provided that both detects a potential changepoint and determines its significance under the assumption that at most one changepoint is present. Simulation results are provided to validate the method as well as the suggested metrics for detection. Data from Kenya is used to provide four examples where the technique is implemented on observed rainfall data.