Improving the interpretation of standardized precipitation index estimates to capture drought characteristics in changing climate conditions

Abstract

AbstractThe standardized precipitation index (SPI) was developed as a stationary method in which the parameters of the distribution used to estimate the cumulative probability of rainfall amounts do not vary over time. Considering that changing climatic conditions may violate the assumption of stationarity, a nonstationary version of the SPI (NSPI) has been proposed. However, since time‐varying parametric distributions tend to remove the effect of rainfall trends on NSPI estimates, this latter index may fail to quantify the severity of drought events. In this study, we developed a four‐step algorithm that uses information generated by the NSPI algorithm to improve the interpretation of SPI estimates in terms of their probability of occurrence under changing climate conditions. This statistical/computational algorithm uses a nonstationary approach to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a particular SPI value occurring. The suitability of this algorithm was evaluated through Monte Carlo experiments and through two case study applications. The first study case applied the algorithm to historical rainfall series for the UK. The second applied the algorithm to gridded rainfall data for South America. The results found in this study demonstrated that, in the presence of temporal changes in rainfall frequency distributions, the probabilities of drought events estimated by the four‐step algorithm were consistently closer to the observed frequency of SPI than the probabilities estimated by the original SPI algorithm. In conclusion, the four‐step algorithm improved the interpretation of SPI values in terms of their expected frequencies under climate changing conditions. This algorithm is able to detect changes in the frequency of SPI values, to quantify how these changes affect the probability of drought events according to the SPI classification system, isolating its effect on the central tendency and on the dispersion of SPI distributions.