An Analysis of Spatio-Temporal Changes in Drought Characteristics over India

Abstract

Regional drought mitigation efforts depend on reliable estimates of intensity and severity of drought events. Most operational methods used for drought classification do not account modeling uncertainties and provide discrete drought classification. However, when uncertainty estimates in classification are available, they can be used to make informed decisions. This study compares a gamma-mixture-model-based probabilistic drought classification method that quantifies uncertainties in drought classification with the standardized precipitation index (SPI) that provides discrete classification. Further, if the precipitation data are nonstationary, then classical methods of drought classification are not applicable, and an alternate method for drought classification for trend stationary precipitation series is presented. This method is tested on synthetic and real-world precipitation data over India. The alternate method offers flexibility in modeling nonstationary time series. The advantages and limitations of this method are discussed along with a set of concluding remarks.