A Markov chain approach for analyzing Palmer drought severity index

Abstract

Drought is perceived differently by different people, but in general, it is conceived as a period of below normal precipitation or moisture deficiency that would affect the social and economic activities of a region. Many numerical indices are used to quantify the effect of drought. The Pahner Drought Severity Index (PDSI) is the most and widely used drought indicator parameter in most recent applications. The PDSI takes into account precipitation, temperature, and soil moisture and depicts prolonged abnormal dryness or wetness.

A Markov chain model was developed to analyze the likelihood of occurrences of the seven types of weather spells, defined by the National Oceanic and Atmospheric Administration (NOAA). The spells are classified, using the PDSI computed monthly by the NOAA. The model predicts both short and long term drought status over an entire climatic division. Twelve monthly transition matrices and one annual transition matrix were computed. The matrices show the transition patterns between months and between drought states.

The model was applied to the Tidewater area (climatic division l) and the Southwest mountains (climatic division 6) of Virginia. The model predictions reflect the reality and compare very well with the observed data for these two climatic divisions. This model can potentially be used as a tool for water resource planning and design of drought assistance plans by water resource managers.