Drought class transition analysis through Markov and Loglinear models, an approach to early warning

The standardized precipitation index (SPI) based on 68 years of precipitation data was computed for several sites of Alentejo, a drought prone region of southern Portugal. Drought classes were derived from SPI values. Markov chain modelling was used in order to estimate: (a) the probability of different drought severity classes; (b) the expected time in each class of severity; (c) the recurrence time to a particular drought class; (d) the expected time for the SPI to change from a particular class to another. A short-term conditional prediction scheme of drought classes is tested. The non-homogeneous Markov chains formulation produced better predictive results since probabilities are tied to each month. However, the persistence of recent climate conditions tend to dominate, so limiting the prediction capability of Markov chains modelling. Several Loglinear models were fitted to the drought class transition matrices and the computed odds and the respective confidence intervals were used to predict drought class transitions. Generally, the odds show lower values as the drought severity increases for the initial month and decreases for the following months, thus showing that odds of transition to the non-drought class versus transition to any drought class decrease when the drought severity of the present class increases. If the present drought class is moderate or severe, the probability of being 1 month from now in a drought class is higher than the probability of being in the non-drought class. Results show the utility of using the above-mentioned stochastic models to support monitoring the evolution of droughts and to produce early warning in combination with other indicators.