Abstract
Drought hazards have complex climatic and spatio-temporal features. Therefore, its accurate monitoring is a challenging task in hydrological research. In recent, the use of standardized drought indices for drought monitoring is common in practice. However, the existence of several drought indices creates chaotic problems in data mining and decision making. This article presents a new weighting scheme for combining multiple drought indices. We propagated steady-state probabilities of Markov chain as weights in the Probabilistic Weighted Joint Aggregative Index (PWJADI) criterion. Hence, to aggregate drought characterization two or more indices, averaged long term behavior of drought classification states observed in the individual drought index is considered as a weighting characteristic. The proposed algorithm is rather general and can be used for any standardized type of drought indices. The new procedure is named as Long Averaged Weighted Joint Aggregative Criterion (LAWJAC). In this research, we focused on the three multi-scalar drought indices namely, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Standardized Precipitation Temperature Index (SPTI). The selection of these indices is due to their similar computational procedures. In the evolution of LAWJAC, three meteorological stations of the Northern Area of Pakistan are considered. A comparison of LAWJAC is made with PWJADI. Results show significant deviations between existing and proposed methods. By the rationale of the proposed algorithm, these deviations strongly advocate the use of LAWJAC for more accuracy in drought characterization.