A New Comprehensive Framework for Identifying and Monitoring the Interseasonal Characteristics of Meteorological Drought

Abstract

The current study aimed to examine the interseasonal characteristics of meteorological drought. For this purpose, a new comprehensive framework is proposed. The framework consists of two major stages. In the first stage of the framework, the K-means method is utilized to identify homogeneous clusters. Besides, the Monte Carlo feature selection (MCFS) is applied to select more important stations from the varying clusters. In the second stage, the standardized precipitation index at a three-time scale (SPI-3), the conditional fixed effect binary logistic regression model (CFEBLRM), and the random effect binary logistic regression model (REBLRM) are utilized. The significance of CFEBLRM and REBLRM is measured by log-likelihood values, log-likelihood ratio chi-square test (LRCST), Wald chi-square tests (WCT), and $p$ values. The Hausman test (HT) is applied to identify endogeneity and suggests the appropriate model in CFEBLRM and REBLRM. The results from the proposed framework indicate that the drought persists in the summer to autumn and autumn to winter seasons between 90 and 99 percent. The odds ratio of CFEBLRM for the summer-autumn season indicates that the increment in precipitation will decrease the drought persistence in the autumn season. The result of the current study facilitates the decision-makers to understand the effects of meteorological drought occurrences better and improve strategies for mitigating drought effects and managing seasonal crops in the Punjab province in Pakistan.