New comprehensive ensemble-based approaches for characterizing and monitoring the spatial inter-seasonal characteristics of meteorological drought

Abstract

The current study aimed to examine the inter-seasonal 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 Test (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 to 99 percent. The odd ratio of CFEBLRM for the Summer-Autumn season indicates that the increment in temperature 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 Province of Punjab in Pakistan.