Identifying inter-seasonal drought characteristics using binary outcome panel data models

This study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteorological drought in selected stations. The log-likelihood Ratio Chi-Square (LRCST) and Wald chi-square tests (WCTs) are used to assess the significance of RELRM and CFELRM. The Hausman test (HT) is applied to select the appropriate model between RELRM and CFELRM. For instance, HT suggests the CFELRM as an appropriate model in spring-to-summer spatiotemporal drought modelling. The significant coefficient from CFELRM indicates that an increment in moisture conditions of the spring season will decrease the probability of drought in the summer. The odds ratio of 0.1942 means that 19.42% chance of being in a higher category. Similarly, in summer-to-autumn using RELRM the computed odds ratio of 0.0673 shows that 6.73% chance of being in a higher category.

Automated summary

This study focuses on the characteristics of spatiotemporal and inter-seasonal meteorological drought. The Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to analyze the drought in selected stations. The results show that an increase in moisture conditions during the spring season will decrease the probability of drought in the summer, while in the summer-to-autumn transition, there is a 6.73% chance of being in a higher category.