Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence
Drought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding the spatial patterns of drought and predicting the likelihood of inter-seasonal drought persistence can provide substantial operational guidelines for water resource management and agricultural...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2211041 |
| _version_ | 1811153890120302592 |
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| author | Muhammad Ahmad Raza Mohammed M. A. Almazah Ijaz Hussain Fuad S. Al-Duais A. Y. Al-Rezami Talha Omer |
| author_facet | Muhammad Ahmad Raza Mohammed M. A. Almazah Ijaz Hussain Fuad S. Al-Duais A. Y. Al-Rezami Talha Omer |
| author_sort | Muhammad Ahmad Raza |
| collection | DOAJ |
| description | Drought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding the spatial patterns of drought and predicting the likelihood of inter-seasonal drought persistence can provide substantial operational guidelines for water resource management and agricultural production. This study examines drought persistence by identifying the spatial patterns of seasonal drought frequency and inter-seasonal drought persistence in the northeastern region of Pakistan. The Standardized Precipitation Index (SPI) with a three-month time scale is used to examine meteorological drought. Furthermore, Bayesian logistic regression is used to calculate the probability and odds ratios of drought occurrence in the current season, given the previous season’s SPI values. For instance, at Balakot station, for the summer-to-autumn season, the value of the odds ratio is significant (6.78). It shows that one unit increase in SPI of the summer season will cause a 5.78 times to increase in odds of autumn drought occurrence. The average drought frequency varies from 37.3 to 89.1%, whereas the average inter-seasonal drought persistence varies from 21.9 to 91.7% in the study region. Results indicate that some areas in the study region, like Kakul and Garhi Dupatta, are more prone to drought and vulnerable to inter-seasonal drought persistence. Furthermore, the Bayesian logistic regression results reveal a negative relationship between spring drought occurrence and winter SPI, demonstrating that the overall study region is more prone to winter-to-spring drought persistence and less vulnerable to summer-to-autumn drought persistence. Overall study has concluded that the region’s seasonal drought forecast is challenging due to uncertain drought persistence patterns. However, the Bayesian logistic regression model provides more accurate and precise regional seasonal drought forecasts. The outcome of the present study provides scientific evidence to develop early warning systems and manage seasonal crops in Pakistan. |
| first_indexed | 2024-03-11T23:46:51Z |
| format | Article |
| id | doaj.art-33e4c9f54e4742b389bafafe61c3154f |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:46:51Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-33e4c9f54e4742b389bafafe61c3154f2023-09-19T09:13:18ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2023.22110412211041Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistenceMuhammad Ahmad Raza0Mohammed M. A. Almazah1Ijaz Hussain2Fuad S. Al-Duais3A. Y. Al-Rezami4Talha Omer5Department of Statistics, Quaid-i-Azam UniversityDepartment of Mathematics, College of Sciences and Arts (Muhyil), King Khalid UniversityDepartment of Statistics, Quaid-i-Azam UniversityMathematics Department, College of Humanities and Science, Prince Sattam Bin Abdulaziz UniversityMathematics Department, Prince Sattam Bin Abdulaziz UniversityDepartment of Economics, Finance and Statistics, JIBS, Jönköping University, JönköpingDrought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding the spatial patterns of drought and predicting the likelihood of inter-seasonal drought persistence can provide substantial operational guidelines for water resource management and agricultural production. This study examines drought persistence by identifying the spatial patterns of seasonal drought frequency and inter-seasonal drought persistence in the northeastern region of Pakistan. The Standardized Precipitation Index (SPI) with a three-month time scale is used to examine meteorological drought. Furthermore, Bayesian logistic regression is used to calculate the probability and odds ratios of drought occurrence in the current season, given the previous season’s SPI values. For instance, at Balakot station, for the summer-to-autumn season, the value of the odds ratio is significant (6.78). It shows that one unit increase in SPI of the summer season will cause a 5.78 times to increase in odds of autumn drought occurrence. The average drought frequency varies from 37.3 to 89.1%, whereas the average inter-seasonal drought persistence varies from 21.9 to 91.7% in the study region. Results indicate that some areas in the study region, like Kakul and Garhi Dupatta, are more prone to drought and vulnerable to inter-seasonal drought persistence. Furthermore, the Bayesian logistic regression results reveal a negative relationship between spring drought occurrence and winter SPI, demonstrating that the overall study region is more prone to winter-to-spring drought persistence and less vulnerable to summer-to-autumn drought persistence. Overall study has concluded that the region’s seasonal drought forecast is challenging due to uncertain drought persistence patterns. However, the Bayesian logistic regression model provides more accurate and precise regional seasonal drought forecasts. The outcome of the present study provides scientific evidence to develop early warning systems and manage seasonal crops in Pakistan.http://dx.doi.org/10.1080/10106049.2023.2211041bayesian logistic regressiondrought persistencegibbs samplinginter-seasonalstandardized precipitation index |
| spellingShingle | Muhammad Ahmad Raza Mohammed M. A. Almazah Ijaz Hussain Fuad S. Al-Duais A. Y. Al-Rezami Talha Omer Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence Geocarto International bayesian logistic regression drought persistence gibbs sampling inter-seasonal standardized precipitation index |
| title | Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence |
| title_full | Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence |
| title_fullStr | Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence |
| title_full_unstemmed | Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence |
| title_short | Bayesian logistic regression analysis for spatial patterns of inter-seasonal drought persistence |
| title_sort | bayesian logistic regression analysis for spatial patterns of inter seasonal drought persistence |
| topic | bayesian logistic regression drought persistence gibbs sampling inter-seasonal standardized precipitation index |
| url | http://dx.doi.org/10.1080/10106049.2023.2211041 |
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