JSTEI | Journal of Science, Technology and Environment Informatics |
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Research article:
Vulnerability assessment of drought prone areas in Bangladesh through extreme temperature modeling
Nasrin Sultana
Dept. of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Salna, Gazipur-1706, Bangladesh
J. Sci. Technol. Environ. Inform. | Volume 03, Issue 01, pp. 151-160 | Date of Publication: 17 February 2016
DOI: http://dx.doi.org/10.18801/jstei.030116.17
Vulnerability assessment of drought prone areas in Bangladesh through extreme temperature modeling
Nasrin Sultana
Dept. of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Salna, Gazipur-1706, Bangladesh
J. Sci. Technol. Environ. Inform. | Volume 03, Issue 01, pp. 151-160 | Date of Publication: 17 February 2016
DOI: http://dx.doi.org/10.18801/jstei.030116.17
| vulnerability_assessment_of_drought_prone_areas_in_bangladesh_through_extreme_temperature_modeling_v.1.pdf |
Title: Vulnerability assessment of drought prone areas in Bangladesh through extreme temperature modeling
Abstract: Bangladesh is commonly known as a disaster prone country and drought is one of the frequent natural phenomenon. A series of daily maximum temperature data from drought prone areas such as Bogra, Dinajpur, Ishsurdi, Faridpur and Rangpur districts over the period 1964-2013 years are analyzed in this study. For modelling purposes annual maximum temperature data fitted to generalize extreme value (GEV) distributions and block maxima approach are applied. The trend in GEV model also considers due to the existence of temporal trend in daily temperature data. Likelihood ratio statistics are used as a tool to compare models with trend and without trend. Drought risk is computed through the quantile of the best fitted GEV model which is popularly known as return levels.
Key words: Drought, Temperature, Generalized extreme value distribution, Non-stationary model and Return levels
Abstract: Bangladesh is commonly known as a disaster prone country and drought is one of the frequent natural phenomenon. A series of daily maximum temperature data from drought prone areas such as Bogra, Dinajpur, Ishsurdi, Faridpur and Rangpur districts over the period 1964-2013 years are analyzed in this study. For modelling purposes annual maximum temperature data fitted to generalize extreme value (GEV) distributions and block maxima approach are applied. The trend in GEV model also considers due to the existence of temporal trend in daily temperature data. Likelihood ratio statistics are used as a tool to compare models with trend and without trend. Drought risk is computed through the quantile of the best fitted GEV model which is popularly known as return levels.
Key words: Drought, Temperature, Generalized extreme value distribution, Non-stationary model and Return levels
Citation for this article (APA Style):
Sultana, N. (2016). Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling. Journal of Science, Technology and Environment Informatics, 03(01), 151-160.
MLA (Modern Language Association)
Sultana, N. “Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling.” Journal of Science, Technology and Environment Informatics, 03.01 (2016): 151-160.
Chicago/Turabian
Sultana, N. “Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling.” Journal of Science, Technology and Environment Informatics, 03, no. 01 (2016): 151-160.
Sultana, N. (2016). Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling. Journal of Science, Technology and Environment Informatics, 03(01), 151-160.
MLA (Modern Language Association)
Sultana, N. “Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling.” Journal of Science, Technology and Environment Informatics, 03.01 (2016): 151-160.
Chicago/Turabian
Sultana, N. “Vulnerability assessment of drought prone areas of Bangladesh through extreme temperature modeling.” Journal of Science, Technology and Environment Informatics, 03, no. 01 (2016): 151-160.
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