Journal of Science, Technology and Environment Informatics |
|
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.
- ADB (2005). Country strategy and program 2006–2010. Bangladesh Resident Mission, Dhaka, Bangladesh.
- Beirlant, J., Dierckx, G. & Guillou, A. (2005). Estimation of the extreme-value index and generalized quantile plots. Bernoulli, 11(6), 949-970. http://dx.doi.org/10.3150/bj/1137421635
- Coles, S., Bawa, J., Trenner, L. & Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208). London: Springer. http://dx.doi.org/10.1007/978-1-4471-3675-0
- DMB, 2010. National Plan for Disaster Management (2010-2015), Disaster Management Bureau (DMB), Disaster Management and Relief Division (DM&RD), Dhaka, Bangladesh.
- Drápela, K. & Drápelová, I. (2011). Application of Mann-Kendall test and the Sen's slope estimates for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997-2010. Beskydy, 4(2), 133-146.
- Embrechts, P., Kluppelberg, C. & Mikosch, T.(1999). Modelling extremal events. British Actuarial Journal, 5(2), 465-465.
- Habiba, U. (2012). Enhancement of Drought Risk Management Policy and Actions Incorporating Farmer's Adaptive Practices in Northwestern Bangladesh.
- Habiba, U., Shaw, R. & Takeuchi, Y. (2011). Socio-economic impact of droughts in Bangladesh. Droughts in Asian monsoon region: community, environment and disaster risk management, 5, 25-48. http://dx.doi.org/10.1108/S2040-7262(2011)0000008008
- Habiba, U., Hassan, A. W. R. & Shaw, R. (2013). Livelihood adaptation in the drought prone areas of Bangladesh. In: Climate Change Adaptation Actions in Bangladesh (pp. 227-252). Springer Japan. http://dx.doi.org/10.1007/978-4-431-54249-0_13
- Kotz, S. & Nadarajah, S. (2000). Extreme value distributions (Vol. 31). Imperial College Press, London.
- Murshid, K. A. S. (1987). Weather, new technology and instability in food grain production in Bangladesh. The Bangladesh Development Studies, 15(1), 31-56.
- Onoz, B. & Bayazit, M. (2003). The power of statistical tests for trend detection. Turkish Journal of Engineering and Environmental Sciences, 27(4), 247-251.
- Paul, B. K. (1995). Flood research in Bangladesh: Major findings and future research direction. In A paper presented at the 1995 Annual Meeting of the Association of American Geographers held in Chicago, IL.
- Shahid, S. & Behrawan, H. (2008). Drought risk assessment in the western part of Bangladesh. Natural Hazards, 46(3), 391-413. http://dx.doi.org/10.1007/s11069-007-9191-5
- Smith, R. L., Tawn, J. A. & Coles, S. G. (1997). Markov chain models for threshold exceedances. Biometrika, 84(2), 249-268. http://dx.doi.org/10.1093/biomet/84.2.249
- WARPO (2005). National Adaptation Program of Action (NAPA): Water, coastal areas, natural disaster and health sector. Water Resources Planning Organization (WARPO), Dhaka.
- World Bank (2000). Bangladesh agriculture in the 21st century, World Bank Publication
- Rootzén, H. & Tajvidi, N. (2006). Multivariate generalized Pareto distributions. Bernoulli, 917-930. http://dx.doi.org/10.3150/bj/116161495
Open Access | Read Article
© Sultana (2016). This article published by Journal BiNET is freely available for anyone to read, share, download, print, permitted for unrestricted use and build upon, provided that the original author(s) and publisher are given due credit. All Published articles are distributed under the Creative Commons Attribution 4.0 International License.