Science Journal of Applied Mathematics and Statistics

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Prediction and Trends of Rainfall Variability over Bangladesh

Received: 03 January 2017    Accepted: 19 January 2017    Published: 01 March 2017
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Abstract

Rainfall is one of the most common natural disasters in Bangladesh which rigorously affect agro-based economy and people’s livelihood in almost every year. The main objective of this study is to examine the variation, prediction and trend of rainfall in Bangladesh. The data for this study have been extracted from the Bangladesh Meteorological Department (BMD). Data used in this study were collected from 31 rain gauge stations located in different parts of the country for a period of 40 years (1975-2014). Linear regression model is used to understand the variation, trend and prediction of rainfall for annual and various climatic seasons such as pre-monsoon, monsoon, post-monsoon and winter. We also estimated mean rainfall with standard deviation of pre-monsoon, monsoon, post-monsoon and winter. Finding reveals that, the trends of mean rainfall of annual, pre-monsoon and winter have decreased, whereas rainfall remained unchanged in monsoon season and has increased in post-monsoon. Data predicts lesser rainfall in the period 1975, 1989, 1992, 1994, 2004, 2009, 2012, 2013 and 2014 years. These results indicate lesser precipitation in future over Bangladesh. The predicted rainfall amount from the best fitted model was compared with the observed data. The predicted values show reasonably good result. Thus the model can be used for future rainfall prediction. It is expected that this long term prediction will help the decision makers in efficient scheduling of flood prediction, urban planning, and rainwater harvesting and crop management. Classification of rainfalls in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development.

DOI 10.11648/j.sjams.20170501.18
Published in Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 1, February 2017)
Page(s) 54-59
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Rainfall, Variability, Prediction, Trend, Regression, Bangladesh

References
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[3] IPCC (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability. In: M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson, Eds., Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge.
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[7] Devkota L. P. (2006), “Rainfall over SAARC region with special focus on tele-connections and long range forecasting of Bangladesh monsoon rainfall, monsoon forecasting with a limited area numerical weather prediction system”, Report No-19, SAARC Meteorological Research Centre (SMRC), Dhaka, Bangladesh.
[8] Mannan M. A., M. A. M. Chowdhury and S. Karmakar (2016), “ Prediction of Rainfall over Southeastern part of Bangladesh during Monsoon Season”, International Journal of Integrated Sciences & Technology, Vol. 2, P. 73-82.
[9] Imon, A. H. M. R, M. C. Roy and S. K. Bhattacharjee (2012), “Prediction of Rainfall Using Logistic Regression”, PJSOR, Vol. 8, No. 3, P. 655-667.
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[12] Rodrigo, S., M. J. Esteban-Parra, D. Pozo-Va ´zquez and Y. Castro-Dı´ez, (2000) “Rainfall variability in southern Spain on decadal to centennial time scales”, International Journal of Climatology, Vol. 20, No. 7, P. 721–732.
[13] Rotstayn, L. D., Lohmann, Ulrike, (2002), “ Tropical rainfall trends and the indirect aerosol effect”, Journal of Climate, Vol. 15, P. 2103–2116.
[14] Murphy, B. F., Timbal, Bertrand, (2007), “A review of recent climate variability and climate change in southeastern Australia”, International Journal of Climatology, doi: 10.1002/joc.1627.
[15] Nicholls, N., Lavery, Beth, (2006), “Australian rainfall trends during the twentieth century”, International Journal of Climatology, Vol. 12, No. 2, P. 153–163. doi: 10.1002/joc.3370120204.
[16] Islam, T., S. Saha, A. A. Evan, N. Halder, S. C. Dey, (2016), “Monthly Weather Forecasting through ANN Model: A Case Study in Barisal, Bangladesh”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 6, Copyright to IJARCCE DOI 10.17148/IJARCCE.2016.5601 1
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[18] Shahid, S., (2009), “Rainfall variability and the trends of wet and dry periods in Bangladesh, International Journal of Climatology.
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[20] Islam, M. N. and H. Uyeda, (2008), “Use of TRMM in determining the climatic characteristics ofrainfall over Bangladesh, Remote Sensing of Environment, Vol. 108, No. 3, P. 264.
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Author Information
  • Department of Mathematics, Islamic University, Kushtia, Bangladesh

  • Department of Mathematics, Islamic University, Kushtia, Bangladesh

  • Department of Mathematics, Islamic University, Kushtia, Bangladesh

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  • APA Style

    Mohammad Anisur Rahman, Sunny Mohammed Mostafa Kamal, Mohammad Maruf Billah. (2017). Prediction and Trends of Rainfall Variability over Bangladesh. Science Journal of Applied Mathematics and Statistics, 5(1), 54-59. https://doi.org/10.11648/j.sjams.20170501.18

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    ACS Style

    Mohammad Anisur Rahman; Sunny Mohammed Mostafa Kamal; Mohammad Maruf Billah. Prediction and Trends of Rainfall Variability over Bangladesh. Sci. J. Appl. Math. Stat. 2017, 5(1), 54-59. doi: 10.11648/j.sjams.20170501.18

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    AMA Style

    Mohammad Anisur Rahman, Sunny Mohammed Mostafa Kamal, Mohammad Maruf Billah. Prediction and Trends of Rainfall Variability over Bangladesh. Sci J Appl Math Stat. 2017;5(1):54-59. doi: 10.11648/j.sjams.20170501.18

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  • @article{10.11648/j.sjams.20170501.18,
      author = {Mohammad Anisur Rahman and Sunny Mohammed Mostafa Kamal and Mohammad Maruf Billah},
      title = {Prediction and Trends of Rainfall Variability over Bangladesh},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {5},
      number = {1},
      pages = {54-59},
      doi = {10.11648/j.sjams.20170501.18},
      url = {https://doi.org/10.11648/j.sjams.20170501.18},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjams.20170501.18},
      abstract = {Rainfall is one of the most common natural disasters in Bangladesh which rigorously affect agro-based economy and people’s livelihood in almost every year. The main objective of this study is to examine the variation, prediction and trend of rainfall in Bangladesh. The data for this study have been extracted from the Bangladesh Meteorological Department (BMD). Data used in this study were collected from 31 rain gauge stations located in different parts of the country for a period of 40 years (1975-2014). Linear regression model is used to understand the variation, trend and prediction of rainfall for annual and various climatic seasons such as pre-monsoon, monsoon, post-monsoon and winter. We also estimated mean rainfall with standard deviation of pre-monsoon, monsoon, post-monsoon and winter. Finding reveals that, the trends of mean rainfall of annual, pre-monsoon and winter have decreased, whereas rainfall remained unchanged in monsoon season and has increased in post-monsoon. Data predicts lesser rainfall in the period 1975, 1989, 1992, 1994, 2004, 2009, 2012, 2013 and 2014 years. These results indicate lesser precipitation in future over Bangladesh. The predicted rainfall amount from the best fitted model was compared with the observed data. The predicted values show reasonably good result. Thus the model can be used for future rainfall prediction. It is expected that this long term prediction will help the decision makers in efficient scheduling of flood prediction, urban planning, and rainwater harvesting and crop management. Classification of rainfalls in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Prediction and Trends of Rainfall Variability over Bangladesh
    AU  - Mohammad Anisur Rahman
    AU  - Sunny Mohammed Mostafa Kamal
    AU  - Mohammad Maruf Billah
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    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
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    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20170501.18
    AB  - Rainfall is one of the most common natural disasters in Bangladesh which rigorously affect agro-based economy and people’s livelihood in almost every year. The main objective of this study is to examine the variation, prediction and trend of rainfall in Bangladesh. The data for this study have been extracted from the Bangladesh Meteorological Department (BMD). Data used in this study were collected from 31 rain gauge stations located in different parts of the country for a period of 40 years (1975-2014). Linear regression model is used to understand the variation, trend and prediction of rainfall for annual and various climatic seasons such as pre-monsoon, monsoon, post-monsoon and winter. We also estimated mean rainfall with standard deviation of pre-monsoon, monsoon, post-monsoon and winter. Finding reveals that, the trends of mean rainfall of annual, pre-monsoon and winter have decreased, whereas rainfall remained unchanged in monsoon season and has increased in post-monsoon. Data predicts lesser rainfall in the period 1975, 1989, 1992, 1994, 2004, 2009, 2012, 2013 and 2014 years. These results indicate lesser precipitation in future over Bangladesh. The predicted rainfall amount from the best fitted model was compared with the observed data. The predicted values show reasonably good result. Thus the model can be used for future rainfall prediction. It is expected that this long term prediction will help the decision makers in efficient scheduling of flood prediction, urban planning, and rainwater harvesting and crop management. Classification of rainfalls in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development.
    VL  - 5
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