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Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought

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Computational Intelligence Techniques in Earth and Environmental Sciences

Abstract

In the semiarid Barind region, episodes of agricultural droughts of varying severity have occurred. The occurrence of these agricultural droughts is associated with rainfall variability and can be reflected by soil moisture deficit that significantly affects crop performance and yield. In the present study, an analysis of long-term (1971–2010) rainfall data of 12 rain monitoring stations in the Barind region was carried out using a Markov chain model which provides a drought index for predicting the spatial and temporal extent of agricultural droughts. Inverse distance weighted interpolation was used to map the spatial extent of drought in a GIS environment. The results indicated that in the Pre-Kharif season drought occurs almost every year in different parts of the study area. Though occurrence of drought is less frequent in the Kharif season the minimum probability of wet weeks leads to reduction in crop yields. Meanwhile, the calculation of 12 months drought suggests that severe to moderate drought is a common phenomenon in this area. Drought index is also found to vary depending on the length of period. The return period analysis suggests that chronic drought is more frequent in the Pre-Kharif season and the frequency of moderate droughts is higher in the Kharif season. On the contrary severe drought is more frequent for a 12-month period.

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Author information

Authors and Affiliations

  1. Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    A. T. M. Jahangir Alam & A. H. M. Sadaat

  2. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    M. Sayedur Rahman

Authors
  1. A. T. M. Jahangir Alam
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  2. M. Sayedur Rahman
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  3. A. H. M. Sadaat
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Corresponding author

Correspondence to A. T. M. Jahangir Alam .

Editor information

Editors and Affiliations

  1. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA, NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA, Fort Collins, CO, USA

    Tanvir Islam

  2. NASA Goddard Space Flight Center, Greenbelt & ESSIC, University of Maryland College Park, MD, USA, Greenbelt, Maryland, USA

    Prashant K. Srivastava

  3. Department of Civil Engineering, IIT Delhi, Delhi, India

    Manika Gupta

  4. School of Geog. and Env. Sciences, Monash University, Melbourne, Australia

    Xuan Zhu

  5. School of Environmental Sciences Remote Sensing, Jawaharlal Nehru University, New Delhi, India

    Saumitra Mukherjee

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Alam, A.T.M.J., Rahman, M.S., Sadaat, A.H.M. (2014). Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought. In: Islam, T., Srivastava, P., Gupta, M., Zhu, X., Mukherjee, S. (eds) Computational Intelligence Techniques in Earth and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8642-3_6

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