ASSESSMENT OF DRY AND WET SPELLS FOR CHANGE IN RAINFALL PATTERN OF THE INDIAN CHHATTISGARH WATERSHED USING THE MARKOV CHAIN APPROACH

D. TANDEL, S. VERMA, M.K. VERMA, D. MEHTA, B. BENZOUGAGH

Abstract


This study uses a Markov chain technique to predict rainfall patterns in the Indian Chhattisgarh watershed, where rainfall is crucial for agriculture and water resource management. By analyzing historical rainfall data from 1986 to 2021, the study builds a Markov chain model that captures the transition probabilities between different rainfall districts, enabling the prediction of future patterns. The research reveals significant variability in rainfall patterns across the region, emphasizing the unpredictability of rainfall. Notably, the likelihood of a wet day decreases, while the probability of a dry day increases, reaching 60% across all districts. The probability of a dry day consistently exceeds 60% in the crop-growing season, while the chance of a wet day ranges from 30% to 50%. The model also highlights that wet days are more likely to occur after dry days, and vice versa. This Markov chain approach provides a robust framework for understanding and forecasting rainfall patterns. Therefore, stakeholders such as policymakers, farmers, and water resource managers should be empowered to develop proactive drought and flood mitigation strategies.


Keywords


Dry and wet spell, Markov chain, Probability distribution, Rainfall pattern, Chhattisgarh region.

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