Assessing the long-term fluctuations in dry-wet spells over Indian region using Markov model in GEE cloud platform
DOI:
https://doi.org/10.54386/jam.v25i2.2184Keywords:
CHIRPS, Markov Chain Model, Dry- wet spells, Agricultural planningAbstract
The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW–39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of co-efficient of variation (CV) showed low CV (<60%) in Pd and high CV (>60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (<40%) in Pw. The drought prone and moisture sufficient zones were indentified based on the analysis of long-term frequency distribution of dry-wet spells and trend. Inter-comparison of IPs between CHIRPs with IMD (Indian Meteorological Department) and NOAA CPC (National Oceanic and Atmospheric Administration/Climate Prediction Centre) showed encouraging results. The study provides baseline reference for climate-resilient agricultural crop planning with respect to food security.
References
Behera, S. and Subudhi, C.R. (2018). Markov chain model of weekly rainfall probability and dry and wet spells for agricultural planning in Ganjam district of Odisha. Int. J. Pharm. Res., 12(3): 422-433.
Dabral, P. P., Purkyastha, K. and Aram, M. (2014). Dry and wet spell probability by Markov chain model-a case study of North Lakhimpur (Assam), India. Int. J. Agric. Biol. Eng,, 7(6): 8-13.
Dash, M. K. and Senapati, P. C. (1992). Forecasting of dry and wet spells at Bhubaneshwar for agricultural planning. Ind. J. Soil Cons., 20(1&2): 75–82.
Das, P.K., Das, D.K., Midya, S.K., Bandyopadhyay, S. and Uday, R. (2020). Spatial analysis of wet spell probability over India (1971-2005) towards agricultural planning. Atmósfera, 33(1): 19-31, doi: 10.20937/ATM.52499.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L. and Hoell, A. (2015). The Climate Hazards Infrared Precipitation with Stations-A New Environmental Record for Monitoring Extremes. Sci. Data, 2, 150066. . 10.1038/sdata.2015.66.
Joseph, A. and Tamilmani, D. (2017). Markov chain model of weekly rainfall probability and dry wet spells for agricultural planning in Coimbtore in western zone of Tamil Nadu. Ind. J. Soil Cons., 43(1): 66–71.
Kendell, M.G. (1975). Rank Correlation Methods (4th ed.). Charles Griffin, London, pp.202.
Mann, H.B. (1945). Non-parametric tests against trend. Econometrica 13(3):163-171.
Makwana, J. J., Deora , B. S., Patel, C. K., Parmer, B. S. and Saini, A. K. (2021). Analysis of rainfall characteristics and moisture availability index for crop planning in semi arid region of north Gujarat. J. Agrometeorol., 23 (4): 409-415. https://doi.org/10.54386/jam.v23i4.145
Mishra, A.K. and Rafiq, M. (2019). Rainfall estimation techniques over India and adjoining oceanic regions. Curr. Sci. 116 (1).
Pandharinath, N. (1991). Markov chain model probability of dry and wet weeks during monsoon period over Andhra Pradesh. Mausam, 42(4): 393-400. https://doi.org/10.54302/mausam.v42i4.3274
Pawar, P.S., Khodke, U.M. and Waikar, A.U. (2019). Dry and wet spell probability by Markov Chain Model for agricultural planning at Parbhani. Int. J. Bio-resour. Stress Manag.,10(3): 233-240.
Pradhan, A., Chandrakar, T., Nag S.K., Dixit, A. and Mukherjee, S.C. (2020). Crop planning based on rainfall variability for Bastar region of Chhattisgarh, India. J. Agrometeorol. 22 (4): 509-517. https://doi.org/10.54386/jam.v22i4.477
Saicharan, V. and Rangaswamy, S.H. (2023). A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India. Sustainability, 15, 5758. https://doi.org/10.3390/su15075758.
Sattar, A., Khan, S.A. and Banerjee, S. (2018). Assured rainfall analysis for enhanced crop production under rainfed condition in Bihar. J. Agrometeorol. 20 (4): 332-335. https://doi.org/10.54386/jam.v20i4.578
Syroka, J. and Toumi, R. (2004). On the withdrawal of the Indian summer monsoon. Q J R Meteorol Soc. 130: 989-1008.
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