Spatio-Temporal Variability and Trend Analysis of Long-Term Rainfall in Parbati River Basin, Rajasthan
DOI:
https://doi.org/10.52151/jae2024612.1848Keywords:
Autocorrelation, Trend analysis, Men-Kendall, Modified Men Kendall, Sen’s slopeAbstract
The examination of rainfall variability and trends can provide valuable insights for climate risk assessment and agricultural water management. This study aims to assess the spatial and temporal variability and trends in annual, seasonal, and monthly rainfall of Parbati River basin, Rajasthan, using 43-year (1981-2023) data. The original and modified Mann-Kendall tests were employed to analyze the trends. The modified Mann-Kendall test was employed when lag-1 auto-correlation was present in the rainfall time series, thus taking care of presence of persistence in rainfall data series. Sen’s slope estimator was utilized to quantify the magnitude of rainfall trends. Results of trend test revealed that annual rainfall exhibited an increasing trend at three stations (Bayana, Baseri, and Karauli), with trend magnitudes ranging from 0.40 to 6.37 mm year-1, whereas the other three stations did not show any discernible trends. Seasonally, significant increase in rainfall occurred at Bari, Bayana, Karauli, and Sapotra stations during the pre-monsoon season, and at Baseri and Bayana stations during monsoon season. No significant trends were observed in post-monsoon and winter seasons at any station. In case of monthly rainfall, a significant positive trend was detected at Baseri station in September, Bari station in March, Bayana and Karauli stations in January, and Sapotra station in January and April. This study emphasized on the use of variance-corrected Mann-Kendall test in order to take care for the presence of serial correlation in time series. The outcomes of the study could play a vital role in ensuring sustainable use and conservation of water resources under changing climate conditions.
Downloads
References
Anand, A., Dinesh, A. S., Srivastava, P. K., Chaudhary, S. K., Varma, A. K., & Kumar, P. (2022). Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system. Geocarto International, 37(21), 6213-6231. https://doi.org/10.1080/10106049.2021.1936208
Anderson, R. L. (1954). The problem of autocorrelation in regression analysis. Journal of the American Statistical Association, 49(265), 113-129. https://doi.org/10.1080/01621459.1954.10501219
Bandyopadhyay, N., Bhuiyan, C., & Saha, A. K. (2016). Heat waves, temperature extremes and their impacts on monsoon rainfall and meteorological drought in Gujarat, India. Natural Hazards, 82, 367-388. https://doi.org/10.1007/s11069-016-2205-4
Bayazit, M., & Onoz, B. J. H. S. J. (2007). To prewhiten or not to prewhiten in trend analysis? Hydrological Sciences Journal, 52(4), 611-624. https://doi.org/10.1623/hysj.52.4.611
Bisht, D. S., Chatterjee, C., Raghuwanshi, N. S., & Sridhar, V. (2018). Spatio-temporal trends of rainfall across Indian river basins. Theoretical and applied climatology, 132, 419-436. https://doi.org/10.1007/s00704-017-2095-8
CGWB. (2017). Report on aquifer mapping and groundwater management, Dholpur district, Rajasthan. Report of the Central Ground Water Board (CGWB), Ministry of Water Resources, Government of India, 7p.
Das, J., & Bhattacharya, S. K. (2018). Trend analysis of long-term climatic parameters in Dinhata of Koch Bihar district, West Bengal. Spatial Information Research, 26, 271-280. https://doi.org/10.1007/s41324-018-0173-3
Das, J., Mandal, T., Rahman, A. S., & Saha, P. (2021). Spatio-temporal characterization of rainfall in Bangladesh: An innovative trend and discrete wavelet transformation approaches. Theoretical and Applied Climatology, 143(3), 1557-1579. https://doi.org/10.1007/s00704-020-03508-6
Feng, X., Porporato, A., & Rodriguez-Iturbe, I. (2013). Changes in rainfall seasonality in the tropics. Nature Climate Change, 3(9), 811-815. https://doi.org/10.1038/nclimate1907
Gangarde, A., Dauji, S., & Londhe, S. (2023). Comparison of long-term and short-term trends of annual rainfall in India: A case study. ISH Journal of Hydraulic Engineering, 29(3), 411-424. https://doi.org/10.1080/09715010.2022.2084351
Gumus, V., Avsaroglu, Y., & Simsek, O. (2022). Streamflow trends in the Tigris river basin using Mann-Kendall and innovative trend analysis methods. Journal of Earth System Science, 131(1), 34. https://doi.org/10.1007/s12040-021-01770-4
Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for auto correlated data. Journal of Hydrology, 204(1-4), 182-196. https://doi.org/10.1016/S0022-1694(97)00125-X
Hamilton, J. P., Whitelaw, G. S., & Fenech, A. (2001). Mean annual temperature and total annual precipitation trends at Canadian biosphere reserves. Environmental Monitoring and Assessment, 67, 239-275. https://doi.org/10.1023/A:1006490707949
Hirapara, J. G., Singh, P. K., Singh, M., & Patel, C. D. (2020). Analysis of rainfall characteristics for crop planning in north and south Saurashtra region of Gujarat. Journal of Agricultural Engineering, 57(2), 162-171.
Hussain, M., & Mahmud, I. (2019). pyMannKendall: A python package for non parametric Mann Kendall family of trend tests. The Journal of Open Source Software, 4(39), 1556. https://doi.org/10.21105/joss.01556
Jayanta, D., Tapash, M., Piu, S., & Kumar, B. S. (2020). Variability and trends of rainfall using non-parametric approaches: A case study of semi-arid area. Mausam, 71(1), 33-44. https://doi.org/10.54302/mausam.v71i1.4
Kamruzzaman, M., Hwang, S., Cho, J., & Jang, M. W. (2019). Assessment of the historical variability of meteorological drought in Bangladesh. Journal of the Korean Society of Agricultural Engineers, 61(3), 77-88. https://doi.org/10.5389/KSAE.2019.61.3.077
Kendall, M. G. (1975). Rank Correlation Methods, Griffin, London, United Kingdom.
Machiwal, D., & Jha, M.K. (2012). Hydrologic Time Series Analysis: Theory and Practice. Capital Publishing Company, New Delhi, India and Springer, The Netherlands, 303p.
Machiwal, D.,& Jha, M.K. (2017). Evaluating persistence, and identifying trends and abrupt changes in monthly and annual rainfalls of a semi-arid region in western India. Theoretical and Applied Climatology, 128(3-4), 689-708. https://doi.org/10.1007/s00704-016-1734-9
Machiwal, D., Dayal, D., & Kumar, S. (2017). Long-term rainfall trends and change points in hot and cold arid regions of India. Hydrological Sciences Journal, 62(7), 1050-1066. https://doi.org/10.1080/02626667.2017.1303705
Machiwal, D., Gupta, A., Jha, M. K., & Kamble, T. (2019). Analysis of trend in temperature and rainfall time series of an Indian arid region: Comparative evaluation of salient techniques. Theoretical and Applied Climatology, 136, 301-320. https://doi.org/10.1007/s00704-018-2487-4
Mahmoud, S. H., & Gan, T. Y. (2018). Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions. Science of the Total Environment, 633, 1329-1344. https://doi.org/10.1016/j.scitotenv.2018.03.290
Malik, A., & Kumar, A. (2020). Spatio-temporal trend analysis of rainfall using parametric and non-parametric tests: Case study in Uttarakhand, India. Theoretical and Applied Climatology, 140, 183-207. https://doi.org/10.1007/s00704-019-03080-8
Mann, H. B. (1945). Non parametric tests against trend, Econometrica, 13, 245-259. https://doi.org/10.2307/1907187
Meena, H.M., Machiwal, D., Santra, P., Moharana, P.C., & Singh, D.V. (2019). Trends and homogeneity of monthly, seasonal and annual rainfall over arid region of Rajasthan, India. Theoretical and Applied Climatology, 136, 795-811,https://doi.org/10.1007/s00704-018-2510-9
Meena, H. M., Santra, P., Moharana, P. C., & Pandey, C. B. (2016). Crop productivity response to rainfall variability in Kharif season in arid western Rajasthan. Journal of Environmental Science, 10(1), 19-23.
Mehta, D., & Yadav, S. M. (2021). Analysis of long-term rainfall trends in Rajasthan, India. In: R. Jha, V. P. Singh, V. Singh, L. B. Roy, R. Thendiyath (Editors), Climate Change Impacts on Water Resources: Hydraulics, Water Resources and Coastal Engineering,Springer International Publishing, 293-306. https://doi.org/10.1007/978-3-030-64202-0_26
Meshram, S. G., Singh, V. P., & Meshram, C. (2017). Long-term trend and variability of precipitation in Chhattisgarh State, India. Theoretical and Applied Climatology, 129, 729-744. https://doi.org/10.1007/s00704-016-1804-z
Ortiz, D. I., Piche-Ovares, M., Romero-Vega, L. M., Wagman, J., & Troyo, A. (2021). The impact of deforestation, urbanization, and changing land use patterns on the ecology of mosquito and tick-borne diseases in Central America. Insects, 13(1), 20. https://doi.org/10.3390/insects13010020
Palsaniya, D. R., Rai, S. K., Sharma, P., Satyapriya, & Ghosh, P. K. (2016). Natural resource conservation through weather-based agro-advisory. Current Science,111(2), 256-258.
Pant, M., Bhatla, R., Ghosh, S., Das, S., & Mall, R. K. (2024). How climate change is affecting the summer monsoon extreme rainfall pattern over the Indo-Gangetic Plains of India: Present and future perspectives. Climate Dynamics, 62(2), 1055-1075. https://doi.org/10.1007/s00382-023-06953-x
Partal, T., & Kahya, E. (2006). Trend analysis in Turkish precipitation data. Hydrological Processes, 20(9), 2011-2026. https://doi.org/10.1002/hyp.5993
Patakamuri, S. K., Muthiah, K., & Sridhar, V. (2020). Long-term homogeneity, trend, and change-point analysis of rainfall in the arid district of Ananthapuramu, Andhra Pradesh State, India. Water, 12(1), 211. https://doi.org/10.3390/w12010211
Pathak, P., Chourasia, A. K., Wani, S. P., & Sudi, R. (2013). Multiple impact of integrated watershed management in low rainfall semi-arid region: A case study from eastern Rajasthan, India. Journal of Water Resource and Protection, 5(1), 27-36. http://dx.doi.org/10.4236/jwarp.2013.51004
Pearson, E. S., & Hartley, H. O. (1966). Biometrika Tables for Statisticians. Third edition, Cambridge University Press, London
Pingale, S. M., Khare, D., Jat, M. K., & Adamowski, J. (2014). Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmospheric Research, 138, 73-90. https://doi.org/10.1016/j.atmosres.2013.10.024
Piyoosh, A. K., & Ghosh, S. K. (2017). Effect of autocorrelation on temporal trends in rainfall in a valley region at the foothills of Indian Himalayas. Stochastic Environmental Research and Risk Assessment, 31, 2075-2096. https://doi.org/10.1007/s00477-016-1347-y
Prasad, S. K., Samota, A., Singh, M. K., & Verma, S. K. (2014). Cultivars and nitrogen levels influence on yield attributes, yield and protein content of pearl millet under semi-arid condition of vindhyan region. The Ecoscan, 6, 47-50.
Praveen, B., Talukdar, S., Shahfahad, Mahato, S., Mondal, J., Sharma, P., Islam, A.R.M.T., & Rahman, A. (2020). Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports, 10(1), 10342. https://doi.org/10.1038/s41598-020-67228-7
Rahman, M. A., Kamal, S., & Billah, M. M. (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
Saha, U., & Sateesh, M. (2022). Rainfall extremes on the rise: Observations during 1951-2020 and bias-corrected CMIP6 projections for near-and late 21st century over Indian landmass. Journal of Hydrology, 608, 127682. https://doi.org/10.1016/j.jhydrol.2022.127682
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63, 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
Saini, D., Bhardwaj, P., & Singh, O. (2022). Recent rainfall variability over Rajasthan, India. Theoretical and Applied Climatology, 148(1), 363-381. https://doi.org/10.1007/s00704-021-03904-6
Sharma, S. K., Sharma, D. P., Sharma, M. K., Gaur, K., & Manohar, P. (2021). Trend analysis of temperature and rainfall of Rajasthan, India. Journal of Probability and Statistics, 2021, 6296709. https://doi.org/10.1155/2021/6296709
Thomas, D. S. (1997). Science and the desertification debate. Journal of Arid Environments, 37(4), 599-608. https://doi.org/10.1006/jare.1997.0293
Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2015). Attribution of climate extreme events. Nature Climate Change, 5(8), 725-730. https://doi.org/10.1038/nclimate2657
UNSO. (1997). Aridity zones and dryland populations: An assessment of population levels in the world’s drylands with particular reference to Africa. UNDP Office to Combat Desertification and Drought (UNSO), New York.
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9), 1807-1829. https://doi.org/10.1002/hyp.1095
Yue, S., & Wang, C. Y. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, 18: 201-218. https://doi.org/10.1023/B:WARM.0000043140.61082.60
