Abstract
India is dependent on rainfall for agriculture, food security and economic advancements. Spatial and temporal variability of rainfall is well reported, as well as the recent upsurge of extremes due to climate change. In Maharashtra, rainfall intensity varies widely from very high (coasts) to low (west-interior). Extreme value analysis (EVA) of heavy annual rainfall for the various districts of Maharashtra was conducted to ascertain the following for each district: suitable extreme value distribution; extreme (high) annual rainfall; efficacy of gap-filling for missing annual record/s. The suitability of various statistical distributions for representing high annual rainfall values was evaluated using Anderson–Darling test (quantitative), quantile and probability plots (qualitative). The gap-filling exercise resulted in minor variations (<10%) in the estimated extremes, positive as well as negative, for different cases. Therefore, the results of EVA on the actual records are advocated. The suitable distributions for 35 districts are presented in maps and Frechet distribution was adjudged suitable for almost 60% of districts. The estimated extremes for all districts are listed for different return periods for ready reference. These values would be useful for the planning and construction of hydraulic structures, water resources and reservoir management, design and planning of irrigation.
Research highlights
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Suitable probability distribution functions for modelling the extremes of annual rainfall in each district of Maharashtra have been identified and the parameters have been provided.
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Extreme values of annual rainfall for each district in Maharashtra for selected return periods have been presented.
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Evaluation was performed on whether the gaps in the annual rainfall records would affect EVA of the annual rainfall in the districts of Maharashtra.
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References
Amrit K, Soni A R, Sunayana Mishra S K, Vijay R and Kumar R 2020 Assessment of frequency and severity of droughts in Maharashtra state of India; Arab. J. Geosci. 13 1294, https://doi.org/10.1007/s12517-020-06400-9.
Ang A and Tang W 2007 Probability concepts in engineering – Emphasis on applications in Civil and Environmental Engineering; John Wiley and Sons Inc, United States of America.
Babar S and Ramesh H 2014 Analysis of extreme rainfall events over Nethravathi basin; ISH J. Hydraul. Eng. 20(2) 212–221, https://doi.org/10.1080/09715010.2013.87235.
Baig I A, Chandio A A, Ozturk I, Kumar P, Khan Z A and Salam M A 2022 Assessing the long- and short-run asymmetrical effects of climate change on rice production: Empirical evidence from India; Environ. Sci. Pollut. Res. 29 34,209–34,230, https://doi.org/10.1007/s11356-021-18014-z.
Bhardwaj M, Kumar P, Kumar S, Dagar V and Kumar A 2022 A district-level analysis for measuring the effects of climate change on production of agricultural crops, i.e., wheat and paddy: Evidence from India; Environ. Sci. Pollut. Res. 29 31,861–31,885, https://doi.org/10.1007/s11356-021-17994-2.
Brilhante M F, Gomes M I and Pestana D 2013 A simple generalisation of the Hill estimator; Comput. Stat. Data Anal. 57(1) 518–535.
British Broadcasting Company (BBC) 2022 Maharashtra floods: The tribal families living on a highway with animals; https://www.bbc.com/news/world-asia-india-62711069.
Caers J, Beirlant J and Maes M A 1999 Statistics for modeling heavy tailed distributions in Geology: Part I – Methodology; Math. Geol. 31(4) 391–410.
Chaudhuri R R and Sharma P 2020 Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: Case study of Delhi, India; Nat. Hazards 104 2307–2324, https://doi.org/10.1007/s11069-020-04273-5.
Chaudhuri R R and Sharma P 2021 An integrated stochastic approach for extreme rainfall analysis in the National Capital Region of India; J. Earth Syst. Sci. 130(16), https://doi.org/10.1007/s12040-020-01510-0.
Coles S 2001 An Introduction to Statistical Modelling of Extreme Values; Springer-Verlag, London Limited, Great Britain.
Daoo V J and Krishnamoorthy T M 2000 Extreme value analysis of meteorological elements at Trombay, Mumbai; Indian J. Radio Space Phys. 29 304–308.
Das S 2018 Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra; India; Arab. J. Geosci. 11 576, https://doi.org/10.1007/s12517-018-3933-4.
Das S, Millington N and Simonovic S P 2013 Distribution choice for the assessment of design rainfall for the city of London (Ontario, Canada) under climate change; Can. J. Civ. Eng. 40 121–129, https://doi.org/10.1139/cjce-2011-0548.
Dauji S 2019 Clock hour correction effect on extreme value analysis of rainfall on Western Coast of India; ISH J. Hydraul. Eng. 28(suppl) 390–402, https://doi.org/10.1080/09715010.2019.1687338.
Dauji S 2020 Author’s reply to discussion of ‘Clock hour correction effect on extreme value analysis of rainfall on Western Coast of India’; ISH J. Hydraul. Eng. 28(suppl) 572–573, https://doi.org/10.1080/09715010.2020.1792363.
Dauji S 2023 Climate change indications in short-period rainfall records from coastal India using trend analysis: A proposed framework; Arab. J. Geosci. 16 189, https://doi.org/10.1007/s12517-023-11269-5.
Davison A C and Smith R L 1990 Models for exceedances over high thresholds; J. R. Stat. Soc. Series B: Stat. Methodol. 52(3) 393–442.
De U S, Singh G P and Rase D M 2013 Urban flooding in recent decades in four mega cities of India; J. Indian Geophys. Union 17(2) 153–165.
Deka S, Borah M and Kakaty S C 2011 Statistical analysis of annual maximum rainfall in North-East India: An application of LH-moments; Theor. Appl. Climatol. 104 111–122, https://doi.org/10.1007/s00704-010-0330-7.
Department of Agriculture (DoA) 2016 Manual for Drought Management; Department of Agriculture, Cooperation & Farmers Welfare, Ministry of Agriculture & Farmers Welfare, Government of India, New Delhi.
Gangarde A, Dauji S and Londhe S 2022 Comparison of long-term and short-term trends of annual rainfall in India: A case study; ISH J. Hydraul. Eng. 29(3) 411–424, https://doi.org/10.1080/09715010.2022.2084351.
Ghandre N, Dauji S and Londhe S 2020 Analysis of precipitation extremes for coastal districts of Maharashtra; Proceedings of Int. Conf. Recent Adv. Comput. Techniques (IC-RACT) 2020, https://doi.org/10.2139/ssrn.3697534.
Ghate A S and Timbadiya P V 2021 Comprehensive extreme rainfall analysis: A study on Ahmedabad region, India; ISH J. Hydraul. Eng. 28(4) 438–448, https://doi.org/10.1080/09715010.2021.1905566.
Ghate A S and Timbadiya P V 2022 Effect of discretisation on extreme rainfall analysis: A study of Jaipur city in India; Mausam 73(2) 341–354, https://doi.org/10.54302/mausam.v73i2.3570.
Ghosh S, Das D, Kao S C and Ganguly A R 2012 Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes; Nat. Clim. Change 2 86–91, https://doi.org/10.1038/nclimate1327.
Guhathakurta P and Rajeevan M 2007 Trends in the rainfall pattern over India; Int. J. Climatol. 28 1453–1469, https://doi.org/10.1002/joc.1640.
Guhathakurta P, Sreejith O P and Menon P A 2011 Impact of climate change on extreme rainfall events and flood risk in India; J. Earth Syst. Sci. 120(3) 359–373.
Gupta A, Dauji S, Srivastava P K and Bhargava K 2018 Extreme value analysis by graphical and Lieblein techniques for rainfall on Western Coast of India; IWRA (India) J. 7(2) 38–45.
Harshanth R, Dauji S and Srivastava P K 2021a Quality checks on continuous rainfall Records: A case study; In: Climate Change Impacts on Water Resources, Water Science and Technology Library (eds) Jha et al., Springer Nature Switzerland AG 98 223–232, https://doi.org/10.1007/978-3-030-64202-0_120.
Harshanth R, Dauji S and Srivastava P K 2021b Evaluation of time discretization of daily rainfall from the literature for a specific site; In: Climate Change Impacts on Water Resources, Water Science and Technology Library (eds) Jha et al., Springer Nature Switzerland AG 98 207–221, https://doi.org/10.1007/978-3-030-64202-0_19.
Harshanth R, Dauji S and Srivastava P K 2023 Development of site-specific time distribution pattern of rainfall for Tarapur, India; J. Earth Syst. Sci. 132 77, https://doi.org/10.1007/s12040-023-02086-1.
http://agricoop.nic.in/sites/default/files/Maharashtra-SAPV1.3-2.pdf; accessed 23 September 2019.
https://censusindia.gov.in/census.website/data/census-tables#, Maharashtra Data; accessed 10 October 2022.
https://www.maharashtra.gov.in/1128/Districts, Maharashtra District Map; accessed 23 September 2019.
Indian Meteorological Department (IMD) 2018 Rainfall data; http://dsp.imdpune.gov.in/; accessed 23 September 2019.
India Today 2019 Floods hit power, water supply in Sangli, Kolhapur in Maharashtra; https://www.indiatoday.in/india/story/floods-hit-power-water-supply-in-sangli-kolhapur-in-maharashtra-1578930-2019-08-09, accessed 10 October 2022.
Khaladkar R M, Mahajan P N and Kulkarni J R 2009 Alarming rise in the number and intensity of extreme point rainfall events over the Indian zone under climate change scenario; RR 123, ISSN 0252-1075, Pune, Indian Institute of Tropical Meteorology.
Kharake A, Pathare J and Deshmukh P 2021 Spatio-temporal variability of intra-monsoon rainfall in Pravara-Mula River Basin, India; Arab. J. Geosci. 14 890, https://doi.org/10.1007/s12517-021-07268-z.
Kothawale D R and Rajeevan M 2017 Monthly, seasonal and annual rainfall time series for all India homogeneous regions and meteorological sub-divisions: 1871–2016; RR-138, ESSO/IITM/STCVP/SR/02(2017)/189, ISSN 0252-1075, Pune, Indian Institute of Tropical Meteorology.
Kulanthaivelu R K, Iyyanar S and Ramakrishnan S 2022 Climate change and agricultural losses in India; Am. J. Econ. Sociol. 81(2) 339–358, https://doi.org/10.1111/ajes.12461.
Laio F 2004 Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters; Water Resour. Res. 40 W09308, https://doi.org/10.1029/2004WR003204.
Mumbai Metropolitan Region Development Authority (MMRDA) 2006 Fact Finding Committee on Mumbai Floods: Final Report; Volume 1, March 2006. https://mmrda.maharashtra.gov.in/documents/10180/11271261/1/7026649b-8587-43f7-b04a-caf4aa2678b7.
Naghavi B, Yu F X and Singh V P 1993 Comparative evaluation of frequency distributions for Louisiana extreme rainfall; Water Resour. Bull. Am. Water Resour. Assoc. 29(2) 211–219.
Nair S C and Mirajkar A 2022 Drought vulnerability assessment across Vidarbha region, Maharashtra, India; Arab. J. Geosci. 15 355, https://doi.org/10.1007/s12517-022-09623-0.
Parida B P 1999 Modelling of Indian summer monsoon rainfall using a four-parameter Kappa distribution; Int. J. Climatol. 19 1389–1398, https://doi.org/10.1002/(SICI)1097-0088(199910)19:12%3c1389::AID-JOC435%3e3.0CO;2-T.
Patel Z B and Yadav S M 2022 Analysis of changes in extreme rainfall characteristics over Ambica river basin, India; ISH J. Hydraul. Eng. 29(4) 569–579, https://doi.org/10.1080/09715010.2022.2098681.
Pawar U V 2022 Rainfall distribution and trends over the semi-arid Marathwada region of Maharashtra, India; Arab. J. Geosci. 15 1738, https://doi.org/10.1007/s12517-022-11006-4.
Pentapati S, Deo M C, Kerkar J and Vethamony P 2015 Projected impact of climate change on waves at Mumbai High; Inst. Civ. Eng. Proc. Marit. Eng. 168(MA1) 20–29, https://doi.org/10.1680/maen.14.00017.
Ramesh C, Vivekanandan N, Surwade K B, Bapat A D, Govindan S and Mathew F T 2008 Extreme value analysis of rainfall in Mumbai region to aid estimation of severe flood; ISH J. Hydraul. Eng. 14(2) 102–117, https://doi.org/10.1080/09715010.2008.10514908.
Revee D E 1996 Estimation of extreme Indian monsoon rainfall; Int. J. Climatol. 16 105–112.
Roth M, Jongbloed G and Buishand T A 2015 Threshold selection for regional peaks-over-threshold data; J. Appl. Stat. 43 1291–1309, https://doi.org/10.1080/02664763.2015.1100589.
Sabarish R M, Narasimhan R, Chandhru A R, Suribabu C R, Sudharsan J and Nithiyanantham S 2017 Probability analysis for consecutive-day maximum rainfall for Tiruchirapalli City (south India, Asia); Appl. Water Sci. 7 1033–1042, https://doi.org/10.1007/s13201-015-0307-x.
Sardana D, Kumar P, Weller E and Rajni 2022 Seasonal extreme rainfall variability over India and its association with surface air temperature; Theor. Appl. Climatol. 149 185–205, https://doi.org/10.1007/s00704-022-04045-0.
Sarkar S and Maity R 2020 Increase in probable maximum precipitation in a changing climate over India; J. Hydrol. 585 124806, https://doi.org/10.1016/j.jhydrol.2020.124806.
Scarrott C and MacDonald A 2012 A review of extreme value threshold estimation and uncertainty quantification; Revstat. Stat. J. 10(1) 33–60.
Shrestha S, Bae D H, Hok P, Ghimire S and Pokhrel Y 2021 Future hydrology and hydrological extremes under climate change in Asian river basins; Sci. Rep. 11 17089, https://doi.org/10.1038/s41598-021-96656-2.
Solari S, Eguen M, Polo M J and Losada M A 2017 Peaks Over Threshold (POT): A methodology for automatic threshold estimation using goodness of fit p-value; Water Resour. Res. 53 2833–2849, https://doi.org/10.1002/2016WR019426.
Srivastava P K, Dauji S and Bhargava K 2023 Site-specific rainfall analysis to generate design parameters: A case study; AIP Conf. Proc. 2721 040001-1–040001-23, https://doi.org/10.1063/5.0153909
Tabari H 2021 Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation; J. Hydrol. 593 125932, https://doi.org/10.1016/j.jhydrol.2020.125932.
Vitthal H, Karmakar S and Ghosh S 2013 Diametric changes in trends and patterns of extreme rainfall over India from pre-1950 to post-1950; Geophys. Res. Lett. 40 3253–3258, https://doi.org/10.1002/grl.50631.
Vivekanandan N 2014 Rainfall frequency analysis using order statistics approach of extreme value distributions; SSRG Int. J. Civ. Eng. 1(4) 6–12, https://doi.org/10.14445/23488352/IJCE-V1I4P102.
Vivekanandan N 2015 Assessing the adequacy of the parameter estimation methods of the Gumbel distribution for modeling the seasonal and annual rainfall; Int. J. Manag. Sci. Eng. Manag. 10(4) 253–259, https://doi.org/10.1080/17509653.2014.953612.
Wikipedia 2021 Maharashtra floods; https://en.wikipedia.org/wiki/2021_Maharashtra_floods.
Yang X, Zhang J and Ren W X 2017 Threshold selection for extreme strain extrapolation due to vehicles on bridges; Procedia Struct. Integr. 5 1176–1183, https://doi.org/10.1016/j.prostr.2017.07.030.
Yang X, Zhang J and Ren W X 2018 Threshold selection for extreme value estimation of vehicle load effect on bridges; Int. J. Distrib. Sens. Netw. 14(2), https://doi.org/10.1177/1550147718757698.
Zope P E, Eldho T I and Jothiprakash V 2012 Study of spatio-temporal variations of rainfall pattern in Mumbai City; India; J. Environ. Res. Dev. 6(3) 545–553.
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Saha Dauji and Shreenivas Londhe: Conceptualization and methodology; Nikhilesh Gandhre: Formal analysis and investigation; Saha Dauji: Writing – original draft preparation; All authors: Writing – review and editing; Nikhilesh Gandhre and Saha Dauji: Revision. All authors read and approved the final version of manuscript.
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Communicated by Parthasarathi Mukhopadhyay
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Gandhre, N., Dauji, S. & Londhe, S. Extreme value analysis of annual precipitation in districts of Maharashtra, India. J Earth Syst Sci 133, 53 (2024). https://doi.org/10.1007/s12040-023-02243-6
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DOI: https://doi.org/10.1007/s12040-023-02243-6