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Investigating Spatio-Temporal Trends and Anomalies in Long-Term Meteorological Variables to Determine If Maharashtra is an Emerging Warming State in India

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Civil Engineering for Multi-Hazard Risk Reduction (IACESD 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 457))

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Abstract

This paper offers a comprehensive investigation of crucial meteorological variables [rainfall (P), surface temperature (T), and relative humidity (RH)] for Maharashtra (307,690 km2 area), a dry-arid state in the western Indian subcontinent, encompassing four meteorological subdivisions: Konkan and Madhya Maharashtra (west) and Marathwada and Vidarbha (east). The central hypothesis posits that Maharashtra is rapidly becoming an emerging warming state. To examine this hypothesis, long-term hydroclimatic time series (1980–2020) data for P, T, and RH were derived from the ECMWF-ERA5 dataset and analyzed using non-parametric Mann–Kendall and Sen's Slope methods at α = 0.05 significance level. Pearson correlation coefficient (PCC) was applied for time series and scatter plot interpretation. Anomalies were identified by comparing data from 2011 to 2020 to the baseline (1981–2020). The results showed significant and positive trends in temperature (T) and rainfall (P) across Maharashtra and its subdivisions. Relative humidity (RH) had an insignificant but positive trend. The highest correlation was between RH and P, followed by T and P, with the weakest association between T and RH. Konkan had the highest RH and P values, while Vidarbha experienced the highest temperatures. Temperature anomalies ranged from 0.19 to 1.29 °C in Maharashtra, with the most significant anomaly in Marathwada (0.62–1.77 °C) and Vidarbha (0.67–1.56 °C). RH and P anomaly values decreased with rising temperatures, especially during summer, winter, and in the eastern region, potentially leading to hotter summers and less cool winters. In conclusion, the findings provide robust evidence of Maharashtra's emergence as a warming state, particularly during the recent decade (2011–2020).

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References

  1. IPCC (2021) Summary for policymakers. In: Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change

    Google Scholar 

  2. Wang Y, Dong P, Hu W, Chen G, Zhang D, Chen B, Lei G (2022) Modeling the climate suitability of northernmost mangroves in China under climate change scenarios. Forests 13(1):64. https://doi.org/10.3390/f13010064

    Article  Google Scholar 

  3. Byrne MP, O’Gorman PA (2018) Trends in continental temperature and humidity directly linked to ocean warming. Proc Natl Acad Sci 115(19):4863–4868. https://doi.org/10.1073/pnas.1722312115

    Article  Google Scholar 

  4. Gunawardhana LN, Al-Rawas GA, Kazama S (2017) An alternative method for predicting relative humidity for climate change studies. Meteorol Appl 24(4):551–559. https://doi.org/10.1002/met.1641

    Article  Google Scholar 

  5. Srivastava A, Maity R, Desai VR (2022a) Assessing global-scale synergy between adaptation, mitigation, and sustainable development for projected climate change. In: Chatterjee U et al (eds) Ecological footprints of climate change. Springer Climate. https://doi.org/10.1007/978-3-031-15501-7_2

  6. Tung YS, Wang CY, Weng SP, Yang CD (2022) Extreme index trends of daily gridded rainfall dataset (1960–2017) in Taiwan. Terr Atmos Oceanic Sci 33(1):1–16. https://doi.org/10.1007/s44195-022-00009-z

    Article  Google Scholar 

  7. Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteor Soc 84(9):1205–1218. https://doi.org/10.1175/BAMS-84-9-1205

    Article  Google Scholar 

  8. Rusca M, Messori G, Di Baldassarre G (2021) Scenarios of human responses to unprecedented social‐environmental extreme events. Earth's Future, 9(4), e2020EF001911. https://doi.org/10.1029/2020EF001911

  9. Ummenhofer CC, Meehl GA (2017) Extreme weather and climate events with ecological relevance: a review. Philos Trans Royal Soc B Bio Sci 372(1723):20160135. https://doi.org/10.1098/rstb.2016.0135

    Article  Google Scholar 

  10. Pande CB, Al-Ansari N, Kushwaha NL, Srivastava A, Noor R, Kumar M et al (2022) Forecasting of SPI and meteorological drought based on the artificial neural network and M5P model tree. Land 11(11):2040. https://doi.org/10.3390/land11112040

    Article  Google Scholar 

  11. Winsemius HC, Jongman B, Veldkamp TI, Hallegatte S, Bangalore M, Ward PJ (2018) Disaster risk, climate change, and poverty: assessing the global exposure of poor people to floods and droughts. Environ Dev Econ 23(3):328–348. https://doi.org/10.1017/S1355770X17000444

    Article  Google Scholar 

  12. Yang TH, Liu WC (2020) A general overview of the risk-reduction strategies for floods and droughts. Sustainability 12(7):2687. https://doi.org/10.3390/su12072687

    Article  Google Scholar 

  13. Arora NK (2019) Impact of climate change on agriculture production and its sustainable solutions. Environ Sustain 2(2):95–96. https://doi.org/10.1007/s42398-019-00078-w

    Article  Google Scholar 

  14. De Silva MMGT, Kawasaki A (2018) Socioeconomic vulnerability to disaster risk: a case study of flood and drought impact in a rural Sri Lankan community. Ecol Econ 152:131–140. https://doi.org/10.1016/j.ecolecon.2018.05.010

    Article  Google Scholar 

  15. Dhanuka A, Srivastava A, Khadke L, Kushwaha NL (2023) Smart geometric design of highways using HTML programming for sustainable and climate resilient cities. In: Chatterjee U, Bandyopadhyay N, Setiawati MD, SarkarS (eds) Urban commons, future smart cities and sustainability. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-24767-5_39

  16. Elbeltagi A, Raza A, Hu Y, Al-Ansari N, Kushwaha NL et al (2022) Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration. Appl Water Sci 12(7):152. https://doi.org/10.1007/s13201-022-01667-7

    Article  Google Scholar 

  17. Kalantari Z, Ferreira CSS, Keesstra S, Destouni G (2018) Nature-based solutions for flood-drought risk mitigation in vulnerable urbanizing parts of East-Africa. Current Opin Environ Sci Health 5:73–78. https://doi.org/10.1016/j.coesh.2018.06.003

    Article  Google Scholar 

  18. Raikes J, Smith TF, Jacobson C, Baldwin C (2019) Pre-disaster planning and preparedness for floods and droughts: a systematic review. Int J Disaster Risk Reduction 38:101207. https://doi.org/10.1016/j.ijdrr.2019.101207

    Article  Google Scholar 

  19. Srivastava A, Jain S, Maity R, Desai VR (2022b) Demystifying artificial intelligence amidst sustainable agricultural water management. In: Zakwan M, Wahid A, Niazkar M, Chatterjee U (eds) Water resource modeling and computational technologies. Current directions in water scarcity research, vol 7. Elsevier. https://doi.org/10.1016/B978-0-323-91910-4.00002-9

  20. Elbeltagi A, Srivastava A, Kushwaha NL, Juhász C, Tamás J, Nagy A (2023a) Meteorological data fusion approach for modeling crop water productivity based on ensemble machine learning. Water 15(1):30. https://doi.org/10.3390/w15010030

    Article  Google Scholar 

  21. Elbeltagi A, Srivastava A, Al-Saeedi AH, Raza A, Abd-Elaty I, El-Rawy M (2023b) Forecasting long-series daily reference evapotranspiration based on best subset regression and machine learning in Egypt. Water 15(6):1149. https://doi.org/10.3390/w15061149

    Article  Google Scholar 

  22. Elbeltagi A, Srivastava A, Deng J, Li Z, Raza A, Khadke L, Yu Z, El-Rawy M (2023c) Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments. Agric Water Manag 283:108302. https://doi.org/10.1016/j.agwat.2023.108302

    Article  Google Scholar 

  23. Li X, Zhang K, Gu P, Feng H, Yin Y, Chen W, Cheng B (2021) Changes in precipitation extremes in the Yangtze River Basin during 1960–2019 and the association with global warming, ENSO, and local effects. Sci Total Environ 760:144244. https://doi.org/10.1016/j.scitotenv.2020.144244

    Article  Google Scholar 

  24. Mahajan DR, Dodamani BM (2015) Trend analysis of drought events over upper Krishna basin in Maharashtra. Aquat Procedia 4:1250–1257. https://doi.org/10.1016/j.aqpro.2015.02.163

    Article  Google Scholar 

  25. Singh C, Deshpande T, Basu R (2017) How do we assess vulnerability to climate change in India? A systematic review of literature. Reg Environ Change 17(2):527–538. https://doi.org/10.1007/s10113-016-1043-y

    Article  Google Scholar 

  26. NIH—National Institute of Hydrology (2020). Hydrology and water resources information system for India, Water Resources Systems Division, National Institute of Hydrology. http://117.252.14.242/rbis/India_Information/Water%C2%A0Budget.htm. Accessed 3 Apr 2022

  27. NWM—National Water Mission (2018) State-water budgeting report. Ministry of Jal Shakti, Department of Water Resources, Government of India. http://nwm.gov.in/sites/default/files/A%20note%20on%20State%20Water%20Budgeting%2019.3.2018.pdf . Accessed 3 Apr 2022

  28. Goyal MK, Surampalli RY (2018) Impact of climate change on water resources in India. J Environ Eng 144(7):04018054. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001394

    Article  Google Scholar 

  29. Singh D, Tsiang M, Rajaratnam B, Diffenbaugh NS (2014) Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nat Clim Chang 4(6):456–461. https://doi.org/10.1038/nclimate2208

    Article  Google Scholar 

  30. Kumar V, Jain SK, Singh Y (2010) Analysis of long-term rainfall trends in India. Hydrol Sci J 55(4):484–496. https://doi.org/10.1080/02626667.2010.481373

    Article  Google Scholar 

  31. Mondal A, Khare D, Kundu S (2015) Spatial and temporal analysis of rainfall and temperature trend of India. Theoret Appl Climatol 122(1):143–158. https://doi.org/10.1007/s00704-014-1283-z

    Article  Google Scholar 

  32. Dhawan V (2017) Water and agriculture in India. In: Background paper for the South Asia expert panel during the Global Forum for Food and Agriculture (Vol. 28). https://www.oav.de/fileadmin/user_upload/5_Publikationen/5_Studien/170118_Study_Water_Agriculture_India.pdf. Accessed 3 Apr 2022

  33. Mehta L (2005) The politics and poetics of water: The naturalisation of scarcity in Western India. Orient Blackswan.

    Google Scholar 

  34. GoM—Government of Maharashtra (2020) Economic survey of Maharashtra 2019–20. Directorate of Economics and Statistics, Planning Department, Government of Maharashtra, Mumbai. https://www.maharashtra.gov.in/Site/upload/WhatsNew/ESM_2019_20_Eng_Book.pdf. Accessed 3 Apr 2022

  35. Singh RN, Sah S, Das B, Vishnoi L, Pathak H (2021) Spatio-temporal trends and variability of rainfall in Maharashtra, India: Analysis of 118 years. Theoret Appl Climatol 143(3):883–900. https://doi.org/10.1007/s00704-020-03452-5

    Article  Google Scholar 

  36. Guhathakurta P, Saji E (2013) Detecting changes in rainfall pattern and seasonality index vis-à-vis increasing water scarcity in Maharashtra. J Earth Syst Sci 122(3):639–649. https://doi.org/10.1007/s12040-013-0294-y

    Article  Google Scholar 

  37. Ingle ST, Patil SN, Mahale NK, Mahajan YJ (2018) Analyzing rainfall seasonality and trends in the North Maharashtra region. Environ Earth Sci 77(18):1–12. https://doi.org/10.1007/s12665-018-7837-0

    Article  Google Scholar 

  38. Mandale VP, Jedhe SH, Khadtare MY (2019) Spatio-temporal trends of rainfall and rainy days in the Marathwada Region of Maharashtra State. Clim Change 5(17):55–61. http://www.discoveryjournals.org/climate_change/current_issue/v5/n17/A7.pdf

  39. Jedhe SH, Kadam US, Mane MS, Mahale DM, Nandgude SB, Thokal RT (2018) Trends of rainfall and temperature in Konkan region of Maharashtra. J Agrometeorol 20(1), 80–83. http://krishi.icar.gov.in/jspui/handle/123456789/48447

  40. Dawoodi HH (2021) Rainfall prediction in North Maharashtra region using support vector machine. Turk J Comput Mathematics Educ 12(7):1501–1505. https://doi.org/10.17762/turcomat.v12i7.2957

  41. Kelkar SM, Kulkarni A, Rao KK (2020) Impact of climate variability and change on crop production in Maharashtra, India. Curr Sci 118(8):1235–1245. https://www.currentscience.ac.in/Volumes/118/08/1235.pdf

  42. Masroor M, Rehman S, Avtar R, Sahana M, Ahmed R, Sajjad H (2020) Exploring climate variability and its impact on drought occurrence: evidence from Godavari Middle sub-basin India. Weather Clim Extremes 30:100277. https://doi.org/10.1016/j.wace.2020.100277

    Article  Google Scholar 

  43. Masroor M, Avtar R, Sajjad H, Choudhari P, Kulimushi LC, Khedher KM et al (2022) Assessing the influence of land use/land cover alteration on climate variability: an analysis in the Aurangabad district of Maharashtra state India. Sustainability 14(2):642. https://doi.org/10.3390/su14020642

    Article  Google Scholar 

  44. Shah SH, Harris LM, Johnson MS, Wittman H (2021) A ‘drought-free’ Maharashtra? politicising water conservation for rain-dependent agriculture. Water Altern 14(2):573–596. https://www.water-alternatives.org/index.php/alldoc/articles/vol14/v14issue2/628-a14-2-6/file

  45. Kaicker N, Imai KS, Gaiha R (2020) Severity of the Covid-19 Pandemic in India. The Case of three States: Maharashtra, Jharkhand and Meghalaya. GDI Working Paper, 2020–047. https://doi.org/10.2139/ssrn.3709831

  46. Pol SS, Rajderkar SS, Dhabekar PD, Bansode Gokhe SS (2021). Effect of climatic factors like rainfall, humidity and temperature on the dengue cases in the metropolitan city of Maharashtra. Int J Community Med Public Heal, 8(2):672–677. https://doi.org/10.18203/2394-6040.ijcmph20210220

  47. Sasane SA, Jadhav AS, Barik RK, Kumar GK, Raghavswamy V (2019) Application of spatial technology in malaria information infrastructure mapping with climate change perspective in Maharashtra, India. MAUSAM 70(4):787–806. https://metnet.imd.gov.in/mausamdocs/170411.pdf

  48. Saunik S, Shil P, Das SN, Rajankar SP, Khare O, Hosalikar KA, Kabir Y (2021) Predictions of disease spikes induced by climate variability: a pilot real time forecasting model project from Maharashtra, India. In: Kumar MD, Kabir Y, Hemani R, Bassi N (eds) Management of irrigation and water supply under climatic extremes. Global Issues in Water Policy, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-59459-6_9

  49. Kendall MG (1975) Rank correlation methods. Edition 4. Charles Griffin, London

    Google Scholar 

  50. Mann HB (1945) Nonparametric tests against trend. Econ J Econometric Soc 245–259. https://doi.org/10.2307/1907187

  51. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  MathSciNet  Google Scholar 

  52. Jena P, Azad S, Rajeevan MN (2015) Statistical selection of the optimum models in the CMIP5 dataset for climate change projections of Indian monsoon rainfall. Climate 3(4):858–875. https://doi.org/10.3390/cli3040858

    Article  Google Scholar 

  53. Dhorde AG, Korade MS, Dhorde AA (2017) Spatial distribution of temperature trends and extremes over Maharashtra and Karnataka States of India. Theoret Appl Climatol 130(1):191–204. https://doi.org/10.1007/s00704-016-1876-9

    Article  Google Scholar 

  54. Korade MS, Dhorde AG (2016) Trends in surface temperature variability over Mumbai and Ratnagiri cities of coastal Maharashtra, India. Mausam 67(2):455–462. https://doi.org/10.54302/mausam.v67i2.1352

  55. Khan MHR, Rahman A, Luo C, Kumar S, Islam GA, Hossain MA (2019) Detection of changes and trends in climatic variables in Bangladesh during 1988–2017. Heliyon 5(3):e01268. https://doi.org/10.1016/j.heliyon.2019.e01268

    Article  Google Scholar 

  56. Ali H, Fowler HJ, Mishra V (2018) Global observational evidence of strong linkage between dew point temperature and precipitation extremes. Geophys Res Lett 45(22):12–320. https://doi.org/10.1029/2018GL080557

    Article  Google Scholar 

  57. Dash SK, Jenamani RK, Kalsi SR, Panda SK (2007) Some evidence of climate change in twentieth-century India. Clim Change 85(3):299–321. https://doi.org/10.1007/s10584-007-9305-9

    Article  Google Scholar 

  58. Niu X, Wang S, Tang J, Lee DK, Gutowski W, Dairaku K et al (2015) Projection of Indian summer monsoon climate in 2041–2060 by multiregional and global climate models. J Geophys Res Atmos 120(5):1776–1793. https://doi.org/10.1002/2014JD022620

    Article  Google Scholar 

  59. Allan RP, Barlow M, Byrne MP, Cherchi A, Douville H, Fowler HJ et al (2020) Advances in understanding large-scale responses of the water cycle to climate change. Ann N Y Acad Sci 1472(1):49–75. https://doi.org/10.1038/s41598-020-70816-2

    Article  Google Scholar 

  60. Cox DT, Maclean IM, Gardner AS, Gaston KJ (2020) Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. Glob Change Biol 26(12):7099–7111. https://doi.org/10.1111/gcb.15336

    Article  Google Scholar 

  61. Fowler HJ, Lenderink G, Prein AF, Westra S, Allan RP, Ban N et al (2021) Anthropogenic intensification of short-duration rainfall extremes. Nat Rev Earth Environ 2(2):107–122. https://doi.org/10.1038/s43017-020-00128-6

    Article  Google Scholar 

  62. Hardwick JR, Westra S, Sharma A (2010) Observed relationships between extreme sub‐daily precipitation, surface temperature, and relative humidity. Geophys Res Lett 37(22). https://doi.org/10.1029/2010GL045081

  63. Kousari MR, Ekhtesasi MR, Tazeh M, Saremi Naeini MA, Asadi Zarch MA (2011) An investigation of the Iranian climatic changes by considering the precipitation, temperature, and relative humidity parameters. Theoret Appl Climatol 103(3):321–335. https://doi.org/10.1007/s00704-010-0304-9

    Article  Google Scholar 

  64. Singh P, Kumar V, Thomas T, Arora M (2008) Changes in rainfall and relative humidity in river basins in northwest and central India. Hydrol Processes Int J 22(16):2982–2992. https://doi.org/10.1002/hyp.6871

    Article  Google Scholar 

  65. Kousari MR, Asadi Zarch MA (2011) Minimum, maximum, and mean annual temperatures, relative humidity, and precipitation trends in arid and semi-arid regions of Iran. Arab J Geosci 4(5):907–914. https://doi.org/10.1007/s12517-009-0113-6

    Article  Google Scholar 

  66. Bhimala KR, Gouda KC, Himesh S (2021) Evaluating the spatial distribution of WRF-simulated rainfall, 2-m air temperature, and 2-m relative humidity over the urban region of Bangalore India. Pure Appl Geophys 178(3):1105–1120. https://doi.org/10.1007/s00024-021-02676-4

    Article  Google Scholar 

  67. Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change 119(2):345–357. https://doi.org/10.1007/s10584-013-0705-8

    Article  Google Scholar 

  68. Simmons AJ, Willett KM, Jones PD, Thorne PW, Dee DP (2010) Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets. J Geophys Res Atmos 115(D1). https://doi.org/10.1029/2009JD012442

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Acknowledgements

This work is partially supported by the Ministry of Earth Sciences (MoES) under the Government of India. The funding for the student (AS) was supported by Prime Minister’s Research Fellowship (PMRF/2401746/21CE91R03) under the Ministry of Education, Government of India. Technical assistance received from Leena Khadke is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, 721302, India

    Aman Srivastava & Rajib Maity

  2. Indian Institute of Technology (IIT) Dharwad, Dharwad, Karnataka, 580001, India

    Venkappayya R. Desai

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  1. Aman Srivastava
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Contributions

All authors contributed to the study's conception and design. AS collected the data and conducted the analysis, while RM and VRD guided the material preparation, data collection, and analysis. AS wrote the first draft of the manuscript, while RM and VRD commented on previous versions. All authors read and approved the final manuscript.

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Correspondence to Rajib Maity or Venkappayya R. Desai .

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Editors and Affiliations

  1. Department of Civil Engineering, Jyothy Institute of Technology, Bengaluru, Karnataka, India

    K. S. Sreekeshava

  2. Department of Civil Engineering, National Institute of Technology, Mangalore, Karnataka, India

    Sreevalsa Kolathayar

  3. Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

    N. Vinod Chandra Menon

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.

Data Availability Statement

The datasets generated during the current study and acquired from the sources will be made available from the corresponding author upon reasonable request.

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Srivastava, A., Maity, R., Desai, V.R. (2024). Investigating Spatio-Temporal Trends and Anomalies in Long-Term Meteorological Variables to Determine If Maharashtra is an Emerging Warming State in India. In: Sreekeshava, K.S., Kolathayar, S., Vinod Chandra Menon, N. (eds) Civil Engineering for Multi-Hazard Risk Reduction. IACESD 2023. Lecture Notes in Civil Engineering, vol 457. Springer, Singapore. https://doi.org/10.1007/978-981-99-9610-0_25

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