Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of daily average temperature trends in Kerala, India, using MERRA-2 reanalysis data: a climate change perspective

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data for a period of 40 years (1980–2019) was used to detect the trend characteristics of daily average temperature in the state of Kerala, India. Data extracted from a total of fifty locations in the state were statistically processed using Mann–Kendall (MK) and Spearman’s Rho (SR) tests to detect the trend, Pettitt test to identify the single change point, and Theil-Sen’s method for the calculation of the rate of change. The MERRA-2 product is validated for the study region according to statistical indicators. The daily average temperature in the state during the period of study varies between 16.56 and 32.64 °C. The spatial pattern of daily average maximum temperature shows higher temperature domains in the central and southern parts of the state. Trend characteristics of daily average temperature assessed through MK and SR tests show a significant increasing trend in all stations, with maximum values in stations located in the northern part of the state. Change point detected through the Pettitt test divided the sampling stations into three groups based on the change in daily average temperature characteristics in the years 2002 (north zone), 2009 (south zone), and 2012 (central zone), indicating nonunique spatial variability in temperature characteristics in the state. The rate of change in the daily average temperature assessed indicates an increase at the rate of an average of 0.013 °C.year−1. During the whole study period, the daily average temperature showed an overall increase of 0.54 °C, and for the 100-year futuristic prediction, the daily average temperature in the state shows an average increase of 1.35 °C. Among the stations, a higher rate of increase in daily average temperature is shown by stations located in the eastern part of the Pathanamthitta, Idukki, and Kollam districts. Though the rise in daily average temperature is not much higher, its spatial characteristics require more attention because, in recent times, the study area has faced repeated, severe, and long drought conditions along with sunburn incidents. As an agrarian state, a change in the temperature domain will adversely affect the overall agricultural production and will evoke not only a food crisis but also economic as well as water resources issues. The result obtained can be used as holistic basic information for understanding the impending effect of climate change in the state to frame better mitigation as well as management strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All the datasets used in this research is collected from open source. Temperature data was collected from NASA’s Global Modeling and Assimilation Office (GMAO)https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ and web service of Solar Radiation Data (SoDa).

References

  • Abbasnia M, Toros H (2020) Trend analysis of weather extremes across the coastal and non-coastal areas (case study: Turkey). J Earth Syst Sci 129:1–13. https://doi.org/10.1007/s12040-020-1359-3

    Article  Google Scholar 

  • Achilleos GA (2011) The inverse distance weighted interpolation method and error propagation mechanism–creating a DEM from an analogue topographical map. J Spat Sci 56:283–304. https://doi.org/10.1080/14498596.2011.623348

    Article  Google Scholar 

  • Adamala S, Velmurugan A (2020) Trend analysis of rainfall and air temperature in Andaman & Nicobar Islands, India. J Indian Soc Coastal Agric Res 38:95–103

    Google Scholar 

  • Agarwal S, Suchithra AS, Singh SP (2021) Analysis and interpretation of rainfall trend using Mann-Kendall’s and Sen’s Slope method. Indian J Ecol 48:453–457

    Google Scholar 

  • Aggarwal D, Attada R, Shukla KK, Chakraborty R, Kunchala RK (2021) Monsoon precipitation characteristics and extreme precipitation events over Northwest India using Indian high resolution regional reanalysis. Atmos Res 267:105993. https://doi.org/10.1016/j.atmosres.2021.105993

    Article  Google Scholar 

  • Ajithkumar B, Vysakh A, Sreekala AA (2017) Effect of El Nino on rainfall and thermal regime of Kerala. Contempo Res India 7(2):279–285

    Google Scholar 

  • Akinsanola AA, Ogunjobi KO (2017) Recent homogeneity analysis and long-term spatio-temporal rainfall trends in Nigeria. Theo Appl Climat 28(1):275–289

    Article  Google Scholar 

  • Almazroui M, Şen Z (2020) Trend analyses methodologies in hydro-meteorological records. Earth Sys Environ: 1–26. https://doi.org/10.1007/s41748-020-00190-6

  • Alonso-González E, López-Moreno JI, Gascoin S, García-ValdecasasOjeda M, Sanmiguel-Vallelado A, Navarro-Serrano F, Revuelto J, Ceballos A, Esteban-Parra MJ, Essery R (2018) Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014. Earth Sys Sci Data 10:303–315. https://doi.org/10.5194/essd-10-303-2018

    Article  Google Scholar 

  • Animashaun IM, Oguntunde PG, Akinwumiju AS, Olubanjo OO (2020) Rainfall analysis over the Niger Central Hydrological Area, Nigeria: variability, trend, and change point detection. Sci African 8:e00419. https://doi.org/10.1016/j.sciaf.2020.e00419

    Article  Google Scholar 

  • Asfaw A, Simane B, Hassen A, Bantider A (2018) Variability and time series trend analysis of rainfall and temperature in north central Ethiopia: a case study in Woleka sub-basin. Weather Clim Extrem 19:29–41. https://doi.org/10.1016/j.wace.2017.12.002

    Article  Google Scholar 

  • Ashraf MS, Ahmad I, Khan NM, Zhang F, Bilal A, Guo J (2021) Streamflow variations in monthly, seasonal, annual and extreme values using Mann-Kendall, Spearmen’s rho and innovative trend analysis. Water Res Manag 35:243–261. https://doi.org/10.1007/s11269-020-02723-0

    Article  Google Scholar 

  • Ashrit R, Indira Rani S, Kumar S, Karunasagar S, Arulalan T, Francis T, Routray A, Laskar SI, Mahmood S, Jermey P, Maycock A (2020) Imdaa regional reanalysis: performance evaluation during Indian summer monsoon season. J. Geophys. Res: Atmos. 125:e2019JD030973. https://doi.org/10.1029/2019JD030973

    Article  Google Scholar 

  • Bağdatlı MC, Can E (2019) Analysis of precipitation datas by Mann Kendall and Spermans rho rank correlation statistical approaches in Nevsehir Province of Turkey. Rec Res Sci Technol 11:24–31. https://doi.org/10.25081/rrst.2019.11.6082

    Article  Google Scholar 

  • Bandyopadhyay A, Bhadra A, Raghuwanshi NS, Singh R (2009) Temporal trends in estimates of reference evapotranspiration over India. J Hydrol Eng 14:508–515. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000006

    Article  Google Scholar 

  • Beier CM, Signell SA, Luttman A, DeGaetano AT (2012) High-resolution climate change mapping with gridded historical climate products. Landsc Ecol 27:327–342. https://doi.org/10.1007/s10980-011-9698-8

    Article  Google Scholar 

  • Bhatla R, Tripathi A (2014) The study of rainfall and temperature variability over Varanasi. Int J Earth Atmos Sci 1:90–94

    Google Scholar 

  • Bhattacharyya P, Munda S, Dash PK (2020) Climate change and greenhouse gas emission. New India Publishing Agency, New Delhi, p 110088

    Google Scholar 

  • Bhattacharyya S, Sreekesh S, King A (2022) Characteristics of extreme rainfall in different gridded datasets over India during 1983-2015. Atmos. Res. 267:105930. https://doi.org/10.1016/j.atmosres.2021.105930

    Article  Google Scholar 

  • Burns DA, Klaus J, McHale MR (2007) Recent climate trends and implications for water resources in the Catskill Mountain region, New York. USA J Hydrol 336:155–170. https://doi.org/10.1016/j.jhydrol.2006.12.019

    Article  Google Scholar 

  • Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, London

    Google Scholar 

  • Chand MB, Bhattarai BC, Pradhananga NS, Baral P (2021) Trend analysis of temperature data for the Narayani River Basin. Nepal Sci 3:1. https://doi.org/10.3390/sci3010001

    Article  Google Scholar 

  • Chowdhury MA, Hasan MK, Hasan MR, Younos TB (2020) Climate change impacts and adaptations on health of internally displaced people (IDP): an exploratory study on coastal areas of Bangladesh. Heliyon 6:e05018. https://doi.org/10.1016/j.heliyon.2020.e05018

    Article  Google Scholar 

  • Conte LC, Bayer DM, Bayer FM (2019) Bootstrap Pettitt test for detecting change points in hydroclimatological data: case study of Itaipu Hydroelectric Plant. Brazil Hydrol Sci J 64:1312–1326. https://doi.org/10.1080/02626667.2019.1632461

    Article  Google Scholar 

  • Dodman D (2009) Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories. Environ Urban 21:185–201. https://doi.org/10.1177/0956247809103016

    Article  Google Scholar 

  • Dorji T, Tamang TB (2019) Report on the analysis of historical climate and climate projection for Bhutan. Natl Center Hydrol Meteorol R Govern Bhutan

  • Dragaud ICD, Soares da Silva M, Assad LPDF, Cataldi M, Landau L, Elias RN, Pimentel LCG (2019) The impact of SST on the wind and air temperature simulations: a case study for the coastal region of the Rio de Janeiro state. Meteorol Atmos Phys 131(4):1083–1097. https://doi.org/10.1007/s00703-018-0622-5

    Article  Google Scholar 

  • Egbinola CN, Amanambu CA (2013) Climate variation assessment based on rainfall and temperature in Ibadan, South-Western. Nigeria J Environ Earth Sci 3:32–45

    Google Scholar 

  • Ensor LA, Robeson SM (2008) Statistical characteristics of daily precipitation: comparisons of gridded and point datasets. J Appl Meteorol Climatol 47:2468–2476. https://doi.org/10.1175/2008JAMC1757.1

    Article  Google Scholar 

  • Gable FJ. 1997. Climate change impacts on Caribbean coastal areas and tourism. J. Coast. Res., 49–69.

  • Gebrechorkos SH, Hülsmann S, Bernhofer C (2019) Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa. Sci Rep 9:1–9

    Article  CAS  Google Scholar 

  • Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, Randles CA, Darmenov A, Bosilovich MG, Reichle R, Wargan K (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim 30:5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1

    Article  Google Scholar 

  • Ghodichore N, Vinnarasi R, Dhanya CT, Roy SB (2018) Reliability of reanalyses products in simulating precipitation and temperature characteristics over India. J Earth Sys Sci 127:1–21. https://doi.org/10.1007/s12040-018-1024-2

    Article  Google Scholar 

  • Gilbert RO (1987) Statistical methods for environmental pollution monitoring. John Wiley & Sons, New York

    Google Scholar 

  • Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob Planet Change 100:172–182. https://doi.org/10.1016/j.gloplacha.2012.10.014

    Article  Google Scholar 

  • Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-45213-z

    Article  CAS  Google Scholar 

  • Gupta P, Verma S, Bhatla R, Chandel AS, Singh J, Payra S (2020) Validation of surface temperature derived from MERRA-2 Reanalysis against IMD gridded data set over India. Earth Space Sci 7:e2019EA000910. https://doi.org/10.1029/2019EA000910

    Article  Google Scholar 

  • Gupta SK, Gupta N, Singh VP (2021) Variable-sized cluster analysis for 3D pattern characterization of trends in precipitation and change-point detection. J Hydrol Eng 26:04020056

    Article  Google Scholar 

  • Hajani E, Rahman A, Haddad K (2014) Trend analysis for extreme rainfall events in New South Wales, Australia. Inter Scho Sci Res Innov 8(12):754–59

    Google Scholar 

  • Hordofa AT, Leta OT, Alamirew T, Chukalla AD (2022) Spatiotemporal trend analysis of temperature and rainfall over Ziway Lake Basin. Ethiopia Hydrol 9:2. https://doi.org/10.3390/hydrology9010002

    Article  Google Scholar 

  • Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Zhang HM (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and inter comparisons. J Clim 30:8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1

    Article  Google Scholar 

  • Hussain A, Cao J, Hussain I, Begum S, Akhtar M, Wu X, Guan Y, Zhou J (2021) Observed trends and variability of temperature and precipitation and their global teleconnections in the Upper Indus Basin, Hindukush-Karakoram-Himalaya. Atmos 12:973. https://doi.org/10.3390/atmos12080973

    Article  Google Scholar 

  • IMD (2010) Climate profile of India. Met Monograph No. Environment Meteorology-01/2010. India Meteorological Department, Government of India, pp 1–129

  • IPCC (2014) Impacts, adaptation and vulnerability. Part A: global and sectoral aspects contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change, in Climate Change 2014 ed Field C B et al Cambridge: Cambridge University Press p. 1132

  • IPCC: climate change (2021) the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

  • Jaiswal RK, Lohani AK, Tiwari HL (2015) Statistical analysis for change detection and trend assessment in climatological parameters. Environ Proc 2:729–749. https://doi.org/10.1007/s40710-015-0105-3

    Article  Google Scholar 

  • Jisha NV, Varma GG, Gleeja V, Prasad A, Beena V, Karthiayini K, Sejian V (2021) Annual temperature profile of Thrissur: a climate change perspective. J Vet Anim Sci 52:26–31

    Article  Google Scholar 

  • Jourdain NC, Gupta AS, Taschetto AS, Ummenhofer CC, Moise AF, Ashok K (2013) The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Clim Dyn 41:3073–3102. https://doi.org/10.1007/s00382-013-1676-1

    Article  Google Scholar 

  • Kalita RDK, Yubaraj S, Devesh W, Atul Saxena (2020) Hill geographer trends in extreme climate indices in Cherrapunji for the period 1979 to 2020. https://doi.org/10.21203/rs.3.rs-706445/v1

  • Karimi T, Stöckle CO, Higgins SS, Nelson RL (2021) Impact of climate change on greenhouse gas emissions and water balance in a dryland-cropping region with variable precipitation. J Environ Manage 287:112301. https://doi.org/10.1016/j.jenvman.2021.112301

    Article  CAS  Google Scholar 

  • Kendall MG (1948) Rank correlation methods. Published by C, Griffin, London. https://doi.org/10.2307/2333282

    Book  Google Scholar 

  • KFS (2018) Kerala forest statistics, Kerala Forest Department, Government of Kerala. Thiruvananthapuram. (accessed on 01.06.2021)

  • KFWD (2021) Kerala Forest and Wildlife Department, forest statistics, Government of Kerala. https://forest.kerala.gov.in/index.php/forest/forest-area. Accessed 01.06.2021

  • Kishore P, Jyothi S, Basha G, Rao SVB, Rajeevan M, Velicogna I, Sutterley TC (2016) Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends. Clim Dyn 46:541–556. https://doi.org/10.1007/s00382-015-2597-y

    Article  Google Scholar 

  • Kişi Ö, Guimaraes CAS, Marques da Silva R, Zounemat-Kermani M (2018) Trend analysis of monthly streamflows using Şen’s innovative trend method. Geofizika 35:53–68. https://doi.org/10.15233/gfz.2018.35.3

    Article  Google Scholar 

  • Koutavarapu R, Umakanth N, Satyanarayana T, Kumar MS, Rao MC, Lee DY, Shim J (2021) Study of statistical estimated parameters using ERA5 reanalysis data over Khulna region during monsoon season. Acta Geophys 69:1963–1978. https://doi.org/10.1007/s11600-021-00662-x

    Article  Google Scholar 

  • Krishnakumar KN, Rao GP, Gopakumar CS (2009) Rainfall trends in twentieth century over Kerala. India. Atmos Envir 43(11):1940–4

    Article  CAS  Google Scholar 

  • KSAPCC (2014) Kerala State action plan on climate change. Department of Environment and Climate Change, Government of Kerala, pp 150

  • Kumar N, Tischbein B, Beg MK (2019) Multiple trend analysis of rainfall and temperature for a monsoon-dominated catchment in India. Meteorol Atmos Phys 131:1019–1033. https://doi.org/10.1007/s00703-018-0617-2

    Article  Google Scholar 

  • Lehodey P, Alheit J, Barange M, Baumgartner G, Beaugrand K, Drinkwater JM, Fromentin SR, Hare G, Ottersen RI, Perry C, Roy Van der Lingen CD, Werner F (2006) Climate variability, fish and fisheries. J Clim 10:5009–5030. https://doi.org/10.1175/JCLI3898.1

    Article  Google Scholar 

  • Li J, Tan S, Wei Z, Chen F, Feng P (2014) A new method of change point detection using variable fuzzy sets under environmental change. Water Res Manag 28:5125–5138. https://doi.org/10.1007/s11269-014-0798-5

    Article  Google Scholar 

  • Livneh B, Bohn TJ, Pierce DW, Munozarriola F, Nijssen B, Vose R, Cayan DR, Brekke L (2015) A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Sci Data 2(1):12. https://doi.org/10.1038/sdata.2015.42

    Article  Google Scholar 

  • Maleika W (2020) Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multi beam echo sounder. Appl Geomat 12:397–407. https://doi.org/10.1007/s12518-020-00307-6

    Article  Google Scholar 

  • Malik A, Kumar A (2022) Trend analysis of annual, seasonal, and monthly streamflow in Naula Watershed, Uttarakhand (India). Advanced Modelling and Innovations in Water Resources Engineering. Springer, Singapore, pp 205–218

    Chapter  Google Scholar 

  • Mallakpour I, Villarini G (2016) A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrolog Sci J 61:245–254. https://doi.org/10.1080/02626667.2015.1008482

    Article  Google Scholar 

  • Mallick J, Talukdar S, Alsubih M, Salam R, Ahmed M, Kahla NB, Shamimuzzaman M (2021) Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theor Appl Climatol 143:823–841. https://doi.org/10.1007/s00704-020-03448-1

    Article  Google Scholar 

  • Mandal S, Islam M, Biswas M, Ali H (2021) Modeling the potential impact of climate change on living beings near coastal areas. Model Earth Syst Environ 7:1783–1796. https://doi.org/10.1007/s40808-020-00897-5

    Article  Google Scholar 

  • Mann HB. 1945. Nonparametric tests against trend. Econometrica: J. Econometric Soc. 245–259.

  • McCarty WL, Coy L, Gelaro R, Huang A, Merkova D, Smith EB, Sienkiewicz M, Wargan K (2016) MERRA-2 input observations: summary and assessment (NASA TM-2016-104606). NASA Global Model Assimil Off 46:64

    Google Scholar 

  • Mohsin T, Gough WA (2010) Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA). Theor Appl Climatol 101:311–327. https://doi.org/10.1007/s00704-009-0214-x

    Article  Google Scholar 

  • Moreno MV, Conedera M, Chuvieco E, Pezzatti GB (2014) Fire regime changes and major driving forces in Spain from 1968 to 2010. Environ Sci Policy 1(37):11–22

    Article  Google Scholar 

  • Ndhlovu GZ, Woyessa YE (2021) Use of gridded climate data for hydrological modelling in the Zambezi River Basin. Southern Africa J Hydrol 602:126749. https://doi.org/10.1016/j.jhydrol.2021.126749

    Article  Google Scholar 

  • Newman AJ, Clark MP, Craig J, Nijssen B, Wood A, Gutmann E, Mizukami N, Brekke L, Arnold JR (2015) Gridded ensemble precipitation and temperature estimates for the contiguous United States. J. Hydromet 16:2481–2500. https://doi.org/10.1175/JHM-D-15-0026.1

    Article  Google Scholar 

  • Newman AJ, Clark MP, Longman RJ, Giambelluca TW (2019) Methodological inter comparisons of station-based gridded meteorological products: utility, limitations, and paths forward. J Hydromet 20:531–547. https://doi.org/10.1175/JHM-D-18-0114.1

    Article  Google Scholar 

  • NOAA/CPC - National Oceanic and Atmospheric Administration/Climate Prediction Center (2021) Cold & Warm Episodes by Season. [online]. Available at: <http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml>. Accessed 03 March 2021

  • Oliveira-Júnior JF, Silva Junior CA, Teodoro PE, Rossi FS, Blanco CJC, Lima M, Gois G, CorreiaFilho WLF, Barros Santiago D, Santos Vanderley MHG (2021) Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest. Int J Climatol 41:4478–4493. https://doi.org/10.1002/joc.7080

    Article  Google Scholar 

  • Oreggioni GD, Ferraio FM, Crippa M, Muntean M, Schaaf E, Guizzardi D, Vignati E (2021) Climate change in a changing world: socio-economic and technological transitions, regulatory frameworks and trends on global greenhouse gas emissions from EDGAR v. 5.0. Glob Environ Change 70:102350. https://doi.org/10.1016/j.gloenvcha.2021.102350

    Article  Google Scholar 

  • Palaniswami S, Muthiah K (2018) Change point detection and trend analysis of rainfall and temperature series over the Vellar River Basin. Pol. J. Environ. Stud. 27:1673–1681. https://doi.org/10.15244/pjoes/77080

    Article  Google Scholar 

  • Pandey BK, Khare D (2018) Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India). Glob Planet Change 161:172–182. https://doi.org/10.1016/j.gloplacha.2017.12.017

    Article  Google Scholar 

  • Pandey V, Taloh I, Pandey PK (2021) Effect of de-trending climatic parameters on temporal changes of reference evapotranspiration in the eastern Himalayan region of Sikkim. India J Water Clim Change 12:2797–2813. https://doi.org/10.2166/wcc.2021.160

    Article  Google Scholar 

  • Persaud BD, Whitfield PH, Quinton WL, Stone LE (2020) Evaluating the suitability of three gridded-datasets and their impacts on hydrological simulation at Scotty Creek in the southern Northwest Territories, Canada. Hydrol Process 34:898–913. https://doi.org/10.1002/hyp.13663

    Article  Google Scholar 

  • Pettitt AN (1979) A non-parametric approach to the change-point problem. J R Stat Soc: Series C (applied Statistics) 28(2):126–135

    Google Scholar 

  • Pezzatti GB, Zumbrunnen T, Bürgi M, Ambrosetti P, Conedera M (2013) Fire regime shifts as a consequence of fire policy and socio-economic development: an analysis based on the change point approach. For Policy Econ 1(29):7–18

    Article  Google Scholar 

  • Phuong J, Bandaragoda C, Istanbulluoglu E, Beveridge C, Strauch R, Setiawan L, Mooney SD (2019) Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and inter comparison. Environ Model Softw 116:119–130. https://doi.org/10.1016/j.envsoft.2019.01.007

    Article  Google Scholar 

  • Pingale SM, Khare D, Jat MK, 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. Atmos Res 138:73–90. https://doi.org/10.1016/j.atmosres.2013.10.024

    Article  Google Scholar 

  • Preston BL, Jones RN (2006) Climate change impacts on Australia and the benefits of early action to reduce global greenhouse gas emissions. CSIRO, Canberra, p 41

    Google Scholar 

  • Purnadurga G, Kumar TL, Rao KK, Barbosa H, Mall RK (2019) Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region. Int J Climatol 39:5791–5800. https://doi.org/10.1002/joc.6189

    Article  Google Scholar 

  • PWD (1974) Public Works Department, Govt. of Kerala. Physiographical divisions of Kerala

  • Quagliolo C, Pezzoli A, Ignaccolo R, Davila JLS (2020) Time-lagged inverse-distance weighting for air temperature analysis in an equatorial urban area (Guayaquil, Ecuador). Meteorol Applic 27(4):e1938. https://doi.org/10.1002/met.1938

    Article  Google Scholar 

  • R Core Team (2021) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna (https://www.R-project.org/)

    Google Scholar 

  • Rahman MA, Yunsheng L, Sultana N (2017) Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman’s rho tests and ARIMA model. Meteorol Atmos Phys 129:409–424. https://doi.org/10.1007/s00703-016-0479-4

    Article  Google Scholar 

  • Rajib MA, Rahman MM, Rakib ZB, Khondoker MTH, Iskander SM, Hassan MM, Mortuza MR (2012) Application of RCM-based climate change indices in assessing future climate. I. Temperature extremes. In: Loucks ED (ed) Proc World Environmental and Water Resources Congress 2012: crossing boundaries, 20–24 May 2012, Albuquerque, NM. American Society of Civil Engineers, Reston, pp 1779–1786

    Chapter  Google Scholar 

  • Rakib Z (2018) Characterization of climate change in southwestern Bangladesh: trend analyses of temperature, humidity, heat index, and rainfall. Clim Res 76:241–252. https://doi.org/10.3354/cr01535

    Article  Google Scholar 

  • Rana S, McGregor J, Renwick J (2015) Precipitation seasonality over the Indian subcontinent: an evaluation of gauge, reanalyses, and satellite retrievals. J Hydromet 16:631–651. https://doi.org/10.1175/JHM-D-14-0106.1

    Article  Google Scholar 

  • Rocha RV, Souza Filho FDA (2020) Mapping abrupt stream flow shift in an abrupt climate shift through multiple change point methodologies: Brazil case study. Hydrol Sci J 65:2783–2796. https://doi.org/10.1080/02626667.2020.1843657

    Article  Google Scholar 

  • Rosa EA, Dietz T (2012) Human drivers of national greenhouse-gas emissions. Nat Clim Change 2:581–586. https://doi.org/10.1038/nclimate1506

    Article  CAS  Google Scholar 

  • RStudio Team (2019) RStudio: integrated development for R. RStudio Inc., Boston. http://www.rstudio.com

  • Saha U, Singh T, Sharma P, Gupta MD, Prasad VS (2020) Deciphering the extreme rainfall scenario over Indian landmass using satellite observations, reanalysis and model forecast: case studies. Atmos Res 240:104943. https://doi.org/10.1016/j.atmosres.2020.104943

    Article  Google Scholar 

  • Sayemuzzaman M, Jha MK (2014) Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmos Res 137:183–194. https://doi.org/10.1016/j.atmosres.2013.10.012

    Article  Google Scholar 

  • Seenu PZ, Jayakumar KV (2021) Comparative study of innovative trend analysis technique with Mann-Kendall tests for extreme rainfall. Arab J Geosci 14:1–15. https://doi.org/10.1007/s12517-019-4903-1

    Article  Google Scholar 

  • Sein KK, Chidthaisong A, Oo KL (2018) Observed trends and changes in temperature and precipitation extreme indices over Myanmar. Atmos 9:477. https://doi.org/10.3390/atmos9120477

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–89

    Article  Google Scholar 

  • Shelia V, Hansen J, Sharda V, Porter C, Aggarwal P, Wilkerson CJ, Hoogenboom G (2019) A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environ Model Softw 115:144–154. https://doi.org/10.1016/j.envsoft.2019.02.006

    Article  Google Scholar 

  • Singh B, Singh K, Kumar R, Sihag P (2018) Future prediction and trend analysis of temperature of Haryana. J Indian Water Resour Soc 38:24–27

    Google Scholar 

  • Sippel S, Meinshausen N, Fischer EM, Székely E, Knutti R (2020) Climate change now detectable from any single day of weather at global scale. Nat Clim Change 10:35–41. https://doi.org/10.1038/s41558-019-0666-7

    Article  Google Scholar 

  • Sobral BS, Oliveira-Júnior JF, Alecrim F, Gois G, Muniz-Júnior JG, Terassi PMB, Pereira-Júnior ER, Lyra GB, Zeri M (2020) PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio de Janeiro State, Brazil. Atmos Res 238:104873. https://doi.org/10.1016/j.atmosres.2020.104873

    Article  Google Scholar 

  • Souza A, Santos CM, Ihaddadene R, Cavazzana G, Abreu MC, Oliveira-Júnior JF, Pobocikova I, Gois G, Lins TMP (2021) Analysis of extreme monthly and annual air temperatures variability using regression model in Mato Grosso do Sul, Brazil. Model Earth Syst Environ 8:1–17. https://doi.org/10.1007/s40808-021-01096-6

    Article  Google Scholar 

  • Sreeraj A, Vijayakumar S (2021) On the analysis of temperature changes over Kottayam district of Kerala. Turk J Comput Math Educ 12:1999–2004

    Google Scholar 

  • Sridhara S, Gopakkali P, Nandini R (2020) Trend analysis of precipitation and temperature over different districts of Karnataka: an aid to climate change detection and cropping system option. Int J Environ Clim Change 10:15–25. https://doi.org/10.9734/ijecc/2020/v10i330184

    Article  Google Scholar 

  • Srilakshmi M, Jhajharia D, Gupta S, Yurembam GS, Patle GT (2022) Analysis of spatio-temporal variations and change point detection in pan coefficients in the northeastern region of India. Theor Appl Climatol 147:1–15. https://doi.org/10.1007/s00704-021-03888-3

    Article  Google Scholar 

  • Stocker BD, Roth R, Joos F, Spahni R, Steinacher M, Zaehle S, Prentice IC (2013) Multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios. Nat Clim Change 3:666–672. https://doi.org/10.1038/nclimate1864

    Article  CAS  Google Scholar 

  • Subash N, Sikka AK (2014) Trend analysis of rainfall and temperature and its relationship over India. Theor Appl Climatol 117:449–462. https://doi.org/10.1007/s00704-013-1015-9

    Article  Google Scholar 

  • Suhaila J, Yusop Z (2018) Trend analysis and change point detection of annual and seasonal temperature series in Peninsular Malaysia. Meteo Atmo Phy 130(5):565–581

    Article  Google Scholar 

  • Sujeewa K (2011) Empirical analysis of temperature change in Sri Lanka during the last 140 years (1871–2010). University of the Philippines Diliman. Masters dissertation

  • Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2018) A review of global precipitation data sets: data sources, estimation, and inter comparisons. Rev Geophys 56:79–107. https://doi.org/10.1002/2017RG000574

    Article  Google Scholar 

  • Tangang FT, Juneng L, Ahmad S (2007) Trend and interannual variability of temperature in Malaysia: 1961–2002. Theo Appl Climat 89(3):127–141

    Article  Google Scholar 

  • Tarek M, Brissette FP, Arsenault R (2020) Large-scale analysis of global gridded precipitation and temperature datasets for climate change impact studies. J Hydrometeorol 21:2623–2640. https://doi.org/10.1175/JHM-D-20-0100.1

    Article  Google Scholar 

  • Teng TY, Liu TM, Tung YS, Cheng KS (2021) Converting climate change gridded daily rainfall to station daily rainfall-a case study at Zengwen Reservoir. Water 13:1516. https://doi.org/10.3390/w13111516

    Article  CAS  Google Scholar 

  • Tostes JO, Lyra GB, Oliveira-Júnior JF, Francelino MR (2017) Assessment of gridded precipitation and air temperature products for the State of Acre, southwestern Amazonia, Brazil. Environ Earth Sci 153:1–18. https://doi.org/10.1007/s12665-017-6467-2

    Article  CAS  Google Scholar 

  • Tosunoglu F (2017) Trend analysis of daily maximum rainfall series in Çoruh Basin, Turkey. J Institute of Sci Tech 7(1):195–205

    Article  Google Scholar 

  • Varughese A, Hajilal MS, George B (2017) Analysis of historical climate change trends in Bharathapuzha River Basin, Kerala, India. Nature Environ Pol Tech 16:237

    Google Scholar 

  • Vengateswari M et al (2019) Variability and trends in rainfall and temperature over Tamil Nadu. Int J Agric Sci 11(10):8463–8466

    Google Scholar 

  • Von Storch H (1999) On the use of inflation in statistical downscaling. J Clim 12(12):3505–3506

    Article  Google Scholar 

  • Wang S, Zhang X (2012) Long-term trend analysis for temperature in the Jinsha River Basin in China. Theor Appl Climatol 109:591–603. https://doi.org/10.1007/s00704-012-0603-4

    Article  Google Scholar 

  • Wang J, Guo L, Shi X, Pan W, Bai Y, Ai H (2012) Real-time elastography with a novel quantitative technology for assessment of liver fibrosis in chronic hepatitis B. Eur J Radiol 81(1):e31–e36

    Article  Google Scholar 

  • Wang Y, Xu Y, Tabari H, Wang J, Wang Q, Song S, Hu Z (2020) Innovative trend analysis of annual and seasonal rainfall in the Yangtze River Delta, Eastern China. Atmos Res 231:104673. https://doi.org/10.1016/j.atmosres.2019.104673

    Article  Google Scholar 

  • Werner AT, Schnorbus MA, Shrestha RR, Cannon AJ, Zwiers FW, Dayon G, Anslow F (2019) A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Sci Data 6:1–16. https://doi.org/10.1038/sdata.2018.299

    Article  Google Scholar 

  • Wong CL, Yusop Z, Ismail T (2018) Trend of daily rainfall and temperature in Peninsular Malaysia based on gridded data set. Int J Geomate 14:65–72

    Article  Google Scholar 

  • Wu Y, Guo L, Zheng H, Zhang B, Li M (2019) Hydroclimate assessment of gridded precipitation products for the Tibetan Plateau. Sci Total Environ 660:1555–1564. https://doi.org/10.1016/j.scitotenv.2019.01.119

    Article  CAS  Google Scholar 

  • XLSTAT (2016) Statistical Software for Excel. https://www.xlstat.com

  • Yadav R, Tripathi SK, Pranuthi G, Dubey SK (2014) Trend analysis by Mann-Kendall test for precipitation and temperature for thirteen districts of Uttarakhand. J Agrometeor 16(2):164–71

    Article  Google Scholar 

  • Yue S, Pilon P, Cavadias G (2002) Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259(1–4):254–71

    Article  Google Scholar 

  • Zhao J, Huang Q, Chang J, Liu D, Huang S, Shi X (2015) Analysis of temporal and spatial trends of hydro-climatic variables in the Wei River Basin. Envir Res 139:55–64

    Article  CAS  Google Scholar 

  • Zheng X, Streimikiene D, Balezentis T, Mardani A, Cavallaro F, Liao H (2019) A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players. J Clean Prod 234:1113–1133. https://doi.org/10.1016/j.jclepro.2019.06.140

    Article  Google Scholar 

  • Zikra M (2015) Climate change impacts on Indonesian coastal areas. Proc Earth Planet Sci 14:57–63. https://doi.org/10.1016/j.proeps.2015.07.085

    Article  Google Scholar 

  • Zubair L (2017) Climate change assessment in Sri Lanka using quality evaluated surface temperature data. Columbia University Water Center, New York, USA, FECT Technical Report 2017–06, Foundation for Environment, Climate and Technology, Digana Village, Sri Lanka, pp 30

Download references

Acknowledgements

The third author thanks CNPq for its Level 2 Research Productivity Scholarship (no. 309681/2019-7). Authors are thankful to the anonymous reviewers and Editor-in-Chief Dr. Philippe Garrigues for their critical review and constructive comments and suggestions, which improved the quality of the manuscript.

Author information

Authors and Affiliations

  1. Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, Rua Do Matão, 1226, São Paulo, SP, 055508-090, Brazil

    Ninu Krishnan Modon Valappil

  2. Federal University of Technology, Parana, Londriná, 86036-370, Brazil

    Ninu Krishnan Modon Valappil

  3. Kerala State Disaster Management Authority, Thiruvananthapuram, Kerala, 695033, India

    Vijith Hamza

  4. Laboratory of Applied Meteorology and Environment (LAMMA), Institute of Atmospheric Sciences, Federal University of Alagoas, Lourival Melo Mota Avenue, S/N, CEP: 57072-970, Tabuleiro Dos Martins, Maceio, Alagoas, Brazil

    José Francisco de Oliveira Júnior

Authors
  1. Ninu Krishnan Modon Valappil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijith Hamza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Francisco de Oliveira Júnior
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation and data collection and analysis were performed by Ninu Krishnan Modon Valappil, Vijith Hamza, and José Francisco de Oliveira Júnior. The first draft of the manuscript was written by Ninu Krishnan Modon Valappil, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Vijith Hamza.

Ethics declarations

Ethics approval

All accepted principles of ethical and professional conduct have been followed during this research in accordance with Springer’s standards.

Consent to participate

The authors have given informed consent to publish this article in the journal of Environmental Science and Pollution Research if accepted.

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valappil, N.K.M., Hamza, V. & de Oliveira Júnior, J.F. Evaluation of daily average temperature trends in Kerala, India, using MERRA-2 reanalysis data: a climate change perspective. Environ Sci Pollut Res 30, 26663–26686 (2023). https://doi.org/10.1007/s11356-022-23895-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-23895-9

Keywords

Search

Navigation