Abstract
Climate change is a serious issue resulting in global variation in the temperature and precipitation pattern. In this study, changes in rainfall trend in India for 141 years (1871–2011) and temperature trend for 107 years (1901–2007) were analysed. The annual, seasonal and monthly changes in different regions of India were investigated to see the climate change in different parts of the country, and the net excess or deficit of rainfall and temperature in India was obtained. Statistical non-parametric tests were performed to see the trend magnitude with the Mann-Kendall (MK) test and Sen’s slope. Mann-Whitney-Pettitt (MWP) test was used for probable break point detection in the series, and change percentage was calculated over 30 sub-divisions and 7 broad regions. The results indicate decreasing annual and monsoon rainfall of India in most of the sub-divisions, and temperature fluctuations were observed in all the places. Temperatures (minimum, maximum and mean) were showing a significant increase, particularly in the winter and post-monsoon time. Wide variation was noticed all over India in the case of the minimum temperature. Variation was also observed at different spatial scales of sub-divisions and regions. This study gives the net impact of climate change in India which shows net excess of temperature and net deficit of rainfall.
Similar content being viewed by others
References
Akinremi OO, McGinn SM, Cutforth HW (2001) Seasonal and spatial patterns of rainfall trends on the Canadian prairies. J Clim 14(9):2177–2182
Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675
Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: a homogeneity test for linear trends. Int J Climatol 17:25–34
Andrighetti M, Zardi D, Franceschi M (2009) History and analysis of the temperature series of Verona (1769–2006). Meteorol Atmos Phys 103:267–277
Arora M, Goel NK, Singh P (2005) Evaluation of temperature trends over India. Hydrol Sci J 50(1):81–93
Ati OF, Muhammed SQ, Ati MH (2008) Variations and trends in annual rainfall amounts and the onset of the rainy season for Kano for 87 years (1916–2002). J Appl Sci Res 4(12):1959–1962
Batisani N, Yarnal B (2010) Rainfall variability and trends in semi-arid Botswana: implications for climate change adaptation policy. Appl Geogr 30:483–489
Buffoni L, Maugeri M, Nanni T (1999) Precipitation in Italy from 1833 to 1996. Theor Appl Climatol 63:33–40
Burn DH, Cunderlik JM, Pietroniro A (2004) Hydrological trends and variability in the Liard river basin. Hydrol Sci J 49(1):53–67
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
Conway D, Persechino A, Ardoin-Bardin S, Hamandawana H, Dieulin C, Mahé G (2009) Rainfall and water resources variability in sub-Saharan Africa during the twentieth century. J Hydrometeor 10:41–59
Cunderlik JM, Burn DH (2002) Local and regional trends in monthly maximum flows in Southern British Columbia. Can Water Resour J 27(2):191–212
Cunderlik JM, Burn DH (2004) Linkages between regional trends in monthly maximum flows and selected climatic variables. ASCE J Hydrol Eng 9(4):246–256
Dash SK, Jenamani RK, Kalsi SR, Panda SK (2007) Some evidence of climate change in twentieth-century India. Clim Chang 85:299–321
Dimri AP (2013) Interannual variability of Indian winter monsoon over the Western Himalayas. Glob Planet Chang 106:39–50
Du J, Shi C (2012) Effects of climatic factors and human activities on runoff of the Weihe River in recent decades. Quatern Int 1–8. doi:10.1016/j.quaint.2012.06.036
Feidas H, Makrogiannis T, Bora-Senta E (2004) Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001. Theor Appl Climatol 79:185–208
Fisk D (1997) Climate change and its impacts: a global perspective. Department of the Environment, Transport and the Regions, The Met Office Hadley Centre Brochure, UK
Ghahraman B (2006) Time trend in the mean annual temperature of Iran. Turk J Agric For 30:439–448
González JM, Cháidez JJN, Ontiveros VG (2008) Analysis of rainfall trends (1920–2004) in Mexico. Investigaciones Geográficas, Boletín del Instituto de Geografía, UNAM 65:38–55
Hamed KH, Rao AR (1998) A modified Mann-Kendall trend test for auto correlated data. J Hydrol 204:182–196
IPCC (Intergovernmental Panel for Climate Change) (2007) Climate Change 2007 – the physical scientific basis, working group I contribution to the fourth assessment report of the intergovernmental panel for climate change. Cambridge University Press, 237–336
Jhajharia D, Singh VP (2011) Trends in temperature, diurnal temperature range and sunshine duration in Northeast India. Int J Climatol 31:1353–1367
Kendall MG (1975) Rank correlation methods. Charles Griffin, London
Khaliq MN, Ouarda TBMJ (2007) Short communication on the critical values of the standard normal homogeneity test (SNHT). Int J Climatol 27:681–687
Kipkorir EC (2002) Analysis of rainfall climate on the Njemps Flats, Baringo District, Kenya. J Arid Environ 50:445–458
Kothawale DR, Rupa Kumar K (2005) One the recent changes in surface temperature trends over India. Geophys Res Lett 32: L18714. doi:10.101029/2005GL023528
Kripalani RH, Kulkarni A, Sabade SS (2003) Indian monsoon variability in a global warming scenario. Nat Hazards 29:189–206
Kumar V, Jain SK, Singh Y (2010) Analysis of long-term rainfall trends in India. Hydrol Sci J 55(4):484–496
Ludwig W, Serrat P, Cesmat L, Garcia-Esteves J (2004) Evaluating the impact of the recent temperature increase on the hydrology of the Teˆt River (Southern France). J Hydrol 289:204–221
Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259
Modarres R, Silva VPR (2007) Rainfall trends in arid and semi-arid regions of Iran. J Arid Environ 70:344–355
Nicholls N, Lavery B (1992) Australian rainfall trends during the twentieth century. Int J Climatol 12(2):153–163
Pal I, Al-Tabbaa A (2010) Long-term changes and variability of monthly extreme temperatures in India. Theor Appl Climatol 100:45–56
Pal I, Al-Tabbaa A (2011) Assessing seasonal precipitation trends in India using parametric and non-parametric statistical techniques. Theor Appl Climatol 103:1–11
Pettitt AN (1979) A non-parametric approach to the change-point detection. Appl Stat 28(2):126–135
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
Ramesh Kumar MR, Krishnan R, Sankar S, Unnikrishnan AS, Pai DS (2009) Increasing trend of “break-monsoon” conditions over India—role of ocean–atmosphere processes in the Indian Ocean. IEEE Geosci Remote Sens Lett 6(2):332–336
Reiter A, Weidinger R, Mauser W (2012) Recent climate change at the upper Danube—a temporal and spatial analysis of temperature and precipitation time series. Clim Chang 111:665–696
Rodrigo S, Esteban-Parra MJ, Pozo-Vázquez D, Castro-Díez Y (2000) Rainfall variability in southern Spain on decadal to centennial time scales. Int J Climatol 20(7):721–732
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389
Sen Z (1998) Average areal precipitation by percentage weighted polygon method. J Hydrol Eng 3(1):69–72
Sen Roy S, Balling RC Jr (2005) Analysis of trends in maximum and minimum temperature, diurnal temperature range, and cloud cover over India. Geophys Res Lett 32:L12702. doi:10.1029/2004GL022201
Silva VPR (2004) On climate variability in northeast of Brazil. J Arid Environ 58:575–596
Singh P, Kumar V, Thomas T, Arora M (2008) Changes in rainfall and relative humidity in different river basins in the northwest and central India. Hydrol Process 22:2982–2992
Sonali P, Nagesh KD (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227
Storch HV (1993) Misuses of statistical analysis in climate research. Analysis of climate variability: applications of statistical techniques (2nd ed). Proceedings of an autumn school organized by the Commission of the European Community on Elba from October 30 to November 6, 1993. Springer, Berlin 11–26
Subash N, Sikka AK (2014) Trend analysis of rainfall and temperature and its relationship over India. Theor Appl Climatol 117(3–4):449–462
Suppiah R, Hennessy KJ (1998) Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990. Int J Climatol 10:1141–1164
Tabari H, Marofi S, Ahmadi M (2010) Long-term variations of water quality parameters in the Maroon River, Iran. Environ Monit Assess 177:273–287
Tabari H, Marofi S, Aeini A, Hosseinzadeh Talaee P, Mohammadi K (2011) Trend analysis of reference evapotranspiration in the western half of Iran. Agric For Meteorol 151:128–136
Tabari H, Hosseinzadeh Talaee P, Ezani A, Shifteh Some’e B (2012) Shift changes and monotonic trends in autocorrelated temperature series over Iran. Theor Appl Climatol 109:95–108. doi:10.1007/s00704-011-0568-8
Theil H (1950) A rank invariant method of linear and polynomial regression analysis, part 3. Netherlands Akademie van Wettenschappen Proceedings 53:1397–1412
Turkes M, Sumer UM (2004) Spatial and temporal patterns of trends and variability in diurnal temperature ranges of Turkey. Theor Appl Climatol 77:195–227
Ventura F, Rossi Pisa P, Ardizzoni E (2002) Temperature and precipitation trends in Bologna (Italy) from 1952 to 1999. Atmos Res 61:203–214
Vose RS, Easterling DR, Gleason B (2005) Maximum and minimum temperature trends for the globe: an update through 2004. Geophys Res Lett 32:L23822. doi:10.1029/2005GL024379
Wang S, Yan M, Yan Y, Shi C, He L (2012) Contributions of climate change and human activities to the changes in runoff increment in different sections of the Yellow River. Quatern Int 1–12. doi:10.1016/j.quaint.2012.07.011
Xu ZX, Takeuchi K, Ishidaira H (2003) Monotonic trend and step changes in Japanese precipitation. J Hydrol 279:144–150
Yue S, Hashino M (2003a) Temperature trends in Japan: 1900–1990. Theor Appl Climatol 75:15–27
Yue S, Hashino M (2003b) Long term trends of annual and monthly precipitation in Japan. J Am Water Resour Assoc 39(3):587–596
Yue S, Pilon P, Phinney R, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16:1807–1829
Yue S, Pilon P, Phinney B (2003) Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol Sci J 48(1):51–63
Acknowledgment
The authors are thankful to the Council of Scientific and Industrial Research (CSIR), New Delhi, for providing financial support during the study.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
1.1 Method
1.1.1 Serial correlation and pre-whitening
Detection of trend in a series is affected by the presence of a positive or negative autocorrelation (Hamed and Rao 1998; Yue et al. 2003). The autocorrelation coefficient of ρk for a discrete time series for lag-k is given as
where \( {\overline{x}}_t \) and Var (x t ) are represented as the sample mean and sample variance of the first (n − k) terms, respectively; \( {\overline{x}}_{t+k} \) and Var (x t + k) stand for the sample mean and sample variance of the last (n − k) terms correspondingly. Again, the hypothesis of no correlation is examined by the lag-1 autocorrelation coefficient as H 0: ρ 1 = 0 against H 1: |ρ 1| > 0:
Here, the t test is the Student’s t distribution with (n − 2) degrees of freedom (Cunderlik and Burn 2002, 2004). If |t| ≥ t α/2, the null hypothesis about no serial correlation is rejected at the significance level α.
Pre-whitening method is used to remove the serial correlation effect on the MK test (Storch 1993). Pre-whitening method with no trend was applied by Yue et al. (2002) with modification in the technique.
Here, β is Theil-Sen’s estimator. The r 1 (lag-1 serial correlation coefficient) has been computed for new series. If r 1 does not vary significantly from zero, then the data will be used without serial correlation, and the MK test will be applicable to the sample data directly. But, if it is opposite, then method of pre-whitening will be applied before the testing of trend.
The β × i value is added to the residual data set of Eq. 4
This Y ″ i is the final pre-whitened series.
1.1.2 Mann-Kendall test and Theil-Sen’s estimator
The statistic of the MK test is given as
where
Here, x j and x i are data values that are in sequence with n data; sgn (θ) is equivalent to 1, 0 and −1 if θ is more than, equal to or less than 0 respectively. If Z c appears to be greater than Z α/2, then the trend is considered as significant, where α represents the level of significance (Xu et al. 2003).
The rainfall trend magnitude is calculated by Theil-Sen’s estimator (Theil 1950; Sen 1968).
where 1 < j < i < n and β estimator stands for the median of the entire data set of all combination of pairs and is resistant to the effect of extreme values (Xu et al. 2003).
1.1.3 Percentage of mean
The change percentage is calculated by its approximation with linear trend. So, change percentage is equal to the median slope multiplied with the length of the period and the whole divided by the corresponding mean value which is given in percentage (Yue and Hashino 2003b).
1.1.4 Mann-Whitney-Pettitt method (MWP)
A time series {X 1, X 2…, X n} with length n is considered. Let t be taken as the time of the most expected change point. Two samples of {X 1, X 2, …, X t} and {Xt + 1, X t + 2, …, X n} can be then obtained by dividing the time series at t time. The U t index is derived in the following way:
where
Plotting the U t value against t in a time series with no change point will result in a continuously increasing value of |Ut|. Nevertheless, if there is a presence of change point (even a local change point), then |U t | will increase up to the level of the change point, and then, it will begin to decrease. The main significant change point t is considered as the point where the value of |U t | remains highest:
The estimated significant probability p(t) for a change point (Pettitt 1979) is given as:
The change point becomes statistically significant at t time with the significance level of α when probability p(t) surpasses (1 − α).
Rights and permissions
About this article
Cite this article
Mondal, A., Khare, D. & Kundu, S. Spatial and temporal analysis of rainfall and temperature trend of India. Theor Appl Climatol 122, 143–158 (2015). https://doi.org/10.1007/s00704-014-1283-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-014-1283-z