Advertisement

Climate Dynamics

, Volume 43, Issue 12, pp 3419–3437 | Cite as

Dry spell characteristics over India based on IMD and APHRODITE datasets

  • L. Sushama
  • S. Ben Said
  • M. N. Khaliq
  • D. Nagesh Kumar
  • R. Laprise
Article

Abstract

Selected characteristics of dry spells and associated trends over India during the 1951–2007 period is studied using two gridded datasets: the Indian Meteorological Department (IMD) and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE) datasets. Two precipitation thresholds, 1 and 3 mm, are used to define a dry day (and therefore dry spells) in this study. Comparison of the spatial patterns of the dry spell characteristics (mean number of dry days, mean number of dry spells, mean and maximum duration of dry spells) for the annual and summer monsoon period obtained with both datasets agree overall, except for the northernmost part of India. The number of dry days obtained with APHRODITE is larger for this region compared to IMD, which is consistent with the smaller precipitation for the region in APHRODITE. These differences are also visible in the spatial patterns of mean and maximum dry spell durations. Analysis of field significance associated with trends, at the level of 34 predefined meteorological subdivisions over the mainland, suggests better agreement between the two datasets in positive trends associated with number of dry days for the annual and summer monsoon period, for both thresholds. Important differences between the two datasets are noted in the field significance associated with the negative trends. While negative trends in annual maximum duration of dry spells appear field significant for the desert regions according to both datasets, they are found field significant for two regions (Punjab and South Interior Karnataka) for the monsoon period for both datasets. This study, in addition to providing information on the spatial and temporal patterns associated with dry spell characteristics, also allows identification of regions and characteristics where the two datasets agree/disagree.

Keywords

Dry spells Gridded precipitation data India Trends 

Notes

Acknowledgments

The authors would like to thank the Indian Meteorological Department for providing the third version IMD (1° × 1°) daily gridded precipitation data, and to the Research Institute for Humanity and Nature (RIHN) and the Meteorological Research Institute of Japan Meteorological Agency (MRI/JMA), for the APHRODITE (0.5º × 0.5º) gridded daily precipitation dataset. The authors would also like to thank the three anonymous referees for their very helpful comments. This work was financially supported by Quebec’s Ministère du Développement économique, de l’Innovation et de l’Exportation (MDEIE) through a PSR-SIIRI grant.

References

  1. Beniston M, Stephenson DB, Christenson OB, Ferro CAT, Frei C, Goyette S, Halsnaes K, Holt T, Jylhä K, Koffi B, Palutikof J, Schöll R, Semmler T, Woth K (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81:71–95CrossRefGoogle Scholar
  2. Bouagila B, Sushama L (2013) On the current and future dry spell characteristics over Africa. Atmosphere 4:272–298CrossRefGoogle Scholar
  3. Cohn TA, Lins HF (2005) Nature’s style: naturally trendy. Geophys Res Lett 32:L23402. doi: 10.1029/2005GL024476 CrossRefGoogle Scholar
  4. Elmore KL, Baldwin ME, Schultz DM (2006) Field significance revisited: spatial bias errors in forecasts as applied to the Eta model. Mon Weather Rev 134:519–531CrossRefGoogle Scholar
  5. Ghosh S, Luniya V, Gupta A (2009) Trend analysis of Indian summer monsoon rainfall at different spatial scales. Roy Meteorol Soc 10(4):285–290Google Scholar
  6. Gong DY, Shi PJ, Wang JA (2004) Daily precipitation changes in the semi-arid region over northern China. J Arid Environ 59:771–784CrossRefGoogle Scholar
  7. Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Prince KX (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314:1442–1445CrossRefGoogle Scholar
  8. Guhathakurta P, Rajeevan M (2008) Trends in the rainfall pattern over India. Int J Climatol 28(11):1453–1469CrossRefGoogle Scholar
  9. Hamed KH, Rao AR (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204:219–246Google Scholar
  10. Joshi UR, Rajeevan M (2006) Trends in precipitation extremes over India. National Climate Centre, PuneGoogle Scholar
  11. Kendall MG (1975) Rank correlation methods. Charles Griffin, LondonGoogle Scholar
  12. Khaliq MN, Ouarda TBMJ, Gachon P, Sushama L, St-Hilaire A (2009a) Identification of hydrological trends in the presence of serial and cross correlations: review of selected methods and their application to annual flow regimes of Canadian rivers. J Hydrol 368(1–4):117–130CrossRefGoogle Scholar
  13. Khaliq MN, Ouarda TBMJ, Gachon P (2009b) Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: the effect of short- and long-term persistence. J Hydrol 369:183–197CrossRefGoogle Scholar
  14. Lucas-Picher P, Christensen JH, Saeed F, Kumar P, Asharaf S, Ahrens B, Wiltshire AJ, Jacob D, Hagemann S (2011) Can regional climate models represent the Indian monsoon? J Hydrometeor 12:849–868Google Scholar
  15. Kulkarni A, von Storch H (1995) Monte Carlo experiments on the effect of serial correlation on the Mann-Kendall test of trend. Meteorol Z 4(2):82–85Google Scholar
  16. Lana X, Burgueno A, Martinez MD, Serra C (2006) Statistical distributions and sampling strategies for the analysis of extreme dry spells in Catalaonia (NE Spain). J Hydrol 324:94–114CrossRefGoogle Scholar
  17. Livezey RE, Chen WY (1983) Statistical field significance and its determination by Monte Carlo techniques. Mon Weather Rev 111:46–59CrossRefGoogle Scholar
  18. Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259CrossRefGoogle Scholar
  19. May W (2008) Potential future changes in the characteristics of daily precipitation in Europe simulated by the HIRHAM regional climate model. Clim Dyn 30:581–603CrossRefGoogle Scholar
  20. Rajeevan M, Bhate J (2009) A high resolution daily gridded rainfall dataset (1971–2005) for meso-scale meteorological studies. Curr Sci 96(4):558–562Google Scholar
  21. Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region, analysis of break and active monsoon spells. Curr Sci 91(3):296–306Google Scholar
  22. Rajeevan M, Bhate J, Jaswal AK (2008) Correction to “Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data”. Geophys Res Lett 35:L23701. doi: 10.1029/2008GL036105 CrossRefGoogle Scholar
  23. Ray KCS, Srivastava AK (2000) Is there any change in extreme events like heavy rainfall? Curr Sci 79(2):155–158Google Scholar
  24. Revadekar JV, Preethi B (2011) Statistical analysis of the relationship between summer monsoon precipitation extremes and food grain yield over India. Int J Climatol 1–10. doi:  10.1002/joc.2282
  25. Saeed F, Hagemann S, Jacob D (2011) A framework for the evaluation of the South Asian summer monsoon in a regional climate model applied to REMO. Int J Climatol 32:430–440Google Scholar
  26. Serra C, Burgueno A, Martinez MD, Lana X (2006) Trends in dry spells across Catalonia (NE Spain) during the recent half of the 20th century. Theoret Appl Climatol 85:165–183CrossRefGoogle Scholar
  27. Shepard D (1968) A two-dimensional interpolation function for irregularly spaced data. In Proceedings of 1968 ACM national conference, pp 517–524Google Scholar
  28. Singh N, Ranade A (2010) The wet and dry spells across India during 1951–2007. J Hydrometeorol 11:26–45CrossRefGoogle Scholar
  29. Subbaramayya I, Naidu CV (1992) Spatial variations and trends in the Indian monsoon rainfall. Int J Climatol 12:597–609CrossRefGoogle Scholar
  30. Suppiah R, Hennessy KJ (1998) Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990. Int J Climatol 10:1141–1164CrossRefGoogle Scholar
  31. Sushama L, Khaliq MN, Laprise R (2010) Dry spell characteristics over Canada in a changing climate as simulated by the Canadian RCM. Global Planet Change 74:1–14CrossRefGoogle Scholar
  32. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79:185–211CrossRefGoogle Scholar
  33. Uma R, Lakshmi Kumar TV, Narayanan MS, Rajeevan M, Bhate J, Niranjan Kumar K (2013) Large scale features and assessment of spatial scale correspondence between TMPA and IMD rainfall datasets over Indian landmass. J Earth Syst Sci 122(3):573–588CrossRefGoogle Scholar
  34. Ventura V, Paciorek CJ, Risbey JS (2004) Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J Clim 17:4343–4356CrossRefGoogle Scholar
  35. Vigaud N, Vrac M, Caballero Y (2012) Probabilistic downscaling of GCM scenarios over southern India. Int J Climatol. doi: 10.1002/joc.3509 Google Scholar
  36. Wang XL, Swail VR (2001) Changes of extreme wave heights in northern hemisphere oceans and related atmospheric circulation regimes. J Clim 14:2204–2221CrossRefGoogle Scholar
  37. Wilks DS (2006) On “field significance” and false discovery rate. J Appl Meteorol Climatol 45:1181–1189CrossRefGoogle Scholar
  38. Yatagai A, Arakawa O, Kamiguchi K, Kawamoto H, Nodzu MI, Hamada A (2009) A 44-years daily gridded precipitation dataset for Asia based on dense network of rain gauges. SOLA 5:137–140. doi: 10.2151/sola.2009-035 CrossRefGoogle Scholar
  39. Yue S, Pilon P, Phinney B (2003) Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol Sci J 48(1):51–63CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • L. Sushama
    • 1
  • S. Ben Said
    • 1
  • M. N. Khaliq
    • 1
    • 2
  • D. Nagesh Kumar
    • 3
  • R. Laprise
    • 1
  1. 1.Centre ESCERUniversity of Quebec at MontrealMontrealCanada
  2. 2.Global Institute for Water Security and School of Environment and SustainabilityUniversity of SaskatchewanSaskatoonCanada
  3. 3.Department of Civil EngineeringIndian Institute of ScienceBangaloreIndia

Personalised recommendations