Trend Analysis of Precipitation and Temperature over Different Districts of Karnataka: An Aid to Climate Change Detection and Cropping System Option
International Journal of Environment and Climate Change,
Aims: To know the rainfall and temperature trend for all the districts of Karnataka state to develop suitable coping mechanisms for changing weather conditions during the cropping season.
Study Design: The available daily data of rainfall (1971-2011) and minimum and maximum temperature (1971-2007) for each district was collected from NICRA-ICAR website. A non-parametric model such as the Mann-Kendall (MK) test complemented with Sen’s slope estimator was used to determine the magnitude of the trend.
Place and Duration of Study: The rainfall data of 41 years (1971-2011) and temperature data of 37 years (1971-2007) was collected for all 27 districts of Karnataka.
Methodology: Basic statistics related to rainfall like mean, standard deviation (SD), the coefficient of variation (CV) and the percentage contribution to annual rainfall were computed for monthly and season-wise. Mann-Kendall test was used to detect trend for rainfall as well as temperature.
Results: An increasing trend in rainfall during winter, monsoon and annual basis for all most all the districts of Karnataka and decreasing trend of rainfall during pre and post-monsoon season was noticed. An early cessation of rainfall during September month in all most all the districts of Karnataka was observed. Similarly, monthly mean, maximum and the minimum temperature had shown an increasing trend over the past 37 years for all the districts of Karnataka.
Conclusion: The more variation in rainfall during the pre-monsoon season was observed, which is more important for land preparation and other operations. The increasing trend of maximum and minimum temperature throughout the year may often cause a reduction in crop yield. It is necessary to change crops with its short duration varieties in order to avoid late season drought.
- Mann-kendall test
- Sen’s slope
- trend analysis.
How to Cite
Sahu RK, Khare D. Spatial and temporal analysis of rainfall trend for 30 districts of a coastal state (Odisha) of India. International Journal of Geology, Earth and Environmental Sciences. 2015;5(1):40- 53.
Dore MHI. Climate change and changes in global precipitation patterns: what do we know? Environment International. 2005;31: 1167–1181.
Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N. A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change. 2014;4(4):287-291.
Mohan Singh, Ram Niwas. Rainfall variability analysis over North-West India in context to climate change using GIS. Climate Change. 2018;4(13): 12-28.
Mavromatis T, Stathis D. Response of the water balance in greece to temperature and precipitation trends. Theoretical and Applied Climatology. 2011;104:13- 24.
Yue S, Wang, C. The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management. 2004;18:201–218.
Tabari H, Marofi S, Aeini A, Talaee PH, Mohammadi K. Trend Analysis of Reference Evapotranspiration in the Western half of Iran. Agricultural and Forest Meteorology. 2011a;151:128-136.
Onoz B, Bayazit M. The power of statistical tests for trend detection. Turkish Journal of Engineering & Environmental Sciences. 2012;27(2003):247–251.
Drapela K, Drapelova I. Application of Mann-Kendall test and the Sen’s slope estimates for trend detection in deposition data from BílýKríž (Beskydy Mts., the Czech Republic) 1997–2010. Beskdy Mendel University in Brno. 2011;4(2):133–146.
Motiee H, McBean E. An Assessment of long term trends in hydrologic components and implications for water levels in lake Superior, Hydrology Research. 2009;40 (6):564-579.
Hamed KH, Rao AR. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology. 1998;204(1-4):182-196.
NCDC website, National Climatic Data Center; 2012.
Lanzante, JR. Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. International Journal of Climatology. 1996;16:1197-1226.
Davis JC, Statistics and data analysis in geology. 2nd ed. Wiley, New York; 1986.
Rossi R, Mulla D, Journel A, Franz E. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs.1992;62:277-314.
Mann HB. Nonparametric tests against trend. Econometrica. 1945;13:245–259.
Kendall MG. Rank correlation methods. 4th ed. Charles Griffin, London; 1975.
Mishra AK, Zger MO, Singh VP. Trend and persistence of precipitation under climate change scenarios for Kansabati basin, India. Hydrological Proceedings. 2009;23: 2345–2357.
Deni SM, Suhaila J, Zin WZW, Jemain AA. Spatial trends of dry spells over Peninsular Malaysia during monsoon seasons. Theoretical and Applied Climatology. 2010; 99(3):357–371.
Sen PK. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association. 1968;63(324): 1379–1389.
Tabari H, Somee BS, Zadeh RM. Testing for long-term trends in climatic variables in Iran. Atmos- pheric Research. 2011b;100:132– 140.
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