Trend Analysis of Rainfall in Two Contrasting Regional Environments

Chapter

Abstract

The study of climate change has become the most intriguing aspect of scientific research all over the world. Particular attention has been paid to analyze the regional changes under well debated global warming, i.e. changes in air temperature, with less focus on the impact of climate change on other weather elements including rainfall. While global warming has been attributed to anthropogenic impacts by many scientists, changes in rainfall amounts are still without determining clear causal relationships. Teleconnections viz., El Niño and North Atlantic Oscillation etc. and respective changes in cyclogenesis frequencies are possible causes. The consequences of climate variability and climate change are potentially more significant for the poor in developing countries than for those living in more prosperous nations. Vulnerability to the impacts of climate change is a function of exposure to climate variables, sensitivity to those variables and the adaptive capacity of the affected community. Climate variability can cause abrupt disruptions, such as floods, droughts or tropical storms. These disruptions can take a major toll on a country’s economy if a significant part of economic activity is sensitive to the weather and climate.

Keywords

Rainfall Amount Annual Rainfall Amount Weather Element Scale Climate Change Slope Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alexandersson, H. (1986). A homogeneity test applied to precipitation data. International Journal of Climatology, 6: 661–675.CrossRefGoogle Scholar
  2. Dessens, J. and Bucher, A. (1995). Changes in minimum and maximum temperatures at the Pic du Midi relation with humidity and cloudiness, 1882–1984. Atmospheric Research, 37: 147–162.CrossRefGoogle Scholar
  3. Glickman, T.S. (Managing Editor) (2000). Glossary of meteorology. American Meteorological Society.Google Scholar
  4. Houghton, J.T., Jenkins, G.J. and Ephraum, J.J. (1990). Climate Change, the IPCC Scientific Assessment. Cambridge Univ. Press, New York, Melbourne.Google Scholar
  5. IPCC (2007). Climate Change 2007: Climate change impacts, adaptation and vulnerability. Working Group II contribution to the Intergovernmental Panel on Climate Change FourthAssessment Report. Summary for policymakers, 23. Google Scholar
  6. Kalnay, E. and Cai, M. (2003). Impact of urbanization and land-use change on climate. Nature, 423: 528–531.CrossRefGoogle Scholar
  7. Kendall, M.G. (1975). Rank Correlation Methods, 4th edition.. Charles Griffin, London, U.K.Google Scholar
  8. Marengo, J.A. (2004). Interdecadal variability and trends of rainfall across the Amazon basin. Theoretical and Applied Climatology, 78: 79–96.CrossRefGoogle Scholar
  9. Salmi, T., Maata, A., Antilla, P., Ruoho-Airola, T. and Amnell, T. (2002). Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen´s slope estimates – the Excel template application Makesens. Finish Meteorological Institute, Helsinki, Finland.Google Scholar
  10. Sen, P.K. (1968). Estimates of the regression coefficient based on Kendal´s tau. Journal of the American Statistical Association, 63: 1379–1389.CrossRefGoogle Scholar
  11. Serra, C., Burgueno, A. and Lana, X. (2001). Analysis of maximum and minimum daily temperatures recorded at Fabra observatory (Barcelona, NE Spain) in the period 1917–1998. International Journal of Climatology, 21: 617–636.CrossRefGoogle Scholar
  12. Shahid, S. (2011). Trends in extreme rainfall events of Bangladesh. Theoretical and Applied Climatology, 104: 489–499.CrossRefGoogle Scholar
  13. Sirois, A. (1998). A brief and biased overview of time series analysis or how to find that evasive trend. In: WMO report No. 133 WMO/EMEP workshop on advanced statistical methods and their application to air quality data sets. Helsinki, September 1998, 14–18.Google Scholar
  14. Štěpánek, P. (2008). AnClim – Software for time series analysis. Dept. of Geography, Faculty of Natural Sciences, MU, Brno. http://www.climahom.eu/AnClim.html

Copyright information

© Capital Publishing Company 2015

Authors and Affiliations

  1. 1.Division for Weather and Climate Obsevation, Meteorological and Hydrological ServiceZagrebCroatia
  2. 2.Department of AgroMeteorologyChaudhary Charan Singh Haryana Agricultural UniversityHisarIndia

Personalised recommendations