Trends in Precipitation Extremes over India - (IMD), Pune

From the list prescribed by WMO, we have not considered indices of R10mmand R20mm, because rainfall events of 10 or 20 mm are quite common in the Indiansub-continent during the southwest monsoon season. Hence, we have consideredR7.5cm (75 mm) and R12.5cm (125mm) to study the trends in heavy precipitationfrequencies of rainfall greater than or equal to 7.5cm and 12.5cm.The definitions of indices allow seasonal and monthly partitions. As mentionedearlier, here, we have considered only the southwest monsoon season (June –September), the main rainy season in India. Most of the indices are defined in termsof counts of days crossing the thresholds either absolute (fixed) thresholds orpercentile (variable) thresholds. Annual or seasonal day-count indices based onpercentile thresholds are expressions of anomalies relative to the local climate.Consequently, the value of the thresholds is site specific. Such indices allow forspatial comparisons, because they sample same part of precipitation (probabilitydensity) distributions at each station. On the other hand, annual/seasonal day-countindices based on absolute thresholds are less suitable for spatial comparisons. Thereason being, over an area as large as India, thresholds of sample may vary differentfor different parts of the precipitation distributions. This means that in another climateregime the variability in such indices readily stems from another season. Forexample, R7.5cm may be an extreme event in North-west India, while such eventsare quite common along the west coast and NE India.Extreme rainfall indices considered for the analysis are calculated for eachyear for each station. Trends of these indices are calculated using linear regressionmodel to calculate the magnitude of the trends. Since most of the indices are countsof days, a non-parametric model will be the best suited to test the significance ratherthan using Students’ t test. Hence, we have used the Kendall -Tau test (Press et al.,1986) to examine the significance of the trend. The trends referred to as significantexceed the 95% or 99% confidence limits in the Kendall–Tau test. As mentionedearlier, missing data was not filled up while calculating the indices and trends.6