INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 24: 457–466 (2004) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.995
TRENDS IN EXTREME DAILY PRECIPITATION INDICES IN INDIA SHOURASENI SEN ROY and ROBERT C. BALLING JR* Department of Geography, Arizona State University, Tempe, AZ, USA Received 21 July 2003 Revised 12 November 2003 Accepted 12 November 2003
ABSTRACT We assembled daily precipitation records, initially for 3838 stations, throughout India and ultimately identified 129 stations with reasonably complete records over the period 1910 to 2000. From these daily records, we generated annual time series of seven different indices of extreme precipitation events, including total precipitation, largest 1, 5, and 30 day totals, and the number of daily events above the amount that marks the 90th, 95th, and 97.5th percentiles of all precipitation at each station. Of the 903 different time series (seven variables for 129 stations), 114 had a significant upward trend and 61 had a significant downward trend; overall, 61% of the time series showed an upward trend. The standard regression coefficients showing the strength and sign of the trend were highly correlated across the network. They generally showed increasing values in a contiguous region extending from the northwestern Himalayas in Kashmir through most of the Deccan Plateau in the south and decreasing values in the eastern part of the Gangetic Plain and parts of Uttaranchal. Our results are in general agreement with the prediction from numerical models for an increase in extreme precipitation events in India given the ongoing build-up of greenhouse gases. Copyright 2004 Royal Meteorological Society. KEY WORDS:
India; precipitation; extreme precipitation events; climate change
1. INTRODUCTION One of the most significant consequences of global warming would be an increase in the magnitude and frequency of extreme precipitation events brought about by increased atmospheric moisture levels, thunderstorm activity, and/or large-scale storm activity. As noted in the latest assessment of the Intergovernmental Panel on Climate Change (Houghton et al., 2001), climate models generally predict an increase in extreme precipitation events given a build-up of greenhouse gases, and in many parts of the world an increase in these large precipitation events has been observed during the period of historical records. The issue of extreme events remains a focus of the numerical modelling community, with a relatively steady stream of results all showing an increase in large precipitation events given elevated greenhouse gas concentrations (e.g. Kharin and Zwiers, 2000; Meehl et al., 2000; Durman et al., 2001; Yonetani and Gordon, 2001; Wilby and Wigley, 2002; Huntingford et al., 2003; Watterson and Dix, 2003). Durman et al. (2001) warned that models may overpredict the future probability of extreme events; but, even when the predictions are adjusted to fit empirical data better, they still show a substantial rise in the probability of large precipitation events throughout the year. Given the ongoing interest in the modelling community, empirical scientists continue to assemble databases and analyse them for trends in extreme precipitation events. Limiting the literature to 2000 onward, researchers have found an increasing trend for extreme precipitation events in the USA and Australia (Easterling et al., 2000; Haylock and Nicholls, 2000; Groisman et al., 2001; Kunkel, 2003), western New Zealand (Salinger and Griffiths, 2001), French Polynesia, Fiji, and other parts of the South Pacific (Manton et al., 2001; Griffiths * Correspondence to: Robert C. Balling Jr, Department of Geography, Arizona State University, Tempe, Arizona, USA; e-mail:
[email protected]
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et al., 2003), Italy and other areas in the Mediterranean basin (Brunetti et al., 2001a,b; Alpert et al., 2002), the UK in winter (Osborn et al., 2000), and in South Africa (Fauchereau et al., 2003). Other scientists reported no trend in extreme rainfall events in Canada (Zhang et al., 2001; Kunkel, 2003) or the Tuscany region of Italy (Crisci, 2002). Although the evidence for increasing trends appears in most regions, statistically significant decreasing trends in extreme rainfall events have been found in western Australia (Haylock and Nicholls, 2000), Southeast Asia and parts of the central Pacific (Manton et al., 2001; Griffiths et al., 2003), northern and eastern New Zealand (Salinger and Griffiths, 2001), the UK in summer (Osborn et al., 2000), and in Poland (Bielec, 2001). In this investigation, we turn our attention to India, where a large agricultural economy increases the importance of any changes in precipitation distributions. It is noteworthy that several numerical modelling studies (Bhaskaran et al., 1995; May, 2002) have found that a substantial rise in moisture transport into India in a doubled CO2 world leads to an increase in extreme precipitation events in the area. On the empirical side, Soman et al. (1988) analysed annual extreme rainfall for stations in the Kerala state of southern India and generally found decreasing trends, particularly for stations in hilly terrain. Later, Rakhecha and Soman (1994) analysed extreme events of from 1 to 3 days’ duration for 316 stations across India for the period 1901 to 1980. Generally, they found that trends in these events were not statistically significant at most stations. However, Rakhecha and Soman (1994: 227) reported that ‘the extreme rainfall series at stations over the west coast north of 12-degrees-N and at some stations to the east of the Western Ghats over the central parts of the Peninsula showed a significant increasing trend at 95% level of confidence. Stations over the southern Peninsula and over the lower Ganga valley have been found to exhibit a decreasing trend at the same level of significance’. Given the ongoing interest and importance of possible trends in extreme precipitation events, in this paper we assemble a database of daily precipitation totals for stations throughout India, employ a variety of definitions of extreme events, examine all records for trends, and attempt to explain the variations and trends with external variables, including sea-surface temperatures (SSTs), regional air temperatures, indices of El Ni˜no–southern oscillation (ENSO), the Pacific decadal oscillation (PDO), and the atmospheric concentration of CO2 .
2. DAILY PRECIPITATION DATA We began our search for daily precipitation records in India by contacting scientists at the US National Climatic Data Center regarding their newly developed Global Daily Climatology Network database (#TD9101). They sent us 3838 files containing daily precipitation of varying length and completeness; there was one file per station. Unfortunately, many files contained only a few decades of data, many other files contained an overwhelming number of missing records, and all files ended in 1980. Given our interest in determining trends in extreme events, we placed a premium on length of record, and found 138 stations with relatively complete records (>90%) from 1910 to 1980. The precipitation data are recorded by rain gauges all over the subcontinent, which in some cases are self-recording rain gauges. In the case of snowfall, the snow gauges have a tube-like structure and the water equivalent of the collected snow is recorded as the precipitation for that station. Owing to a final inter-calibration algorithm used by the India Meteorological Department, the final precipitation value is not influenced substantially by the type of gauge used. We next contacted the National Data Center of the India Meteorological Department to locate the records for these stations from 1981 to the near present. They supplied us with daily precipitation data from 1980 to 2000 for 130 of our 138 stations. We merged the appropriate files, giving us a database of daily precipitation from 1910 to 2000 for 130 stations across India (Figure 1). We calculated the nearest-neighbour statistics for the distribution of stations as the ratio between the observed mean distance among the sites and the expected mean distance given a random distribution (Clark and Evans, 1954). The ratio of 1.30 for our network throughout India falls into the desirable ‘random to uniform’ category. Copyright 2004 Royal Meteorological Society
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Figure 1. Distribution of the 130 stations in our study (the star symbol represents the one station eliminated from the analysis)
We immediately recognized that the years 1965 and 1970–74 were dominated by missing records (many stations had no data whatsoever for these years) and so these were dropped from the analyses. For all remaining years, the number of days with missing data averaged 10.2% for all stations.
3. EXTREME PRECIPITATION VARIABLES From the daily precipitation records of the original 130 stations, we created annual values of the following seven selected variables. If more than 30 days had missing daily data in a given year, then we considered all seven variables to be missing for that calendar year and station. We used a variety of other rules and missingvalue definitions in determining whether a year should be considered missing and found no appreciable effect on our final results. Copyright 2004 Royal Meteorological Society
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1. Total precipitation. We summed the daily precipitation records to create the total annual precipitation. We adjusted the final value by multiplying by N/(N − m), where N is the number of days in a year and m is the number of days with missing values. We conducted all analyses with and without this adjustment and found only trivial differences in our results (this is also true for similar adjustments in the variables below). 2. Largest 1 day event. We scanned the daily records for each station and year to determine the largest 1 day value; no adjustments were made for missing data. 3. Largest 5 day total. We determined the total precipitation occurring in the wettest consecutive 5 day period in each year. If the 5 day window contained more than one missing value, then the 5 day period was disqualified. However, if 1 day was missing, then the 5 day total was increased by 5/4. 4. Largest 30 day total. We determined the total precipitation occurring in the wettest consecutive 30 day period in each year. If the 30 day window contained more than two missing values, then the 30 day period was disqualified. However, if 1 or 2 days were missing, then the 30 day total was increased by 30/(30 − m), where m is the number of missing days in the period. 5. Extreme frequency (90th percentile). Any number of methods could be used to establish what represents an ‘extreme’ 1 day event in the highly variable climate types existing across India (e.g. Karl et al., 1995; Karl and Easterling, 1999; Manton et al., 2001). In this study, we selected a daily total for each station based on the frequency distribution of precipitation for the entire 1910 to 2000 period. We basically integrated the frequency distribution with precipitation ≥ 0.1 mm to determine the percentage of total precipitation attributed to all events below a given daily total. We based this variable on the daily total below which contributed 90% of all the precipitation that fell at that station over the entire time period. Given that the specific daily amount is based on all years of record, we then rescanned each year to determine the frequency of days with precipitation at or above the threshold. There was no adjustment to this integer for missing days in a given year. 6. Extreme frequency (95th percentile). All procedures for the previous variable were repeated using the threshold for 95% of total precipitation. 7. Extreme frequency (97.5th percentile). All procedures for the previous two variables were repeated using the threshold for 97.5% of total precipitation. We looked at a variety of other choices but found the results to be highly correlated through time with the seven variables described above, thereby adding little new information. Furthermore, we conducted a series of tests on our decisions regarding missing data and found that the choices we made had little impact on the final results. Finally, we used many of the quality assurance procedures suggested by Peterson et al. (1998) and eliminated one station completely for having a highly unrealistic upward trend while extreme 1 or 2 year outliers found at 24 stations were eliminated from further analysis. In the end, we found that the results were not impacted by our handling of the outliers at these 24 stations.
4. ANALYSES AND RESULTS For each station, we constructed a matrix of 91 rows, one for each year from 1910 to 2000, and eight columns containing the year and each of the seven variables described above. We next used simple linear regression analysis with the year of record serving as the independent variable to establish the standardized regression coefficient, representing the strength and sign of any trend, for each of the seven variables. The standardized regression coefficient, also referred to as a beta weight, is equal to the unstandardized regression coefficient multiplied by (Sx /Sy ), where Sx is the standard deviation of the independent variable and Sy is the standard deviation of the dependent variable. In simple regression, the value of the standardized regression coefficient is equal to the Pearson product-moment correlation coefficient between the independent and dependent variables. Absolute values of these coefficients above 0.20 (appropriate threshold value given an N-size of 91 years) indicated a statistically significant trend at the 0.05 level. Of the 903 regression coefficients (seven variables for 129 stations), 434 were positive but not significant and 294 were negative but not significant (Figure 2). Copyright 2004 Royal Meteorological Society
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Of the standardized regression coefficients judged to be significant, 114 were positive and 61 were negative. These findings suggest that there has been a tendency for extreme events in India to have increased over the period 1910 to 2000. The simple regression analyses resulted in a new matrix of 129 rows, one for each station, and seven columns containing the standardized regression coefficients for each of the seven variables. This new 129 × 7 matrix was subjected to principal components analysis and, as seen in Table I, all variables were reasonably highly correlated with one dominant component that explained 73.5% of the variance in the matrix of standardized regression coefficients. The component scores, with a mean of zero and a standard deviation of one, would show the underlying spatial distribution of the standardized regression coefficients. Using the means and standard deviations of the standardized regression coefficients of the seven variables, we reinterpreted the component scores as standardized regression coefficients by altering their standard deviation from 1 to 0.15 (the mean standard deviation of the seven variables) and their mean from 0 to 0.3 (the average mean of the seven variables). A map of the reinterpreted standardized regression coefficients (Figure 3) was produced using the spline interpolation method in Arcview; the interpolation has a smoothing effect on the coefficients, thereby lowering the absolute values in Figure 3. As seen in that figure, positive values extend from the northwestern Himalayas through most of the Deccan Plateau, including the states of Orissa, Andhra Pradesh, and Madhya Pradesh. The regions of slight declining trends depicted by the negative values are located in the eastern part of the Gangetic Plain extending into the Chota Nagpur Plateau and in the southern tip of peninsular India. Our results are in agreement with Rakhecha and Soman (1994), who identified an upward trend in extreme 1 to 3 day events in these same areas, and Sinha Ray and De (2003), who found increases there in the number of days with >7 cm of precipitation. The decrease we found in the eastern part of the Gangetic Plain was also Table I. Selected statistics for standard regression coefficients for 129 stations Variable Annual precipitation Largest 1 day event Largest 5 day event Largest 30 day event Extreme event frequency (90th percentile) Extreme event frequency (95th percentile) Extreme event frequency (97.5th percentile) Copyright 2004 Royal Meteorological Society
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−0.10 0.05 0.02 0.02 0.04 0.05 0.03
0.19 0.14 0.15 0.16 0.15 0.13 0.12
0.85 0.90 0.90 0.89 0.86 0.86 0.72
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Figure 3. Map of reinterpreted standardized regression coefficients showing trends in the composited extreme events throughout India
reported by Rakhecha and Soman (1994). It is of significance to note that the regions of heavy precipitation on the west coast of the Indian subcontinent showed increasing trends in the occurrence of extreme events. Rather than show the composite for all seven variables, we also present the trend maps for total annual precipitation and large 5-day events. The trends in the total annual precipitation (Figure 4) show decreasing values for the northeastern states and parts of the eastern Gangetic Plain, and increasing trends over most of the Deccan Plateau and the northwestern Himalayas. The trends in the large 5-day events (Figure 5) show declining values similar to the annual total precipitation in the northeast, the eastern Gangetic Plain, and parts of Uttaranchal. Climatologically, the eastern part of the Gangetic Plain and parts of Uttaranchal receive precipitation predominantly from the Bay of Bengal branch of the southwest monsoon. The performance of the Bay of Bengal branch of the southwest monsoon is, to a large extent, determined by the formation of low-pressure centres over the head of the Bay of Bengal. Next, we averaged the ‘extreme frequency (90th percentile)’ variable for each year across the network of 129 stations. As seen in Figure 6, there is a highly statistically significant trend in the array, indicating an increase in extreme events across India. We repeated the analysis for the 95th and 97.5th percentile variables and found the same significant upward trend across India. In an attempt to explain the temporal variance and trend seen in Figure 6, we assembled annual and July SST anomalies from the widely used Jones et al. (1999) temperature time series for grid cells in the Indian Ocean, Arabian Sea, and Bay of Bengal; near-surface air temperature anomalies for all of India from the Jones et al. (1999) database; a southern oscillation index based on the difference between standardized sealevel pressures at Tahiti and Darwin, SSTs in an area bounded by 5 ° N to 5 ° S and 90° to 150 ° W thought to be particularly good indicators of ENSO (Trenberth, 1997); a binary variable describing the phase of the PDO (Mantua et al., 1997); and the global concentration of atmospheric CO2 . Using stepwise multiple Copyright 2004 Royal Meteorological Society
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Figure 4. Map of standardized regression coefficients showing trends in the total annual precipitation throughout India
regression analyses, the only variable selected as a significant predictor of the extreme events time series was atmospheric CO2 (r = +0.41). Even when the large autocorrelation in the CO2 series is considered and the degrees of freedom are reduced by nearly a factor of three, the positive link between CO2 and the extreme events remains statistically significant. However, this may be a result of both variables having an upward trend through the study period.
5. CONCLUSIONS We assembled daily precipitation records for 3838 stations in India and identified 129 stations randomly to uniformly distributed across the country with reasonably complete records from 1910 to 2000. From those daily records, we created annual time series of seven different indices of extreme precipitation events, including total precipitation, largest 1, 5, and 30 day totals, and the number of daily events above the amount that marks the 90th, 95th, and 97.5th percentiles of all precipitation at each station. Analyses of these data revealed the following fundamental results of our study: 1. In general, evidence exists for an increase in the frequency of extreme precipitation events over the period 1910 to 2000. 2. The increase in extreme events is strongest in a contiguous region extending from the northwestern Himalayas in Kashmir through most of the Deccan Plateau in the southern peninsular region of India; evidence for a decrease in these events is found in the eastern part of the Gangetic Plain and parts of Uttaranchal. Copyright 2004 Royal Meteorological Society
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Figure 5. Map of standardized regression coefficients showing trends in the largest 5-day precipitation total throughout India
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3. The eastern Gangetic Plain, extending into the Chota Nagpur Plateau and parts of the northwestern Himalayas, also showed declining trends in the total annual precipitation and the large 5-day events. The overall decline in the total annual precipitation can partly explain the decline in the heavy precipitation events in these regions. 4. Although we selected seven different variables to represent various dimensions of precipitation extremes, we found them to be highly correlated through time at individual stations and highly correlated in space in Copyright 2004 Royal Meteorological Society
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terms of trends. Across the country, extreme events are most common in July, during the monsoon season, and least common during the winter months. 5. Interannual time series of extreme precipitation events were significantly and positively correlated with the global CO2 concentration, although this may be due to upward trends in all variables. 6. Our results are in general agreement with the prediction from numerical climate models for an increase in extreme precipitation events in India given the ongoing build-up of greenhouse gases. REFERENCES Alpert P, Ben-Gai T, Baharad A, Benjamini Y, Yekutieli D, Colacino M, Diodato L, Ramis C, Homar V, Romero R, Michaelides S, Manes A. 2002. The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values. Geophysical Research Letters 29: DOI: 10.1029/2001GL013554. Bhaskaran B, Mitchell JFB, Lavery JR, Lal M. 1995. Climatic response of the Indian subcontinent to doubled CO2 concentrations. 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