OCTOBER 2006
NCC 3
NCC RESEARCH REPORT
Trends in Precipitation Extremes over India U. R. Joshi M. Rajeevan
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IN D I A M ET
EN T RT M PA
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NATIONAL CLIMATE CENTRE OFFICE OF THE ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH) INDIA METEOROLOGICAL DEPARTMENT PUNE - 411 005
LOGICAL RO DE EO
E L C L A TE C IM
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National Climate Centre Research Report No: 3/2006
Trends in Precipitation Extremes over India
U. R. Joshi and M. Rajeevan
National Climate Centre India Meteorological Department PUNE. INDIA 411005
[email protected]
Abstract
One of the most significant consequences of global warming due to increase in greenhouse gases would be an increase in magnitude and frequency of extreme precipitation events. In the present study the trends in extreme rainfall indices for the period 1901-2000 were examined for 100 stations over India. The trends for the southwest monsoon season and annual period were calculated separately.
The
results show that most of the extreme rainfall indices have shown significant positive trends over the west coast and northwestern parts of Peninsula. However, two hilly stations considered (Shimla and Mahabaleshwar) have shown decreasing trend in some of the extreme rainfall indices.
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1.
Introduction
About 60-90% of the annual rainfall over India is received during the southwest monsoon season (June to September), which is vital for the economy of the country. Inter-annual variation of seasonal and annual rainfall is a subject for more serious research work in India. However, information about the long term trends of rainfall is also important. Previous studies have addressed the issue of changes in the mean rainfall. For example, Guhathakurta and Rajeevan (2006) have shown that there is no long term trend in the southwest monsoon seasonal rainfall over the country as a whole, but there are significant regional variations. However, changes in extreme precipitation are also equally important to investigate. Impact of climate changes are felt most strongly through changes in climate extremes. Any positive or increasing trend in the extreme rainfall events is also a serious concern. The recent extreme heavy rainfall event occurred over Mumbai on 26th July 2005 prompts us to think whether there is any significant trend in extreme rainfall events over different parts of India. One of the most significant consequences of global warming due to increase in greenhouse gases would be an increase in magnitude and frequency of extreme precipitation events. These increased extreme precipitation events can be attributed to increase in moisture levels, thunderstorm activities and large scale storm activity. In the global warming scenario, climate models generally predict an increase in large precipitation events (Houghton et al 2001). The numerical modelling community and data analysts have shown interest on the issue of extreme events occurring around the world. The recent studies have shown that there is an increasing trend of extreme precipitation events in USA and Australia (Easterling et al. 2000, Haylock and Nicholls 2000, Groisman et al. 2001; Kunkel 2003), western New Zealand(Salinger and Griffiths 2001), the UK in winter (Osborn et al. 2000), and south Africa (Fauchereau et al. 2003). Extreme rainfall events in Canada show no trend (Zhang
2
et al. 2001; Kunkel 2003). Significantly decreasing trends in extreme rainfall events have been found in Western Australia (Haylock and Nicholls, 2000), south-east Asia and parts of central Pacific (Griffiths et al. 2003), northern and eastern New Zealand (Salinger and Griffiths 2001), UK in summer (Osborn et al, 2000). Haylock et al. (2006) have recently addressed the trends in total and extreme rainfall over South America and their links with sea surface temperatures. In India also, some studies have addressed this important issue. Rupa Kumar et al. (1992) examined the trends in the total precipitation during 1871-1984 and found increasing trends in the precipitation amounts all along the west coast and northwest India. Their study also suggested a decreasing trend in the overall precipitation in the eastern Madhya Pradesh. The study of Chhabra et al. (1997) indicates a decrease in the precipitation in hilly stations and an increase in the precipitation in the urbanized/industrialized cities. Singh and Sontakke (2002) studied the fluctuations of precipitation amounts during 1829-1999 for the IndoGangetic Region. Their study indicates a significant trend from 1939 over the central part, and a significant decreasing trend over eastern parts of the country. Soman et al (1988) analysed annual extreme rainfall for the stations in Kerala state and found that stations in hilly terrain show a decreasing trends. Guhathakurta and Rajeevan (2006) analysed rainfall trends over 36 meteorological sub-divisions using a fixed rain-gauge network of over 1460 stations. Their study revealed significant decreasing trends in rainfall over 3 meteorological sub-divisions (Jharkhand, Chattishgarh and Kerala) during the southwest monsoon season (June to September). However, there are only a couple of studies on addressing the changes in extreme precipitation events. Sinha Ray and Srivastava (2000) examined the trend in the occurrence of heavy rainfall events in India. They analysed rainfall data of 151 stations and considered a threshold of 7 cm and above. Sen Roy and Balling (2004) analysed the trends in the patterns of extreme precipitation events from 1910 to 2000 and showed an increasing trend over most of western India including Deccan Plateau and a decreasing to a neutral trend over the eastern half of the country except the northeastern corner. Sen Roy and Balling (2006) analysed the spatial patterns of trends in the frequency and intensity of precipitation over India and 3
concluded that most extreme events have become more frequent, particularly in the western half of the country. Francis and Gadgil (2006) using 37 years of rainfall data examined intense rainfall events over the west coast of India. The probability of occurrence of intense rainfall events is high from mid June to mid August. They have analysed the synoptic features associated with these intense rainfall events. Klein Tank (2006) examined the changes in daily temperatures and precipitation extremes in central and south Asia. For this study, they have used daily data of 1961-2000. However, no robust signal of changes in precipitation extremes is observed over the region. The only index with a significant (5% level) positive trend is the precipitation amount on very wet days. Also, the increase in the contribution of very wet days to the total amounts between 1961 and 2000 is significant at 5% level, implying disproportionate changes of the precipitation extremes.
Alexander et al (2005)
examined global observed changes in daily climate extremes of temperature and precipitation using a suite of climate change indices derived from daily data. They have considered the data of 1951-2003 for the analysis. They have gridded the seasonal and annual climate change indices for the analysis. Their results indicate a general tendency towards wetter conditions throughout the 20th century. There are many indices for examining the extreme rainfall events (Peterson et al. 2001). The earlier studies on extreme rainfall over India examined only a couple of such indices. The joint working group on climate change detection of World Meteorological Organisation (WMO-CCL) and the research program on Climate Variability and Prediction CLIVAR (Peterson et al., 2001) recommended 15 indices on extreme rainfall. In this study, we have considered all these 15 indices and examined the long term changes associated with these indices using 100 years of data. About half of the indices considered are expressions of anomalies relative to the local climatology in the standard-normal period 1961-90 enabling comparisons between stations in different countries and regions. We have considered daily data of longer period (1901-2000) for the present analysis. The present study also deals with analysis for the extreme rainfall during the southwest monsoon season as well as annual rainfall over India. However, in this report, only the results of the analysis for the southwest monsoon season (June to September) are discussed, which are found similar with the annual rainfall data also.
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In section 2, details of data used and the methodology adopted are discussed. In section 3, the results are discussed in detail and in section 4 conclusions are drawn.
2.
Data and Methodology For calculating extreme rainfall indices, we have considered daily rainfall data
recorded at rain-gauge stations over India. India Meteorological Department (IMD), maintains rainfall data observed at more than 6000 raingauge stations. However, only 199 observatories are maintained by the IMD personnel. Rest of the observatories is manned by other state government agencies. For better quality, we have considered only rainfall recorded at 199 IMD observatory stations. Daily rainfall data of IMD stations for the period 1901-2000 are considered for the present study. Data for a year is considered to be missing if the daily rainfall data are not available for more than10% for the year. Filling of the missing daily data was not considered as rainfall is a highly variable parameter. Indices of extremes are sensitive to changes in station location, exposure, equipments and observing practices. The daily rainfall data archived at the National Data Centre (NDC) of IMD are quality controlled and the outliers were identified. These outliers have been cross checked with the manuscripts and corrections were carried out whenever needed. After filtering the data, we could get only 100 stations for the period 1901-2000, in which more than 90% of time daily data are available. It may be mentioned that Sinha Ray and Srivastava (2000) considered stations with data more than 60% of time, which may not be suitable for studies on extreme rainfall. Locations of these selected stations are shown in Fig 1. Since we have put the condition of maximum 10% missing days for annual rainfall values, few stations are available in the eastern part of central India. Maximum stations are available from peninsular India. A list of over 50 internationally agreed climate change indices for temperature and rainfall (WMO – CCl / CLIVAR) are available on web site (http://www.wmo.int) with their explanation and equations for calculating them. For the present study, we have considered a set of 10 rainfall indices (Table -1). Detailed procedures to calculate these indices are available at http://www.knmi.nl/samenw/eca.
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From the list prescribed by WMO, we have not considered indices of R10mm and R20mm, because rainfall events of 10 or 20 mm are quite common in the Indian sub-continent during the southwest monsoon season. Hence, we have considered R7.5cm (75 mm) and R12.5cm (125mm) to study the trends in heavy precipitation frequencies of rainfall greater than or equal to 7.5cm and 12.5cm. The definitions of indices allow seasonal and monthly partitions. As mentioned earlier, here, we have considered only the southwest monsoon season (June – September), the main rainy season in India. Most of the indices are defined in terms of counts of days crossing the thresholds either absolute (fixed) thresholds or percentile (variable) thresholds. Annual or seasonal day-count indices based on percentile thresholds are expressions of anomalies relative to the local climate. Consequently, the value of the thresholds is site specific. Such indices allow for spatial comparisons, because they sample same part of precipitation (probability density) distributions at each station. On the other hand, annual/seasonal day-count indices based on absolute thresholds are less suitable for spatial comparisons. The reason being, over an area as large as India, thresholds of sample may vary different for different parts of the precipitation distributions. This means that in another climate regime the variability in such indices readily stems from another season. For example, R7.5cm may be an extreme event in North-west India, while such events are quite common along the west coast and NE India. Extreme rainfall indices considered for the analysis are calculated for each year for each station. Trends of these indices are calculated using linear regression model to calculate the magnitude of the trends. Since most of the indices are counts of days, a non-parametric model will be the best suited to test the significance rather than 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 significant exceed the 95% or 99% confidence limits in the Kendall–Tau test. As mentioned earlier, missing data was not filled up while calculating the indices and trends.
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3.
Results and Discussions We have calculated the trends of 16 indices for monsoon season and on
annual scale and estimated the statistical significance using the Kendal-Tau test. For the present study, results and discussions are presented for the monsoon season in respect of the indices listed in Table-1. However, the trends with the annual data are also found similar. Table – 2 shows the percentage of stations showing significant trends in each index. The details of the trends in extreme indices are given below. Significant trend values are plotted in Figs 2-6 and magnitude of the trends per decade are listed in Table-3. R7.5cm – Number of days with rainfall 7.5cm or more For the monsoon season, 63% of stations did not show any trend. 17% of the stations have shown positive significant trends. The significant positive trends are observed (Fig. 2a) mainly along the west coast and some parts of central India and over Bihar. Higher values of trends are observed over stations of northern parts of west-coast. However, Mahabaleshwar has shown a significant negative trend in number of days with 7.5cm or more. R12.5cm – Number of days with rainfall 12.5cm or more West coast stations and two stations of foothills of Himalayas have significant positive trends (Fig. 2b) during the monsoon season. Similar to R7.5cm, Mahabaleshwar has shown a significant negative trend in number of days with 12.5cm or more. RX1day – Highest one day total and RX5day – highest 5 day total Out of the 100 stations, 73% of the stations during the monsoon season have shown positive trend. However, the trends at only 15% of stations are statistically significant. These stations (Fig. 2c) are mainly along the west coast of India, parts of central India, northern parts of peninsular India and few stations in West Bengal and
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Bihar. Over these regions, one day total rainfall has shown an increasing trend. Similar results are also observed for the highest Five day precipitation (RX5day). The regions of significant positive trends (Fig. 2d) are mainly along the west coast and some parts of west central India. The magnitude of trends is also higher in these areas. R75p – Moderate wet days – Days with rainfall more than 75 percentile of the long period average. Seventeen percent of stations have shown significant positive trends. These stations are distributed (Fig. 3a) along the west coast of India, northern parts of India and west central India (west Madhya Pradesh). Only 5% of stations have shown significant negative trends. R75pTOT – Precipitation fraction due to moderate wet days The index R75pTOT determines the contribution of moderate wet days (rainfall with more than 75 percentile) on the seasonal rainfall total. During the monsoon season, 19% of stations showed significant positive trend. These stations (Fig. 3b) are mainly over along the west coast, southwest parts of central India, northern peninsular India, and few stations in Orissa.
Over these regions, the
contribution from the rainfall amount higher than 75 percentile has increased. Only 3% stations have shown significant negative trend. In a similar fashion, two more indices for 95 percentile and 99 percentile are also calculated. Similar to R75p, the R95p index also shows significant positive trends for 16% of stations. Significant positive trends are also noticed for 15% of the stations during the monsoon season for the index R95pTOT. These significant trends (Fig. 3c & 3d) are along the west coast and northwestern parts of Peninsular India, which includes Maharashtra and parts of northern Karnataka. The indices of R99p and R99pTOT showed (Fig. 4a and 4b) significant positive trends along the foothills of Himalayas and south peninsular India.
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Regional Average Trends In the time series of station data, changes in routine observing practices may have introduced inhomogeneities of non-climatic origin that severely affect the extremes (Klein Tank et al. 2006). The results for individual stations may be affected by inhomogeneities in the underlying series that were not detected. Therefore, robust conclusions can be drawn from tendencies over large areas and regional average trends. Moreover, changes in extreme precipitation due to global warming may be better revealed in regional average trends and not at the station level. This is due to the fact that due to global warming, enhancement of moisture level may take place on a larger scale and the associated enhancement in convection may take place over a larger area. Therefore, we have repeated the analysis by dividing the country into 17 areas as shown in Fig. 5. Number of stations used for area averaging is also indicated in the regional boxes. The region-1 is the west coast with 10 stations. The results are shown in Table – 4. The changes observed at the stations along the west coast are realized even if we average the data over the west coast. The results from individual stations and area average data are consistent. Similarly, over the region-11 (northern parts of India), the signals are consistent. However, stations over the region-2 (south peninsula) have not shown much significant changes in the climate indices. But in the area averaged analysis, many indices (RX1DAY, R75p, R75pTOT and R95p) have shown significant increasing trends. Similarly, individual stations over NE India (Region-14) have not shown any significant changes in the climate indices. However, when we made area average over the region, many indices have shown significant trends. Fig.6. shows the time series of RX1DAY, RX5DAY, R95p and R95pTOT averaged over the west coast (region). All the time series are showing an increasing trend, especially the R95pTOT. During the recent years (after 1990), the per cent of seasonal rainfall from very wet days (R95pTOT) is consistently more than 25%.
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6.
Conclusions
From the present study, the following main conclusions can be drawn. 1. Most of the extreme rainfall indices have shown significant positive trends over the west coast stations and northwestern parts of Peninsula (Maharashtra). Along the west coast, contribution from the heaviest (95 and 99 percentile) events to the total seasonal rainfall has increased significantly. This result is important in view of the recent deluge occurred over Mumbai. 2. Positive trends were also observed over the region 200 to 300 N and 750 to 800 E.
3. Individual stations over North east India did not show any significant trends. However, averaged over the area, some climate indices (R75pTOT, R95p, R95pTOT) showed increasing trends. 4. It is interesting to note that two hilly stations considered in this study (Simla and Mahabaleshwar) have shown significant decreasing trends of some extreme rainfall indices. Changes in the climate indices revealed in this study may be associated with variability in the large-scale atmospheric circulation patterns. However, to what extent the trends observed in this study are related with atmospheric circulation changes needs to be addressed in future studies. The area of concern is northern parts of west coast and adjoining Maharashtra. This study suggested that extreme rainfall events over this region has shown a significant increasing trend. The study of Guhathakurta and Rajeevan (2006) has brought out the result that this region has also witnessed significant increase in monsoon seasonal and annual rainfall. This inference was also made using about 100 years of rainfall data. Using a high resolution regional climate model, Rupa Kumar et al. (2006) have shown in the global warming scenario, summer monsoon precipitation over most parts of Maharashtra and adjoining south Gujarat region is expected to increase on an average by 5 mm/day. This will
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translate into a huge increase in seasonal rainfall over this region. If we assume the observed trend in increase of intense rainfall events will also persist into future, as suggested by the climate models, probability of intense rainfall events (like the one experienced at Mumbai in 2005) in near future will also increase proportionally.
Acknowledgements We are thankful to Dr (Mrs) N.Jayanthi, LACD ADGM (Research) and Shri. Thakur Prasad, DDGM (Climatology) for providing facilities to do this work and for encouragement. We also thank Shri.G.S.Prakasa Rao, Director for his support for this work. The first author participated in the WMO CLIVAR International Workshop on Enhancing South-central Asian Climate Change Monitoring and Indices held at Pune in Feb 2005 and benefitted from the workshop.
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References Alexander, L. et al., 2005. Global observed changes in daily climate extremes of temperature
and
precipitation.
J.
Geophys.Res.,
32:
D05109,
doi
10.1029/2005/JD006290. Chhabra, B. M., Prakasa Rao, G. S. and Joshi U. R., 1997. A comparative study of differences in the averages of temperatures and rainfall over the Indian stations during the periods 1931 – 60 and 1961-90. Mausam 48: 1, 65-70. Easterling D R, Evans J L Groisman P Y, Karl T R, Kumbel K E, Ambenje P. 2000. Observed variability and trends in extreme climate events: a brief review. Bulletin of the American Meteorological Society 81: 417 – 425. Faucherean N. Trzasku S. Roualt M, Richard Y. 2003. Rainfall variability and changes in the southern Africa during twentieth century in the global warming context. Natural Hazards 29: 139 – 154. Francis, P.A. and Gadgil, S., 2006. Intense rainfall events over the west coast of India. Meteorol Atmos. Phys., doi 10.1007/s00703-005-0167-2. Griffiths G M, Salinger M J, Leleu I. 2003. Trends in extreme daily rainfall across the south Pacific and relationship to the South Pacific convergence zone. International Journal of Climatology 23: 847 – 869. Groisman P Y, Knight R W, Karl T R. 2001. Heavy precipitation and high stream flow in the contiguous United States : Trends in the twentieth century. Bulletin of the American Meteorological Society 82: 219 – 246. Guhathakurta. P., and Rajeevan. M., 2006, Trends in the rainfall pattern over India, NCC Research Report No 2/2006, May 2006, India Meteorological Department, pp 23. Haylock M. R., Nicholls N, 2000. Trends in extreme rainfall indices for an updated high quality data set for Australia 1910 – 1998. International Journal of Climatology 20: 1533 – 1541. Haylock M. R. et al., 2006. Trends in total and extreme south American rainfall in 1960-2000 and links with sea surface temperature. Journal of Climate 19:1490-1512.
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Houghton Jr, Ding Y, Griggs Dj, Noguer M, Van der Linden PJ, Dai X, Maskell K, Johson CA (eds)., 2001. Climate Change 2001 ‘The Scientific Basis’, Cambridge University Press, Cambridge, UK. Klein Tank et al., 2006. Changes in daily temperature and precipitation extremes in Central and South Asia. J.Geophys.Res., 111: No D16, D16105, doi 10.1029/2005JD006316. Kunkel K E . 2003. North American trends in extreme precipitation. Natural Hazards 29: 291 – 305. Osborn T J, Hulme M, Jones P D, Basnett T A. 2000. Observed trends in the daily intensity of United Kingdom precipitation. International Journal of Climatology 20: 347 – 364. Peterson T. C, Folland C. Gruza G, Hogg W, Mokssit A and Plummer, 2001. Report on the activities of the working group on climate change detection and related rapporteurs 1998 – 2001. WMO Rep. WCDMP 47, WMO – TD 1071, Geneva, Switzerland 143 pp. Press W. H., Flannery B. P., Teukalsky, S. A., and Vetterling, W. T. 1986. Numerical Recipes: The art of scientific computing; Cambridge Univ., Press Cambridge pp 488 – 493. Rupa Kumar, K., Pant, G. B., Parthasarathy, B. and Sontakke, N., 1992. Spatial and sub-seasonal patterns of the long-term trends of Indian summer monsoon rainfall. Int. Jou. Clim.12: 257-268. Rupa Kumar, K., and co-authors, 2006, High resolution climate change scenarios for India for the 21st century. Current Science. 90: 3, 334-345. Salinger M J, and Griffiths G M 2001. Trends in New Zealand daily temperature and rainfall extremes. International Journal of Climatology 2; 1437 – 1452. Shouraseni Sen Roy and Balling Jr R C. 2004; Trends in extreme daily precipitation on indices in India., Int. J. Climatol., 24: 457 – 466. Shouraseni Sen Roy and Balling Jr R C. 2006; Analysis of spatial patterns of trends in the frequency and intensity of Indian precipitation. Mausam, 57: 3,431-436. Singh, N. and Sontakke, N. A., 2002, On climate fluctuations and environmental changes of the Indo Gangetic plains in India, Climate Change, 52: 287-313. Sinha Ray, K.C. and Srivastava, A. K. 2000, Is there any change in extreme events like heavy rainfall? Current Science, 79: 2, 155 – 158.
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Soman M K, Krishnakumar K, Singh N. 1988. Decreasing trend in the rainfall of Kerala. Current Science 57: 5 – 12. Zang X, Hogg W D, Mekis F. 2001. Spatial and temporal characteristics of heavy precipitation events over Canada. Journal of Climatology 14: 1923 – 1936.
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Table – 1 Extreme Rainfall Indices considered for the study S. N.
Description
Code
1.
Heavy Precipitation days (Precipitation ≥ 7.5 cm)
R7.5cm
2.
Very Heavy Precipitation Days (Precipitation ≥ 12.5 cm)
R12.5cm
3.
Highest 1 day precipitation amount
RX1day
4.
Highest 5 day precipitation amount
RX5day
Moderate wet days 5.
(days with RR > 75th percentile of daily precipitation
R75p
amount) 6.
Precipitation fraction due to Moderate wet Days
R75pTOT
7.
Very wet days (days with RR > 95th percentile)
R95p
8.
Precipitation fraction due to Very wet days
R95pTOT
9.
Extremely wet days (days with RR > 99th percentile)
R99p
10.
Precipitation fraction due to extremely wet days
R99pTOT
Table -2
RX1DAY
RX5DAY
R75P
R75PTOT
R95P
R95PTOT
R99P
R99PTOT
11 2
73 26
78 22
57 22
70 22
42 6
75 21
13 3
70 28
+ve Sig. trend -ve Sig. trend
8 1
15 1
9 3
17 5
19 3
16 1
17 1
10 3
15 1
R7.5cm
Index Ö % No.of Stations Having Ø +ve trend 32 -ve trend 5
R12.5cm
Per cent of stations showing significant trends
17 2
15
Table - 3
R99pTOT
R95pTOT
0.10
1.40 1.30
R99p
R95p
R75pTOT
R75p
RX5day
RX1day
27.17 23.07 26.45 22.53 30.38 20.93 20.83 27.57 21.50 12.97 17.45 15.85 16.82 18.90 26.17 24.75 22.77 22.72 19.08 26.53 16.95 21.83 9.97 15.83 30.93 17.93 27.23 12.87 14.45 28.58 13.07 15.48 19.27 27.13 11.67 19.80 25.77 16.20 16.98 31.10 34.08 21.20 26.62 10.77 20.90 17.72
R12.5cm
78.03 72.63 74.62 88.33 76.77 77.78 85.10 81.60 86.93 77.58 78.47 74.53 75.72 72.82 85.90 84.95 77.77 75.80 82.03 88.72 82.23 76.37 76.23 78.07 75.87 73.67 79.05 74.85 79.98 77.20 80.25 73.82 76.77 72.37 92.72 85.82 87.47 77.35 73.33 77.17 74.83 72.83 92.78 78.72 70.37 83.23
R7.5cm
Lat
AGRA AHMEDABAD AERO AJMER ALIPUR AMBALA AMRAOTI ANGUL BAHRAICH BALASORE BANGALORE C.O. BEGUMPET/HYD.AER BELGAUM BIJAPUR BOMBAY COLABA DARBHANGA GAYA AERODROME HOSHANGABAD INDORE JAGDALPUR JALPAIGURI KAKINADA KHANDWA KOCHI FORT KURNOOL LUDHIANA MAHABALESHWAR MAINPURI MANGALORE NELLORE NEW DELHI/SAFDAR NUNGAMBAKKAM PANJIM PARBHANI PHALODI PORT BLAIR PURI PURNEA RAICHUR RATNAGIRI SIMLA SRINAGAR SURAT TEZPUR TIRUCHI.PALLI(A) VERAVAL VISAKHAPATNAM AP
Long
Station
Significant trend values (per decade) for the rainfall indices
0.10 0.10
0.80 1.00
0.10 0.10
0.80 0.90
0.40 6.60 0.10
0.10
3.30
5.90
0.50 0.40
0.10 0.10 0.10
1.20
0.10
0.90
0.20
1.30
8.70 4.70
0.80 0.20
0.10
2.80
0.70 0.60
0.30
0.10
-0.10
1.80 8.00 -3.60
0.10
3.80
12.10 -8.00 -5.20 10.50
0.20 0.60 -0.70
5.50
0.30 0.50 0.30 0.20 -0.70 0.40
-0.40 0.10
0.90
0.20
0.50
2.60 -0.70 0.10 0.30
0.20 -1.20
0.30 0.30 -0.50
9.10 0.10 0.10
1.00 -1.40
4.60
0.10 -0.10 -0.10
0.60 -1.30
0.10
1.10
0.10
0.60 0.40
1.60 1.00
0.80 1.00 1.60
0.10
1.40
0.20
1.60
-0.70 1.00 0.80
-0.20 0.10 0.20
0.80
0.40 0.30 0.10
0.20
7.60
13.80
0.80 0.90
0.30
1.20
0.10
1.20 1.10 1.10 1.50
0.10
0.50 0.70
-0.20 0.10 0.10 0.20
0.40 0.80 0.10
5.40
0.50
0.20
6.30
11.00 -5.70
3.50
10.20
0.80 -0.60
1.10 -0.60
0.10 0.40
0.10 0.10 -0.10
0.80 0.80 0.60
1.10 0.20
0.90 0.40
0.10
0.90 1.40
0.2
0.10
6.90
16
17.00
0.50 0.30
1.70
0.20
2.20
Table - 4
R99pTOT
R99p
R95pTOT
R95p
R75pTOT
R75p
RX5DAY
RX1DAY
R12.5cm
R7.5cm
Index
Rainfall Indices (1901-2000) – Significant Trends
Region 1
+ ve
N
+ ve
+ ve
N
+ ve
+ ve
+ ve
+ ve
+ ve
2
N
N
+ ve
N
+ ve
+ ve
+ ve
N
N
N
3
N
N
N
N
N
N
N
N
N
N
4
N
N
N
+ ve
N
N
N
N
N
N
5
N
N
N
N
N
N
N
N
N
+ ve
6
N
N
N
N
N
N
N
+ ve
N
N
7
N
N
N
+ ve
N
N
+ ve
N
+ ve
N
8
N
N
N
N
N
N
N
N
N
N
9
N
N
N
N
N
N
N
N
N
N
10
N
N
N
N
N
N
N
N
N
N
11
N
N
N
N
N
+ ve
+ ve
+ ve
N
N
12
N
+ ve
N
+ ve
N
N
N
N
+ ve
+ ve
13
N
N
N
N
N
N
N
+ ve
N
N
14
N
N
N
N
N
+ ve
+ ve
+ ve
N
N
15
N
N
N
─ ve
─ ve
N
N
N
N
N
16
N
N
N
N
N
N
N
N
N
N
17
+ ve
N
N
N
N
+ ve
N
N
N
N
N : No Significant Trend
+ ve
: Positive Significant Trend
17
─ ve : Negative Significant Trend
Fig. 1 : Stations considered for the study
18
a
b
c
d - Significant Positive Trends
- Significant Negative Trends
Fig. 2 : Significant trends of rainfall indices (a) R7.5cm (b) R12.5cm (c) RX1day and (d) RX5day for Southwest Monsoon Season (June to September).
19
a
b
c
d
- Significant Positive Trends
- Significant Negative Trends
Fig. 3 : Significant trends of rainfall indices (a) R75p (b) R75pTOT(c) R95p and (d) R95pTOT for the Southwest Monsoon Season (June to September).
20
a
b
- Significant Positive Trends
- Significant Negative Trends
Fig. 4 : Significant trends of rainfall indices (a) R99p (b) R99pTOT for the southwest Monsoon season (June to September).
21
Figures in the parenthesis indicate the number of stations averaged in the region Region 1 : West coast with 10 stations. Region 2 & 3 are without the west coast stations.
Fig. 5 : Regions considered for the study
22
Rainfall Index RX1day for Region 1 Rainfall in mm
200 Trend = 2.43 m m / decade
150 100 50
1991
1981
1971
1961
1951
1941
1931
1921
1911
1901
0
Rainfall Index RX5day for Region 1
450 400 350 300 250 200 150 100 50 0
1991
1981
Year
1971
1961
1951
1941
1931
1921
1911
Trend = 5.98 m m / decade
1901
Rainfall in mm
Year
Fig 6: Time series of the Extreme Rainfall Indices for the region 1
23
Rainfall Index R95pTOT for Region 1
Percentage
50
Trends = 1 percent / decade
40 30 20 10 1991
1981
1971
Year
1961
1951
1941
1931
1921
1911
1901
0
Rainfall Index R95p for Region 1 20
Trend = 0.3 days / decade
Days
15 10 5
1991
1981
Year
1971
1961
1951
1941
1931
1921
1911
1901
0
Fig 6 (contd) : Time series of the Extreme Rainfall Indices for the region 1
24
N C C RESEARCH REPORTS X New statistical models for long range forecasting of southwest monsoon rainfall over India, M. Rajeevan, D. S. Pai and Anil Kumar Rohilla, Sept. 2005. X Trends in the rainfall pattern over India, P. Guhathakurta and M. Rajeevan, May 2006
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