J. Himalayan Ecol. Sustain. Dev. Vol.8 (2013) ISSN 0973-7502

RECENT VARIATION IN TEMPERATURE TRENDS IN ()

Sumira Nazir Zaz and Shakil Ahmad Romshoo Deptt. of Earth sciences, University of Kashmir, , , 190006 Corresponding authors: Email: [email protected]; [email protected]

ABSTRACT Climate change and global warming are widely recognized as the most significant environmental dilemma today. Studies have shown that Himalayan region as a whole has warmed by about 1.8°F since 1970‘s, which has alerted scientists to lead several studies on climate trend detection at different scales. This paper examines the recent variation in air temperature in Kashmir valley (India). Time series of near surface air temperature data for the period ranging from 1980 to 2010 of five weather observatories were collected from the Indian Meteorological Department (Pune) on which Mann-Kendall Rank Statistic and Regression tests were performed for examination of temperature trends and its significance. Both the tests showed significant increase in the mean Annual, mean Minimum as well as in mean Maximum temperature at a confidence level of 90% -99% at all the five stations. Seasonally very significant increase was recorded in Spring and Winter temperature (90-99%) at all stations. The analysis reveals that such increase in the temperature particularly in spring can occur due to decrease in winter snowfall and its early melting as less snow cover/depth melts within short period of time there by leaving more period of time for warming the surface of earth. Thus, such variation in temperature can lead to water scarcity throughout the valley.

Key words: Nonparametric, parametric; mankendall test, linear regression test, western disturbances 2010). The high mountains of INTRODUCTION South Asia covering the Hindu- Kush, Karakoram Himalaya Prevalence of varied clima- (HKKH) belt have reported tic conditions that are similar to warming trend in the past few those of widely separated latitu- decades (Viviroli et al., 2007; dinal belt, within a limited area, Immerzeel et al., 2010).The make the high mountain areas exhibit a stronger such as the Himalaya, the Alps, warming trend for every season the Andes, the Rockies etc. the (Immerzeel et al., 2010). Snow ideal sites for the study of cover is one of the important temperature change (Singh et al., climatic elements which interact 42

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with the global atmosphere by paper attempts to understand the changing the energy regime as a temperature changes in the valley result of the large albedo and net of Kashmir that lies between the radiation loss (Namias, 1985; Himalayan range in the north and Sarthi et al., 2011). Several the in the south studies have also shown that for a period of three decades from decrease in snowfall days in 1980 to 2010. This region has western Himalayas is related to abundant water resources in the the occurrences of western dist- form of glaciers, snow and lakes urbance (Archer, 2004; Fowler that feed water to number of river and Archer, 2006; Dimiri et al., tributaries which finally drains in 2013; Hatwar et al., 2005; the river Jhelum. The Himalaya Wiltshire, 2013). Various work- exercises a dominant control over ers have suggested that decrease the meteorological and hydr- in snowfall has resulted in the ological conditions in the valley increase of temperature in of Kashmir. Even a minor change Himalayas (Kulkarni et al., 2002; in their climate has a potential to Negi 2005a; Fowler and Archer, cause disastrous consequences on 2006; Negi 2009b; Jeelani 2012; the socio-economic survival of Sarthi, et al., 2011, Kaab et al., millions of people (Archer, 2012). Concern on climate change 2004;Fowler and Archer, has brought several studies on 2006;Jeelani et al., 2012). Runoff temperature trend detection from the melting of winter snow (Brohan, 2006; Jones, 2003; and perennial ice makes a Landscheidt, 2000; Vinnikov and significant contribution to river Grody, 2003, Joeri et al., flow during the summer season 2011).Water resources are consi- that is vital for irrigation and dered vulnerable in the region due hydropower production in the to increasing temperature (Barnett region. et al., 2005). Due to increase in temperature seasonal storage of STUDY AREA water in snow and ice results early runoff (Immerzeel et al., lies 2010; Kaser et al., 2010; Schaner between the Himalayan range in et al., 2012; Siderius et al., 2013). the north and the Pir Panjal range Considering this importance, this in the south, situated between 43

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latitude 33°55' to 34°50' and dominated by midlatitude frontal longitude, 74° 30' to 75° 35' in disturbances. The region experi- India. The Kashmir valley is ences four distinct seasons: winter located at the elevation of (December to February), spring approximately 1500mts above the (March to May), summer (June to sea level. This region has very August), and autumn (September rugged topography and the to November). The average rain- highest elevation is around fall, as observed from the nearest 5600mts above the sea level. The meteorological station at Srinagar, location of the study area is is 650 mm, and average temp- shown in Fig. 1. On the Greater erature ranges from 2.5°C in Himalayan tracts, bordering the winter to 23.8°C in summer north-western part of Kashmir (Jeelani, 2012). valley are Ladakh, Baltistan and Gilgit (Raza et al., 1978). The total area is approximately more than 15,836 km2. The river Jhelum originates from the in the Pir Panjal ranges passes through the middle of the valley and has a length of 160kms in the Indian territory of Kashmir (Wadia, 1979). The river receives water from more than twenty four tributaries and some of them are fed by the glaciers important among them is the 'Kolahoi glacier' in lidder watershed that joins river Jhelum near Sangam station. River Jhelum drains alluvial lands in the Kashmir Valley that is known as the rice bowl of Kashmir. The weather in the Kashmir Himalaya has a marked seasonality in temperature and precipitation, which is 44

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Fig. 1. Map of study area

MATERIAL AND METHODS

Temperature data was These tests were performed using procured from Indian Metrolo- the trend statistical software. gical center (IMD) Pune for five -

stations of different elevations, RESULTS located at , , The results of mean Annual, Srinagar, Kokarnag and . mean Maximum, mean Minimum The average minimum, Maxi- and Seasonal temperature data mum, Annual and Seasonal (Win- using parametric and nonpara- ter, spring, summer, autumn) te- metric test for the Gulmarg, mperature data was analyzed at all Pahalgam, , , stations. The magnitude of trend Kup-wara and Srinagar stations and statistical significance was for last 30 years from 1980-2010 carried out using Mann-kendall are shown as under. (non-parametric) and linear

regression (parametric) tests.

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Gulmarg station This station lies to the north of the valley at an elevation of 2949 mts at latitude of 33°50' and 74° 21' longitude. The average temperature at this station is 7.8 °C. From 1980-2010 the mean Annual and mean Minimum temperature showed a significant increasing trend at a confidence level of 99% using Mann-Kendall and linear regression tests. (Table1 and Fig 2). Analysis of mean Maximum temperature at this station showed an increasing trend at confidence level of 90% using both the test. The temperature in Winter, Spring and Autumn season showed an increasing trend at confidence level of 95%. However, the Summer temperature showed insignificant increasing trend during these 30 years (Table 1 and Fig. 2).

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Table 1. Annual and Seasonal temperature trends at Gulmarg station

Temperature of Gulmarg station in°C

Statistical tests Names Test a=0.1 Result statistic a=0.05 a=0.01 2.923 1.645 1.96 2.576 S (0.01) Annual average 1.564 1.645 1.96 2.576 S (0.1) Average maximum

3.059 1.645 1.96 2.576 S (0.01) Mankendall Average minimum 2.43 1.645 1.96 2.576 S (0.05) test Winter Season 2.006 1.645 1.96 2.576 S (0.05) Spring Season 0.986 1.645 1.96 2.576 NS Summer Season 2.159 1.645 1.96 2.576 S (0.05) Autumn Season 3.12 1.699 2.045 2.756 S (0.01) Linear regression Annual average test 1.942 1.699 2.045 2.756 S (0.1) Average maximum 3.79 1.699 2.045 2.756 S (0.01) Average minimum 2.259 1.699 2.045 2.756 S (0.05) Winter Season 2.224 1.699 2.045 2.756 S (0.05) Spring Season 0.829 1.699 2.045 2.756 NS Summer Season 2.32 1.699 2.045 2.756 S (0.05) Autumn Season NS= Non significant; S=Significant. S= Significance 0.01=99% , 0.05=95%, 0.1=90%

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Fig. 2. Annual and seasonal temperature trends at Gulmarg station Srinagar station level of 90% using both This station located at the parametric and nonpara-metric latitude 34º 00´ and 75º 00´ tests (Fig. 3). The Maximum longitudes at the elevation of temperature showed significant 1500mts. The average tempe- increa-sing trend at confidence rature is 12°C. The average tem- level of 99% (Table 2). perature at this station showed Seasonally, Summer and Autumn significant increasing trend from showed insignificant increasing 1980-2010 at confidence level of trend, while the Spring season 95% using both the tests (Table 2 showed a significant increase at and Fig. 3). The analysis of confidence level of 95% using Minimum and winter temperature Mann-Kendall test and 99% and during these years showed linear regression test. increasing trend at significant 48

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Table 2. Annual and seasonal temperature trends at Srinagar station

Temperature at Srinagar station in°C Statistical test Name of the Season Test a=0.1 a=0.05 a=0.01 Result statistic Mankendall Annual average 2.108 1.645 1.96 2.576 S (0.05) test Annual maximum 2.804 1.645 1.96 2.576 S (0.01) Annual minimum 1.391 1.645 1.96 2.576 S(0.1) Winter Season 1.394 1.645 1.96 2.576 S(0.1) Spring Season 2.413 1.645 1.96 2.576 S (0.05) Summer Season 0.374 1.645 1.96 2.576 NS Autumn Season 0.918 1.645 1.96 2.576 NS

Linear Annual average 2.243 1.699 2.045 2.756 S (0.05) Regression Annual Maximum 3.27 1.699 2.045 2.756 S (0.01) Annual Minimum 1 1.699 2.045 2.756 S(0.1) Winter Season 1.271 1.699 2.045 2.756 S(0.1) Spring Season 3.164 1.699 2.045 2.756 S (0.01) Summer Season 0.273 1.699 2.045 2.756 NS Autumn Season 1.099 1.699 2.045 2.756 NS

NS=Non significant; S=Significant. S 0.01=99% , 0.05=95%, 0.1=90%

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Fig. 3. Annual and seasonal temperature trends at Srinagar station Pahalgam station Pahalgam station is located of mean Maximum, mean in the Lidder valley of Kashmir at Minimum and mean Spring and the elevation of 2730 mts between Winter temperature showed a 75° 20' longitude and 34° 00' significant increase at a confi- latitude as shown in Fig 1. The dence level of 99% using both the mean Annual temperature at the test. Summer temperature showed Pahalgam station is 9ºC. The increase at a confidence level of average temperature at this station 90% as shown in Table 3 and Fig. showed a significant increasing 4. The Autumn temperature sho- trend as shown in Table 3 and wed significant increasing trend at Fig. 4 at confidence level of 99% confidence level of 95% using using both the tests. The analysis both the test. 50

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Table 3. Annual and seasonal temperature trends at Phalgam station

Statistical test Temperature at Phalgam station in°C

Name of the Test a=0.1 a=0.05 a=0.01 Result Season statistic 4.119 1.645 1.96 2.576 S (0.01) Mankendall Annual average test 3.519 1.645 1.96 2.576 S (0.01) Annual maximum 3.6 1.645 1.96 2.576 S (0.01) Annual minimum 3.811 1.645 1.96 2.576 S (0.01) Winter Season 3.438 1.645 1.96 2.576 S (0.01) Spring Season 1.719 1.645 1.96 2.576 S (0.1) Summer Season 2.416 1.645 1.96 2.576 S (0.05) Autumn Season 5.087 1.697 2.042 2.75 S (0.01) Linear Annual average Regression 3.519 1.645 1.96 2.576 S (0.01) Annual Minimum 4.457 1.697 2.042 2.75 S (0.01) Annual Maximum 3.856 1.697 2.042 2.75 S (0.01) Winter Season 4.597 1.697 2.042 2.75 S (0.01) Spring Season 1.915 1.697 2.042 2.75 S (0.1) Summer Season

Autumn Season 2.46 1.697 2.042 2.75 S (0.05)

NS= Non significant; S=Significant. S= Significance 0.01=99% ,0.05=95%, 0.1=90%

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Fig. 4. Annual and seasonal temperature trends at Phalgam station Kokernag station increasing trend from last three The Kokernag is located decades at the confidence level of towards the southern part of the 95% as shown in Fig 5 and Table valley at the elevation of 2000mts 4. The analysis of temperature in at the latitude of 33° 40' and Winter Spring and Autumn sea- longitude of 75° 00'. The mean son shows a significant increa- Maximum and mean Annual sing trend at confidence level of temperature at this station showed 99% using both the trend test an increasing trend at confidence while the Summer temperature level of 99% and the mean shows insignificant increasing Minimum temperature showed an trend.

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Table 4. Annual and seasonal temperature trends at Kokarnag station

Temperature of Kokarnag station in°C

Statistical tests Names Test a=0.1 a=0.05 a=0.01 Result statistic 3.433 1.645 1.96 2.576 S (0.01) Annual average 3.246 1.645 1.96 2.576 S (0.01) Average maximum

1.819 1.645 1.96 2.576 S (0.5) Mankendall Average minimum test 1.785 1.645 1.96 2.576 S (0.01) Winter Season -2.176 1.645 1.96 2.576 S (0.01) Spring Season 0.187 1.645 1.96 2.576 NS Summer Season 2.685 1.645 1.96 2.576 S (0.01) Autumn Season 3.745 1.699 2.045 2.756 S (0.01) Linear regression Annual average test 3.842 1.699 2.045 2.756 S (0.01) Average maximum 2.331 1.699 2.045 2.756 S (0.05) Average minimum 1.797 1.699 2.045 2.756 S (0.01) Winter Season -2.525 1.699 2.045 2.756 S (0.01) Spring Season -0.154 1.699 2.045 2.756 NS Summer Season 2.903 1.699 2.045 2.756 S (0.01) Autumn Season NS= Non significant; S=Significant. S= Significance 0.01=99% ,0.05=95%, 0.1=90%

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Fig. 5. Annual and Seasonal temperature trends at Kokarnag station Kupwara station showing increasing trend at a confi- The Kupwara station lies at dence level of 95% using Mann the altitude of 2400 mts at the Kendall test and 99% and 95% using latitude of 34º 25' and longitude 74º Linear Regression tests. 18'. The average temperature at this station is 12ºC. The analysis of mean The result of this study based Annual, mean Maximum, mean Mi- on observation of the existing data nimum and Winter temperature reveal that there has been increasing from last 30 years showed significant trend in the seasonal and annual increase at confidence level of 99% average temperature at all the five using Mann Kendall and Linear stations in particular and in Kashmir regression tests while the mean valley as a whole. Further analysis Summer and Autumn temperature also reveals that in Kashmir valley showed insignificant increasing trend winter and spring seasons have been as shown in Fig. 6 and Table 5. The warming at all the stations (statis- mean temperature in Spring season is tically significant at 0.01-0.05 or 54

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95%-99%). Whereas summer and 2010; Schaner et al., 2012; Siderius autumn seasons have comparatively et al., 2013), which influence the increased statistically at lower to water resources, economy and the insignificant rates. These obser- tourism in Himalayas, besides snow vations are matching with the findi- cover has been shown to exert a ngs of other studies on temperature considerable local influence on changes, in the Himalaya (Agrawal weather variables, so this can be one et al., 1989; Khan 2001; Archer and of the important bases for prediction Flower, 2004; Kumar and Jain, of enhanced warming in seasonally 2010). Shrestha et al., (1999) in his snow covered regions. The Hima- observations also revealed that the layas receives most of its preci- Himalayan region as a whole has pitation in the form of snow by the warmed by about 1.8°F since 1970‘s. Western disturbances during Winter Similar results too have been months (Karl 1993, Groisman et al., observed by Fowler and Archer 1994, Robinson and Serreze, 1995). (2006) in Upper Indus basin (North From last few years various Western Himalayas) where tempe- researchers have reported decreasing rature has been found increasing at snowfall during winter months due higher rate in winter season. The to variations in Western disturbances Tibetan Plateau region, the Kosi (Karl, 1993; Groisman et al., 1994; Basin in the Central Himalaya and Robinson and Serreze, 1995; the Nepal Himalaya in the eastern Hengchun and Mather, 1997; Fallot part of Himalayas have experienced et al., 1997; IPCC 2001; Ye and Bao, similar positive increasing trend in 2001, Raicich et al., 2003; Choi et temperature during the last century al., 2010 and Brown and Robinson, (Sharma et al., 2000). Since the 2011). Such decreasing snowfall Himalayan Mountains are the results in less snow cover/depth in greatest resources of snow and these regions. This less snow cover/ glaciers after the Polar Regions they depth melts within a short period of are the major sources of water for time during winter season and leaves irrigation, drinking water, hydro scope for early spring season which project etc for south east Asia. results in early increase in temp- Recent studies revealed that erature and direct heating of earth‘s Himalayan glaciers/snow are melting surface that has also been established and receding at much faster rate due to early flowering of plants in (Kaser et al., 2010; Immerzeel et al., this region also noted by various 55

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workers (Walker et al., 1995, during summer seasons which has Houghton, 2001, Dunne et al., 2003). simultaneously affected the irrigation Early melting of snow has resulted in and hydropower sector adversely increasing flow of rivers during (Dhanju, 1983; Negi et al., 2005, spring season and very less flow 2009; Sarthi et al., 2011.)

Table 5. Annual and seasonal temperature trends at Kupwara station

Temperature of Kupwara station in°C Statistical tests Names Test a=0.1 a=0.05 a=0.01 Result statistic Annual average 3.62 1.645 1.96 2.576 S (0.01) Average maximum 3.11 1.645 1.96 2.576 S (0.01) Average minimum 2.363 1.645 1.96 2.576 S (0.01) Mankendall Winter Season 2.43 1.645 1.96 2.576 S (0.01) test Spring Season 3.195 1.645 1.96 2.576 S (0.05) Summer Season 1.462 1.645 1.96 2.576 NS Autumn Season 0.68 1.645 1.96 2.576 NS Linear regression Annual average 3.998 1.699 2.045 2.756 S (0.01) test Average maximum 3.622 1.699 2.045 2.756 S (0.01) Average minimum 2.376 1.699 2.045 2.756 S (0.01) Winter Season 2.259 1.699 2.045 2.756 S (0.01) Spring Season 3.469 1.699 2.045 2.756 S (0.01) Summer Season 1.108 1.699 2.045 2.756 NS Autumn Season 1.023 1.699 2.045 2.756 NS

NS= Non significant; S=Significant. S= Significance 0.01=99% ,0.05=95%, 0.1=90%

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Fig. 6. Annual and seasonal temperature trends at Kupwara station CONCLUSIONS climate change in the Himalaya. The results of the present Further analysis shows that there has study based on observation of the been less snowfall in winter season existing data reveal that there has resulting in less snow cover/depth. been significant increasing trends in This less snow requires less amount the seasonal and the annual surface of temperature for melting paving air temperatures in Kashmir valley as way for early springs due to increase a whole during the period, 1980- in temperature. This increase in 2010. Moreover, the winter and temperature throughout the valley spring seasons have been warming with significant increase in spring more significantly (0.01-0.05) at all temperature can have serious stations. The results are in agreement consequences on agriculture, hydro with the findings of other studies on power and drinking water supply. 57

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