Temperature Variability Analysis of Gulmarg and Srinagar, Kashmir Valley, J&K, India

Climate is a complex and chaotic system having non-linear links between its component variables. Study on temperature trends at macro level may be used to assess the climate variability of a region over the period of time as it is the main component of the climate system. For trend analysis, non-parametric Mann-Kendall test, which is an important tool to find out the existence and magnitude of any statistically significant trend in the climatic data, has been employed in the present study. Another index called Sen Slope has been used to quantify the magnitude of such trend. Temperature data of 39 years (1970-2009) were used for two selected stations over different altitudinal zones of Kashmir valley, viz; Srinagar (1600 metres) and Gulmarg (2644 metres). Both the maximum and minimum temperatures were found to be rising. The overall increase in temperature for Gulmarg is found to be much more than that of Srinagar.


INTRODUCTION
Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased. Each of the last three decades has been successively warmer at the Earth's surface than any preceding decade since 1850. In the Northern Hemisphere, 1983-2012 was likely the warmest 30-year period of the last 1400 years [1]. There is now clear evidence for an observed increase in global average temperature and change in rainfall pattern during the 20th century around the world [2]. There is a close association between climate change and water resources variability [3]. Climate change scenario in India revealed that the annual mean temperature has increased by 0.48°C in the past 100 years [4]. Many studies have shown that there is observed increasing trend in surface temperature in different place of India [5]. Temporal trend analysis is a tool to understand variations of different meteorological parameters with time. The knowledge of increasing/decreasing or no trend of an individual climatic parameter may lead to safer designs, proper planning, required corrective measures, and sustainable practices. Surface temperature data series for India shows a rising trend in annual mean temperature and is comparable with the reported rise of global surface temperature by 0.6°C [6]. In India, several meteorological studies have been conducted in the context of climate change using observed data as well as model results [7] and [8]. The mean annual rainfall of India is of the order of 1190 mm with a standard deviation of 95 mm [9].Assessing temporal trends for different meteorological parameters are essential to understand the local climate change pattern of a region [10]. It helps in forecasting the future climate and its effect on cultural landscapes.

STUDY AREA
The study has been carried out for Gulmarg and Srinagar located at two different altitudinal zones of Kashmir Valley. Gulmarg is located at an altitude of 2644 metres in Middle Himalayas, locally called Pir Panjal mountains on the south of the valley while as Srinagar is located at an altitude of 1600 metres above mean sea level. Srinagar lies approximately at the centre of the valley. The location of the stations is shown in Fig. 1.

METHODOLOGY
The general statistical analysis may be performed to take a general overview of the data collected. The statistical parameters which are usually considered to look for gross data errors/outliers are:

Minimum and maximum 2. Mean 3. Standard deviation 4. Coefficient of variation
The trend analysis may be performed to determine the existence and magnitude of any statistically significant trend in meteorological parameters over the time period considered. The Mann-Kendall (MK) test has been used in this study for detection of trend. This is a nonparametric test which makes no assumption for probability distribution of the variate and is less affected by missing values or outliers. However, MK test is a statistical yes/no type hypothesis testing procedure and, therefore, another index, Sen Slope [11] has been used to quantify the magnitude of such trend. Being non-parametric, Sen Slope also enjoys the same advantages mentioned earlier for the MK test. In Mann-Kendall test, The Kendall's statistic S is given by [12]: Where, t is the extent (number of x involved) of any given tie and ∑ t denotes the summation over all ties. Then Z c follows the standard normal distribution where: The null hypothesis that there is no trend is rejected when: |Zc| > Z1− ⍺ ଶ Where, Z is the standard normal variate and ⍺ is the level of significance for the test.
To find out the effect of auto-correlation on MK test results, the modified MK test with effective sample size (ESS) approach, as suggested in [13], was attempted on detrended series. In this method, the variance ߪ ଶ ௦ is modified as: Wherein n* is the effective sample size calculated as [14]: The mean Minimum annual temperature of Srinagar has shown a slight increasing trend; however, it is not statistically significant. Examination of the mean monthly temperature indicates that the months of December, February and March show warming trend as can be seen from the sharp increase in the mean monthly temperature of the said months while the months of April, August, November and October show a slight increasing trend in mean monthly temperature. The month of May, July and September is trend less while a slight decreasing trend can be seen for the month of June. Examination of the seasonal mean temperature series indicates that the winter season (December, January and February or DJF) and spring season (March, April and May or MAM) show warming trend as the seasons have shown a sharp increase in trend especially for winter season (DJF). It is surprising to note that slight decrease in trend can be seen for the summer season (June, July and August or JJA), However, it is not statistically significant. The autumn season also shows a slight increasing trend which is not statistically significant. Figs. 3 and 4 depicts the variation in monthly mean Temperature over Srinagar. Analysis of the monthly data depicts that a sharp increasing trend is seen for the months of February and December which is not a good sign as rise in temperature in winter does not allow snow to freeze for a longer time which could lead to floods and even shortage of water in summer time as the valley is dependent on snow and glaciers for its water. A slight increase can also be seen for the months of January, April, August, October and November, but statistically it is not very significant. The months of May, July and September are trend less while a slight decreasing trend can be seen for the months of June.

CONCLUSION
Kashmir valley has distinct seasonal calendar. The region is mountainous in nature with fragile ecology and is bearing the brunt of increasing human interference. Following conclusion has been drawn from the study: