Characteristics and trend of extreme temperature in Spatio-temporal context, South Xinjiang China.

. Evaluation of climate change study is vital for appropriate management of hydrological resources and future planning. 27 South Xinjiang is comprising various sort of climatic conditions. The focal point of this study is to assess the 28 dissemination and pattern of temperature for as far back as 39 years in south Xinjiang China. The time series data 29 recorded as maximum, minimum and mean monthly temperature at different metrological stations. For trend detection 30 Mann-Kendall tests and Sen's slope estimation model were applied for appropriate results. The statistical analysis of 31 the study indicates a significant upward trend in three types mean max. min. and average temperature on seasonal & 32 monthly scale. Change points find out in the four decades show an increasing trend of temperature. Results found 33 from Sen's slope magnitudes vary from 0.010ºC to 0.070ºC in T max per annum. Further, Sen's slope from - 0.150ºC to 34 0.080ºC and - 0.080ºC to 0.060ºC every year for both T min and T mean . So for the increasing trend in all temperature is 35 a get way to a dangerous atmospheric devastation and environmental change. Seasonal evaluation of temperature (JJA) 36 June, July and August detected upward trend of temperature while the rainfall occurring months (NDJF), November, 37 December, January and February found significantly dry. The seasonal changeability of temperature is 38 straightforwardly responsible for desertification in the area. The conclusion of the research study that southern 39 Xinjiang facing severe dry conditions are essential to highlight this burning issue for further development and 40 sustainability of water resources. 41

maximum. Different developing studies of climate change show variety in environment is quick in metropolitan 60 regions because of congested population and more use of natural assets (Kalnay and Cai, 2003) (Karl et al., 2009).

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In higher altitude the temperature map worldwide shows a critical change over the most recent couple of many years 62 and its insufficiency lead to dry spell circumstance in any area. Investigation of temperature information at worldwide 63 level uncovers that earth surface temperature increment 0.86 ˚C since 1860 and especially, during the last five to six 64 decades, significant warming trend occurred in the environment (Gu et al., 2016), (Kramer et al., 2017), (Dawood et

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The MKT tests are used widely for time series data and yield much more accurate results for a broad range of situation 70 (Tabari and Marofi, 2011).

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No doubt climatic variability has been considered as one of the complex challenge for upcoming decades on all 72 geographical and economic levels (Solomon et al., 2007). A main part of logical writing uncovers that environment 73 changing occasions causing enormous human misfortunes and financial annoyance and debasement of vegetation 74 cover (Fu et al., 2013). According to recent statistics, 10-fold increase has been recorded in economic losses in the 75 last four decades, among them natural disasters counted more than 90 percent of the total losses due to reoccurrence

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Such increase in temperature not same throughout the world and influences hydrological cycle 81 which badly affect the precipitation regime in any region of the world (Shi et al., 2007), (Cook et al., 2014). The main 82 consequences of these progressions are alteration in climatic boundaries across the world and especially influencing 83 scene and temperature of China (Zhang et al., 2016), (Kramer et al., 2017). In recent 50 years the environment of 84 China shows critical variety with an increment of 0.31 ºC/10a in yearly mean temperature. In twentieth century the 85 variety in temperature isn't a lot of critical, while the adjustment in precipitation situation is a stressed circumstance 86 and measurable (Burns et al., 2007), (Zhang et al., 2008).

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Climate change in china has been experienced severe drought period from 1991 to 2009 leads to loss of 16 88 billion Chines Yuan and about more than 10 million peoples face shortage of drinking water (Ge et al., 2008). The 89 western china, particularly Xinjiang gets warm and dry due to increase rate of temperature 90 specially Taklimakan desert (Lianmei, 2003). Beside these drastic changes in the earth atmosphere and 91 warming lead to change the weather pattern, increasing aridity, rising of ocean and sea level 92 and results in melting of glaciers (Zhang et al., 2009b). These progressions additionally seriously influenced the actual 93 climate of Xinjiang and prompts dry season circumstance for all time (Kalnay and Cai, 2003). Slight changes in temperature can straightforwardly influence the hydrological pattern of a region and altogether influence water assets 95 and climate (Fu et al., 2013).

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Temperature and precipitation generally used to discover the extent and pattern of environmental change by utilizing 97 distinctive measurable techniques in environment study (Dawood et al., 2018). The climate change impacts the 98 creation of flora (Tamarix Plants) and as of now their development and sustainability in the south Xinjiang (Tabari 99 and Marofi, 2011). The extent of this study is to recognize spatial and temporal trend in temperature at Xinjiang from 100 1980 to 2018 and to predict future trend by utilizing Sen's slope estimation (SSE), Mann-Kendall (MKT) and by 101 interpolation inverse distance weighting approaches.

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Keeping in mind the climatic scenario of the area, the main aims of study are the following:

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(1) To find out trend of minimum, maximum and average temperature on monthly, seasonally, and 104 annual basis.

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(3) To highlight some suggestions for the reclamation of such situation.

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(4) To check the specific points of change.

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Evaluation and analysis of temporal temperature data is very important for managing future environmental and water 109 resources in Xinjiang for ecological equilibrium and sustainability (Tabari andMarofi 2011, Fu, Chen et al. 2013

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The yearly precipitation is less than 158 millimeter considered dry region.

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The main river in the region is Hotian River originated from Kunlun Mountain in south, Aksu River north of Tianshan

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(1) Check on the effects of serial correlation.

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(2) In order to detect trend by apply MK test.

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(3) To find Spatial-temporal magnitude and direction of temperature through Sen's slope.

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There are many statistical tools and models used by different scholars for trend detection. For climate study like 162 precipitation, temperature data, and environment related sciences (MKT) are widely used. Statistical analysis is of more values to detect change in a variable with time and space in a uniform sequence, generally increase and decrease 164 trend of temperature (Gu et al., 2016). Mostly widely this test is referred as are rank based approach use to obtain 165 appropriate results (Christopherson, 2015). Henceforth, for this research study MKTT has been used to detect trend 166 temperature over last four decades. This is a non-parametric test used to detect monotonic (linear / non-linear) trend 167 in a variable statistically computing by the following equations (Zhang et al., 2009a). (1) The variance of statistics is found as (3)

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Two hypothesis are generally used to interpret MK test results. If the value of P is lower than significant value level,

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we reject the (Ho) Null hypothesis. In time series data, a significant trend is calculated when the probability value is  Sen's slope estimation of (SSE) approach is used widely to find out magnitude and direction of temperature trend at 181 different spatial and temporal scale (Tabari and Marofi, 2011). This is a slope base test (referred as SS). It is non-182 parametric test uniformly applied when there is space exist between the data. This method is used in hydro-met data 183 to find linear and non-linear trend of temperature data, whether it's increasing or decreasing. For all pairs of data 184 slope (Ti) as find as Sen's slope. 185 In equation (4), show Sen's slope estimation and its permanent value. To find out the slope approximation) equation

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(5) firstly value of all data was calculated.
In equation (5), i = 1,3,5……. N in which time j and k (j ˃ k), Xj and Xk is pairs data value. N value of Ti median is 190 determined as Sen's slope estimation, as below.
192 If the value of N is calculated odd, the Sen's slope will be when the value is even than the N is when value become     Maximum variation is detected in minimum temperature as compared to maximum temperature in (CV) coefficient 230 of variation calculation. The CV indicates the continuity of temperature increasing obviously maximum going upward 231 and minimum downward trend. The concern area receives little amount of rainfall and located on rain shadows having 232 less monsoon occurrence. Due to these uncertainties the range of temperature is maximum. In current situation, 233 minimum temperature has an upward direction with respect to space and time. So the consequences are hard to control 234 the upward rate of minimum temperature for its more variability to maximum temperature.

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The Figure 2 shows the mean month to month interpolation of maximum temperature and trend for a particular time-236 frame. Trend increases as we go towards north to southern direction and east to western with a range from 3.48ºC-    The Mann-Kendall test applied on information of all met-stations. The data was checked cautiously to eliminate any 246 error and homogeneity test was conducted.

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In Table 3

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The ratio is more significant in maximum temperature. The Sen's slope all values were interpolated by IDW to find

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The Table 4

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There is slight descending trend in least temperature from (January to February and November to December), 323 otherwise, least temperature gradually increases in rest of months. In graphical representation, the average temperature 324 has also an upward direction having severe cold months like Jan., Feb., Nov. and Dec. Most extreme temperature is 325 consistently expanding and most elevated proportion is recorded in July 41.97ºC with the remainder of months having 326 additionally upward situation. The rate of monsoon rainfall is less than normal specially in south Xinjiang. Almost the 327 environment is dry and warm.

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This is a situation of uncertainty that mostly the minimum temperature goes upward in recent times. The main reason

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The Figure 8 shows monthly insightful normal dissemination of different sorts of temperature in various stations. As 335 from above figure greatest scope of temperatrue is found in July (41.97 ºC). May, June, July and August is nearly 336 distinguished most sweltering months. In minimum temperature most extreme is distinguished in Jan and month of 337 December. Contrasting with aside from Jan, Feb, Nov and December proportion is going upward. This is extremely 338 pivotal circumstance that base temperature is in most elevated increment rate and outfitting the general climate.

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Normal temperature is likewise more in speeding up extraordinarily in months like June and July.

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The least mean month to month temperatrue is found in Jan and December. In general investigation month savvy

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Percentage of trend in monthly average temperature is nearly 85% which is alarming situation in any locale. In