International Journal of Agricultural Science and Research (IJASR) ISSN 2250-0057 Vol.

3, Issue 2, Jun 2013, 317-330 TJPRC Pvt. Ltd.

INVESTIGATION OF THE VARIATION TRENDS OF PRECIPITATION IN LAST 50 YEAR IN NORTHERN KAROON WATERSHED OF IRAN
PEZHMAN ALAHBAKHSHIAN FARSANI1, MAHMOUD HABIBNEJAD ROSHAN2, GHORBAN VAHBZADE3 & KARIM SOLAIMANI4
1

Student, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources of University, Sari, Iran

2,4

Professor, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources of University, Sari, Iran

Assistant Professor, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources of University, Sari, Iran

ABSTRACT
The hydrological parameters of rainfall, evaporation and runoff are influenced by the global warming as a new challenge in the world which affects on natural resources especially water sources. Climate change has many effects on hydrological cycle and consequently water resources and frequency and intensity if drought and flood in natural environment, society and economic. So, evaluating the variations in spatial and temporal pattern of precipitation is necessary to manage water resource in a region. The objective of this research was to assess the trend of the precipitation variations using Mann-kendall and Sen test in monthly, seasonal and annual scale and determine the variations and its direction using CUMSUM chart in last 50 year in northen Karoon watershed. Results showed that the annual precipitation of watershed has increased, but this increasing was not significant. As, 90%of stations shows the increasing trend in annual precipitation, but only 18% of stations has significant trend. In seasonal scale the maximum increasing in precipitation was observed in autumn and the maximum decreasing trend in precipitation was recorded in spring. The maximum increasing trend in precipitation was recorded in December, whereas the maximum decreasing trend in precipitation was recorded in October and March. Annual variations in precipitation have been only occurred in Hana, MasjedSoleiman and PoulZamankhan stations which is for the end of the forth decade to the prime of sixth decade. The results of this study are used to predicting and locating the future drought and scheduling and managing water resources of region.

KEYWORDS: Climate Change, Variations Trend, Mann-Kendall Test, Northern Karoon INTRODUCTION
The variations of weather are one of the consequences of global warming which affects on environment, water resource, industrial and agricultural activities and human life (shi and xu, 2008). Global warming is due to the green house gases produced by human activities which lead to intensification in hydrological cycle of earth (Alan et al., 2003; Allen and Ingram, 2002; Arnell,1999). Moreover, intensification in hydrological cycle cause to changing temporal and spatial pattern of precipitation as a result cause to increase maximum precipitation which lead to increase disturbances such as flood and drought in many regions of world (Allan and Soden, 2008; Easterling et al., 2000a; IPCC, 2001; Mirza, 2002; Qiu, 2010). The international institute of climate change (IPCC) from 1900-2005 about the precipitation variation reported that the moist climate is exist in north east and south of America, north of Europe, north and central part Asia and in coastal area, south of Africa, Mediterranean region and south of Asia (Trenberth et al., 2007). Water is the most important

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resources to develop economic security, ecosystem and biodiversity among vital resources. So, the determination of the temporal and spatial variations and evaluation of the climate and human activities effects on human is necessary to schedule and sustainable manage of water resources which is required to sufficient hydrological data and information about flow pattern and qualitative and quantitative data of water resource in watershed (Oybande, 2001). In recent years the worries increased about the maximum incidents of climate and weather, because it has destructive effects in human community and nature (Easterling et al., 2000b; Karl and Esterling, 1999). Variability of climate associated with human activities in production of green house gases cause to increase global temperature (zhang et al., 2010), evaporation and exchange of vapor in atmosphere and finally intensification of the hydrological cycle of earth (Menzel and Buger, 2002). Variation pattern of Atmosphere in large scale of spatial and temporal distribution of temperature and precipitation is controlled. One of the consequences of climate change may be the variations in regional hydrologic cycle and change in quality and quantity of stream regimes. The water is very important in society and nature, so understanding of variation is necessary. In the other hand global warming cause to change in cycle of global hydrological and runoff. Therefore, access to water resources changes from a region to another region due to climate change (Xu and Singh, 2004; labat et al., 2004). In last mid-decades the access to sweet water with suitable quality has been converted to a public worry especially according to population pressure and weather change (Gleick, 1993; Gleick, 2000; Milliman et al., 2008; Shiklomanov and Rodda, 2003; Vorosmarty et al., 2002). The accelerated increasing in water consumption due to increasing population and enhancement of standard level of life and economic development which this status cause to intensification pollution in water resources which lead to negative effects on economic and social sustainable development in human community. These problems are not only bacuase of the natural factors such as lack of spatial and temporal distribution of precipitation but are due to lack of knowledge about the issues of water resource (Zhang et al., 2005). Climate change has many effects on hydrological cycle and consequence on water resources, frequency and intensity of drought and flood, natural area and society and economic. The earth has experienced the increasing temperature and change in rainfall pattern in three previous decades and it seems that this condition can continue in future. Such variations in weather may include the considerable effects on hydrological cycle (Ramos, 2001). The amount of stored water in water resources in different times of hydrological cycle changes because of increasing precipitation variability and increasing temperature. Knowing of trend and variability of flow and previous hydro-climate variables is necessary to future development and sustainable management of water resources in a region especially in field of global warming, water and energy cycle and increasing demand for water due to population and economic development. One of the most important studies in field of climate change is analysis and determining of previous variations in climate system. Recently the extensive researches have been concluded in field of climate change and variability which many studies have focused on trend determination. Carbajal et al. (1993) in watershed of Aconcagua stream in chili investigated the precipitation and temperature using Mann-kendall. This research doesnt show significant trend in precipitation whereas the temperature had significant trend. Gonzales-Hidalgo et al. (2001) showed that the trend of rainfall was decreased significantly in variability among annual rainfall in moist region of Valencia of Spain. Xu et al (2003) investigated the trend of precipitation variations in Japan using parametric and non parametric tests and concluded that the mean rainfall in Japan has suddenly changed, whereas there are no signs of even decreasing or increasing trend. Gemmer et al. (2004) investigated the annual series of rainfall in 160 stations in china and observed that there is a trend in determined months in eastern and north east of china. Partal and Kahya (2006) investigated the precipitation trend in Turkish. Results showed that there is significant trend in January,

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February, September and mean annual. Rose (2007) conducted a study on precipitation and runoff trend in south east of USA. Results showed that there is no significant trend in precipitation and runoff in study area. The purpose of this study was to investigate the existence trend in precipitation in monthly, seasonal and annual scale in northern Karoon watershed.

MATERIALS AND METHODS

Study Area The study watershed is a section of a Karoon watershed in middle heights of Zagroos and is located in northern latitude of 3350010 to 3612112.1 and eastern longitude of 344408.6 to 595339.6 based on UTM image system. Northern Karoon watershed has an area of 2707200 hectare (To Shahid Abbaspoor dam) and is one of the large watersheds of Persian Gulf. This steam includes four main branches entitled Khorasan, Abvanak Ab kiar and Bazyaft. Mean annual rainfall in study watershed is about 315 mm per year in south east parts of watershed and 1376 mm per year in North West parts of watershed (adjacent heights of Koohrang). Temperature regime of Karoon watershed is affected by the atmosphere masses. For example the minimum absolute temperature in Koohrang station is from -46 to -7 C in Izea station. The Karoon watershed in classification system of corrected Domarton have cold mid-dry climate to temperate mid-dry. Karoon stream is largest stream of Iran. This stream is initiated from Zagros Mountain which is similar to Alpena Mountains and has mean discharge of 425.3 cubic meters per seconds.

Figure 1: The Position of the Rain Guaging and Climatology Stations in Northern Karoon Watershed Data Data series must be long term. Short term data can be affected by climate change and so these change the results. At least 50 year statistics has been suggested to investigate climate change, which may not enough (WMO, 2000). Therefore this study attempts to select stations which have this provision. So, 17 rain gauge stations were selected in study area. Table 1: Geographical Position of the Selected Rain Gauge and Metrology Stations in Northern Karoon Watersheds Station Name Hana Hamgin Shahmokhtar Botari Elevation (m) 2300 2256 1730 1520 Longititude 568286 544438 549486 531871 Latitude 3450141 3530368 3394622 3413026 Station Type Climatology- Rain guaging Climatology Rain guaging Rain guaging

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Pole Shalo Dehkadeh Shahid Shahrekord Bootvand Gotvand Koohrang Lordegan Masjed Solaiman Tale Zang Emam Gheys Pole Zamankhan Tang Panj Haftapeh Mann-Kendall Test

700 2220 2050 140 75 2372 1580 362 480 2285 1883 540 80

Table 1 Contd., 417915 3513052 570130 3411361 484311 3575549 322094 3541985 294312 3570238 417598 3519622 482589 3485031 336236 3539900 290918 3633171 533156 3510984 489666 3595674 291192 3646109 265573 3550522

Rain guaging Rain guaging Rain guaging Rain guaging Climatology Climatology- Rain guaging Climatology Climatology Rain guaging Climatology Climatology Climatology Climatology

Mann-kendalll test is one of the most common non-parametric methods for analyses of the series hydrological and climate trend. The studies conducted by this method indicate its importance and application in analysis of the trend of temporal series. This study was introduced by Men in 1945 and then developed by Kendall in 1948. The application of this method was recommended by world meteology organization (Kendalll, 1975). This method is used to test the random hypothesis of data succession against the existent of trend. Suitability of the application of Mann-kendall method in temporal series is that it is not function of any distribution and it is not necessary to normalize and linearity data (mann,1945; wang and zhou,2005; allan and sodden,2008;kampata et al.,2008). This method is not affected by the limited value which is observed in temporal series. The null model of this test shows the randomiza shape and lack of trend in data series. The acceptance of one model is the indicator of existence trend in series (rejection of null model). The stages of the statistical calculation of this test are as following: The calculation of the difference of each observation to each other and applying sign function and extracting S parameter as: (1) Which n is the number of observations in series and and are calculated as following:

(2)

Calculation of variance using a following equation: if n>10 (3)

if n< 10

(4)

Which n is the number of observations, m is the number of series which have at least one repeated data and t shows the frequency of data with even value.

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Finally Z is determined using a following equation:

(5)

In two sides test to trending data series the null model is accepted if: (6) Significant level is which was used according to two range test. In this study, Mann-kendall test at

probability level of 95 and 99% was used. The trend of data series is increasing if Z is positive and the trend of data series is decreasing if Z is negative. Sen Test This test was introduced by Sen (1968) and uses the analysis of difference among observations in a temporal series. The benefits of mann-kendall test are true for this test. Moreover, this test is used if there is missed data (BouzaDeano et al., 2008). The null model of this test is the indicator of randomize and lack of trend in precipitation data. The acceptance of one model or in another word null model shows the existent of trend in temporal series. The basis of this method is calculating a median gradient for temporal series and judge about significant at different confidence level. The stages of calculation of this static are as following: Calculation of the gradient between each pair observation data using following equation: (7) In this equation and are the observation data in times of t and s, respectively which t is a time unit after

time of s. with using this equation for each pair observation data a temporal series is achieved from calculated gradient. The gradient of trend line ( ) is obtained with calculating median of this temporal series. The positive value of

shows the increasing trend and the negative value shows the decreasing trend (Xu et al, 2007). Calculation of parameter at tested confidence level is calculated as:

(8) Which, Z is the values of standard normal distribution and in two-side test have different values based on different confidence level. This value was 1.96 and 2.58 for confidence levels of 95 and 99%, respectively. Calculation of the up and down confidence limits (M1 and M2) is done using following equation:

(9) Which N is the number of calculated gradients in section a.

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The final stage of Sent set is the investigation of the calculated confidence limit. Among calculated gradients using equation [7] M1 and M2+1 of gradients are extracted. If the zero is in the range between two extracted gradient, the null model would accept and no trend can be determined for the considered temporal series. In this case the null model is rejected and this shows the existent of significant trend in temporal series.

Cumulative Sum Charts (CUSUM) This procedure was used by Wayne (2000) and is used for performing a change point analysis and detection using cumulative sum charts (CUSUM) and bootstrapping. Let X1 ,X2,,Xn represent the n data points. The cumulative sums S0, S1,, Sn are calculated iteratively as illustrated in the following three steps: Calculate the average (10) Set S0 = 0. Calculate Si recursively: (11) A sudden change in the direction of the CUSUM indicates a sudden shift in the average. A period where the CUSUM chart follows a relatively straight path indicates a period where the average does not change. The confidence level can be determined by performing bootstrap analysis. Before performing the bootstrap analysis, the magnitude of change Sdiff which is defined as: (12) Where (13) Then, the bootstrap analysis can be performed as follows: Generate a bootstrap sample of n units, denoted as values. Based on the bootstrap sample, calculate the bootstrap CUSUM, denoted as Calculate the maximum, the minimum and the difference of the bootstrap CUSUM, respectively. Determine whether the bootstrap difference Iterate the above procedure (1)-(4) N times. Let X be the number of bootstraps for which point occurred is, < , then, the confidence level (CL) at which a change is less than the original difference or not. . ,, and , by randomly reordering the original n

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(14) To estimate the location of the change point, define m such that: (15) S is the point furthest from 0 in the CUSUM chart. The point m estimates the last point before the occurrence of the change point. The above-mentioned control chart and change point analysis are based on the assumption that the observations are independent and identically distributed. The performance of CUSUM chart deteriorates when the process observations are autocorrelated.

RESULTS
In this study at first the data were evaluated for the outline data. Results showed that no stations had outline data at probability level of 95%. Then the homogenous Ran test was applied at probability level of 95% to confirm the homogenous status of data. The results of this study showed that the data were homogenous. After these two tests, it was attempt to investigate the existence or lack of trend in different stations and seasonal and annual temporal series. Mannkendall non-parametric test was used to determine the trend in precipitation of study stations. Moreover, Sen Test was used to determine the gradient existent trend. Annual Scale Table2 shows the mean annual precipitation and standard deviation of mean annual precipitation in 50-year period. Maximum and minimum precipitation was recorded in Koohrang and Haft tapeh stations with 1376.1 and 262.9 mm, respectively. The amount of Z shows the Mann-kendall test and Q shows Sen Test in annual scale. Table 2: Results of the Mann-Kendall and Sen Test and Sum of the Annual Precipitation and its Coefficient of Variation Annual Mean Variation Precipitation Coefficient Hana 315.7 0.4 Hamgin 282.9 0.4 Shahmokhtar 719.6 0.5 Botari 475.2 0.4 Pole Shalo 787.4 0.5 Dehkadeh Shahid 520.3 0.4 Shahrekord 325.4 0.4 Bootvand 271.3 0.5 Gotvand 373.4 0.4 Koohrang 1376.1 0.3 Lordegan 549.9 0.4 Masjed Solaiman 472.6 0.4 Tale Zang 868.6 0.3 Emam Gheys 563.4 0.3 Pole Zamankhan 331.3 0.3 Tang Panj 1083.4 0.3 Haftapeh 262.9 0.4 *Significant at p<0.05; **Significant at p<0.10 Station Name Z 1.52 0.94 0.82 0.07 0.86 -0.06 0.98 0.58 0.55 0.75 2.58 0.82 -1.04 2.17 2.2 -1.53 -0.39 Q 1.4 1.61 2.62 0.33 2.78 -0.17 0.86 0.96 0.67 2.64 3.69 0.9 -3.8 3.88 2.3 -5.5 -0.25 Significant ** * * -

Results showed that Lordegan stations in annual scale and at probability level of 99% and Pol Zama khan and Emam Gheis station at probability level of 95% had increasing trend and other stations had no significant trend at probability level of 95 and 99%.

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Seasonal Scale Sum precipitation of stations in each season was used to investigate the temporal series of precipitation in seasonal scale. Results of the Table3 showed that in autumn season the Lordegan and Pol Zaman Khan stations have increasing trend at probability level of 95%. In winter season only the Emam gheis station has increasing trend at probability level of 95% whereas in spring no stations show the significant trend at probability level of 95% and 99% but in PolShalo, Lordegan and Pol zaman khan had increasing trend in summer at probability level of 95%. Table 3: Results of the Mann-Kendall and Sen Test for the Sum Precipitation in Seasonal Scale Autumn Z Q Hana 0.24 0.15 Hamgin 0.83 0.43 Shahmokhtar 0.36 0.41 Botari -0.42 -0.35 Pole Shalo 0.52 0.8 Dehkadeh Shahid 0.08 0.08 Shahrekord 1.72 0.81 Bootvand 1.7 1.22 Gotvand 0.2 0.07 Koohrang 0.95 1.67 Lordegan 2.34* 1.22 Masjed Solaiman 0.92 0.6 Tale Zang 0.11 0.26 Emam Gheys 1.28 0.8 Pole Zamankhan 2.34* 1.08 Tang Panj -0.41 -1.2 Haftapeh 0.44 0.28 *Significant at p<0.05 Station Name Monthly Scale In order to investigate the existent trend in monthly scale the sum of the precipitation of each station in each month was used. No months of October, November, December, January, march, April, July, August, September shows the significant trend at probability level of 95% and 99%, but in February month the Tele Zang station had decreasing trend at probability level of 95% and Emam gheis station had increasing trend at probability level of 99%. In May only Gotvand and Koohrang stations has increasing trend at probability level of 95%. Moreover, in May Koohrang stations has significant increasing trend at probability level of 99%. Table 4 shows the results of the test of trend for monthly scale precipitation. Table 4: Results of the Mann-Kendall and Sen Test for the Sum Precipitation in Monthly Scale Station Name Hana Hamgin Shahmokhtar Botari Pole Shalo Dehkadeh Shahid Shahrekord Bootvand Gotvand Oct Z -0.51 -0.15 -0.3 -0.24 -0.4 -0.11 -0.15 -0.53 -0.52 Q 0 0 0 0 0 0 0 0 0 Nov Z Q 0.03 0 0.94 0.04 0.07 0 0.55 0 0.4 0.13 0 0 1.85 0.33 0.47 0 -1.09 -0.26 Dec Z 0.2 1.44 0.51 0.04 0.48 0.38 1.31 1.86 0.65 Q 0.08 0.5 0.58 0.03 0.45 0.34 0.41 0.84 0.3 Z -0.3 1.05 -0.29 -0.31 0.09 -0.24 -0.31 0.28 0.08 Jan Q 1.7 1.17 0.74 0.32 0.64 0.91 0.21 -1.37 1.18 Feb Z Q 0.56 0.39 0.38 0.27 0.5 -0.76 0.18 -0.49 0.49 -1.3 0.56 -1.07 0.04 0.31 -0.5 -0.98 0.37 -0.68 Mar Z 0.08 0.09 -0.87 -0.42 -1 -0.87 0.08 -0.24 -0.22 Q 0 0 0 0 0 0 0 0 0 Winter Z Q 1.11 0.79 1 0.83 0.02 0.14 0.2 0.25 0.75 0.2 -0.34 -0.51 0.04 0.01 -0.24 -0.21 0.35 0.24 -0.56 -1.4 0.82 0.91 0.44 0.4 -1.47 -3.57 2.25* 2.3 0.6 0.27 -1.58 4.8 0.44 0.28 Spring Z Q 1.21 0.44 0.35 0.13 -0.15 -0.16 -0.78 0.6 0.49 0.47 -0.16 -0.53 0.21 0.1 -0.61 -0.16 0.75 0.21 1.51 1.74 0.66 0.5 -0.44 -0.13 -1.06 -1.4 -0.3 -0.2 0.49 0.21 -0.63 -1.12 0.52 0.11 Summer Z Q 0.41 0 0.55 0 1.11 0 0.42 0 1.96* 0 -0.47 0 1.1 0 -.02 0 0.79 0 0.86 0 2.07* 0 1.3 0 0.54 0 1.16 0 2.32* 0 0.32 0 0.61 0

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Table 4 Contd., Koohrang -0.8 0 0.39 0.35 0.92 0.96 Lordegan 0.49 0 1.3 0.26 1.3 0.55 Masjed Solaiman 0 0 -0.63 -0.15 1.48 0.62 Tale Zang 0.19 0 0.73 0.45 -.0.12 -0.16 Emam Gheys -0.09 0 0.42 0.04 1.29 0.7 Pole Zamankhan 0.39 0 1.91 0.34 1.13 0.32 Tang Panj -0.05 0 -0.61 -0.56 -0.37 -0.63 Haftapeh 0.84 0 -1.64 -0.31 1.15 0.33 *Significant at p<0.05; **Significant at p<0.10

-1.06 0 0.3 -0.39 0.72 -0.37 -1.3 -0.2

-0.06 1.04 -1.3 -2.06* 2.7** 1.3 -1.75 -0.67

-0.02 0.45 -0.02 -1.8 1.2 0.28 -1.9 -0.16

-0.38 0.59 -0.2 -1.65 -1.28 0.44 -1.89 -0.25

-0.55 -0.2 -0.1 -1.68 0.72 0.15 -2.1* 0.03

0 0 0 0 0 0 0 0

Table 5: Results of the Mann-Kendall and Sen Test for the Sum Precipitation in Monthly Scale Apr May Z Q Z Q Hana 1.28 0.35 0.78 0.01 Hamgin 0.58 0.18 0 0 Shahmokhtar -0.35 -0.28 -1.05 -0.2 Botari -0.78 -0.3 -1.7 -0.47 Pole Shalo 0.47 0.41 -0.36 -.09 Dehkadeh Shahid -0.22 -0.21 -1.81 -0.29 Shahrekord 0.32 0.13 0.31 0.01 Bootvand -0.2 -.02 -0.22 0 Gotvand 0.33 0.04 2.13* 0.05 Koohrang 0.16 0.12 2.06* 1.05 Lordegan 0.85 0.4 -0.11 -0.01 Masjed Solaiman -0.8 -0.19 0.6 0 Tale Zang -0.36 0.35 -0.13 -0.46 Emam Gheys 0.48 0.15 -1.5 -0.44 Pole Zamankhan 0.96 0.32 -0.22 -0.03 Tang Panj -0.27 -0.3 -1.4 -0.4 Haftapeh 1.45 0.21 1.01 0.01 *Significant at p<0.05; **Significant at p<0.10 Station Name Analysis of the Temporal Variations of Trend CUMSUM chart was used to investigate the variation points in climatic and hydrological temporal series and following results was achieved. According to Figure2 the change point of Hana station is year of 1968 which this change is confirmed at probability level of 99% and variations period is from 1967 to 1995. The mean before the variation of temporal series is 212.23mm this shows the mean of series reach to 342.16 mm after variations. The results show that the precipitation increased after the change point. According to the figure3 the change point of Masjed Soleiman station is year of 1973 which this change is confirmed at probability level of 98% and variations period is from 1968 to 1989. The mean before the variation of temporal series is 237.06 mm and the mean of series reach to 481.84 mm after variations which this shows the precipitation increased after the 1973. The change point of Pol zaman khan station is year of 1968 which this change is confirmed at probability level of 98% and variations period is from 1968 to 1989. The mean before the 1968 was 237.06 mm and the mean after the 1968 reach to 355.44 mm which this shows the precipitation increased (Figure4). Other stations showed no changes. Results have been illustrated in Table 6. Jun Z 1.23 1.34 -0.27 -0.27 0 -0.29 1.52 -0.07 0.56 2.71** 0.78 0.66 0.48 0.23 -0.48 -1.18 0.46 Q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jul Z 0 -0.42 -0.04 0.39 0.66 -0.22 1.32 0 0 -.04 1.21 0.62 0 0.83 1.01 0 0.7 Q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Aug Z 0.07 0.83 -0.04 -0.25 0.69 -0.24 -0.21 -0.02 0.05 -0.39 0.5 0.96 0.15 -0.48 0.73 -0.1 0.26 Sep Q Z 0 0.65 0 0.43 0 0.78 0 -0.04 0 1.07 0 0.16 0 1.32 0 0 0 0.71 0 0.91 0 1.05 0 0.38 0 0.36 0 1.8 0 1.79 0 0.37 0 0.7 Q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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Figure 2

Figure 3

Figure 4 Table 6: Result CUSUM Chart Station Name Hana Masjed Solaiman Pole Zamankhan Change Annual 1968 1973 1968 Confidence Level(%) 99 100 98 Changes Period 1967-1995 1964-2000 1968-1989 Mean Before Changes Period 212.23 351.83 237.06 Mean After Changes Period 342.16 481.84 355.44 Standard Deviation 95.4 185.4 55.56

DISCUSSIONS AND CONCLUSIONS

Almost 90% of stations in annual rainfall are increased but only 18% of stations had significant trend. In seasonal scale the maximum increasing of precipitation was observed in autumn and the maximum decreasing trend in precipitation was recorded in spring. The maximum increasing trend was observed in December but the maximum decreasing trend was recorded in months of October and March. Annual variations in precipitation have been only occurred in Hana, Masjed Soleiman and Poul Zaman khan stations which is for the end of the forth decade to the prime of sixth decade. Moreover, according to the achieved results from previous season the annual precipitation has increased but this increasing was not significant which was in agreement with the findings of Ross (2007). Their results in south east of USA showed no significant trend in precipitation. It seems that the climatic changes are sum of the phenomena which is occurred in a climatic system and it can not possible to relate climate change to one or several factors. So, sum of reasons which may not be awareness about it may be effective in appearance of variations but the proportion of some factors may be low. The

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climate change has different consequences and effects on human community and ecosystem. Climate change which is occurred in future can affect agriculture, water resource, global warming and cooling and etc. so the society is exposure by sum of problems which their negative effects is harmful for human. The climate change is among ten dangerous factors in twenty one century such as food shortage and poverty (IPCC, 2007). Therefore attention to climate change and its prediction is very important.

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