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印度南部半岛的布拉玛普德拉河流域上的温度趋势

11级农业水利工程2班肖继琛

摘要:毋容置疑的是全球的表面气温在逐渐的增加。人为-感应的全球变暖可能在一个河川流域的水资源的经营扮演重要角色。因此,这项研究的主要研究对象是在印度的一个大的南方半岛的合川流域——布拉玛普德拉河河川流域。在移除前弄白程序的重大落后-1 序列相关的效应之后,曼-肯德尔的试验主要是用盆地当中测得的的35个最高温度,最小温度和平均温度来观察布拉玛普德拉河流域上的温度趋势。在季节性的(每月)时间比例,大多数的测站数据显示在平均气温,最高气温和最低气温的任何四个季节(12个月)布拉玛普德拉河流域上的温度趋势没有明显的趋势,除了小季节气候外。大约有60%(45%)测站显示在不同的延时最大气温和最小时布拉玛普德拉河流域上的温度呈现上升的趋势。十二月和后季候风季节(七月和九月和季候风季节)显示在戈达瓦里河盆地上的温度是呈现循环的趋势出现的。由范贝尔和Hughes趋势的空间和时间的均匀性试验的结果表明,在温度超过戈达瓦里盆地的温度趋势不均是相对于不同的数月或不同的测站。尽管在温暖气候的盆地,戈达瓦里盆地地区的

蒸发已经呈现显著降低的趋势。风速强劲减少和增加的相对湿度实际上可能造成印度半岛南部地区的蒸发降低。

关键词:温度;戈达瓦里河流域;南印度半岛;曼 - 肯德尔测试;泰尔森斜率;趋势同质化

1 引言

水是地球系统的一个基本组成部分,控制着全球的天气和气候。淡水资源分布在亚洲的不同地区,特别是在大江大河刘玉忠淡水的分布更为的广泛,但是由于气候的变化和人口增长,出现对更高生活标准的要求导致淡水资源人均占有量减少,21世纪50年代可能会超过十亿人面临吃水困难的问题(德雷珀和Kundell ,2007年)。随着气候变化和全球变暖之间相关性的增加,现有的水资源管理实践可能证明是事倍功半(弗洛斯马提,2002)。同样,在Riha 等人(1996)的报告中指出,温度的升高变异可能会粮食的生产。当不断增长的季节温度超出作物的最佳的生长温度的时候,会产生比较小的平均收益率(Riha 等人,1996)。

温度是气候当中的最重要的参数之一,常常用来检测气候的波动。由于温度和气候在云量,湿度,大气环流模式和土壤水分上的联系,Karl 等人(1993)采用最高温度(x T ma )和最低温度(min T )来表示升温或降温的迹象。Tayanc 等人(2009)

在报告中指出,在土耳其一系列最低气温表明由于城市化而导致显著变暖的情况遍布全国。 近几年来的气候变暖与最小温度之间的联系明显比最高温度之间联系更加的紧密一些。因此,全球的昼夜温差也在不断的开始减小。然而,观察印度的昼夜温差增长和观察全球其他地方昼夜温差减小是完全不同(Srivastava 等人,1992;Rupa Kumar 等人, 1994)。昼夜温差的增加导致了在喜马拉雅的西部地区(Yadav 等人,2004),在印度河上游流域的喀喇昆仑山和兴都库什山脉地区 (Fowler and Archer ,2006),在西北部喜马拉雅山

(Bhutiyani 等人,2007),在印度河流域的西北和中部(Singh 等人,2008),在一些印度东北部的湿润区(Jhajharia and Singh ,2011)最近,Yang 等人(2011)在报告中指出,在1973至2007期间在中国喜马拉雅山的整个区域平均气温每

十年上升C .31

.0。报告中还指出,在中国喜马拉雅山的西部地区气候开始变暖和干燥。

时空的均匀性趋势是对任何一个地区进行水文气象参数分析的一个重要的方面。1984年Van Belle and Hughes 发明了一种通过曼 - 肯德尔(MK )方法来测试均匀性趋势的方法。 均匀性试验是在一个数据集相结合的几个观测站的数据以获得可能的单一的全球趋势得到的。一些研究人员,比如,

Gan(1995, 1998),Kahya and Kalayci (2004), Panda 等人(2007), Kampata 等人(2008), Dinpashoh 等人(2011)和Jhajharia等人 (2012)在世界的不同地方用这种方法在世界的不同地区应用趋势同质化的测试时间序列的降水,气温,径流,地下水水位和蒸散量。

在印度半岛南部的戈达瓦里盆地上,平均温度,最大温度,最小温度的趋势表明少量的信息是可以用的。此外,关于在印度河流域中空气温度变化趋势的研究似乎没有文献可以用。因此,这项研究的主要目的是研究温度(平均温度,最高温度和最低温度)在年度,季节性和每月的时间上,使用的测量数据来自于测量站还是位于或者是靠近印度流域的戈达瓦里盆地。呈现出的趋势是趋势是使用非参数方法MK修正为显著滞后1序列相关后的效果调查。趋势是使用非参数方法——MK方法修正为显著滞后1序列相关后的效果。我们还测试了在盆地气候温度变化的均一性。这项研究可能是确定温度变化的趋势的均匀性在印度流域的首次实验。在流域地区的研究结果在水资源的规划与管理以及在全球变暖的情况下优化水资源的利用和管理。全球变暖已经成为21世纪最紧迫的问题,由于水库的规划和管理会对温度的上升产生极大的影响。在流域范围内的温度变化趋势的研究结果可能有助

于水资源规划者和水资源开发机构将气候的影响纳入决策的事实,一些协议是不受审查的,直接成为永远有效的,比如戈达瓦里水争议法庭(GWDT )奖(Richards and Singh ,2002)。 2 相关资料和研究方法

2.1戈达瓦里河流域

戈达瓦里河流域(经度:东经、。2673和、。0783;纬度:北纬

、。1616和,。4323)延伸312802m K ,大约占印度总面积的

9.5%。戈达瓦里河,印度半岛最大的河流,穿越穿越马哈拉施特拉邦,安得拉邦,中央邦,卡纳塔克邦,奥里萨邦和新创建的切蒂斯格尔邦。戈达瓦里河在在1067米点,S. L.在马哈拉施特拉,向东穿过德高原的纳西克区从西高止山脉海拔的Triambakeshwar 。流过1465公里的东南方向通过马哈拉施特拉安得拉邦州后,戈达瓦里河流入孟加拉湾。戈达瓦里河的主要的支流有布勒沃拉,穆拉,印度,manjra ,Maner ,本根加,沃尔塔,韦恩根格,Pranhita ,Indravati 和Sabari. 戈达瓦里盆地由大起伏的平原分为低平顶山脉。在盆地发现的重要土壤类型是黑色的土壤,红壤,红壤,冲积层,混合土和盐碱土。戈达瓦里盆地属热带气候,在盆地的不同部分蒸发损失在1800mm 到2440mm 之间(CWC ,1999)。戈达瓦里河流域的年降雨量为1132mm ,其中季风季节的降雨量占总降

雨量的84%。戈达瓦里河流域的降雨量分布的极不均匀和稳定,共有42个地区的盆地出现了13个易干旱的地区(CWC,1987)。在季风时期,戈达瓦里河流域雨水非常的充足,大量的雨水,会淹没安得拉邦,马哈拉施特拉邦,恰蒂斯加尔邦和奥里萨邦地区。

2.2. 温度数据

从印度气象部门获得的普纳地区的不同的时间段月度最高气温,平均气温和最低气温,主要是从1962年到2004年。共有35个测验站的数据在戈达瓦里河流域中被选出来。测验站是通过6个地区流域和2个子流域的综合来选择,就如GWDT 所定义的在戈达瓦里盆地共同分享水资源,在这个研究得到了很好的体现。所选的35个站的细节在表1中给出。在某时间所缺失的温度数据有最近的测验站的平均值代替。戈达瓦里盆地35个测验站的平均气温在表2 中已经给出,数据表明帕奇马尔希一月份的平均气温14.5°变化为钱德拉布尔五月份的35.5°。

2.3趋势分析

在这项研究中,非参数MK方法可进行温度趋势为非参数检验,非正态分布和截尾数据的缺失值是更适合的检测,而不受数据中的异常值的存在的影响(Jhajharia 等人,2009;

Wilks ,2011)。温度趋势测验和理解最大的影响是序列依赖性的影响。很明显,如果时间序列比较的持久,那么非参数的测验显示出比重大的水平更随意的结果(Zhang 等,2011)。首先,滞后-1序列相关(r1)的意义用来测试温度时间序列消除序列相关的影响。如果r1的绝对值小于显着性水平值,则原来的MK 方法是可以使用的。否则的话,序列相关性的影响进行预白化应用MK 测试之前从时间序列中删除。有兴趣的读者可以参考Kumaret 等人(2009),Kahya 和Kalayci

(2004),Partal 和Kahya (2006),以及Dinpashoh 等人(2011)对文中的细节进行详细的分析。

2.3.1 MK 实验

MK 检验首次进行了统计计算,S 为:

∑∑-=+=-=1

n 11)sgn(i n i j i j x x S (1)

式中,n 观测次数;j x 是th j 的观测数;)sgn(?可以被

定义为:

??

????????<=>=01-0001sgn θθθθ若若若)( (2)

假设数据是独立同分布的,均值和S 统计方差给出(Kendall, 1975)

0)(=S E (3)

18)

52)(1()52)(1(n 1∑=+--+-=m

i i i i t t t n n S V )( (4)

其中,m 是并列行列组的数量,随着i t 的变化而变化,原来的MK 统计量,被定义为Z ,Z 为:

????

??????????????<+=>-=0)(1000)(1S S Va r S S S S Va r s Z (5) 如果2121-αα--≤≤Z Z Z ,那么没有趋势可以在显着性水平

的α被接受。另外,零假设可以被舍去,假设可以在显著性水平接受α(jhajharia 等人,2012)。比如,如果计算检验统计量值得范围在1.96到-1.96之间的话,那么该系列中任何趋势的零假设可以接受5%(Jhajharia 等人,2012)。

2.3.2塞尔–森估计法

用塞尔–森估计法来计算n 对数据点的斜率,定义如下:

j l l

j x x Median l j <

式中,β是塞尔森估计值,泰尔先生的估计的方法已被广泛应用于识别水文时间序列趋势线的斜率。最近,Jhajharia (2012,2013)等人使用塞尔森估计法对印度东北地区的参考作物蒸发量的趋势进行和在印度西北地区的塔尔沙漠的恶性疟原虫和间日疟原虫疟疾发病率进行相关的研究和调查。

2.4 趋势均匀性研究

为了测试测站的最高温度,平均温度和最低温度,本文中采用了由van Belle 和Hughes (1984)基于平方和分割概念提出的过程均匀性,该过程采用卡方检验来测试各个测站之间的均匀性的趋势。测试温度均匀性的第一步是计算出所有月的MK 函数—j Z (j=1,2,...,12),然后再分析j Z 的平方。在零假设下相对于每个月的j 和j Z 2有一个自由度的卡方分布没有明显的温度趋势。与此同时,如果季节性的观测时足够独立的,那么Z 也将接近于独立。对于m 的总体统计是:

∑==m j j Z

x 122

共 (7)

遵循在零假设下没有温度变化趋势的m 月的卡方分配。这样打的一个统计值是没有任何意义的,因为它没有从整个趋势中分析单个j Z 2之间的不均匀性,解决这个问题的方法是将

2共x 分割为2同种x 和2倾向x (2

倾向x =2m Z ,其中其中Z 是下标j 平均值)。2同种x 的值可以从2共x 中减去2倾向x 。

在零假设下,对于所有的月份Z 的值都是相同的,2同种x 和

2倾向x 的卡方自由度分别是(m-1)和1。温度变化趋势的均

匀性可以通过卡方表中的计算出的2

同种x 和相应的21-m x 来表示。如果2

同种x 并不显著,那么对于常见的趋势有效的测试可

以参照2倾向x 到相应的表列值;若2同种x 是显著的,那么2倾向x 的固执是不可取的。在此条件下,每个月的趋势的检查只能从独立的j Z 当中来观察。在这种情况下总的卡方是:

∑∑===k

p jp m j z x 1212

,我们可以近似的认为一个卡方分布d.f.和mk 是相等。以下步骤用于测试趋势的均匀性:

第一步:计算卡方值。2-月站x 就等于从2同种x 当中减去2

2月站x x +。 第二步:用计算出来的2站x 来测试温度随着不同的测站变化的均匀性。

第三步:用计算出来的2

月x 来测试温度随着月份变化的均匀性。

第四步:用计算出来的2-月站x 测试月—站之间相互的影响的均匀性。

第五步:如果从第二步,第三步,第四步开始测试的结果都

x测试整个地区的趋势变化九可以是无意义的,那么用2倾向

表示成为d.f.=1。如果时间是均匀的,而测站是不均匀的,

用2

mZ(p=1,2,3,...k)进行单个站的趋势测验。然而,若所p

有的测站和时间都是没有顺序的,或者有一个站—月之间的

影响是重大的,那么用

Z进行月—站之间的测验是有重要的

jp

意义的。最近,Dinpashoh等人(2011)和Jhajharia 等人(2012)也用类似的方法测验位于全球不同地区的数据的趋势变化。

3 结果与讨论

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Journal of Industrial Textiles https://www.doczj.com/doc/a713763622.html,/ Optimization of Parameters for the Production of Needlepunched Nonwoven Geotextiles Amit Rawal, Subhash Anand and Tahir Shah 2008 37: 341Journal of Industrial Textiles DOI: 10.1177/1528083707081594 The online version of this article can be found at: https://www.doczj.com/doc/a713763622.html,/content/37/4/341 Published by: https://www.doczj.com/doc/a713763622.html, can be found at:Journal of Industrial TextilesAdditional services and information for https://www.doczj.com/doc/a713763622.html,/cgi/alertsEmail Alerts: https://www.doczj.com/doc/a713763622.html,/subscriptionsSubscriptions: https://www.doczj.com/doc/a713763622.html,/journalsReprints.navReprints: https://www.doczj.com/doc/a713763622.html,/journalsPermissions.navPermissions: https://www.doczj.com/doc/a713763622.html,/content/37/4/341.refs.htmlCitations: - Mar 28, 2008Version of Record >>

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