研究论文 正式出版 版本 3 Vol 10 (1) : 38-48 2019
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气候变化与人类活动对陆地水储量的影响
Impacts of climate change and human activities on terrestrial water storage
: 2018 - 04 - 09
: 2018 - 10 - 12
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摘要&关键词
摘要:陆地水储量(TWS)是气候变化的重要指示,针对TWS研究有助于我们理解气候变化是如何影响水资源的循环。在本研究中,我们用GRACE重力卫星数据与气象资料恢复了2002 -2015年间中国陆地水储量的时空分布变化,M-K趋势分析被运用于判断陆地水储量与气候数据的趋势,并将具有显著趋势的地域划分为10个关键区域,研究发现松花江流域、长江中下游流域、珠江流域、三江源自然保护区及青藏高原中部TWS趋于增加(2.76~7.14 mm/a),而华北平原,黄土高原,辽河流域、天山山脉及雅鲁藏布江流域陆地水储量趋于减少(-1.47~-8.93 mm/a)。TWS与气候数据、气候环流指数的Spearman相关性的结果指出:TWS的变化主要受气候变化影响,气候变化是造成陆地水储量变化的主要因素,但在人口密集区域,人类活动对TWS的影响也不可忽视,如华北平原过度汲取地下水是造成TWS减少的重要原因。
关键词:GRACE数据;气候变化;人类活动;气候环流指数
Abstract & Keywords
Abstract: Background, aim, and scope The gravity satellite represented by GRACE in recent years has shown a unique advantage in the detection of terrestrial water storage (TWS). Until 2002, the implementation of the GRACE gravity satellite program makes it possible to obtain the information of the mass distribution of the earth system with high precision, and the application of the gravity satellite detection technology in hydrometeorology is greatly promoted. Materials and methods This paper adopts the product data from August 2002 to July 2016 released by the Space Research Center of the University of Texas. The product data are filtered by Gauss with a smooth radius of 500km, and the "strip" errors of the north and south are removed from the monthly data. At the same time, various tidal effects and non-tidal atmospheric and oceanic effects are removed. The trends of TWS, temperature and precipitation are studied by using M-K trend, and the temporal and spatial variations are shown by ArcGIS. Results The results show that the TWS of Songhua River Basin, Middle and Lower Reaches of the Yangtze River, Pearl River Basin, Sanjiangyuan Nature Reserve and Central Qinghai-Tibet Plateau tend to increase, while North China Plain, Loess Plateau, Liaohe River Basin, Tianshan Mountain and Yaluzangbu River Basin terrestrial water storage tend to decrease. Four densely populated areas are selected in the critical area to analyze the relationship between TWS and climate circulation index. The TWS of four regions can be considered to be affected more severely by human activities, showing a positive correlation between the climate circulation factor and TWS. Discussion The reason for the change of TWS is complex, which is the result of the interaction between climate change and human activities. It is a huge challenge to divide the change of TWS into natural or human factors in detail. There has been no previous study on the natural and human factors for the change of terrestrial water storage in China. Research findings, the changes of water storage in China are mainly affected by climate change, but the impact of human activities on TWS is also extremely profound, especially in irrigated areas, human activities may be an important reason for the decrease of TWS. Conclusions The TWS in the Middle and Lower Reaches of Yangtze River, Pearl River Basin, Sanjiangyuan Nature Reserve and the Yaluzangbu River Basin are mainly affected by climate change, while North China Plain may be more affected by human activities. Human activities also have a positive effect on the increase of TWS, such as water transfer projects across river basins, which can improve the spatial distribution of water resources. The establishment of nature reserves also has a positive effect on the increase of TWS. in addition , Even though human intervention is intense, climate change has always been a major factor in TWS. Recommendations and perspectives It is a great challenge to distinguish the impact of natural and human factors on TWS, and it is difficult to quantify and analyze the effects of natural and human factors in a precise way, and to use climate data as auxiliary data. It is very valuable to judge the influence of climate change on TWS change, especially the climatic circulation index is used to study the change factors of TWS for the first time in this paper , Future research should focus on the use of hydrological models and statistical methods to explore the real causes of terrestrial water storage change.
Keywords: GRACE; Climate Change; Human activities; Climatic circulation index
NASA(National Aeronautics and Space Administration)的重力恢复与气候实验GRACE(Gravity Recovery And Climate Experiment)卫星具有监测高度集成陆地水储量 (TWS: Terrestrial water storage)变化的能力(Tapley et al,2004),GRACE对雪冰、地表水、土壤水、植物冠层截流水及地下水的综合变化能提供实时动态的监测(Scanlon et al,2016)。
GRACE卫星自2002年3月发射以来,基于GRACE重力卫星数据的的陆地水储量的研究展示了在水文学研究的广阔前景(Schmidt et al,2008)。研究表明GRACE在监测极端水文事件(Andersen et al,2005;Frappart et al,2008)、地质含水层储量(Rodell et al,2007;Leblanc et al,2009)及冰雪变化(Ramillien et al,2006)等方面具有极大的潜力。但是,目前关于专门探讨陆地水储量气候变化与人类活动影响的研究较少,且仅局限在中亚或沿海平原(Deng et al,2016;Chen et al,2017)。对于中国而言,研究陆地水储量的变化是制定水资源管理策略的重要依据,特别是人口密集区域,人类活动对陆地水储量变化影响程度对水资源管理策略的制定具有指导意义,同时探讨分析气候变化与人类活动对陆地水储量的相对贡献有助于更好的理解地球系统的水循环。
气候变化是影响水资源供应与损耗的关键因素,会对全球水资源产生重大影响(Stocker et al,2013),当前全球变暖的趋势已非常明显,这无疑会急剧增加未来水资源的供给压力(Vörösmarty et al,2000),并将对地球系统产生广泛多样的影响(Stocker et al,2013)。人类活动也可以改变水循环的进程,如汲取地下水用于农业灌溉,工业耗水及生活用水、建设大坝水库、跨流域调水及自然保护工程等(Grasby et al,2004;Rodell et al,2009;Liu et al,2012,Feng et al,2013),特别是农田灌溉用水,占全球用水的70%,消耗用水的近90%( Rodell et al,2009),这无疑会对全球长时期陆地水量的平衡产生影响。
定量精确的评估TWS的时空变化为实现水资源可持续管理提供科学支持。但是,传统现场监测难以获取全国尺度的数据,凭少量零散的点推断全国TWS的变化是极其困难的。GRACE卫星重力产品数据(Tapley et al,2004;Zhao et al,2017),可以克服这个采样问题,是用于地表水和地下水监测的有效手段。近年来,国内研究小组利用GRACE数据来评估水资源储量(Moiwo et al,2009;Moiwo et al;2013;Zhao et al,2017)与地下水(Shen et al,2015)的消耗速率方面做了研究。本研究用168个月(2002年8月至2016年7月)间的GRACE数据来评估水储量的时空变化,并根据TWS的变化趋势划分出10个关键地区,进而来分析气候变化与人类活动在关键区域对TWS的主导影响因素。
1   数据与方法
1.1   数据收集
1.1.1   GRACE数据
GRACE卫星利用高轨GPS卫星对低轨双星精密跟踪定位,同时两颗低轨卫星在同一轨道平面内前后相互跟踪编队飞行,利用共轨双星轨道摄动之差高精度测量地球重力场,并将地球重力场等价转换为地球表面的质量变化,除以水的密度反演得到TWS的变化。地球表面的质量处理成格网的数据可从Jet Propulsion Laboratory(JPL)网站获取(https://grace.jpl.nasa.gov/),数据集的分辨率是1°(Deng et al,2016)。
1.1.2   CRU数据
格网的降雨与气温数据在Climatic Research Unit(CRU TS v.3.23)获取(http://www.cru.uea.ac.uk/cru/data/hrg/),用户可以公开下载免费的气候资料,CRU数据基于全球的气象站并模拟出分辨率为0.5°的月值数据,它与其他数据集如GPCC(Global Precipitation Climatology Centre)对应较好,是较为可靠的气候数据(Harris et al,2014)。本文对CRU数据重采样成1°的分辨率,以便于GRACE数据比较。
1.1.3   环流指数资料
气候环流指数在中国气象局气象数据中心获取(http://data.cma.cn/),该数据集提供了74项环流特征指数,以月值数据提供。本文选取了影响我国东部地区的13个环流指数用于研究。
1.2   方法
1.2.1   陆地水储量(TWS)的计算
本研究的GRACE重力球谐系数(斯托克斯系数)由CSR(Center for Space Research at University of Texas, Austin)提供,Wahr等人(1998)指出,根据重力球谐系数在月尺度的GRACE卫星资料,陆地水储量是能够恢复的。由于C20具有非常大的不确定性(Chen et al,2005),故用卫星激光测距代替低阶纬向谐波系数C20(Cheng et al, 2013),然后利用Swenson和Wahr(2006)支持的去相关算法消除消除南北条纹误差,并应用了平滑半径为300 km的高斯滤波(Wahr et al, 1998;Deng et al, 2016),得到基于球谐系数的等效水高,等效水高实际上就是TWS。
\(∆\phi \left(\theta ,\varnothing \right)=a{\rho }_{ave}/3{\sum }_{n=0}^{\infty }{\sum }_{m=0}^{n}\left(2n+1\right)/\left(1+{k}_{n}\right)\left(∆{C}_{nm}cos\left(m\varnothing \right)+∆{S}_{nm}sin\left(m\varnothing \right)\right){P}_{nm}\left(sin\left(\theta \right)\right)\) (1-1)
式中\(\phi \)代表是等效水高,\(\theta \)为纬度,\(\varnothing \)为经度,\(a\)代表地球的半径,\({\rho }_{ave}\)代表地球的平均密度,\({k}_{n}\)是love系数,\({C}_{nm}\)\({S}_{nm}\)是球面谐波系数,\({P}_{nm}\left(sin\left(\theta \right)\right)\)是n阶和m阶完全归一化的勒让德函数。
因GRACE数据在处理过程中造成信号衰减,Landerer 等提出尺度因子来恢复损失的相关信号(2012),
尺度因子可以在JPL网站(ftp://podaacftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/)查询下载。对每个网格的尺度因素可以通过以下公式计算:
\(M={\sum }_{i}^{t}{\left(∆{S}_{T,i}-k∆{S}_{F,i}\right)}^{2}\) (1-2)
公式中t代表本研究中总的月数,\(∆{S}_{T,i}\)是第i个月没有经过CLM4.0模型滤波的TWS异常值,\(∆{S}_{F,i}\)代表第i个月经过CLM4.0模型滤波的TWS异常值,处理过程与GRACE的滤波过程一致,k是从最小二乘法回归得到的尺度因子,M为目标函数。
1.2.2   Mann-Kendal 趋势分析
Mann-Kendal 非参数趋势检验常用于评估时间序列气象和水文数据(Hirsch et al,1984),本文中M-K趋势工具用来研究TWS、气温及降雨的长期趋势。
1.2.3   Spearman相关分析
Spearman相关是根据等级资料研究两个变量间相关关系的方法,依据两列成对等级的各对等级数之差来进行计算(Gazzaz et al,2013;Deng et al,2016)。本文将同一时段内的TWS排成序列,分别与同期的气温,降水序列做Spearman相关。
2   结论与分析
2.1   TWS的时空分布
图1   显示出由168月GRACE数据反演得到的TWS空间变化趋势,趋于红色的地域说明TWS在过去14年间是亏损的,而趋于蓝色的地域是处于TWS盈余的状态,空白区域表示变化幅度在±1.5 mm/a之间。从地图中可以看出不同地区的TWS具有显著的差异性,但是季节间差异并不明显。根据地域间的差异,将其划分出10个关键区域进行研究,其中包括六个流域与四个地区,分别在地图中以字母(A-J)代替。




图1   TWS在2003-2015年间的空间变化趋势及季节差异,图左起依次代表春季(3-5月),夏季(6-8月),秋季(9-11月),冬季(12-2月)及全年。地图中字母代表不同区域:A(松花江流域),B(辽河流域),C(华北平原),D(长江中下游),E(珠江流域),F(黄土高原),G(天山山脉),H(青藏高原中部),I(三江源自然保护区),J(雅鲁藏布江流域)。A,B,D,E,J的矢量范围从湖泊流域科学数据中心(http://lake.geodata.cn)获取;C,F,G,H,I矢量范围来源于其他文献。
Fig.1 Spatial trend and seasonal variation of TWS in 2003-2015. Fig. a. b. c. d. represents spring(3-5month), summer(6-8month), autumn(9-11month) and winter(12-2month) in turn. The letters in the map represent different regions: A (Songhua River Basin), B (Liaohe River Basin), C (North China Plain), D (Middle and Lower Reaches of Yangtze River), E (Pearl River Basin), F(Loess plateau), G (Tianshan Mountains), H (Middle of Tibetan Plateau), I (Sanjiang Source Nature Reserve), J (Yaluzangbujiang River Basin). The vector range of A,B,D,E and J is obtained from the Lake Basin Scientific data Center(http://lake.geodata.cn), The vector range of C,F,G,H and I is derived from other literatures.




 




 
Fig.1 Spatial trend and seasonal variation of TWS in 2003-2015. Fig. a. b. c. d. represents spring(3-5month), summer(6-8month), autumn(9-11month) and winter(12-2month) in turn. The letters in the map represent different regions: A (Songhua River Basin), B (Liaohe River Basin), C (North China Plain), D (Middle and Lower Reaches of Yangtze River), E (Pearl River Basin), F(Loess plateau), G (Tianshan Mountains), H (Middle of Tibetan Plateau), I (Sanjiang Source Nature Reserve), J (Yaluzangbujiang River Basin). The vector range of A,B,D,E and J is obtained from the Lake Basin Scientific data Center(http://lake.geodata.cn), The vector range of C,F,G,H and I is derived from other literatures.
图2   显示出十个关键区域TWS的时间变化曲线,TWS在区域A,D,E,H,I呈现出增加的趋势(2.76~7.14 mm/a),而在区域B,C,F,G,J呈现出减少的趋势(-1.47~-8.93 mm/a),增加与减少的趋势则分别对应的是TWS的盈余与亏损,图中显示区域D与E的增长幅度均超过3.5 mm/a,区域C,F,G与J的减少幅度也均超过3.5 mm/a,也就是说D,E的TWS处于盈余的状态,相反的,C,F,G与J则处于严重亏损的状态,尤其是C(华北平原)区域,TWS年亏损率接近9mm/a。根据地理位置将10个关键区域分为北部(A,B,C,D,)、南部(D,E)与西部(G,H,I,J),可以看出我国北方地区TWS除松花江流域外普遍处于亏损(-2.74~-8.93 mm/a)的状态,南方地区绝大部分的TWS则处于盈余(3.43-7.14 mm/a),西部地区随纬度增高,呈现出亏损-盈余-亏损的空间分布,区域H与I的TWS盈余(1.55-1.62 mm/a),但增量较小,区域G与J的TWS则亏损,且幅度较大,特别是区域J的TWS以7.85 mm/a的速率亏损。


图2   2003-2015年间10个关键区域TWS的时间变化趋势。图中绿色线条代表0值线,红色线条代表趋势线,蓝色的点代表该月的等效水高,灰色的线段代表该点的误差值。
Fig.2 Time change trend of TWS in 10 key regions between 2003 and 2015. The green line in the graph represents the zero line, red lines represent trend lines, the blue dot represents the equivalent water height of the month, the grey segment represents the error of the point.


 














 
2.2   降水和气温的变化趋势




图3   2003-2015年间的降雨与气温空间变化趋势。左图代表的是降雨(mm/a),右图代表的是气温(℃/a)。
图3表示的是与GRACE同时期的气候因子的空间变化趋势,左图中红色的区域说明在过去14年间年降雨量趋于减少,颜色越蓝则说明年降雨量呈现出上升的趋势,可以看出,我国东部地区的绝大部分年降雨量是增加的,特别是区域E与区域D南部,但在区域C与汉江流域(-0.1~-1.5 mm/a),年降雨量则与之相反,具有减少的趋势。西部地区降雨量总体呈现微弱的减少趋势,但因西部实际年降雨量稀少,可以认为其对年降雨量的多年趋势影响不大,需注意区域I(三江源自然保护区),降雨量呈现出增加的趋势(0.1~0.5 mm/a)。右图代表气温的变化趋势,红色网格代表降温区域,而蓝色代表升温趋势,可以得知我国大部分区域气温趋于增加,甚至局部地区增幅达到0.11 ℃/a,降温的区域集中在汉江流域与甘肃中部。
特别需要注意的是青藏高原升温明显,在大部分区域升温幅度在(0.03~0.11 ℃/a),尤其以区域J(雅鲁藏布江流域)升温最为强烈。从青藏高原的气温的长期趋势来看(图4),青藏高原的升温趋势极为明显,增幅达到0.0064 ℃/a,即百年间增温0.64 ℃。


图4   1900-2015年间的青藏高原的气温变化趋势,蓝色的点代表2002-2015年间的气温。
Fig.4 The time series of monthly temperature of the Qinghai-tibet Plateau is from 1900 to 2015,and blue dot is from 2002 to 2015.
2.3   TWS与降水和气温的相关性




图5   TWS与气候数据的相关性,左图为TWS与降雨的Spearman相关,右图是TWS与气温的Spearman相关,斜线填充的方格表示通过了P<0.05水平的显著检验。
图5   表示了2002-2015年间TWS与气候数据的相关性,红色的区域代表TWS与气候或降雨成负相关,蓝色的区域则代表正相关。从图中可以看出TWS与气温和降雨的相关性具有相似性,南部的区域D,E及西部的I,J,K均呈现出正相关,但在北方的区域B,C,F则以负相关为主。进一步发现左右图中区域D,E,I和J均呈现出正相关(0.2~0.7),且P<0.05,这说明这些区域的TWS很大程度上受气候变化影响,图中区域C的TWS与气温和降水均呈现出负相关,且P<0.05,这说明区域C的TWS很可能受到了人类活动的影响。
2.4   TWS与气候环流指数的相关性
大气环流与TWS在物理成因上有着比较密贴的关系,本文选取中国境内的13个气候环流指数与TWS做同期的相关性分析。在10个关键区域中选择4个人口密集的区域来分析TWS与大尺度气候环流的关系,这4个区域的TWS可以认为是受人类活动影响较严重的区域,包括北方的C(华北平原),F(黄土高原)与南方的D(长江中下游流域),E(珠江流域)。结果显示气候环流因子与指定区域之间具有很好的相关性,特别区域D与E,相关性大多数在0.4-0.7之间,且极显著,这说明我国南方的大尺度气候环流是影响TWS变化的主导因素。华北平原与黄土高原两者之间同样具有较高的相关性(均大于0.17),且大部分值极显著,这说明北方的TWS变化与大尺度的气候环流也有密切的关系,但受大气环流的影响程度较南方要低一个量级。在此需特别指出,区域F的TWS对气候环流指数的响应有三个月的延迟,这可能是区域F地表特征复杂或参数不确定性所导致的滞后效应。
表1   四个人口密集区域TWS与气候环流指数相关性的绝对值,**是极显著,*是显著
华北平原
North China Plain
黄土高原
Loess Plateau
长江中下游Middle and Lower Reaches of Yangtze River珠江流域
Pearl River Basin
1亚洲区极涡面积指数0.357**0.432**0.532**0.445**
Index of the area of the polar vortex in the Asia
2亚洲区极涡强度指数0.364**0.420**0.557**0.432**
Index of the strength of the polar vortex in the Asia
3欧亚纬向环流指数0.233**0.244**0.348**0.134**
Zonal index over Eurasian continent
4欧亚经向环流指数0.221**0.277**0.512**0.517**
Meridional index over Eurasian continent
5亚洲纬向环流指数0.202**0.214**0.295**0.096
Zonal index over Asia
6亚洲经向环流指数0.241**0.251**0.492**0.542**
Meridional index over Asia
7北半球副高北界0.172*0.389**0.624**0.674**
Index of the northern extend of the Northern Hemisphere subtropical high
8北半球副高面积指数0.200**0.442**0.571**0.592**
Index of the area of the Northern Hemisphere subtropical high
9北半球副高强度指数0.247**0.439**0.601**0.594**
Index of the strength of the Northern Hemisphere subtropical high
10南海副高北界0.644**
Index of the northern extend of the subtropical high over the South China Sea
11南海副高脊线0.604**
The ridge line of the subtropical high over the South China Sea
12南海副高面积指数0.0461
Index of the area of the subtropical high over the South China Sea
13南海副高强度指数0.0415
Index of the strength of the subtropical high over the South China Sea
3   讨论
3.1   降水变化的影响
降雨是补充地表水资源的最主要的来源,长期降雨的短缺很可能导致TWS的亏损(Shen et al,2015),结论表明选择的关键区域的 TWS均受降雨量的影响,特别是在南方区域。Chen et al(2017)用GLDAS数据反演长时间序列的TWS也得出了一致的结论,但Chen认为区域A(松花江流域)的TWS显示出减少的趋势,并认为是气候变化与湿地退化是造成这种现象的原因。本研究发现GRACE反演出区域A的TWS趋于增加,可能是因为 GLDAS数据反演精度不高,同时发现区域A降雨量同期呈现出增加的趋势,造成TWS盈余的真正原因是降水的增加。
我国南方地区(区域D,E)降雨量充沛且近年降雨具有增加的趋势,且降雨与TWS的为正相关,本研究认为该区域的TWS主要受到气候变化的影响,与当地长时期的大气环流状况联系紧密。该区是我国人口密集的区域,工业与生活耗水也相应较多,汲取地下水用于农田灌溉会造成TWS的亏损,但地下水可以通过强降雨渗漏补给(Taylor et al,2013),南方发生强降雨时间的概率要远远高于我国其他区域(Chen et al,2014; Zheng et al,2017),所以区域D,E因丰富的降水总能让TWS处于盈余。
研究的结果显示区域I(三江源自然保护区)TWS也处于增加的趋势,该区域位于青藏高原地区,蒸发微弱(Wang et al,2013),且在过去14年间降雨量呈现出增加的趋势,Du et al(2017)的结果也证实区域I的降水是显著增加的,所以在区域I相对稳定的降水是造成TWS盈余的主要原因。
3.2   冰川退缩与高原湖泊变化
高海拔意味着更低的气温,西部区域G(天山山脉),H(青藏高原中部),J(雅鲁藏布江流域)均位于高山地区,北半球的泛青藏高原区及周边分布着大量的冰川(Yao et al,2012),已有研究发现冰川退缩与高原湖泊变化是影响该区域TWS变化的主要因素(Zhang et al,2013,Deng et al,2016),近年来全球气候变暖进一步加剧区域G和青藏高原的冰川快速消融(Cui et al,2014;Farinotti et al,2015)。
区域G的TWS亏损主要是由于气温上升与天山山脉相对较低的海拔(Tian et al,2016),冰川质量平衡在过去几十年间损失约5±6亿立方米(Jacob et al,2012),况且本研究发现区域G没有得到持续有效的降雨补给,这些共同的因素造成区域G的TWS是减少的,并且会延续现在的状态。
冰川融水是青藏高原高原湖泊水位与体积增加的最主要原因,区域H(闭合流域)拥有大量的湖泊(118),这些湖泊中约85%(100)的湖泊水位正在增加(速率0.25m/a),剩下的15%(18)的湖泊平均水位降低速率为-0.07m/a(Zhang et al,2011;Zhang et al,2013),区域I的大部分湖泊的质量增加且增幅不小,这是导致TWS盈余的重要原因,同时Xiang(2016)发现昆仑山脉融水的增加与降雨的增强,这些因素共同促成是区域I的TWS增加。区域J(开放流域)超过62%的湖泊水位下降(速率-0.14m/a),且同期区域J的降雨量呈现减少的趋势,雅鲁藏布江流域将消融的雪水流出了境外,且雨季的降雨量的减少(Sang et al,2016)共同造成该区域的TWS趋于减少的趋势。
3.3   人类活动的影响
人类活动可以强烈影响全球水循环。一些地区过度依赖地下水,消耗地下水的速度比自然补充的速度快,就会导致地下水位不断下降(P. Döll et al,2012)。没有约束的从深层岩层中汲取地下水用于灌溉或其他用途用水可能是造成区域B(辽河),C(华北平原),F(黄土高原)的重要原因。区域B,C我国是主要的小麦与玉米产区,而TWS的亏损对区域B,C的社会经济发展具有极大的限制(Fu et al,2004;Yang et al,2006),特别是区域C的人均供水量是全球最低的(Nakayama et al,2010),为了满足农田灌溉与其他用水,大量的地下水被过度开采利用,人类活动对TWS的影响不容忽视(Haddeland et al,2014),根据Siebert等人绘制的地下水灌溉面积比重图发现(2010),我国的区域B,C的灌溉用水比例比重超过了20%,且局部地区高于50%(Feng et al,2013),所以本研究认为区域C地下水的过度开采是造成TWS亏损的重要原因。区域F位于黄河中游,暴雨侵蚀输送土壤,每年往黄河输送泥沙1.6 Gt (Giordano et al,2004),Schnitzer等(2013)也用GRACE卫星与水文模型估算出每年黄土高原水土流失约0.61Gt/a,进一步佐证了区域F的TWS亏损很可能是人类活动破坏植被所导致。
此外,人类的工程建设与自然保护区的设立对TWS的变化也有重要的影响。例如南水北调工程旨在解决中国北方水资源短缺(Liu et al,2010),计划每年在中线将13km3的水从汉江流域的丹江口水库运送到京津地区(Liu et al,2012),这对缓解区域C长期的水资源短缺具有极大的战略意义。
4   结论
TWS的变化的原因较为复杂,其是气候变化与人类活动共同作用的结果,详细划分TWS变化归因于自然或人为因素,具有很大的难度与挑战性。通过借助GRACE卫星获取到全国大尺度TWS的时空变化,客观的探讨了气候与人为因素对TWS变化的相对贡献因素,这却是极有必要。本文得出以下结论:
(1)松花江流域、长江中下游流域、珠江流域、三江源自然保护区及青藏高原中部的TWS处于增加的趋势,尤其是长江中下游流域,增长幅度为7.14 mm/a,华北平原,黄土高原,辽河流域、天山山脉及雅鲁藏布江流域的TWS处于减少的趋势,特别是华北平原,减少幅度为-8.93 mm/a。
(2)长江中下游流域,珠江流域,三江源自然保护区及雅鲁藏布江流域的TWS主要受气候变化影响,而华北平原则可能受人类活动的影响更大一点,人为过度汲取地下水用于灌溉是造成TWS减少的重要原因。人类活动对TWS的增加也有积极作用,如跨流域调水工程可以改善水资源的空间分布,自然保护区的建立对TWS的增加也有一定的积极作用。
(3)人口密集区域的TWS与大气环流因素关系紧密,与环流指数相关性均较高,且南方受大气环流的影响程度超过了北方。这说明即使人类活动干预强烈,但气候变化始终是影响TWS的主要因素。
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稿件与作者信息
何盘星1
He Panxing1
胡鹏飞2
Hu Pengfei2
孟晓于3
Meng Xiaoyu3
马俊4
Ma Jun4
出版历史
出版时间: 2018年10月12日 (版本3
参考文献列表中查看
地球环境学报
Journal of Earth Environment