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Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China

机译:用月水量平衡模型量化气候变化和人类活动对径流的影响 - 以中国北方潮白河流域为例

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摘要

The Chaobai River basin in northern China consists of two major tributaries, the Chao River and Bai River. Monthly observations of precipitation, streamfiow, and panevaporation data are available for 35 years (1961-1966 and 1973-2001). Using the annual time series of the observed streamfiow, one break point at 1979 is detected and is adopted to divide the data set into two study periods, the "before" and "after" periods marking the onset of significant anthropogenic alteration of the flow (reservoirs and silt retention dams, five times increase in population) and significant changes in land use (conversion to terraced fields versus sloping fields). The distributed time-variant gain model (DTVGM) was used to evaluate the water resources of the area. Furthermore, the Bayesian method used by Engeland et al. (2005) was used in this paper to evaluate two uncertainty sources (i.e., the model parameter and model structure) and for assessing the DTVGM's performance over the Chaobai River basin. Comparing the annual precipitation means over 13 years (1961-1966 and 1973-1979), the means of the second period (1980-2001) decreased by 5.4% and 4.9% in the Chao River and Bai River basins, respectively. However, the related annual runoff decreased by 40.3% and 52.8%, respectively, a much greater decline than exhibited by precipitation. Through the monthly model simulation and the fixing-changing method, it is determined that decreases in runoff between the two periods can be attributed to 35% (31%) from climate variations and 68% (70%) from human activities in the Chao River (Bai River). Thus, human impact exerts a dominant influence upon runoff decline in the Chaobai River basin compared to climate. This study enhances our understanding of the relative roles of climate variations and human activities on runoff. © 2009 by the American Geophysical Union.
机译:中国北方的潮白河流域由两条主要支流-潮河和白河组成。可获取35年(1961-1966年和1973-2001年)的月度降水,水流和窗格蒸发数据的月度观测值。使用观测流的年度时间序列,检测到1979年的一个断点,并采用该断点将数据集分为两个研究时期,“前”时期和“后”时期标志着人为流量发生重大变化(水库和淤泥坝,人口增加五倍)和土地利用发生重大变化(转换为梯田与坡地)。分布式时变增益模型(DTVGM)用于评估该地区的水资源。此外,Engeland等人使用的贝叶斯方法。本文(2005)用于评估两个不确定性源(即模型参数和模型结构)以及评估DTVGM在潮白河流域的表现。比较13年(1961-1966年和1973-1979年)的年降水量平均值,潮河流域和白河流域第二个时期(1980-2001年)的平均值分别下降了5.4%和4.9%。但是,相关的年径流量分别减少了40.3%和52.8%,比降水减少的幅度要大得多。通过月度模型模拟和固定改变方法,确定两个时期之间的径流量减少可归因于潮河地区的35%(31%)和人类活动的68%(70%) (白河)。因此,与气候相比,人为影响对潮白河流域径流下降具有主要影响。这项研究增进了我们对气候变化和人类活动对径流的相对作用的理解。 ©2009,美国地球物理联盟。

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    Xia J; Wang G; Che J;

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  • 年度 2009
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