Changes identification of the Three Gorges reservoir inflow and the driving factors quantification
Changes identification of the Three Gorges reservoir inflow and the driving factors quantification
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三峡水库入库流量变化识别及驱动因素量化
DOI：
10.1016/j.quaint.2016.02.064
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发表时间：
2016-03
影响因子：
2.2
通讯作者：
Chen Juan
中科院分区：
文献类型：
--
作者：
Zhang Yu;Zhong Ping an;Wang Manlin;Xu Bin;Chen Juan
径流序列变化研究对流域水资源开发利用具有重要意义。利用1951 - 2013年三峡水库及上游5个集水区的径流序列资料，通过Mann-Kendall检验分析了三峡水库入库流量序列的变化。基于同期水文数据（降水、气温）和人类活动数据（城市化率、有效灌溉面积、造林面积、人口和国内生产总值），应用BP神经网络模型对影响因子进行定量分析。结果显示：（1）研究期内，三峡入库径流量显著减少，年减少率为0.73mm;（2）三峡水库上游5个流域径流量均减少。（3）气候变化和人类活动对三峡入库流量变化的影响分别占36%和64%。在所有驱动因子中，降水是主导影响因子，相对贡献率为25%。气温、城市化率、有效灌溉面积、人口和GDP是影响长江流域生态系统的次要因子，其相对贡献率分别为11%、17%、15%、15%和14%。造林面积是最小的影响因素，相对贡献率为3%。
The study on runoff series variation is of great significance for the development and utilization of water resources in a river basin. In this paper, runoff series data from the Three Gorges reservoir and five upstream catchments observed from 1951 to 2013 are used to analyze the changes in the Three Gorges reservoir inflow series via the Mann–Kendall test. Based on the hydro-metrological data (i.e. precipitation, and temperature) and the human activities data (i.e. urbanization percentage, effective irrigation area, afforestation area, population, and gross domestic product (GDP)) during the same period, the back-propagation artificial neutral network (BP-ANN) model is applied to quantify the influence of the driving factors. The results show: (1) During the study period, there is a significant decrease in the Three Gorges reservoir inflow and the reduction rate is 0.73 mm per year; (2) Runoff from all of the five upstream catchments of the Three Gorges reservoir decrease. Specifically, the decreased trends in the runoff from the Mintuo River catchment and the Jialing River catchment are statistical significant; (3) Impacts of climate change and human activities on changes in the Three Gorges reservoir inflow series account for 36% and 64%, respectively. Among all the driving factors, the precipitation is the dominant influencing factor, accounting for the relative contribution of 25%. The temperature, urbanization percentage, effective irrigation area, population and GDP are the minor factors, accounting for the relative contributions of 11%, 17%, 15%, 15% and 14%, respectively. The afforestation area is the least effective factor with a relative contribution of 3%.
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影响因子：
6.4
作者：
L. Siriwardena;B. Finlayson;T. McMahon
通讯作者：
L. Siriwardena;B. Finlayson;T. McMahon
影响因子：
1.5
作者：
D. Scott;R. E. Smith
通讯作者：
D. Scott;R. E. Smith
影响因子：
5.4
作者：
Gangsheng Wang;J. Xia;Ji Chen
通讯作者：
Gangsheng Wang;J. Xia;Ji Chen
影响因子：
5.2
作者：
Jeannine‐Marie St. Jacques;D. Sauchyn;Yang Zhao
通讯作者：
Jeannine‐Marie St. Jacques;D. Sauchyn;Yang Zhao
影响因子：
6.4
作者：
Patrick N.J. Lane;A. Best;K. Hickel;Lu Zhang
通讯作者：
Patrick N.J. Lane;A. Best;K. Hickel;Lu Zhang