Modeling streamflow driven by climate change in data-scarce mountainous basins
Modeling streamflow driven by climate change in data-scarce mountainous basins
复制标题
在数据稀缺的山区盆地模拟气候变化驱动的水流
DOI：
10.1016/j.scitotenv.2021.148256
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发表时间：
2021
影响因子：
9.8
通讯作者：
Li Weihong
中科院分区：
文献类型：
--
作者：
Fan Mengtian;Xu Jianhua;Chen Yaning;Li Weihong
气候变化对水环境的影响已引起广泛关注。随着全球气候变暖，山区流域面临严峻的供水形势。然而，山区气象站数量有限，这给径流和水资源的准确模拟带来了挑战。为了解决这个问题，本研究开发了一种方法来模拟径流数据稀缺的山区流域。选取天山山脉、阿克苏河和开都河的两个源头沃茨，首先利用地球系统数据产品重建降水和温度动态，然后结合径向基函数人工神经网络和带自适应噪声的完整集合经验模态分解对径流进行模拟。与水文站实测流量的比较表明，该方法具有较高的精度。模拟结果表明，厄尔尼诺南方涛动、气温、降水和北大西洋涛动是影响流域径流的主要因素，2000 - 2017年阿克苏河和开都河径流量均呈减少趋势。
The impacts of climate change on the water environment have aroused widespread concern. With global warming, mountainous basins are facing serious water supply situations. However, there are limited meteorological stations on mountains, which thus creates a challenge in terms of accurate simulation of streamflow and water resources. To solve this problem, this study developed a method to model streamflow in data-scarce mountainous basins. Selecting the two head waters originating in the Tienshan mountains, Aksu and Kaidu Rivers, we firstly reconstructed precipitation and temperature dynamics based on Earth system data products, and then integrated the radial basis function artificial neural network and complete ensemble empirical mode decomposition with adaptive noise to model streamflow. Comparison with the observed streamflow according to hydrological stations indicated that the proposed approach was highly accurate. The modeling results showed that the El-Niño Southern Oscillation, temperature, precipitation, and the North Atlantic Oscillation are the main factors driving streamflow, and the streamflow decreased in both the Aksu River and Kaidu River between 2000 and 2017.