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针对全球气候变暖难以利用水文模型评估大尺度流域径流对气候变化的响应,以松花江流域为例,基于人工神经网络模型,根据全球气候模式ECHAM5/MPI-OM在三种排放情景下对该流域2011～2050年的气候做了预估,并计算了佳木斯站流量变化。结果表明,该站在三种排放情景下年平均流量的年际、年代际变化不明显、周期特征不相同、季节平均流量变化不一致。该法模拟可靠,预报精度高,可用于大流域气候变化影响评估。
In view of the difficulty of global climate warming in assessing the response of large-scale river basin runoff to climate change, taking the Songhua River Basin as an example, based on the artificial neural network model and according to the global climate model ECHAM5 / MPI-OM in three emission scenarios The basin’s climate from 2011 to 2050 was estimated, and the change of the flow at Jiamusi Station was calculated. The results show that the interannual and interdecadal variations of the annual average fluxes of the stations under the three emission scenarios are not obvious, the periodic characteristics are not the same, and the average seasonal flow changes are inconsistent. The method has the advantages of reliable simulation and high prediction accuracy, and can be used for climate change impact assessment of large watershed.