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Machine learning modeling of climate variability impact on river runoff

机译：气候变异影响对河流径流的机器学习建模

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摘要

The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere-ocean system: ENSO (El Nino-Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901-2010 were used. The split-sample approach was applied, with the period 1901-2000 used for training and 2001-2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices.
机译：本研究的假设是河流径流和气候变化指数的时间序列之间的空间组织联系之一，描述了大气 - 海洋系统中的振荡：ENSO（EL Nino-Southern振荡），PDO（太平洋Decadal振荡） ，amo（大西洋多型振荡）和nao（北大西洋振荡）。使用了1901-2010期间六大大陆和气候变异指数的18个研究领域的全球河流流量重建（ERA-20-CM-R）。采用分流方法，采用1901 - 2000年用于训练的时间和2001-2010用于测试。本文中使用的质量措施是指绝对误差，动态时间翘曲和顶级极端事件错误。我们证明，在气候变化指数上培训的机器学习方法（卷积神经网络，CNN）可以在单变量（每个细胞）和多变量（多电池，区域化）设置。我们将模型与热插拔的形式进行了比较到基线，并呈现消融实验的结果（测试时间消融，即千斤顶，训练时间消融），这表明Enso是所考虑的指数中的主要决定因素。

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  • 来源
    《Oceanographic Literature Review》 |2021年第6期|1208-1208|共1页
  • 作者

    M. Norel; K. Krawiec; Z.W. Kundzewicz;

  • 作者单位

    Institute for Agricultural and Forest Environment Polish Academy of Sciences Poznan 60- 809 Poland;

    Institute for Agricultural and Forest Environment Polish Academy of Sciences Poznan 60- 809 Poland;

    Institute for Agricultural and Forest Environment Polish Academy of Sciences Poznan 60- 809 Poland;

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