Some previous studies have proved that prediction models using traditional overall decomposition sampling (ODS) strategy are unreasonable because the subseries obtained by the ODS strategy contain future information to be predicted. It is, therefore, necessary to put forward a new sampling strategy to fix this defect and also to improve the accuracy and reliability of decomposition-based models. In this paper, a stepwise decomposition sampling (SDS) strategy according to the practical prediction process is introduced. Moreover, an innovative input selection framework is proposed to build a strong decomposition-based monthly streamflow prediction model, in which sunspots and atmospheric circulation anomaly factors are employed as candidate input variables to enhance the prediction accuracy of monthly streamflow in addition to regular inputs such as precipitation and evaporation. Meanwhile, the partial correlation algorithm is employed to select optimal input variables from candidate input variables including precipitation, evaporation, sunspots, and atmospheric circulation anomaly factors. Four basins of the U.S. MOPEX project with various climate characteristics were selected as a case study. Results indicate that: (1) adding teleconnection factors into candidate input variables helps enhance the prediction accuracy of the support vector machine (SVM) model in predicting streamflow; (2) the innovative input selection framework helps to improve the prediction capacity of models whose candidate input variables interact with each other compared with traditional selection strategy; (3) the SDS strategy can effectively prevent future information from being included into input variables, which is an appropriate substitute of the ODS strategy in developing prediction models; (4) as for monthly streamflow, the hybrid variable model decomposition-support vector machine (VMD-SVM) models, using an innovative input selection framework and the SDS strategy, perform better than those which have not adopted this framework in all study areas. Generally, the findings of this study showed that the hybrid VMD-SVM model combining the SDS strategy and innovative input selection framework is a useful and powerful tool for practical hydrological prediction work in the context of climate change.

一些先前的研究已经证明，使用传统的整体分解采样（ODS）策略的预测模型是不合理的，因为通过ODS策略获得的子系列包含将来的信息。因此，有必要提出一种新的采样策略来修复该缺陷，并提高基于分解的模型的准确性和可靠性。本文根据实际的预测过程，介绍了一种逐步分解采样（SDS）策略。此外，提出了一种创新的输入选择框架，以建立基于分解的强大每月流量预测模型，其中，黑子和大气环流异常因子被用作候选输入变量，以提高月流量预测的准确性，此外还增加了诸如降水和蒸发之类的常规输入。同时，采用偏相关算法从包括降水，蒸发，黑子和大气环流异常因子在内的候选输入变量中选择最佳输入变量。选择了美国MOPEX项目的四个具有不同气候特征的盆地作为案例研究。结果表明：（1）在候选输入变量中添加遥连接因子有助于提高支持向量机（SVM）模型在预测流量方面的预测准确性；（2）创新的输入选择框架有助于提高候选输入变量与传统选择策略相互影响的模型的预测能力；（3）SDS策略可以有效地防止将来的信息被包含在输入变量中，这在开发预测模型时可以适当替代ODS策略；（4）就每月流量而言，混合变量模型分解支持向量机（VMD-SVM）模型使用创新的输入选择框架和SDS策略，在所有研究领域均比未采用此框架的模型表现更好。一般来说，