Machine learning (ML) models have been applied to monthly streamflow forecasting in recent decades. In this study, forecasting skills of eight ML models are evaluated based on the Model Parameter Estimation Experiment (MOPEX) dataset. We consider two skill scores, i.e., the Nash–Sutcliffe efficiency (NSE) and the adjusted NSE (ANSE), and the latter is the skill score based on the interannual mean monthly value (MMV) as the reference (benchmark) model. Furthermore, NSE of the MMV model (NSE_mmv) is used as a measure of the seasonality of monthly streamflow, as it is the ratio of variance explained by the MMV process. An important result is that forecasting skills of ML models for monthly streamflow are largely controlled by NSE_mmv. Moreover, based on comparisons of different ML models, we have found that the selection of models is not a dominating factor impacting the final skill. Three key factors influencing NSE, i.e., NSE_mmv, the base flow index (BFI) and the aridity index (AI), are explored in this paper. Specifically, NSE_mmv impacts NSE directly and is the predominant factor; BFI influences the memory of the monthly streamflow and therefore influences NSE. The relationship between AI and NSE is much complex and indirect. Firstly, basins with higher AI tend to have lower NSE_mmv, and this will lead to lower NSE; secondly, basins with higher AI tend to have lower BFI, which will also lead to lower NSE; thirdly, for a given BFI level, basins with higher AI tend to have higher memory and higher NSE. For ANSE, basins with AI between 1 and 2 show higher ANSE, which corresponds to higher autocorrelation coefficients.

近几十年来，机器学习（ML）模型已应用于每月流量预测。在这项研究中，基于模型参数估计实验（MOPEX）数据集评估了8个ML模型的预测技能。我们考虑两个技能得分，即纳什-萨特克利夫效率（NSE）和调整后的NSE（ANSE），后者是基于年平均月度值（MMV）作为参考（基准）模型的技能得分。此外，MMV模型的NSE（NSE _mmv）可以用来衡量每月流量的季节性，因为它是MMV过程解释的方差比。一个重要的结果是，ML模型的每月流量预测技能在很大程度上受NSE _mmv控制。此外，基于不同ML模型的比较，我们发现模型的选择不是影响最终技能的主要因素。本文探讨了影响NSE的三个关键因素，即NSE _mmv，基流指数（BFI）和干旱指数（AI）。具体来说，NSE _mmv直接影响NSE，是主要因素。BFI影响每月流量的记忆，因此影响NSE。AI和NSE之间的关系非常复杂和间接。首先，具有较高AI的盆地往往具有较低的NSE _mmv，这会导致NSE降低；其次，具有较高AI的盆地倾向于具有较低的BFI，这也将导致较低的NSE。第三，对于给定的BFI水平，具有较高AI的盆地往往具有较高的内存和较高的NSE。对于ANSE，AI在1到2之间的盆地显示出较高的ANSE，这对应于较高的自相关系数。