Recently, a stochastic data-driven framework was introduced for forecasting uncertain multiscale hydrological and water resources processes (e.g., streamflow, urban water demand (UWD)) that uses wavelet decomposition of input data to address multiscale change and stochastics to account for input variable selection, parameter, and model output uncertainty (Quilty et al., 2019). The former study considered all sources of uncertainty together. In contrast, this study explores how input variable selection uncertainty and wavelet decomposition impact probabilistic forecasting performance by considering eight variations of this framework that either include/ignore wavelet decomposition and varying levels of uncertainty: 1) none; 2) parameter; 3) parameter and model output; and 4) input variable selection, parameter, and model output. For a daily UWD forecasting case study in Montreal (Canada), substantial improvements in forecasting performance (e.g., 16–30% improvement in the mean interval score) was achieved when input variable selection uncertainty and wavelet decomposition were included within the framework.

最近，引入了一种随机数据驱动的框架来预测不确定的多尺度水文和水资源过程（例如，水流，城市需水量（UWD）），该过程使用输入数据的小波分解来解决多尺度变化，并利用随机性来考虑输入变量的选择，参数和模型输出不确定性（Quilty等人，2019）。以前的研究综合考虑了所有不确定因素。相比之下，本研究通过考虑此框架的八种变化来探讨输入变量选择不确定性和小波分解如何影响概率预测性能，这些变化包括/忽略小波分解和不确定性水平的变化：1）没有；2）参数；3）参数及模型输出；和4）输入变量选择，参数和模型输出。