Reply to John Quilty

Comment from John Quilty — ASSESSMENT: This paper introduces a variational mode decomposition (VMD)-based support vector regression (SVR), i.e., VMD-SVR, model for multi-step ahead streamflow forecasting. The authors strive to address the ‘boundary effects’ problem common to many time series decomposition approaches that are typically coupled with data-driven models such as VMD, ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), wavelet transforms (WT), etc. outlined in earlier studies (Du et al., 2017; Maheswaran and Khosa, 2012; Quilty and Adamowski, 2018; Wang and Wu, 2016; Zhang et al., 2015). This is a worthwhile problem to address due to the growing interest in coupling these decomposition methods (VMD, EMD, SSA, WT, etc.) with data-driven models for hydrological forecasting and the vast majority of studies that overlook the impact of boundary effects on hydrological forecasting performance. Many of the just mentioned studies point out flaws in existing strategies for coupling decomposition methods with data-driven models and some go on to identify potential solutions. In this paper, the authors put forth their own approach for addressing boundary effects. The authors claim that the main benefits of their proposed approach include that it “. can reduce the boundary effects, save the modelling time, and improve the prediction performance. This practical streamflow forecasting framework can be outlined as follows: (1) Divide the entire streamflow data into training and validation sets and decompose each of these two sets separately into signal components. This procedure avoids using the validation information for training purposes. (2) Combine the predictors of individual signal components into a final predictors, and select the original streamflow data as the prediction target in order to build only one optimized prediction model. (3) Generate training and validation samples and divide the validation samples into development and testing samples. Mix and shuffle the training and the development samples to optimize the prediction model, and reduce the boundary effects. ” Throughout the MAJOR COMMENTS section below, I raise several issues with how the authors’ proposed approach actually satisfies these points. In my opinion, I think there is much clarification required on the authors’ part to demonstrate that they adequately fulfill these points (in a way that is meaningful for operational forecasting problems, which the present study appears to be concerned with). In particular, the authors’ methodology for how they decompose the time series using VMD (and other comparative approaches) and use it in training and validating their proposed VMD-SVR (and comparative) method(s) is not entirely clear. Out of all issues raised in this review, this point needs the most attention. Nonetheless, I find that the paper is well-written, is properly structured, and is supported by appropriate figures. The references are sufficient. The analysis carried out in the paper is reasonable but the validity of the paper’s results, in terms of how useful the results are to those concerned with developing operational forecasting models, largely depends on how the authors carried out