**2. Model Development**

In this study, multiple linear regression (MLR), artificial neural network (ANN), and the proposed C-vine copula-based quantile regression (CVQR) models are used for streamflow forecasting. In the model development section, the MLR, ANN, and CVQR models are described, which together constitute the main modules of the proposed framework shown in Figure 1. Generally, the framework of this study entails the next four steps: (1–2) fitting and standardizing the predictors (i.e., *x*1, . . . *xn*−1) and predicted variable (*xn*); (3) simulating the monthly streamflow for the calibration process using the MLR, ANN, and proposed CVQR models; and (4) performing monthly streamflow prediction during the calibration and verification periods based on the results of step 3 and comparing the results of R<sup>2</sup> , RMSE, and NSE for each model.
