Probabilistic Assessment of Monthly River Discharge using Copula and OSVR Approaches

In this study, two efficient approaches for bivariate simulation are presented, which include meteorological and hydrological variables. For this purpose, the applicability of support vector regression (SVR) model optimized by Ant colony and Copula-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) algorithms were investigated and compared in simulating the river discharge based on total monthly rainfall in Talezang Basin, Iran. Entropy theory was used to select a suitable meteorological station corresponding to a hydrometric station. The vector autoregressive model was also used as the base model in Copula-GARCH simulations. According to the 99% confidence intervals of the simulations, the accuracy of both models was confirmed. The simulation results showed that the Copula-GARCH model was more accurate than the optimized SVR (OSVR) model. Considering the 90% efficiency (NSE=0.90) of the Copula-GARCH approach, the results show a 36% improvement of RMSE statistics by the Copula-GARCH model compared to the OSVR model in simulating the river discharge on a monthly scale. The results also showed that by combining nonlinear ARCH models with the copula-based simulations, the reliability of the simulation results increases, which was also confirmed using the violin plot. The results also showed an increase in the accuracy of the Copula-GARCH model at the minimum and maximum values of the data.

Automated summary

This study investigates two efficient approaches for bivariate simulation and compares their applicability in simulating the river discharge in Talezang Basin, Iran. The Copula-GARCH model is found to be more accurate than the optimized SVR model, with increased accuracy at the minimum and maximum values of the data.