Post-processing of hydrological model simulations using the convolutional neural network and support vector regression

Post-processing methods can be used to reduce the biases of hydrological models. In this research, six post-processing methods are compared: quantile mapping (QM) methods, which include four kinds of transformations, and two newly established machine learning frameworks [support vector regression (SVR) and convolutional neural network (CNN)] based on meteorological data and variation mode decomposition (VMD)-decomposed streamflow. These post-processing methods are applied to a distributed model (WRF-Hydro), and the evaluation is carried out over five watersheds with different areas in South China. The post-processing methods are separately applied to calibrated and uncalibrated models. The results show that the SVR- and CNN-based post-processing methods perform better than the QM methods in terms of daily streamflow simulations in different areas with different topographies in the Xijiang River basin. There are large uncertainties in the QM post-processing methods. The CNN-based post-processing performs slightly better than the SVR-based post-processing, but both methods can markedly improve the simulated streamflow. The CNN- and SVR-based post-processing frameworks are suitable for both calibration and test periods. The differences between post-processing with uncalibrated and calibrated models are quite small for SVR- and CNN-based post-processing, but large for QM post-processing. For WRF-Hydro, the CNN- and SVR-based post-processing methods consume much less time and computational resources than model calibration.