Hybrid precipitation downscaling over coastal watersheds in Japan using WRF and CNN

Abstract

Study region: Kuma River Watershed in Japan. Study focus: High-quality precipitation information is desirable in hydrological modeling and water resources management. This study aimed to generate long-term fine-resolution precipitation datasets over the study region. A hybrid downscaling framework that integrates a dynamical approach by the Weather Research and Forecasting (WRF) model and a deep learning approach by the Convolutional Neural Network (CNN) model was proposed to derive precipitation information at fine resolutions from ERA-Interim datasets. The proposed hybrid downscaling framework was then applied to a coastal watershed in Japan. The merit of the hybrid downscaling approach in generating precipitation datasets at a 6-km resolution from 80-km ERA-Interim datasets, and 54-km and 18-km WRF simulated gridded datasets was explored as an alternative to pure dynamical downscaling approach by WRF. New hydrological insights for the region: The Nash-Sutcliffe efficiency coefficients of daily basin-averaged precipitation at 6-km resolution obtained by CNN from ERA-Interim, 54-km and 18-km WRF simulated datasets were 0.79, 0.93, and 0.98, respectively for training period; 0.71, 0.85, and 0.96, respectively for validation, when compared to 6-km WRF simulated gridded precipitation. The results demonstrated that CNN can reproduce 6-km WRF simulated precipitation and fine-resolution WRF modeling is needed to further enhance the downscaling performance, especially to capture spatial heterogeneity and extreme events. The hybrid downscaling framework of precipitation is promising to preserve the physics of atmospheric dynamics in precipitation modeling and reduce the computational cost considerably compared to pure dynamical downscaling.

Keywords

• WRF
• Convolutional neural network
• Precipitation
• Downscaling
• Deep learning