Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

Remotely sensing data have advantages in filling spatiotemporal gaps of situ observation networks, showing potential application for monitoring floods data-sparse regions. By using the water level retrievals Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale virtual station (VS) 012 and 077 across midstream Yangtze River Basin during 2009–2016 based on developed Manning formula. Moreover, we calibrate hybrid model combined with Gravity Recovery Climate Experiment (GRACE) data, by coupling GR6J hydrological machine learning to simulate discharge. To physically capture flood processes, random forest (RF) is employed downscale into daily scale. The results show that: (1) from formula good accuracy VS012 VS077 improved Multi-subwaveform Multi-weight Threshold Retracker; (2) combination LSTM models substantially improves performance solely either or models; (3) RF-downscaled demonstrates general consistency where NSE/KGE between them are as high 0.69/0.83. Our approach, multi-source remotely techniques, may benefit poorly gauged areas.