Abstract
Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.
Highlights
A flood is the sudden rise of water volume caused by heavy rain, rapid melting of ice/snow and storm surges [1]
Threshold Retracker; (2) the combination of the GR6J and the Long Short-Term Memory model (LSTM) models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RFdownscaled daily discharge demonstrates a general consistency with in situ data, where Nash–Sutcliffe efficiency coefficient (NSE)/Kling–Gupta efficiency (KGE)
Free from the impact of extremely high-altitude and intense human activities, this study focused on the middle reach of the Yangtze River Basin located between Zhicheng and Huangshigang (ZHY) because the river here is generally wider than 1000 m, and so it is feasible to extract water surface signals from radar waves
Summary
A flood is the sudden rise of water volume caused by heavy rain, rapid melting of ice/snow and storm surges [1]. As one of the most destructive weather-related hazards, flood has caused huge adverse impacts on infrastructures and the ecosystem [2,3]. Gauge stations can provide accurate measurements of river discharge at point scale, facilitating both flood warning and water resources monitoring, which contribute to a better understanding of the water cycle process [4,5]. Considering the unique advantages that covering inaccessible regions and ones not affected by extreme hydrological events, the remote sensing technologies provide a preferable tool to evaluate river discharge, in poorly gauged areas [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
