Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Xiong, Jinghua, Guo, Shenglian, and Yin, Jiabo, 2021. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sensing, 13(12):2272, doi:10.3390/rs13122272.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2021RemS...13.2272X,
author = {{Xiong}, Jinghua and {Guo}, Shenglian and {Yin}, Jiabo},
title = "{Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River}",
journal = {Remote Sensing},
keywords = {satellite altimetry, GRACE, machine learning, hydrological simulation},
year = 2021,
month = jun,
volume = {13},
number = {12},
eid = {2272},
pages = {2272},
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.}",
doi = {10.3390/rs13122272},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13.2272X},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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