Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River
Abstract
:1. Introduction
2. Methods and Data
2.1. Study Area
2.2. Methods
2.2.1. Improved Multi-Subwaveform Multi-Weight Threshold Retracker
- (1)
- Set an empirical threshold (0.5) after repeated experiments.
- (2)
- Implement the MSMWTR algorithm and exclude the subwaveform retracked ranges whose weight is lower than the threshold for each cycle.
- (3)
- Recalculate the weight for each subwaveform and perform steps (2–3) iteratively until no sampling points are excluded under the designated threshold.
- (4)
- Apply the Concentrated Probability Density Function (CPDF) method to detect outliers in each cycle and consider the median of remaining samples as the final range [62].
2.2.2. Developed Manning Formula
2.2.3. Hybrid Model
2.2.4. Random Forest Model
2.2.5. Performance Metrics
2.3. Data
2.3.1. Jason-2/3 Missions
2.3.2. ITSG-Grace 2018 Product
2.3.3. Forcing Data for the Hydrologic Model
2.3.4. In Situ Measurement
3. Results
3.1. Discharge Estimation Based on Satellite Altimetry Data
3.2. Hydrological Simulation Using a Hybrid Model
3.3. Downscaling of Modeling Results at a Daily Timescale
4. Discussion
5. Conclusions
- (1)
- The developed Manning formula shows an effective discharge estimation ability at the VS012 and the VS077. The fitted linear regression line between discharge estimates and in situ observations at the VS012 is with a slope of 0.88 and CC of 0.91 between them. A stronger linear relationship between estimated and observed flow discharges was discovered at the VS077 (k/CC = 0.95/0.96).
- (2)
- The hybrid model clearly shows the improvements of discharge estimation in comparison with either the sole GR6J or LSTM models. The CC, KGE and NSE of the hybrid model increased by 3.4/3.4%, 1.1/2.1% and 8.4/3.4% more than the GR6J/LSTM models, respectively. In addition, a noticeable decrease in NRMSE of 14.3/25.0% and BIAS of 13.3/23.5% was detected by comparing with the GR6J/LSTM models, respectively.
- (3)
- The RF-downscaled daily flow discharge is generally consistent with in situ results, although some underestimations during the flood peaks exist. The KGE and NSE between downscaled results and in situ data are as high as 0.83 and 0.69 with NRMSE and BIAS of 0.15 and 21%, respectively. A strong correlation (CC = 0.87) exists between them.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Vitual Station | Period | NSE | KGE | NRMSE | BIAS |
---|---|---|---|---|---|
VS012 | Calibration | 0.74 | 0.83 | 0.09 | 0.26 |
Validation | 0.66 | 0.70 | 0.08 | 0.20 | |
2010 | 0.77 | 0.82 | 0.06 | 0.13 | |
2011 | 0.67 | 0.71 | 0.09 | 0.21 | |
2012 | 0.69 | 0.76 | 0.07 | 0.16 | |
2013 | 0.70 | 0.75 | 0.08 | 0.17 | |
2014 | 0.71 | 0.77 | 0.07 | 0.15 | |
2015 | 0.63 | 0.66 | 0.10 | 0.23 | |
2016 | 0.79 | 0.85 | 0.06 | 0.16 | |
2017 | 0.66 | 0.69 | 0.08 | 0.18 | |
2018 | 0.70 | 0.73 | 0.08 | 0.18 | |
2019 | 0.70 | 0.74 | 0.07 | 0.17 | |
VS077 | Calibration | 0.93 | 0.96 | 0.05 | 0.11 |
Validation | 0.92 | 0.96 | 0.05 | 0.11 | |
2010 | 0.98 | 0.93 | 0.04 | 0.06 | |
2011 | 0.89 | 0.88 | 0.08 | 0.19 | |
2012 | 0.90 | 0.88 | 0.07 | 0.11 | |
2013 | 0.91 | 0.87 | 0.07 | 0.09 | |
2014 | 0.83 | 0.85 | 0.09 | 0.16 | |
2015 | 0.86 | 0.92 | 0.08 | 0.10 | |
2016 | 0.94 | 0.90 | 0.07 | 0.11 | |
2017 | 0.92 | 0.89 | 0.06 | 0.13 | |
2018 | 0.85 | 0.83 | 0.07 | 0.11 | |
2019 | 0.85 | 0.91 | 0.07 | 0.10 |
Period | Parameters | Hybrid Model | GR6J | Improvement (%) | LSTM | Improvement (%) |
---|---|---|---|---|---|---|
2009–2016 | CC | 0.92 | 0.89 | 3.4 | 0.89 | 3.4 |
KGE | 0.93 | 0.91 | 2.1 | 0.92 | 1.1 | |
NSE | 0.90 | 0.83 | 8.4 | 0.87 | 3.4 | |
NRMSE | 0.06 | 0.07 | 14.3 | 0.08 | 25.0 | |
BIAS | 0.13 | 0.15 | 13.3 | 0.17 | 23.5 | |
High-flows period from May to Oct. | CC | 0.90 | 0.84 | 7.1 | 0.88 | 2.3 |
KGE | 0.88 | 0.81 | 8.6 | 0.86 | 2.3 | |
NSE | 0.77 | 0.63 | 22.2 | 0.73 | 5.5 | |
NRMSE | 0.08 | 0.10 | 20.0 | 0.07 | 14.3 | |
BIAS | 0.12 | 0.17 | 29.4 | 0.13 | 7.7 | |
Low-flows period from Nov. to Apr. | CC | 0.87 | 0.78 | 11.5 | 0.84 | 3.6 |
KGE | 0.72 | 0.71 | 1.4 | 0.69 | 2.9 | |
NSE | 0.68 | 0.65 | 4.6 | 0.62 | 9.7 | |
NRMSE | 0.08 | 0.09 | 11.1 | 0.09 | 11.1 | |
BIAS | 0.13 | 0.18 | 27.8 | 0.14 | 7.1 |
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Xiong, J.; Guo, S.; Yin, J. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sens. 2021, 13, 2272. https://doi.org/10.3390/rs13122272
Xiong J, Guo S, Yin J. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sensing. 2021; 13(12):2272. https://doi.org/10.3390/rs13122272
Chicago/Turabian StyleXiong, Jinghua, Shenglian Guo, and Jiabo Yin. 2021. "Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River" Remote Sensing 13, no. 12: 2272. https://doi.org/10.3390/rs13122272