Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Meteorological Data
2.2.2. GRACE Data
2.3. Methods
2.3.1. TWS Calculations
2.3.2. Design and Architecture of LSTM Deep Learning Models
2.3.3. Performance Metrics
3. Results
3.1. Evaluation of GRACE TWSCs and Their Spatiotemporal Variability
3.2. Estimation of Long-Term Time Series of Meteorological Data and Their Uncertainties
3.3. Terrestrial Water Storage Anomalies Simulated from LSTM Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Acronym | Meaning |
---|---|
LSTM | Long Short-Term Memory |
TWS | Terrestrial Water Storage |
GRACE | Gravity Recovery And Climate Experiment |
TWSA | Terrestrial Water Storage Anomalies |
TRB | Tarim River Basin |
FO | Follow-On |
TWSCs | Terrestrial Water Storage Changes |
CNN | Convolutional Neural Network |
ANN | Artificial Neural Network |
RNN | Recurrent Neural Network |
YRB | Yarkand River Basin |
KGRB | Kaxgar River Basin |
ARB | Aksu River Basin |
HRB | Hotan River Basin |
WKRB | Weigan-Kuqa River Basin |
DRB | Dina River Basin |
KRB | Keriya River Basin |
KKRB | Kaidu-Kongque River Basin |
QRB | Qarqan River Basin |
DEM | Digital Elevation Model |
APHRODITE | Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation |
CRU | Climatic Research Unit |
PUSHGBC | Princeton University and University of Southampton Hydro-climatology Group Bias Corrected |
GPCC | Global Precipitation Climatology Centre |
P | Precipitation |
ET | Evapotranspiration |
SM | Soil Moisture |
T | Temperature |
GLDAS-1 | Global Land Data Assimilation system, version 1 |
GLDAS-2 | Global Land Data Assimilation system, version 2 |
JPL | Jet Propulsion Laboratory |
CSR | Center for Space Research |
r | correlation coefficient |
NRMSE | Normalized Root Mean Square Error |
NSE | Nash–Sutcliffe efficiency |
BIAS | Relative BIAS |
Data | Data Sources | Spatial Resolution | Temporal Resolution | Date |
---|---|---|---|---|
Precipitation | APHRODITE | 0.25° | daily | 1982–2015 |
PUSHGBC | 0.25° | daily | 1982–2016 | |
CRU | 0.5° | Monthly | 1982–2016 | |
GPCC | 0.5° | Monthly | 1982–2016 | |
ET | GLADS1-CLM | 1° | Monthly | 1982–2016 |
GLADS1-Mosaic | 1° | Monthly | 1982–2016 | |
GLADS1-Noah | 1° | Monthly | 1982–2016 | |
GLADS1-VIC | 1° | Monthly | 1982–2016 | |
GLADS2-Noah | 0.25° | Monthly | 1948–2016 | |
Temperature | CRU | 0.5° | Monthly | 1982–2016 |
GLADS2-Noah | 0.25° | Monthly | 1982–2016 | |
Soil moisture | GLADS2-Noah(V2.0) | 0.25° | Monthly | 1948–2014 |
GLADS2-Noah(V2.1) | 0.25° | Monthly | 2000–2016 | |
TWSA | GRACE-JPL | 0.5° | Monthly | 2002–2017 |
GRACE-CSR | 0.5° | Monthly | 2002–2017 |
LSTM Predictors | Performance in Different Stages (r/NRMSE) | |||
---|---|---|---|---|
Training (70%) | Validation (15%) | Test (15%) | All (100%) | |
SM_P | 0.847/0.148 | 0.591/0.197 | 0.596/0.146 | 0.831/0.161 |
SM_T | 0.879/0.136 | 0.659/0.128 | 0.625/0.127 | 0.857/0.135 |
SM_ET | 0.873/0.133 | 0.669/0.161 | 0.633/0.116 | 0.855/0.139 |
SM_P_ET | 0.796/0.161 | 0.877/0.128 | 0.507/0.123 | 0.818/0.150 |
SM_P_T | 0.914/0.118 | 0.673/0.134 | 0.615/0.108 | 0.881/0.122 |
SM_ET_T | 0.930/0.099 | 0.738/0.162 | 0.415/0.142 | 0.890/0.125 |
SM_ET_P_T | 0.935/0.096 | 0.742/0.134 | 0.763/0.095 | 0.922/0.107 |
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Wang, F.; Chen, Y.; Li, Z.; Fang, G.; Li, Y.; Wang, X.; Zhang, X.; Kayumba, P.M. Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China. Remote Sens. 2021, 13, 889. https://doi.org/10.3390/rs13050889
Wang F, Chen Y, Li Z, Fang G, Li Y, Wang X, Zhang X, Kayumba PM. Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China. Remote Sensing. 2021; 13(5):889. https://doi.org/10.3390/rs13050889
Chicago/Turabian StyleWang, Fei, Yaning Chen, Zhi Li, Gonghuan Fang, Yupeng Li, Xuanxuan Wang, Xueqi Zhang, and Patient Mindje Kayumba. 2021. "Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China" Remote Sensing 13, no. 5: 889. https://doi.org/10.3390/rs13050889
APA StyleWang, F., Chen, Y., Li, Z., Fang, G., Li, Y., Wang, X., Zhang, X., & Kayumba, P. M. (2021). Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China. Remote Sensing, 13(5), 889. https://doi.org/10.3390/rs13050889