A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reconstructing GRACE-derived terrestrial water storage anomalies with in-situ groundwater level measurements and meteorological forcing data
Study region: North China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focus: This paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.
Reconstructing GRACE-derived terrestrial water storage anomalies with in-situ groundwater level measurements and meteorological forcing data
Study region: North China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focus: This paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.
Reconstructing GRACE-derived terrestrial water storage anomalies with in-situ groundwater level measurements and meteorological forcing data
Peijun Li (author) / Yuanyuan Zha (author) / Chak-Hau Michael Tso (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Reconstructing terrestrial water storage anomalies using convolution-based support vector machine
| Elsevier | 2023
Reconstructing terrestrial water storage anomalies using convolution-based support vector machine
| DOAJ | 2023
Monitoring Terrestrial Water Storage Using GRACE/GRACE-FO Data over India: A Review
| Springer Verlag | 2024