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Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs
by
Zhong, Detang
, Li, Junhua
, Wang, Shusen
in
Algorithms
/ Aquifers
/ Calibration
/ climate
/ Data assimilation
/ downscaling
/ EALCO
/ Evaluation
/ GRACE
/ Groundwater
/ High resolution
/ Iterative methods
/ land surface model
/ Machine learning
/ Neural networks
/ Remote sensing
/ SCVCM
/ Self calibration
/ Spatial discrimination
/ Spatial resolution
/ Spherical caps
/ Statistical methods
/ Surface water
/ Temporal resolution
/ total water storage
/ Trends
/ uncertainty
/ Water availability
/ Water monitoring
/ Water resources
/ Water storage
2021
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Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs
by
Zhong, Detang
, Li, Junhua
, Wang, Shusen
in
Algorithms
/ Aquifers
/ Calibration
/ climate
/ Data assimilation
/ downscaling
/ EALCO
/ Evaluation
/ GRACE
/ Groundwater
/ High resolution
/ Iterative methods
/ land surface model
/ Machine learning
/ Neural networks
/ Remote sensing
/ SCVCM
/ Self calibration
/ Spatial discrimination
/ Spatial resolution
/ Spherical caps
/ Statistical methods
/ Surface water
/ Temporal resolution
/ total water storage
/ Trends
/ uncertainty
/ Water availability
/ Water monitoring
/ Water resources
/ Water storage
2021
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Do you wish to request the book?
Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs
by
Zhong, Detang
, Li, Junhua
, Wang, Shusen
in
Algorithms
/ Aquifers
/ Calibration
/ climate
/ Data assimilation
/ downscaling
/ EALCO
/ Evaluation
/ GRACE
/ Groundwater
/ High resolution
/ Iterative methods
/ land surface model
/ Machine learning
/ Neural networks
/ Remote sensing
/ SCVCM
/ Self calibration
/ Spatial discrimination
/ Spatial resolution
/ Spherical caps
/ Statistical methods
/ Surface water
/ Temporal resolution
/ total water storage
/ Trends
/ uncertainty
/ Water availability
/ Water monitoring
/ Water resources
/ Water storage
2021
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Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs
Journal Article
Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs
2021
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Overview
High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.
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