Abstract
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.
Highlights
Accurate knowledge on total or terrestrial water storage (TWS) and its spatiotemporal variability play a crucial role in the assessment of climate variation and water resource availability [1]
In an attempt to downscale GRACE TWSA in a gridded format which closely resembles a high resolution TWS product simulated by a land surface model (LSM), Zhong et al [34] presented an iterative adjustment method based on the self-calibration variance-component model (SCVCM) for generating high spatial resolution total water storage anomaly (TWSA)
An iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to downscale the monthly GRACE TWSA G spatially to the high spatial resolution grid of the Ecological Assimilation of Land and Climate Observations (EALCO) TWSA Li (t)
Summary
Accurate knowledge on total or terrestrial water storage (TWS) and its spatiotemporal variability play a crucial role in the assessment of climate variation and water resource availability [1]. The accuracy of TWS simulated by land surface models (LSMs) at high spatial and temporal resolution is commonly limited by uncertainties in meteorological forcing, model parameter calibration, and land surface process representation [2,3,4,5]. To reduce the model uncertainties, researchers have been developing new LSMs that may account for interactions among the different components (e.g., soil moisture, groundwater, surface water, and snow) of TWS. Including groundwater in an LSM enables a more complete simulation of the terrestrial water cycle, but it is subject to large uncertainties since the hydrogeological conditions of aquifers are largely unknown in many circumstances. Due to differences in model physics and parameter values, estimates from various
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