Comparison of Data‐Driven Techniques to Reconstruct (1992–2002) and Predict (2017–2018) GRACE‐Like Gridded Total Water Storage Changes Using Climate Inputs

Fupeng Li,Zhengtao Wang,Christina Lück,Ehsan Forootan,Jürgen Kusche,Kerstin Schulze,Roelof Rietbroek

doi:10.1029/2019wr026551

Abstract

AbstractThe Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow‐On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE‐type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate‐related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data‐driven methods and identifying the more robust alternatives for predicting GRACE‐like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino‐Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long‐period TWSC fields globally when combined with statistical decomposition techniques.

Highlights

The Gravity Recovery and Climate Experiment (GRACE) mission, launched by the National Aeronautical and Spatial Administration (NASA) and the German Aerospace Centre (DLR) and flown from March 2002 to October 2017, was dedicated to observe temporal changes in the Earth’s gravity field (Tapley et al, 2004)
We predict each selected GRACE EOF/independent components (ICs) temporal mode individually based on the methods as described in Section 2 and find that forecasting modes with less energy tend to have relatively higher standard errors as estimated by the Center for Space Research (CSR) mascons, while choosing more dominant EOF/IC modes in the Total Water Storage Change (TWSC) prediction will reduce the signal leakage, this will increase the prediction uncertainties
An optional approach could be that one first predicts the TWSC based on a different number of retained modes, and uses a GRACE solution to test the uncertainties of TWSC predicted by these different numbers of modes to identify a best number for each study region

Summary

Introduction

The Gravity Recovery and Climate Experiment (GRACE) mission, launched by the National Aeronautical and Spatial Administration (NASA) and the German Aerospace Centre (DLR) and flown from March 2002 to October 2017, was dedicated to observe temporal changes in the Earth’s gravity field (Tapley et al, 2004). Several alternative sensors and data processing techniques have been proposed to derive surface mass change maps prior to the GRACE period and during the gap between the two generations of GRACE missions, e.g., from satellite laser ranging (Nerem et al, 2012; Talpe et al, 2017), global GNSS inversions (Wu, 2003; Rietbroek et al, 2013), or from the Swarm satellite mission (Jäggi et al, 2016; Bezděk et al, 2016; Lück et al, 2018; Teixeira Encarnação et al, 2019), none of these appear to be able to provide a spatial resolution or accuracy comparable to that of GRACE

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