Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

Abstract

The Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow‐on, GRACE‐FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The ∼1‐year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. In this study, we applied an automated machine learning (AutoML) workflow to perform gridwise GRACE‐like data reconstruction. AutoML represents a new paradigm for optimal algorithm selection, model structure selection, and hyperparameter tuning, addressing some of the most challenging issues in machine learning applications. We demonstrated the workflow over the conterminous U.S. (CONUS) using six types of machine learning models and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML‐assisted gap filling achieved satisfactory performance over the CONUS. On the testing data, the mean gridwise Nash‐Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root‐mean‐square‐error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE‐FO periods (after June 2017). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end‐to‐end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large‐scale hydrological prediction systems and when predictor importance exhibiting strong spatial variability.

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

“…Sun et al [68] used an automated ML for the prediction of GRACE TWS data from different combinations of meteorological and climatic variables to fill the missing values of TWS data. They obtained a mean NSE of nearly 0.85, an R2 of about 0.95, and an NRMSE (%) equal to 9%.…”

Deep Learning for Geophysics: Current and Future Trends

“…YU AND MA 10.1029/2021RG000742 21 of 36 In addition to oceans, water is stored in different forms, such as rivers, lakes, rain, and ice. DL has found its roles in estimating groundwater storage (Sun et al, 2019), global water storage in the US (Sun, Scanlon, et al, 2020), measuring accurate river widths by super resolution (Ling et al, 2019), predicting the temperature of lake water (Read et al, 2019), predicting rainfall and runoff (Akbari Asanjan et al, 2018), and prediction water vapor retrieval from remote sensing data (Acito et al, 2020).…”

Seasonal Amplitude of Water Storage Variations of the Yangtze–Huai Plain Lake Group: Implicaion for Floodwater Storage Capacity

“…The GRACE and GRACE-FO satellites, which are designed to measure the gravity field over time, are used to evaluate spatialtemporal variability in TWS (including surface water, snow water, groundwater, soil moisture, and biomass) (Song et al, 2015;Yin et al, 2020;Sun et al, 2021). The service period of the GRACE satellites was from March 2002 to June 2017.…”