Reconstructing GRACE-like TWS anomalies for the Canadian landmass using deep learning and land surface model
Bibliographic record
Abstract
Terrestrial water storage (TWS) is an essential part of the global water cycle. Long-term information of observed and modeled TWS is fundamental to analyze water resources, meteorological extreme events (e.g., droughts and floods), and the climate change impacts. Over the past several decades, hydrologists have been applying physically-based hydrological model (GHM) and land surface model (LSM) to simulate TWS and its components (e.g., groundwater storage). However, the reliability of these physically-based models is often affected by uncertainties in climatic forcing data, model parameters, model structure, and mechanisms for physical process representations. Launched in March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission exclusively applies remote sensing techniques to measure the variations in TWS on a global scale. The mission length of GRACE, however, is too short to meet the requirements for analyzing long-term TWS. Therefore, lots of effort have been devoted to the reconstruction of GRACE-like TWS data for the pre-GRACE era. Data-driven methods, such as multilinear regression and machine learning, exhibit a great potential to reconstruct TWS data by integrating GRACE observations and physically-based model simulations. The advances in artificial intelligence enable adaptive learning of correlations between variables in complex spatiotemporal systems. However, the applicability of various deep learning techniques has not been adequately studied for GRACE TWS reconstruction. In this study, three deep learning-based models are developed to reconstruct the historical TWS using LSM outputs for the Canadian landmass from 1979 to 2002. The performance of the models is evaluated against the GRACE-observed TWS in 2002–2004 and 2014–2016. The trained models achieve a mean correlation coefficient of 0.96, with a mean RMSE of 53 mm. The results show that the LSM-based deep learning models significantly improve the correlations between original LSM simulations and GRACE observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".