Refining GNSS-based water storage estimation: Improved hydrological signal extraction using principal component analysis
Bibliographic record
Abstract
The Global Navigation Satellite System (GNSS) is vital for monitoring terrestrial water storage (TWS). However, effectively extracting hydrological load deformation from GNSS observations poses a significant challenge. This study proposes a novel strategy; the seasonal hydrological load signals are removed from the raw data, and the remaining signals use principal component analysis (PCA). Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error (RMSE) is improved by approximately 15 % compared with traditional PCA extraction from raw data. From January 2013 to December 2022, TWS was inverted from 24 GNSS stations in Yunnan Province. The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and the Global Land Data Assimilation System (GLDAS) land surface model. However, the amplitude of the GNSS-inverted TWS is slightly higher. Since GNSS ground stations are more sensitive to hydrological load signals, they show correlations with precipitation data that are 8.6 % and 6.0 % higher than those of GRACE and GLDAS, respectively. In the power spectral density analysis of GRACE/GFO, GLDAS, and GNSS, the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles. These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS, and GNSS observations can help address limitations in GRACE/GFO observations. This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".