Temporal variations of geoid heights over Kazakhstan from GRACE–FO data and their relation with hydrological changes in the Caspian Sea and seismic activity
Bibliographic record
Abstract
Introduction We quantify temporal variations of geoid heights (Δ N ) across Kazakhstan during 2019–2024 using satellite gravimetry, and relate these changes to recent extreme hydrological and seismic events. Methods Monthly Δ N fields were derived from GRACE-FO–based global geopotential models. We applied seasonal-trend decomposition to isolate seasonal and long-term components, mapped linear trends, and assessed 2024 mass-change anomalies. Consistency checks included correlations with Caspian Sea water-level records and a preliminary comparison between Δ N and ellipsoidal height changes from five continuous GPS stations within the study area. Results Δ N exhibits a clear seasonal cycle with amplitudes up to ∼3.5 mm. A persistent decline in geoid heights during 2019–2022 is observed along the Caspian Sea coast, reaching rates of up to −1.57 mm yr −1 . In 2024, mass anomalies associated with seismic and hydrological events interrupted this decline. Δ N correlates strongly with Caspian Sea level variations (r = 0.91). GPS–GRACE-FO comparisons show an inverse seasonal pattern, consistent with mass-loading effects. Discussion/conclusions GRACE-FO robustly captures regional, mass-related geoid variability over Kazakhstan. The results support applications in geodetic infrastructure maintenance, hazard assessment and early warning, and climate-impact analysis. They also provide inputs relevant to the realization of the International Height Reference Frame by accounting for time-variable gravity in Central Asia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".