Assessing Groundwater Depletion and Dynamics Using <scp>GRACE</scp> and <scp>InSAR</scp> : Potential and Limitations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In the last decade, remote sensing of the temporal variation of ground level and gravity has improved our understanding of groundwater dynamics and storage. Mass changes are measured by GRACE (Gravity Recovery and Climate Experiment) satellites, whereas ground deformation is measured by processing synthetic aperture radar satellites data using the InSAR (Interferometry of Synthetic Aperture Radar) techniques. Both methods are complementary and offer different sensitivities to aquifer system processes. GRACE is sensitive to mass changes over large spatial scales (more than 100,000 km 2 ). As such, it fails in providing groundwater storage change estimates at local or regional scales relevant to most aquifer systems, and at which most groundwater management schemes are applied. However, InSAR measures ground displacement due to aquifer response to fluid‐pressure changes. InSAR applications to groundwater depletion assessments are limited to aquifer systems susceptible to measurable deformation. Furthermore, the inversion of InSAR ‐derived displacement maps into volume of depleted groundwater storage (both reversible and largely irreversible) is confounded by vertical and horizontal variability of sediment compressibility. During the last decade, both techniques have shown increasing interest in the scientific community to complement available in situ observations where they are insufficient. In this review, we present the theoretical and conceptual bases of each method, and present idealized scenarios to highlight the potential benefits and challenges of combining these techniques to remotely assess groundwater storage changes and other aspects of the dynamics of aquifer systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it