Groundwater depletion in Central Mexico: Use of GRACE and InSAR to support water resources management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Groundwater deficits occur in several areas of Central Mexico, where water resource assessment is limited by the availability and reliability of field data. In this context, GRACE and InSAR are used to remotely assess groundwater storage loss in one of Mexico's most important watersheds in terms of size and economic activity: the Lerma‐Santiago‐Pacifico (LSP). In situ data and Land Surface Models are used to subtract soil moisture and surface water storage changes from the total water storage change measured by GRACE satellites. As a result, groundwater mass change time‐series are obtained for a 12 years period. ALOS‐PALSAR images acquired from 2007 to 2011 were processed using the SBAS‐InSAR algorithm to reveal areas subject to ground motion related to groundwater over‐exploitation. In the perspective of providing guidance for groundwater management, GRACE and InSAR observations are compared with official water budgets and field observations. InSAR‐derived subsidence mapping generally agrees well with official water budgets, and shows that deficits occur mainly in cities and irrigated agricultural areas. GRACE does not entirely detect the significant groundwater losses largely reported by official water budgets, literature and InSAR observations. The difference is interpreted as returns of wastewater to the groundwater flow systems, which limits the watershed scale groundwater depletion but suggests major impacts on groundwater quality. This phenomenon is enhanced by ground fracturing as noticed in the field. Studying the fate of the extracted groundwater is essential when comparing GRACE data with higher resolution observations, and particularly in the perspective of further InSAR/GRACE combination in hydrogeology.
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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) | 0.000 | 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) | 0.001 | 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 it