Integrating MRS data with hydrologic model ‐ Carrizal Catchment (Spain)
Bibliographic record
Abstract
ABSTRACT Magnetic resonance sounding (MRS) provides quantitative hydrogeological information on hydrostratigraphy and hydraulic parameters of subsurface (e.g., flow and storage property of aquifers) that can be integrated in distributed hydrologic models. The hydraulic parameters are typically obtained by pumping tests. In this study, we propose an MRS integration method based on optimizing MRS estimates of aquifer hydraulic parameters through hydrologic model calibration. The proposed MRS integration method was applied in the 73 km 2 Carrizal Catchment in Spain, characterized by a shallow unconfined aquifer with an unknown aquifer bottom. 12 MRS survey results were inverted with Samovar 11.3, schematized and integrated in the transient, distributed, coupled, hydrologic, MARMITES‐MODFLOW model. As the aquifer bottom was unknown, the aquifer was schematized into one unconfined layer of uniform thickness. For that layer, MRS estimators of specific yield and transmissivity/hydraulic conductivity were calculated as weighted averages of the inverted MRS layers. The MRS integration with hydrologic model was carried out by introducing multipliers of specific yield and transmissivity/hydraulic conductivity that were optimized during transient model calibration using 11 time‐series piezometric observation points. The optimized multipliers were 1.0 for specific yield and 3.5*10 ‐9 for hydraulic conductivity. These multipliers were used, and can be used in future MRS investigations in the Carrizal Catchment (and/or adjacent area with similar hydrogeological conditions), to convert MRS survey results into aquifer hydraulic parameters. The proposed method of MRS data integration in the hydrologic model of Carrizal Catchment not only allowed us to calibrate the model but also to confirm the functional capability of MRS in quantitative groundwater assessment. Most importantly however, it demonstrated that if pumping tests are not available, the use of MRS integrated in distributed coupled hydrological models, or even in standalone groundwater models, provides a valuable aquifer parameterization alternative.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".