Hyper‐Resolution Continental‐Scale 3‐D Aquifer Parameterization for Groundwater Modeling
Bibliographic record
Abstract
Abstract Groundwater is the world's most important freshwater resource. Despite this importance, groundwater flow and interactions between groundwater and other parts of the hydrological cycle are often neglected or simplified in large‐scale hydrological models. One of the challenges in simulating groundwater flow and continental to global scales is the lack of consistent globally available hydrogeological data. These input data are needed for a more realistic physical representation of the groundwater system, enabling the simulation of groundwater head dynamics and lateral flows. A realistic representation of the subsurface is especially important as large‐scale hydrological models move to finer resolutions and aim to provide accurate and locally relevant hydrologic information everywhere. In this study, we aim at improving and extending on current available large‐scale data sets providing information of the subsurface. We present a detailed aquifer representation for the continental United States and Canada at hyper resolution (250 × 250 m). We integrate local hydrogeological information, including observations of aquifer layer thickness, conductivity, and vertical structure, to obtain representative aquifer parameter values applicable to the continental scale. The methods used are simple and can be expanded to other parts of the world. Hydrological simulations were performed using the integrated hydrological model ParFlow and demonstrated improved model performance when using the new aquifer parameterization. Our results support that more detailed and accurate aquifer parameterization will advance our understanding of the groundwater system at larger scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".