An Analytical Framework to Investigate Groundwater‐Atmosphere Interactions Influenced by Soil Properties
Bibliographic record
Abstract
Abstract The interaction between climate and groundwater forms a complex, coupled system that affects land‐atmosphere feedback processes and thus local climatic parameters. We propose an analytical framework that integrates local groundwater information and soil hydrophysical characteristics to identify regions with bidirectional (two‐way) coupling where groundwater is influenced by climatic factors (e.g., precipitation) and may affect local climate (e.g., through surface fluxes). The framework capitalizes on the concept of the evaporation characteristic length to quantify the hydraulic connection of groundwater to the soil surface. To evaluate the framework, we calculate the maximum depth of hydraulic connection ( D max ) between groundwater and the soil surface in Hamburg, Germany. For regions with D max exceeding the groundwater depth ( d ), a bidirectional mode of coupling is defined, while D max < d implies a unidirectional coupling mode. Our results indicate that climate driven evaporation changes potentially alter the coupling between groundwater and climate depending on soil texture. Moreover, soil hydraulic properties and shallow groundwater tables could play a crucial role in shifting land‐atmosphere feedback processes by influencing the coupling mode. This research provides insights into the groundwater‐climate interactions under various climatic conditions and soil textures which could contribute to sustainable land‐use management practices, particularly in regions characterized by bidirectional coupling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".