Impact of horizontal groundwater flow and localized deforestation on the development of shallow temperature anomalies
Bibliographic record
Abstract
In this paper we discuss temperature anomalies that develop in the shallow subsurface as a result of localized deforestation in combination with shallow horizontal groundwater flow. Model results show how a patch‐wise pattern of deforestation at the surface induces significant lateral temperature gradients in the subsurface. Results also indicate that lateral heat transport by advection via horizontal groundwater flow becomes significant above flow rates of about 10−8 m/s. In a steady state situation, reached 1750 a after deforestation, an anomaly of 0.1 K is still present at a distance of ∼2.5 km downstream of the deforested patch at depths between 200 and 575 m for horizontal groundwater flow velocities between 10−7 m/s and 10−8 m/s, respectively. We carried out transient simulations to examine the impact of deforestation on subsurface temperatures during the last century. These experiments include a study of the effects of regional surface warming on the thermal regime of the subsurface. In these scenarios, 100 a after localized deforestation, significant temperature anomalies occur hundreds of meters downstream of the deforested areas. Results show that ground surface temperature history reconstructions based upon synthetic temperature versus depth profiles up‐ and downstream of the deforested patches fail to recover the timing and magnitude of the warming event imposed at the surface. Results from our numerical simulations indicate that lateral heat flow effects should be considered when using subsurface thermal data for constraining land surface schemes in general circulation models.
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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 | 0.000 |
| 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 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".