Shallow groundwater thermal sensitivity to climate change and land cover disturbances: derivation of analytical expressions and implications for stream temperature modeling
Bibliographic record
Abstract
Abstract. Climate change is expected to increase stream temperatures and the projected warming may alter the spatial extent of habitat for cold-water fish and other aquatic taxa. Recent studies have proposed that stream thermal sensitivities, derived from short-term air temperature variations, can be employed to infer future stream warming due to long-term climate change. However, this approach does not consider the potential for streambed heat fluxes to increase due to gradual warming of the shallow subsurface. The temperature of shallow groundwater is particularly important for the thermal regimes of groundwater-dominated streams and rivers. Also, recent studies have investigated how land surface perturbations, such as wildfires or timber harvesting, can influence stream temperatures by changing stream surface heat fluxes, but these studies have typically not considered how these surface disturbances can also alter shallow groundwater temperatures and streambed heat fluxes. In this study, several analytical solutions to the one-dimensional unsteady advection–diffusion equation for subsurface heat transport are employed to estimate the timing and magnitude of groundwater temperature changes due to seasonal and long-term variability in land surface temperatures. Groundwater thermal sensitivity formulae are proposed that accommodate different surface warming scenarios. The thermal sensitivity formulae suggest that shallow groundwater will warm in response to climate change and other surface perturbations, but the timing and magnitude of the subsurface warming depends on the rate of surface warming, subsurface thermal properties, bulk aquifer depth, and groundwater velocity. The results also emphasize the difference between the thermal sensitivity of shallow groundwater to short-term (e.g., seasonal) and long-term (e.g., multi-decadal) land surface-temperature variability, and thus demonstrate the limitations of using short-term air and water temperature records to project future stream warming. Suggestions are provided for implementing these formulae in stream temperature models to accommodate groundwater warming.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".