A new analytical solution for assessing climate change impacts on subsurface temperature
Bibliographic record
Abstract
Abstract Groundwater temperature is an important water quality parameter that affects species distributions in subsurface and surface environments. To investigate the response of subsurface temperature to atmospheric climate change, an analytical solution is derived for a one‐dimensional, transient conduction–advection equation and verified with numerical methods using the finite element code SUTRA. The solution can be directly applied to forward model the impact of future climate change on subsurface temperature profiles or inversely applied to produce a surface temperature history from measured borehole profiles. The initial conditions are represented using superimposed linear and exponential functions, and the boundary condition is expressed as an exponential function. This solution expands on a classic solution in which the initial and boundary conditions were restricted to linear functions. The exponential functions allow more flexibility in matching climate model projections (boundary conditions) and measured temperature–depth profiles (initial conditions). For example, measured borehole temperature data from the Sendai Plain and Tokyo, Japan, were used to demonstrate the improved accuracy of the exponential function for replicating temperature–depth profiles. Also, the improved accuracy of the exponential boundary condition was demonstrated using air temperature anomaly data from the Intergovernmental Panel on Climate Change. These air temperature anomalies were then used to forward model the effect of surficial thermal perturbations in subsurface environments with significant groundwater flow. The simulation results indicate that recharge can accelerate shallow subsurface warming, whereas upward groundwater discharge can enhance deeper subsurface warming. Additionally, the simulation results demonstrate that future groundwater temperatures obtained from the proposed analytical solution can deviate significantly from those produced with the classic solution. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".