Cold Climates, Complex Hydrology: Can A Land Surface Model Accurately Simulate Deep Percolation?
Bibliographic record
Abstract
Abstract. Cold regions present unique challenges for land surface models simulating deep percolation or potential groundwater recharge. Previous model evaluation efforts often overlooked these regions and did not account for various sources of uncertainties influencing model performance and its evaluation. This work addresses these limitations using high-resolution integrated lysimeter measurements to assess the performance of the SVS land surface model in a cold climate. SVS showed promise in the simulation of snowmelt and rainfall-driven deep percolation events. It also simulated daily snow depth well, with a correlation coefficient (r) greater than 0.94 and a mean-bias-error (MBE) smaller than 3.0 cm for most of the simulation period. The newly implemented soil-freezing scheme reasonably simulated the near-surface soil temperature (r = 0.89) with a slight cold bias (MBE = -0.8 °C). However, the model's inability to represent frozen soil infiltration and preferential flow resulted in a significant underestimation of percolation (r: 0.35, MBE: -0.8 mm·day-1) and near-surface soil moisture during cold months (MBE: -0.058 m3 ·m-3). Those findings highlight the importance of a comprehensive model evaluation for improving deep percolation modeling in cold regions. Such improvements can lead to more informed decision-making regarding groundwater resource management in a changing climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".