Understanding the hydrological response of groundwater discharge from freezing soils to a warming climate
Bibliographic record
Abstract
Objective: Estimate the error introduced by the misrepresentation of soil freezing characteristic curves (SFCCs) in hydrological models and propose an improved method for modelling freezing soils.Key FindingsMost SFCCs used in numerical modelling studies are chosen based on convergence behaviour as opposed to physical soil properties. The choice of SFCC affects model outcomes, including governing the ice content of soils, which in turn controls the permeability and the discharge from a hydrogeologic model. AbstractMore than half of the global terrestrial surface is subject to freezing processes, either as seasonally frozen soils or as permafrost. Soil freezing processes are represented by the soil freezing characteristic curve (SFCC) that relates soil temperature to its unfrozen water content. Unfortunately, SFCCs are frequently misrepresented in models, and often chosen based on ease of model convergence behavior as opposed to physical soil properties. With climate change and increased frequency of midwinter melt, SFCCs are becoming increasingly important in accurately predicting the hydrological response of catchments.Two synthetic hillslopes, one for a permafrost system and one for a permafrost-free system affected by seasonal freezing, are simulated using SUTRA-ice and a selection of widely accepted SFCCs. SFCCs are drawn from literature values as well as a repository of collected SFCC data: "A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves". The resulting discharge is compared for each simulation, showing that the choice of SFCC is important in controlling streamflow generation in these landscapes, and the choice of SFCC may be a previously overlooked controlling process in the hydrological behaviour of catchments with freezing soils. Further work upscaling these results to catchment and larger scales is needed.
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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 | 0.002 |
| 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 | 0.001 |
| 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".