Improved representation of surface‐groundwater interaction in the Canadian land surface scheme
Bibliographic record
Abstract
The improvement of surface‐groundwater interactions in land surface models are necessary to determine the evolution of hydrologic variables such as surface runoff, evapotranspiration, soil moisture, and streamflows, especially during dry conditions, when streamflows are largely derived from water releases from groundwater storage. Despite its importance, investigations of the effects of surface‐groundwater interactions on streamflows simulated by large‐scale land surface models are lacking. In this paper, we implement a new parameterization to represent groundwater dynamics in the Canadian Land Surface Scheme (CLASS), which is used for modelling the land surface component in the Canadian regional and global climate models. We compare offline simulations performed with the original and modified versions of CLASS to find the impact of these modifications on the regional hydrology. The offline simulations are driven by ERA‐Interim atmospheric forcing data from the European Centre for Medium‐Range Weather Forecasts reanalysis (for the 1980–2011 period), over a northeast Canadian domain. The original and modified versions of CLASS differ in the soil bottom boundary conditions, with free (gravitational) drainage in the former, while an unconfined aquifer at the depth of bedrock is considered in the latter. Results suggest higher soil moisture levels in the simulation with modified CLASS compared to the original version, particularly for regions with shallow water table. At these locations, summer surface runoff, evapotranspiration and streamflows are also higher in the simulation with modified CLASS, and the simulated low flow values are in better agreement to those observed. This study thus demonstrates the need to account for surface‐groundwater interactions in land surface models for realistic simulation of hydrological processes and streamflows.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".