Data from: A coupled soil water balance model for simulating depression-focused groundwater recharge
Bibliographic record
Abstract
In arid and semi-arid environments focussed infiltration of rain and snowmelt water under topographic depressions is an important mechanism of groundwater recharge. Quantifying the aggregated recharge from numerous small depressions is a major challenge in water resource management. Building on field-based investigations into the surface water-groundwater interaction of individual depressions and their catchments (i.e. uplands) in the Canadian Prairies, we have developed a simple water balance model to simulate groundwater recharge considering the hydrological coupling of a depression-upland system. The model is based on the Versatile Soil Moisture Budget (VSMB), which has been widely used in the Canadian Prairies to simulate soil moisture conditions. We evaluated the new model, VSMB Depression-Upland System (VSMB-DUS) using field data consisting of an artificial flooding experiment and long-term monitoring of a depression in Alberta, Canada. The model captured surface water level, soil moisture, and groundwater responses to the artificial flooding with a reasonable accuracy, and also represented the inter-annual variability of recharge fluxes during a five year period (2007-2011) including dry and wet years. Simulated annual recharge varied between 12 and 45 mm, whereas annual precipitation varied between 453 and 597 mm during the five-year period. The VSMB-DUS tends to over- or under-estimate snowmelt runoff in individual years, however, simulated recharge was only slightly impacted by the errors in snowmelt runoff estimation. Due to its computational efficiency and robust algorithms, the VSMB-DUS will provide a useful tool for estimating aggregated recharge in a large-scale model grid cell containing hundreds of depression-upland systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.005 | 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".