Simulated monthly potential groundwater recharge with land cover changes in southern Quebec Database – period 1951-2100
Bibliographic record
Abstract
The dataset contains potential groundwater recharge (GWR) simulated with a 500 m x 500 m resolution over southern Quebec, with a monthly time step, and with time-variant land cover (LC). The dataset is comprised of the LC classifications and the simulated spatial-temporal GWR. It contains (1) the reclassified, resampled, and annually distributed LC classifications for the 1990-2010 period based on the LC classifications for 1990, 2000, and 2010 (NRCan, 2021); (2) the downscaled LC scenarios from the land use harmonization dataset for RCP4.5 and RCP8.5 (Hurtt et al., 2011); and (3) the reclassified and resampled LC map for 2015 from Bissonnette et al. (2016) used for the GWR simulation of Dubois et al. (2021a, 2022b). The dataset also contains (4) the simulated GWR over the 1990-2010 period simulated with LC changes from the NRCan (2021) classification and (5) 12 GWR scenarios spanning 1951-2100 simulated with the LC scenarios. GWR data were simulated with these LC scenarios using the HydroBudget model (Dubois et al., 2021a, b) and are presented in Dubois et al. (2022a). They were compared to the data presented in Dubois et al. (2021a, 2022b) for constant LC. For the 1990-2010 period, interpolated climate data were provided by the Quebec Ministry of Environment and Climate Change (Ministère de l’Environnement et de la Lutte contre les changements climatiques - MELCC). For the 1951-2100 period, a selection of 12 climate scenarios provided by the Ouranos Consortium from the Coupled Model Intercomparison Project— Phase 5 (CMIP5) ensemble (RCP4.5 and RCP8.5, various global climate models) were used as input. The simulations were performed at UQAM by the team of Pr Marie Larocque’s research Chair on Water and land conservation (Chaire Eau et conservation du territoire), as part of a project funded by the MELCC. The study area is located in the Province of Quebec (humid and cold climate; Canada), between the St. Lawrence River and the Canada–USA border and between the Quebec–Ontario border and Quebec City (35 800 km2). The study area is divided into 140 656 cells of 500 m x 500 m. The simulation results are sorted in netCDF files for each LC classifications (5 classes: agriculture, forest, wetlands, water, urban) and for each GWR simulation. The resampled and reclassified LC map from Bissonnette et al. (2016) is provided as a .csv file. Beside the LC classifications, the available variables are composed of the monthly values of simulated vertical inflow (VI; sum of observed rainfall and simulated snowmelt – mm/month), average temperature (°C), simulated runoff (runoff + excess runoff – mm/month), simulated actual evapotranspiration (mm/month), and simulated GWR (mm/month) for each grid cell. All data have a 500 m x 500 m spatial resolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".