Evaluating the use of a gridded climate surface for modelling groundwater recharge in a semi‐arid region (Okanagan Basin, Canada)
Bibliographic record
Abstract
Abstract Spatially distributed groundwater recharge was simulated for a segment of a semi‐arid valley using three different treatments of meteorological input data and potential evapotranspiration (PET). For the same area, timeframe, land cover characteristics and soil properties, groundwater recharge was estimate using (i) single‐station climate data with monthly PET calculated by the Thornthwaite method; (ii) single‐station climate data with daily PET calculated by the Penman–Monteith method; and (iii) daily gridded climate data with spatially distributed PET calculated using the Penman–Monteith method. For each treatment, the magnitude and distribution of actual evapotranspiration (AET) for summer months compared well with those estimated for a 5‐year crop study, suggesting that the near‐surface hydrological processes were replicated and that subsequent groundwater recharge rates are realistic. However, for winter months, calculated AET was near zero when using the Thornthwaite PET method. Mean annual groundwater recharge varied from ∼3·2 to 10·0 mm when PET was calculated by the Thornthwaite method, and from ∼1·8 to 7·5 mm when PET was calculated by the Penman–Monteith method. Comparisons of bivariate plots of seasonal recharge rates estimated from single‐station versus gridded surface climate reveal that there is greater variability between the different methods for spring months, which is the season of greatest recharge. Furthermore, these seasonal differences are shown to provide different results when compared to the depth to water table, which could lead to different results of evaporative extinction depth. These findings illustrate potential consequences of using different approaches for representing spatial meteorological input data, which could provide conflicting predictions when modelling the influence of climate change on groundwater recharge. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".