Quantifying uncertainty in groundwater recharge due to spatiotemporal rainfall and temporal evapotranspiration variability
Bibliographic record
Abstract
• Stochastic vadose zone water budget used to estimate mean recharge and uncertainty. • Spatial rainfall variability characterized by local network of rain gauges (3 yr). • Actual evapotranspiration developed from empirical dataset for similar watersheds. • Budyko curve framework used to generate annual actual evapotranspiration. • Most of the uncertainty in recharge derives from rainfall variability not actual ET. The sustainable management of public supply wells relies to a significant degree on groundwater recharge estimates. Accuracy of these estimates will depend on the uncertainty within the largest components of the water budget, including precipitation and evapotranspiration. Quantifying this uncertainty and understanding the effect it may have on regional water balances is challenging. To examine the relative contribution of spatiotemporal rainfall variability (SRV) and annual actual evapotranspiration (AET) variability to groundwater recharge uncertainty, a method was developed to calculate a watershed stochastic vadose zone water budget within a Monte Carlo framework. The method incorporates rainfall time series generated through a semi-parametric approach that is constrained by observed local spatial rainfall correlation coefficients. Stochastic annual AET estimates are generated based on Penman-Monteith potential evapotranspiration (PET) estimates and observed variation about the Budyko curve for selected US MOPEX watersheds with PET ¯ / P ¯ ratios similar to the study area. Overland flow is estimated using streamflow records and hydrograph separation results for the study watershed. The method was applied to the Alder Creek watershed (78 km 2 ) in southern Ontario, Canada, over a 46-year period. Results suggested that 84 % of the uncertainty in recharge was related to SRV while 16 % was related to AET. This method could be used to estimate uncertainty in recharge as a context for numerical groundwater modelling and to project changes in this uncertainty based on possible climate-change induced reductions in rainfall correlation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".