Automated Linear and Nonlinear Reservoir Approaches for Estimating Annual Base Flow
Bibliographic record
Abstract
Three automated base flow separation techniques based on linear and nonlinear reservoir approaches are used to identify the seasonal variation of base flow and to quantify the annual base flow for three subwatersheds of the Essex region in Southwestern Ontario, Canada. Significant differences in annual base flow estimated by linear and nonlinear reservoir algorithms are observed. In the nonlinear reservoir approach, the recession parameter is considered to be a seasonally variable parameter. The nonlinear reservoir approach fits streamflow recession better than the linear reservoir approach. The steeper slopes of seasonal flow duration curves in the 90% to 100% flow exceedance range show that the groundwater contribution to streamflow is relatively small in the study area. The precipitation-streamflow relationships show faster response of base flow during the period of high recharge. All of the methods show similar base flow estimation during the period of high evapotranspiration losses. The nonlinear reservoir approach represents the base flow response to precipitation better than the other methods. Therefore, the annual base flow estimated by the nonlinear reservoir approach is considered as the most reasonable estimation for the formulation of water budget of the study area. The method quantifies the occurrence of average annual baseflow as 34% of average annual streamflow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".