Identification of rainfall–runoff model for improved baseflow estimation in ungauged basins
Bibliographic record
Abstract
Abstract Baseflow is an integral component of environmental flow prescriptions to mitigate the impacts of flow regime alteration on the ecological condition of rivers and often the most contentious issue in project planning, particularly for waterpower development. Baseflow prescriptions to meet ecological objectives are increasingly derived from a natural reference condition, often a simulated streamflow time series for an ungauged basin. Accuracy of baseflow estimates is important for identifying ecological water requirements and possible ecosystem and economic tradeoffs with confidence. Estimating baseflow time series in ungauged basins to accurately quantify a natural reference condition in all regions of Ontario is particularly difficult given the heterogeneity in landscape, climate, and size of the basins where simulated time series are required. To identify the optimal hydrologic model for baseflow simulation in all regions of Ontario, five variants of McMaster University‐Hydrologiska Byråns Vattenbalansavdelning (MAC‐HBV) were tested using a combination of possible model parameters, linear and nonlinear storage‐discharge relationship in deep soil layers, and different criteria for optimizing model parameter sets. It was found that the hydrologic model which used a nonlinear storage‐discharge relationship, a larger range of model parameters and included low flow criteria in the optimization procedure showed the best performance for baseflow estimates. The optimal model was then combined with a coupled regionalization method to improve baseflow estimates in ungauged basins compared with the original MAC‐HBV model. This included a ∼20% increase in the median of Nash Sutcliffe efficiencies and a 50% reduction in the volume errors for the tested basins. The resulting model provides a tool that can be used reliably throughout Ontario to simulate streamflow and baseflow time series in ungauged basins and to calculate baseflow indicators and criteria for aquatic ecosystem assessments of planned flow regime alterations. Copyright © 2011 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 | 0.000 | 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".