The accuracy of ecological flow metrics derived using a physics‐based distributed rainfall–runoff model in the Great Plains, USA
Bibliographic record
Abstract
Abstract The development of a hydrologic foundation, essential for advancing our understanding of flow‐ecology relationships, was developed using the high‐resolution physics‐based distributed rainfall–runoff model V flo in a semi‐arid region. We compared the accuracy and bias associated with flow metrics that were generated using V flo , gauge data, and drainage area ratios at both a daily and monthly time step in the Canadian River basin, USA. First, we calibrated and applied bias correction to the V flo model to simulate streamflow at ungauged catchment locations. Next, flow metrics were calculated using simulated and observed data from stream gauge locations. We found discharge predictions using V flo were more accurate than drainage area ratios. General correspondence between predicted discharge and the gauge data was apparent; however, flow metrics calculated using the V flo output did not accurately represent flow variability. Results from the V flo model showed systematic discharge over‐predictions in the upper basin and isolated over‐predictions in the lower basin, likely due to hail events and sparse rainfall data across the large catchment. Goodness‐of‐fit statistics (Nash–Sutcliffe efficiency, root‐mean square error, and the coefficient of variation) indicated the drainage area ratio and V flo were more accurate at a monthly rather than daily time step, even after quantile mapping. This finding limits the number of streamflow metrics available to develop ecological models, but more importantly, the coarser resolution may hinder our understanding of ecological processes that occur at a submonthly time step. Our approach provides a framework for selecting flow metrics that best represent hydrologic patterns across a large semi‐arid catchment with the necessary accuracy to address the ecological questions of interest.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".