Evaluating the ability of a hydrologic model to replicate hydro‐ecologically relevant indicators
Bibliographic record
Abstract
Abstract It is a common practice to employ hydrologic models for assessing alterations to streamflow as a result of anthropogenically driven changes, such as riverine, land use, and climate change. However, the ability of the models to replicate different components of the hydrograph simultaneously is not clear. Hence, this study evaluates the ability of a standard hydrologic model set‐up: Variable Infiltration Capacity (VIC) hydrologic model for two headwater sub‐basins in the Fraser River (Salmon and Willow), British Columbia, Canada, with climate inputs derived from observations and statistically downscaled global climate models (GCMs); to simulate six general water resource indicators (WRIs) and 32 ecologically relevant indicators of hydrologic alterations (IHA). The results show a generally good skill of the observation‐driven VIC model in replicating most of the WRIs and IHAs. Although the WRIs, including annual volume, centre of timing, and seasonal flows, and the IHAs, including maximum and minimum flows, were reasonably well replicated, statistically significant differences in some of the monthly flows, number and duration of flow pulses, rise and fall rates, and reversals were noted. In the case of GCM‐driven results, additional monthly, maximum, and minimum flow indicators produced statistically significant differences. A number of issues with the model input/output data, hydrologic model parametrization and structure as well as downscaling methods were identified, which lead to such discrepancies. Therefore, there is a need to exercise caution in the use of model‐simulated indicators. Overall, the WRIs and IHAs can be useful tools for evaluating changes in an altered hydrologic system, provided the skill and limitations of the model in replicating these indicators are understood. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2013 John Wiley & Sons Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".