Application of the distributed hydrology soil vegetation model to Redfish Creek, British Columbia: model evaluation using internal catchment data
Bibliographic record
Abstract
Abstract The Distributed Hydrology Soil Vegetation Model is applied to the Redfish Creek catchment to investigate the suitability of this model for simulation of forested mountainous watersheds in interior British Columbia and other high‐latitude and high‐altitude areas. On‐site meteorological data and GIS information on terrain parameters, forest cover, and soil cover are used to specify model input. A stepwise approach is taken in calibrating the model, in which snow accumulation and melt parameters for clear‐cut and forested areas were optimized independent of runoff production parameters. The calibrated model performs well in reproducing year‐to‐year variability in the outflow hydrograph, including peak flows. In the subsequent model performance evaluation for simulation of catchment processes, emphasis is put on elevation and temporal differences in snow accumulation and melt, spatial patterns of snowline retreat, water table depth, and internal runoff generation, using internal catchment data as much as possible. Although the overall model performance based on these criteria is found to be good, some issues regarding the simulation of internal catchment processes remain. These issues are related to the distribution of meteorological variables over the catchment and a lack of information on spatial variability in soil properties and soil saturation patterns. Present data limitations for testing internal model accuracy serve to guide future data collection at Redfish Creek. This study also illustrates the challenges that need to be overcome before distributed physically based hydrologic models can be used for simulating catchments with fewer data resources. Copyright © 2003 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".