Using the Distributed Hydrology Soil Vegetation Model and River Basin Model to predict stream discharge and temperature in the Horsefly watershed, British Columbia
Bibliographic record
Abstract
Stream discharge and temperature are variables in the hydrological cycle that directly influence aquatic ecosystems. This study uses the Distributed Hydrology Soil Vegetation Model (DHSVM) and the River Basin Model (RBM) to simulate stream discharge and temperature in three basins of the Horsefly watershed, British Columbia, Canada. Using empirical meteorological and topographical data, we validated and assessed the performance of DHSVM and RBM. The DHSVM demonstrated satisfactory performance in simulating non-dammed streamflow, achieving adjusted R 2 values of 0.50 and 0.54, and Normalized Nash-Sutcliffe Efficiency (NNSE) values of 0.30 and 0.35 during calibration and validation, respectively. However, the model performed less well simulating a managed stream, likely due to coarse meteorological and soil input data. Conversely, the RBM performed well across all studied basins (Moffat: NNSE= 0.63, R 2 =0.57, Mckinley: NNSE= 0.79, R 2 = 0.93, McKusky and McKay: NNSE= 0.73, R 2 = 0.84). Despite data limitations, coupled DHSVM-RBM models predicted stream discharge well in unmanaged streams and stream temperature across the studied basins. Our work demonstrates the usefulness of these models to account for stream discharge and temperature under land cover and climate change scenarios, with linkages to aquatic ecosystem management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".