Hydrological Modeling of Subartic Wetlands: Comparison Between SLURP and WATFLOOD
Bibliographic record
Abstract
Subarctic wetlands span almost one-sixth of the Canadian wetlands and have been acknowledged as an important ecotone between arctic tundra and boreal forest. Their hydrological complexity is determined by the climatological and physiographical characteristics with regard to the spatiotemporal distribution of water resources. In this study, two semi-distributed hydrological models, SLURP (semi-distributed land use-based runoff processes) and WATFLOOD™, were employed to understand their effectiveness in modeling the hydrology of subarctic wetlands. Comparisons of their delineation approaches, formulations, parameters, and simulation results indicated that both models were capable of simulating the hydrological processes. However, differences were also observed. Besides their different segment delineation approaches, snowmelt and spring peak flows simulated by SLURP were 4–7 days earlier than those estimated by WATFLOOD because SLURP predefines snowmelt rates as variables, whereas WATFLOOD applies constants in the degree-day method. Due to the lack of considering the existence of permafrost and numerous seasonal ponds, both models tended to underestimate the spring peak flows. Evapotranspiration estimated by the Morton complementary relationship areal evapotranspiration method adopted in SLURP was lower than that calculated by WATFLOOD. Summer runoff only appeared during intense rainfall events, and its concentration was much faster in both models as compared with the observed records, which may be attributed to the variations of permafrost depth, soil water storage capacity, and seasonal pond levels. These findings will be helpful in improving the modeling quality of the two models and understanding the hydrologic features of subarctic wetlands.
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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) | 0.000 | 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 | 0.001 | 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".