A comparison study on distributed hydrological modelling of a subarctic wetland system
Bibliographic record
Abstract
Wetlands occupy 14% of the Canadian territory and mainly exist as bogs, fens, swamps, marshes, and shallow water. Recently, arctic and subarctic wetlands have attracted much attention due to their unique hydrological characteristics, and vulnerability to climate condition changes. To gain insight of the interactions between hydrology and atmosphere of the second largest wetland in Canada - the Hudson Bay Lowlands (HBL), extensive field investigations were conducted from 2006 to 2008 in the Deer River watershed near Churchill, Manitoba, Canada. Hydrologic and geographic conditions, such as frost table, soil moisture and temperature, and streamflow were monitored to advance the understanding of the wetland systems. Following up the field investigation, two semi-distributed hydrological models, Semi-distributed Land Use-based Runoff Processes (SLURP) and WATFLOOD, were employed to simulate the water cycle in the Deer River watershed. They were further compared from the aspects of modelling structures, formulations, parameters, and simulation results. Regardless the distinct simulation concepts (i.e., aggregated simulation area in SLURP and group response unit in WATFLOOD), the results indicated that snowmelt and peaks of spring runoffs simulated by SLURP were earlier than those simulated by WATFLOOD. This may be explained by the exponentially increasing snowmelt rate adopted by SLURP. Lack of considering the existence of permafrost and seasonal ponds in both models tended to underestimate the peaks of spring runoffs. It was also observed that the Morton CRAE method used in SLURP slightly underestimated the summertime evapotranspiration, meanwhile it was overestimated by the Hargreaves Equation employed in WATFLOOD. This study not only helped to fill the knowledge gaps in how well the two widely used models could fit the requirements of subarctic wetlands modelling, but also showed their strength and limitations as well as the potential for improvement.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".