Assessment of the WATCLASS hydrological model result of the Mackenzie River basin using the GRACE satellite total water storage measurement
Bibliographic record
Abstract
Abstract Earlier efforts have been geared towards modelling the hydrological water balance of the Mackenzie River basin and its sub‐basins using a coupled land surface–hydrological model for the Canadian cold region known as WATCLASS. The goal of this current study is to effectively inter‐compare the resulting total water storage anomalies estimated from the gravity recovery and climate experiment (GRACE) satellite analysis with those estimated from the atmospheric‐based water balance approach as well as the model output from WATCLASS over the 1 · 8 × 106 km2 Mackenzie River basin in Canada. Since the success of the parameter estimation stage of the coupled land surface–hydrological model, WATCLASS over this large catchment, was entirely based on a goodness of fit between the simulated and observed flows, it is often desirable to assess the reliability of the generated state variables prior to concluding on the overall efficiency of this model in reproducing the relevant hydrological processes over this region. A major challenge here lies in finding suitable dataset with which this comparison can be made to further assess the ability of the model in accurately reproducing other mass fluxes. The outcome of this inter‐comparison reveals the potential application of the GRACE‐based approach as a veritable tool required for the closure of the hydrological water balance of the Mackenzie River basin as well as serving as a dependable source of data for the calibration of traditional hydrological models. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 | 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".