Land surface hydrological modelling of the Mackenzie River Basin: Parametrization to simulate streamflow and permafrost dynamics
Bibliographic record
Abstract
• The MESH land surface hydrology model parameterized for the Mackenzie River Basin. • A deep soil profile and organic soils used to simulate permafrost dynamics. • Cold regions processes such as glacier melt, blowing snow, etc. integrated. • Calibrated to streamflow and validated for snow, ET, and permafrost. • Challenges of large-scale hydro-cryosphere land surface modelling addressed. Continental high latitudes have been warming at higher rates than the global average, causing substantial permafrost thaw with widespread effects on soils, vegetation, streamflow seasonality and land subsidence. Complex feedbacks are controlled by precipitation changes and soil hydraulic and thermal properties, amongst many factors. The Mackenzie River Basin (MRB) is the largest drainage basin in Canada (1.8x10 6 km 2 ), underlain by permafrost of various classes for most of its extent (70–80% by area). Changes to the MRB affect atmospheric feedback, inflows to the Arctic Ocean, and local environments and communities. This study aims to parameterize a land surface hydrology model (MESH) for the MRB to simulate the coevolution of hydrology and permafrost dynamics, and hence enable the investigation of future change impacts. MESH, designed to simulate cold region processes, couples energy and water exchanges and requires a deep soil profile and long spin-up periods to initialize the permafrost regime at depth. Calibration was restricted to a limited number of influential physical parameters using streamflow from representative sub-basins. Validation used streamflow, snowpack, and snow cover across the basin. Sensitivity and identifiability analyses at well-instrumented sub-surface temperature gauges identified key parameters for permafrost simulation, which were adjusted for consistency with the spatial distribution of permafrost occurrence provided by available datasets and maps. To confirm permafrost prediction accuracy, validations were performed at gauges where active layer depth or soil temperature were observed. The resulting model has high fidelity in simulating both the hydrology and permafrost dynamics in the basin. This suggests that the model can be used with confidence to predict the impacts of future climate, land use/cover, and management scenarios on the evolution of cold region hydrology and permafrost at large scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".