Benchmarking historical performance and future projections from a large-scale hydrologic model with a watershed hydrologic model
Bibliographic record
Abstract
Large-scale hydrologic models (LHMs) are increasingly relied upon for assessing climate-driven hydrologic changes from watershed to global scales. However, their ability to provide robust projections for a range of hydrologic variables remains unclear. Here, we evaluate the historical performance and future projections from the Community Water Model (CWatM) LHM against the Variable Infiltration Capacity (VIC) watershed hydrologic model for the Liard River basin (drainage area of ∼ 275 000 km 2 ) in subarctic Canada. The model setups have key differences in terms of model configuration (CWatM is set up with two subbasins, and VIC is set up with 28 subbasins) and model structure (e.g., snowmelt and frozen soil representation). We drive both models with an ensemble of eight global climate models from the Coupled Model Intercomparison Project Phase 6, downscaled and bias-corrected with a multivariate method. We analyze a range of hydrologic projections at 1.5 to 4.0 °C global warming levels (GWLs) above the preindustrial period. The historical performance benchmarking shows reasonable goodness-of-fit metrics for both models, with a slightly better performance for VIC. Projected hydrologic responses from CWatM are generally consistent with VIC in terms of annual water balance, as well as monthly snow water equivalent and flow changes, suggesting the robustness of the projections. Both models project coherent hydrologic changes, including progressively higher annual evapotranspiration; increased annual, winter, spring, and maximum flows; increased frequency of extreme flow; and earlier timing of maximum flow, with higher GWLs. However, the magnitudes of maximum flow and late-summer flow diverge between the two models, which can be explained by structural uncertainties associated with the representation of frozen soil and groundwater processes. Thus, our study provides insights into the robustness of hydrologic projections from an LHM and offers a basis for model improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".