Physically Based Mountain Hydrological Modeling Using Reanalysis Data in Patagonia
Bibliographic record
Abstract
Abstract A physically based hydrological model for the upper Baker River basin (UBRB) in Patagonia was developed using the modular Cold Regions Hydrological Model (CRHM) in order to better understand the processes that drive the hydrological response of one of the largest rivers in this region. The model includes a full suite of blowing snow, intercepted snow, and energy balance snowmelt modules that can be used to describe the hydrology of this cold region. Within this watershed, snowfall, wind speed, and radiation are not measured; there are no high-elevation weather stations; and existing weather stations are sparsely distributed. The impact of atmospheric data from ECMWF interim reanalysis (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) on improving model performance by enhancing the representation of forcing variables was evaluated. CRHM parameters were assigned for local physiographic and vegetation characteristics based on satellite land cover classification, a digital elevation model, and parameter transfer from cold region environments in western Canada. It was found that observed precipitation has almost no predictive power [Nash–Sutcliffe coefficient (NS) < 0.3] when used to force the hydrologic model, whereas model performance using any of the reanalysis products—after bias correction—was acceptable with very little calibration (NS > 0.7). The modeled water balance shows that snowfall amounts to about 28% of the total precipitation and that 26% of total river flow stems from snowmelt. Evapotranspiration losses account for 7.2% of total precipitation, whereas sublimation and canopy interception losses represent about 1%. The soil component is the dominant modulator of runoff, with infiltration contributing as much as 73.7% to total basin outflow.
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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 | 0.001 |
| 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) | 0.001 | 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".