Hydrological model uncertainty due to precipitation‐phase partitioning methods
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Bibliographic record
Abstract
Abstract Precipitation‐phase partitioning methods (PPMs) that are used in simulating cold‐region hydrological processes vary significantly. Typically, PPMs are based on empirical algorithms that are driven by readily available near‐surface air temperature but ignore the physical processes controlling precipitation phase by not incorporating humidity. Because these lack any physical basis, there is uncertainty in their spatial and temporal transferability. Recently, humidity‐based methods that have a stronger physical basis and smaller uncertainty have been developed. To quantify the uncertainty that empirical PPMs introduce into hydrological simulations, a cold‐region hydrological modelling platform was used with a physically based PPM and a selection of empirical PPMs to calculate a set of snow regime and streamflow regime indices. The empirical PPMs included a single air temperature threshold and a double air temperature threshold, whereas the physically based PPM used a psychrometric energy balance model. All calculations were run with near‐surface meteorological observations that typically drive hydrological models. Intercomparison of the hydrological responses to the PPMs highlighted substantial differences between the wide range of responses to empirical algorithms and the very small uncertainty due to physically based methods. Uncertainty of hydrological processes, quantified by simulating over a range of air temperature thresholds, reached 20% for the rainfall fraction, 0.4 mm/day for basin discharge, 160 mm of peak snow water equivalent, 36 days for hydrological uncertainty snow cover duration, 26 days for snow‐free date and 10 days for peak discharge date. The implication of this research is that the reduced uncertainty derived from implementing physically based PPMs, for operational or research purposes, are greatest for snowpack prediction in mountain basins. However for streamflow discharge calculations, the reduced uncertainty was greatest in prairie and alpine basins due to the additional effects of precipitation phase calculations on frozen soil infiltration and summer snowmelt processes respectively. Copyright © 2014 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it