Use of Ensemble-Based Gridded Precipitation Products for Assessing Input Data Uncertainty Prior to Hydrologic Modeling
Bibliographic record
Abstract
The spatial and temporal performance of an ensemble of five gridded climate datasets (precipitation) (North American Regional Reanalysis, European Centre for Medium-Range Weather Forecasts interim reanalysis, European Union Water and Global Change (WATCH) Watch Forcing data ERA-Interim, Global Forcing Data-Hydro, and The Australian National University spline interpolation) was evaluated towards quantifying gridded precipitation data ensemble uncertainty for hydrologic model input. Performance was evaluated over the Nelson–Churchill Watershed via comparison to two ground-based climate station datasets for year-to-year and season-to-season periods (1981–2010) at three spatial discretizations: distributed, sub-basin aggregation, and full watershed aggregation. All gridded datasets showed spatial performance variations, most notably in year-to-year total precipitation bias. Absolute minimum and maximum realizations were generated and assumed to represent total possible uncertainty bounds of the ensemble. Analyses showed that high magnitude precipitation events were often outside the uncertainty envelope; some increase in spatial aggregation, however, enveloped more observations. Results suggest that hydrologic models can reduce input uncertainty with some spatial aggregation, but begin to lose information as aggregation increases. Uncertainty bounds also revealed periods of elevated uncertainty. Assessing input ensemble bounds can be used to include high and low uncertainty periods in hydrologic model calibration and validation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".