Uncertainty analysis for propagation effects from statistical downscaling to hydrological modelingUncertainty analysis for propagation effects from statistical downscaling to hydrological modeling
Bibliographic record
Abstract
To understand the water balance and environmental effects under climate change condition, hydrological models are always used to simulate the hydrological cycle and predict future scenarios by using global climate models (GCMs) outputs. Due to the mismatch of the spatial resolution problem, different downscaling techniques are usually applied to GCMs outputs to generate the high resolution data for fitting the data requirement of hydrological models. As it is known, hydrological modeling always suffers from a number of uncertainties and leads to inaccuracy and unreliability of prediction. Uncertainties associated with climate change have been described as irreducible and persistent, and downscaling GCM outputs using downscaling methods also lead to considerable uncertainties. The purpose of this study is to quantify the propagation effects of uncertainties from statistical downscaling to hydrological modeling for improving the accuracy and reliability of hydrological prediction. A real-world case study has been provided in this study to demonstrate the feasibility of the proposed method. Statistical downscaling model (SDSM) was applied to downscale H3A2a (A2 emission scenario in Hadley Centre Coupled Model 3) outputs for uncertainties evaluation during hydrological modeling when the GCM outputs are used as inputs of a distributed hydrological model.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".