Uncertainty quantification in climate change impacts on hydrology using ANOVA
Bibliographic record
Abstract
The climate model projections obtained from the Regional Climate Models (RCMs) and their forcing through the hydrological models are known to include multi-source uncertainties. Using the subsequent modelling data for their intended purposes, such as watershed studies, flood mitigation, and climate change adaptation policies while containing unsupervised uncertainties of such nature, will prove detrimentally misjudged and potentially do more harm than good. The uncertainty propagation takes place at each stage along the modelling process and depends on the choice of climate model projections, Representative Concentration Pathways (RCPs), bias correction methods (BCs), hydrological models, and hydrological parameters among other contributors. The aim of this paper is to quantify the overall uncertainty in climate change impacts using Analysis of Variance (ANOVA) to decompose or disaggregate it into components and assess relative contribution of each of the components. The approach is demonstrated through a case study in Little River Experimental Watershed (LREW watershed) under two different emission scenarios (RCP 4.5 and RCP 8.5), five sets of RCM and driving GCM combinations, two bias correction methods by utilizing the Soil & Water Assessment Tool (SWAT) hydrological model. The results will indicate breakdown of the total uncertainty (T) into the respective uncertainties caused by the climate models (C), emission scenario (R), bias correction method (B) and unlike most of the uncertainty decomposition studies, further uncertainty breakdown due to the interactions between various components are also presented. The findings from this study will be useful to the modelers involved with flood mitigation or policy management by enhancing their understanding about the nature of streamflow projections and effectively aid in better decisions concerned with adapting to a changing climate subjected to uncertainties.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".