Assessment of Uncertainty Propagation from Climate Modeling to Hydrologic Forecasting under Changing Climatic Conditions
Bibliographic record
Abstract
The changing climate has a profound impact on the hydrological cycle and water balance, complicating water resources management. General circulation models (GCMs) and downscaling methods have been widely employed to reflect and quantify climate change effects in hydrological studies. The uncertainties associated with GCMs, downscaling methods, and hydrological modeling mutually interact, significantly amplifying the complexity of uncertainty analysis. To address this challenge, we proposed the Integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) method, specifically designed to assess the uncertainty propagation effect from statistical downscaling and hydrological modeling. This study aims to utilize ISES-UPA to inves-tigate the effects and contributions of different uncertainty components to the total uncertainty in hydrological modeling under changing climatic conditions. Successfully applied to a real case study in Sichuan, China, the results reveal that the total propagated uncertainty significantly surpasses the simple addition of other sources (e.g., about 2.15 times from statistical downscaling and about 4.44 times from hydrological modeling on average). By using ISES-UPA, individual and combined uncertainties from statistical downscaling and hydrological modeling can be compared and quantified, thereby enhancing the reliability of hydrological studies under changing climate conditions.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".