Assessment of Uncertainty Propagation from Climate Modeling to Hydrologic Forecasting under Changing Climatic Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it