Interactive Impacts of Uncertainties in Bias-Corrected Hydrologic Simulations: Southern China
Bibliographic record
Abstract
This study aims to comprehensively examine diverse uncertainties/multiplicities (e.g., performance indicators, bias-correction methods, hydrologic models, bias-correction schemes, predictor combinations, watersheds, streamflow magnitudes, and temporal scales) in bias-corrected hydrologic simulations (BCHS). The focus is placed on the variations of BCHS accuracies (representing climatic impacts on runoffs) with every uncertainty, as well as their interactions with the other uncertainties. To achieve this, an integrated bias-corrected hydro-modeling uncertainty analysis approach (IBCHMUA) is developed based on one advanced hydro-modeling method, i.e., discrete principal-monotonicity inference (DiPMI), and two hydrologic models, i.e., Xin’anjiang and HyMOD. IBCHMUA is applied to two representative watersheds (Xiangxi and Zhongzhou) in southern China. Many findings are revealed. For instance, it is necessary to apply multiple performance indicators and DiPMI is effective in correcting hydro-model biases. Every uncertainty poses significant impacts on BCHS, and the significance of the impacts further varies with all or part of the other uncertainties. BCHS accuracies (or the estimated climatic impacts on runoffs in southern China) increase from daily to monthly scales, from Xiangxi to Zhongzhou Watersheds, from the highest through the lowest to the overall runoff magnitudes, from Xin’anjiang to HyMOD models, and from original to bias-corrected hydrologic simulations. Meanwhile, the impacts of the uncertainties in BCHS decrease from bias-correction schemes, temporal scales, streamflow magnitudes, hydrologic models or predictor combinations, to watersheds. These findings are helpful for reducing the complexity and enhancing the reliability of BCHS under diverse uncertainties, and point out the importance of taking into account the interactions of the uncertainties in BCHS studies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".