Uncertainty Analysis of a 1D River Hydraulic Model with Adaptive Calibration
Bibliographic record
Abstract
Water level modeling is a critical component of flood warning systems. A high-quality forecast requires the development of a hydraulic model that reliably accounts for the main sources of uncertainty. In this paper, a 1D hydraulic model with adaptive flow-based calibration was developed. This calibration resulted in roughness-flow relationships that allow Manning coefficients to be updated as a function of river flow, to limit errors throughout the flood cycle. An uncertainty analysis is then conducted for independent events, considering as the main source of uncertainty the error in the estimated input flows (upstream and lateral), and in the calibrated roughness coefficients. A set of parameters is generated by Latin Hypercube Sampling (LHS) from the characterization of these errors to evaluate their propagation to the variables of interest, namely water level and flow. These are evaluated by performance metrics (scores) such as the reliability diagram and the continuous rank probability score (CRPS). The adaptive flow-based calibration considerably reduced the error of the 1D model and improved its performance over time and throughout the flood events. The uncertainty analysis resulted in consistent accuracy improvements over a deterministic simulation with gains of 20% to 32%, depending on the combined parameters. Good reliability is also reached for most stations, with resulting spreads and Root Mean Square Error (RMSE) close to one another. The proposed methodology has the potential to improve the descriptive capability of 1D river hydraulic models and to increase their reliability when included in forecasting systems.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".