Integrating different types of information into hydrological model parameter estimation: Application to ungauged catchments and land use scenario analysis
Bibliographic record
Abstract
In hydrological modeling, two areas of application present particular challenges, first the modeling of ungauged catchments, and second the modeling of catchment nonstationarity; for example due to effects of land use change. The ungauged catchment problem requires that prior knowledge of the catchment is combined with evidence of behavior; for example from a regionalization exercise and/or spot flow measurements. Simulation of the effects of land use change requires that prior knowledge of the catchment is combined with information on the effects of that change on model parameters, generally in the absence of direct observations with which to condition the parameters. In both cases, ideally, all available sources of information about the behavior should be considered, and integrated in a way that maximizes the value of the information for model identification and uncertainty estimation. Using a formal Bayesian procedure, we combine three different sources of knowledge into a catchment scale conceptual model: (1) small‐scale physical properties; (2) regionalized signatures of flow; and (3) available flow measurements. Applying the methodology to a distributed model for the Hodder catchment, UK, the physics‐based information source contributed most to improving model performance, followed by peak flow times, and lastly the regionalized signatures. The flood frequency curve was evaluated under scenarios of land use change, and those changes that were significant relative to model uncertainty were identified.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".