Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model
Bibliographic record
Abstract
Abstract Spatial parameter fields are required to model hydrological processes across diverse landscapes. Transfer functions are often used to relate parameters to spatial catchment attributes, introducing large uncertainties. Quantifying these uncertainties remains a key challenge for large‐scale modeling. This paper extends the multiscale parameter regionalization (MPR) technique to consider parameter uncertainties. We evaluate this method of producing nationally consistent parameter fields, which maintain a constant relationship between model parameters and catchment attributes, across 437 catchments in Great Britain (GB). By sampling multiple transfer function parameters, we produce thousands of possible model parameter fields which are constrained within an uncertainty framework. This is compared to spatially homogeneous parameter sets constrained for individual catchments. The nationally consistent MPR parameter fields perform well (KGE* > 0.75) across 60% of catchments. Performance is similar or better than catchment‐constrained parameters (KGE* drop < 0.1) across 82% of catchments. Advantages of our national parameter fields include (a) improved representation of flows within catchments, (b) more robust performance between calibration and evaluation periods, and (c) spatial parameter fields reflecting hydrologically meaningful variation in catchment characteristics. By including uncertainties, we show that hydrographs produced using MPR have smaller uncertainty bounds which are better able to encompass flows. As the first application of MPR to both the DECIPHeR modeling framework and GB, we developed transfer functions and identified key catchment attributes to constrain model parameters, which are transferrable to other models alongside the addition of uncertainty. Methodologies presented here are informative for future regionalization efforts in GB and elsewhere.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".