Spatial bootstrapping for model-free estimation of subcatchment parameter uncertainty for a semi-distributed rainfall runoff model
Bibliographic record
Abstract
Semi-distributed rainfall runoff models are widely used in hydrology, offering a compromise between the computational efficiency of lumped models and the representation of spatial heterogeneity offered by fully distributed models. In semi-distribute models, the catchment is divided into subcatchments, which are used as the basis for aggregating spatial characteristics. During model development, uncertainty is usually estimated from literature, however, subcatchment uncertainty is closely related to subcatchment size and level of spatial heterogeneity. Currently, there is no widely accepted systematic method for determining subcatchment size. Typically, subcatchment discretisation is a function of the spatiotemporal resolution of the available data. In our research, we evaluate the relationship between lumped parameter uncertainty and subcatchment size. Models with small subcatchments are expected to have low spatial uncertainty, as the spatial heterogeneity per subcatchment is also low. As subcatchment size increases, as does spatial uncertainty. Our objectives are to study the trade-off between subcatchment size, parameter uncertainty, and computational expense, to outline a systematic and precise framework for subcatchment discretisation. A proof of concept is presented using the Stormwater Management Model (EPA-SWMM) platform, to study a semi-urban catchment in Southwestern Ontario, Canada. Automated model creation is used to create catchment models with varying subcatchment sizes. For each model variation, uncertainty is estimated using spatial statistical bootstrapping. Applying bootstrapping to the spatial parameters directly provides a model free method for calculating the uncertainty of sample estimates. A Monte Carlo simulation is used to propagate uncertainty through the model and spatial resolution is assessed using performance criteria including the percentage of observations captured by the uncertainty envelope, the mean uncertainty envelope width, and rank histograms. The computational expense of simulations is tracked across the varying spatial resolution, achieved through subcatchment discretisation. Initial results suggest that uncertainty estimates often disagree with typical values listed in literature and vary significantly with respect to subcatchment size; this has significant implications on model calibration.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".