Rainfall model comparison for continuous modelling for small and ungauged basins
Bibliographic record
Abstract
The benefit of continuous modelling in hydrological studies is widely recognized since it provides the practitioner with effective hydrological outputs for risk assessment. However, this approach is still not common mainly because it needs as input simulated rainfall time series. Many rainfall generation methods exist yet there are still two main challenges: (i) many rainfall models are available without a clear suggestion on which is the most appropriate to use, (ii) most rainfall models are not user-friendly and require significant theoretical background to successfully be applied. In this contribution, we test eight rainfall models by evaluating the performances of the simulated rainfall time series when used as input for a specific continuous rainfall-runoff model, named COSMO4SUB (COntinuous Simulation MOdel For Small and Ungauged Basin), particularly designed for small and ungauged basins. The rainfall models selected here are: two versions of the Complete Stochastic Modelling Solution (CoSMoS-1s and 2s); three versions of Bootstrap-based models; the classical Bartlett Lewis and Neymann-Scott rectangular pulses models; and a mixed method based on monthly simulation and multifractal cascade disaggregation. The comparison was performed by analyzing runoff time series obtained with the COSMO4SUB model and using as input the rainfall time series simulated by the eight models and the observed one. We selected and investigated several general properties, such as the average number of flood events, the marginal distribution of peak, volume, duration and antecedent dry period before the flood, and their dependence structure. The comparison confirms the capability of all models to provide realistic flood events and allows identifying the models to be further improved and tailored for data scarce hydrological risk applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".