Residual dynamics in hydrological models: insights from a large sample of catchments and models
Bibliographic record
Abstract
The study investigates the properties of residuals from 78 hydrological models applied to 419 distinct catchments over the contiguous United States in a large multi-catchment, multi-model approach. Such dataset provides a foundation for a robust analysis, allowing for an in-depth investigation of residual characteristics. The research focuses on key properties such as sample shape properties (L-skewness and L-kurtosis) investigated with conventional L-moment diagrams (λ 4 /λ 2 vs λ 3 /λ 2 ) and L-moment diagrams adapt for symmetric distributions (λ 6 /λ 2 vs λ 4 /λ 2 ). Other investigated characteristics are residuals heteroscedasticity, and residual correlation. Additional focus of the study is how these characteristics vary across the different models, hydrological regimes, and under the application of different residual transformations. Specifically, the impact of two transformations (Box-Cox and logarithmic) is evaluated on stabilizing such properties. Additionally, the removal of seasonality is analyzed as a separate process, revealing significant effects in stabilizing higher-order moments, greatly reducing heavy-tails in residuals, even in the absence of any transformation. While the removal of seasonality has notable effects on the statistical properties of the residuals, its effect alone is limited in reducing heteroskedasticity, where transformations play instead a key role, effectively approximating a homoscedastic distribution. Upper and lower tails correlations are also investigated, showing distinct patterns different from general correlation behaviors. The findings of this study lays the groundwork for a conscious and informed construction of stochastic error models for uncertainty estimation in hydrological modelling, as well as for the development of new metrics for 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".