Locating the sources of low‐pass behavior within rainfall‐runoff models
Bibliographic record
Abstract
The reasons why most rainfall‐runoff models appear relatively insensitive to potential evapotranspiration (PE) inputs, compared with rainfall inputs, are investigated. To this aim, a methodology is presented providing detailed tracking of the treatment of PE input by two rainfall‐runoff models. Since uncertainties affect both the structures and the inputs of rainfall‐runoff models, the analysis is based on synthetic flow data. Standard synthetic streamflow series were generated using a standard PE input. Then, the PE series were corrupted successively by random and autocorrelated errors, and the propagation of these errors through the models' state variables is followed. For comparison, the same methodology was applied to rainfall data. The analysis is focused on two lumped rainfall‐runoff models (the GR4J model and a lumped version of TOPMODEL) over a large sample of 308 watersheds. The investigation shows that perturbation errors in the potential evapotranspiration are absorbed by the model's production (soil moisture accounting) reservoir, which controls the water losses from the model. Given the slow variations in the soil moisture accounting reservoir, rainfall‐runoff models behave like low‐pass filters, absorbing high‐frequency variations of PE inputs. In contrast, the models tested here do not smooth the rainfall perturbation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".