Identifying hydrological regularities via perceptual models at the regional scale
Bibliographic record
Abstract
Identifying hydrological regularities, such as patterns and laws that explain the observed variability in catchment response, is an important objective of catchment hydrology. These insights could contribute regional knowledge that can be exploited in catchment classification studies and improve model realism and parsimony. But how to infer such regularities, and to what extent are they generalizable, in view of the evidence of uniqueness of place? In this presentation, we propose the development of a regional scale perceptual model as a framework to stimulate the search of hydrological regularities and to represent them visually. Our approach is intended for nested catchments, a scale that we consider is sufficiently large to provide an interesting contrast in hydrological responses, but sufficiently small to encompass local dominant process that may be different elsewhere. Our perceptual model development approach is demonstrated in the 27,000 km2 Moselle catchment, using streamflow data at 26 nested subcatchments, and commonly available data of landscape properties, including topography, vegetation, geology and soil. The identified signatures of streamflow spatial variability highlighted the role of precipitation, geology and topography, which affected, respectively the average flows, base runoff and lag time. Soil and vegetation, on the other hand, were not found to be a dominant cause of hydrograph variability, which might appear surprising, considering that soil properties are one of the key ingredients of many distributed models. The framework undertaken in this study may be useful to develop perceptual models in other basins at regional scale, and to map and regularize the variety of dominant hydrological processes.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".