Interest of spatially distributed data to evaluate the object-oriented PUMMA model on the semi-rural Mercier catchment (Yzeron basin, France)
Bibliographic record
Abstract
Urban growth affects mostly the periphery of large cities, leading to catchments which are a mixture of rural and urban areas, so-called periurban catchments. Increase imperviousness and the setup of various networks (drinking water, sewer systems) lead to the modification of the hydrological cycle components and of water pathways in those catchments. In this paper, we assess the relevance of the The Peri-Urban Model for landscape MAnagement (PUMMA) to represent the hydrological cycle in the Mercier sub-catchment (6.8 km2), located close to Lyon city, France, with 10% of artificialized areas. The model is tested in more rural conditions than in a previous application to the Chaudanne neighbour sub-catchment (2.2 km2), characterized by 24% of artificialized areas. The model mesh is based on an object-oriented approach. It is composed of polygons, as derived from a detailed land use map (forest, agricultural fields, urban cadastral units and lakes). The river network is composed of the natural river, ditches and the rain water network. All the units are interconnected for surface and sub-surface flow transfer, and river routing. Model parameters are specified using in situ information or the results of previous studies, without any calibration, in order to assess the relevance of various functioning hypotheses. The model is run for two contrasted years: 2008 (wet) and 2009 (dry) with a variable time step for rainfall and an hourly time step for reference evapotranspiration. Simulated discharge is compared to measurements at the outlet and statistical performance criteria are computed. The model is able to represent the contrast between higher values of base flow in winter and very low flow in summer. Annual runoff volume underestimated (-21%) in 2008 and overestimated (+10%) in 2009.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".