Radar imagery and saturated areas: decreasing model equifinality
Bibliographic record
Abstract
Remote sensing data not only permit the local measurement of a phenomenon within a catchment, but also permit the examination of internal fine features and heterogeneity to obtain better knowledge of elementary hydrological processes and compare observed and predicted elementary hydrological processes. The images also enable running the models more effectively when ground truth given by the images fits with the model results. An application is shown for a small catchment in French Brittany (Coët-Dan) through several series of radar images from European Remote Sensing Satellite 1 (ERS-1) and ground observations of saturated areas. ERS radar signals are related not only to soil moisture but also to vegetation and roughness, so these images seem incapable of providing reliable soil moisture mapping directly. Nevertheless, a time series over a short period may yield useful information on soil moisture variations and saturation within a catchment, allowing a comparison of the saturated ground areas with model predictions at the catchment scale. We used Topmodel modelling, which computes the saturated areas at each time step during a rain event. In fact, one ERS-1 image does not permit the detection of the saturated areas, but a time series reveals the wettest areas, which appear to provide valuable assistance for better modelling. Using the global likelihood uncertainty estimation (GLUE) methodology opens an interesting research domain: indeed, models often accept numerous sets of parameters that give quite acceptable flow-rate simulations (equifinality), and the radar observations may help to choose within all the different parameter sets those with internal behaviour close to that of the physical modelling assumptions. Radar data thus help to retain only numerical solutions that are physically consistent and consequently to reduce predictive uncertainty caused by equifinality. This considerably improves our knowledge of elementary 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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".