Re-initiating depth-discharge monitoring in small-sized ungauged watersheds by combining remote sensing and hydrological modelling: a case study in Madagascar
Bibliographic record
Abstract
This work presents a practical approach to reconstructing past and present discharge and water depth time series for operational monitoring of small-sized ungauged watersheds using remotely sensed and freely accessible datasets in conjunction with hydrological models. The methodology was applied to the Tsiribihina watershed in Madagascar. Mostly, satellite data are used, such as water levels from satellite altimetry missions and rainfall from the African Rainfall Climatology 2 product. In contrast, the Modelo de Grandes Bacias is calibrated using historical discharge measurements to simulate distributed discharge in the basin. Rating curves are computed by crossing the altimetry information of water height with the simulated discharge outputs. These rating curves enable the conversion of water levels, discharge and depth interchangeably. Present-day discharge and depth can thus be estimated in near real time with any update of satellite rainfall data and/or water level gained by altimetry missions currently flying in operational mode.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".