Stereo bathymetry to monitor small seasonal agriculture water ponds in ungauged areas
Bibliographic record
Abstract
Abstract. Small reservoirs represent a critical water supply to farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Over 200,000 ancient rainwater harvesting reservoirs (“tanks”) exist across South India, but with their complex history, considerable size variation, and widespread distribution, understanding the hydrological role of these tanks has been difficult. Fortunately, the last decade has seen improvements in sensors and technologies that enhance our ability to assess the hydrological role of these tanks. In particular, high-resolution Digital Elevation Model (DEMs), now much easier to produce, can be used to improve the characterization of tanks and their surrounding watersheds. Here, a high-resolution DEM is created during the lowest reservoir conditions using Pléiades stereoscopy, and along with two global DEMs, compared with volume estimates from a field-derived UAV reference DEM for a set of tanks in South India. This study demonstrates that a Pléiades-derived DEM can capture accurate reservoir geometry and, when simulating water volume, achieves volumetric differences of 2–8% compared to our UAV reference data. The Pléiades-derived-DEM produced an equivalent height bias less than the expected bias for the recently launched Surface Water and Ocean Topography (SWOT) mission. Deriving high-resolution tank bathymetry from space during low-water conditions provides an opportunity to systematically and repeatably measure tank volume. These results are encouraging in efforts to utilize very high resolution DEMs chosen at an appropriate hydrological time, particularly in regions where water management and security is paramount.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".