Assessing current tank storage state from multi-mission satellite observations to support water management in southern India
Bibliographic record
Abstract
Small reservoirs represent a critical water supply to farmers across semi-arid regions. Managing these water resources is challenging because hydrological forecasting suffers from sparse rainfall measurements that do not capture highly localised rainfall accumulations. Small reservoir tank structures across South India form part of a complex ancient traditional water distribution system that has historically supplied irrigation to cropped fields during the dry-season. Despite their historical significance and the critical need for water storage in an agrarian dominated country with unpredictable rainfall, thousands of tanks have fallen into a state of disrepair with the introduction of groundwater wells and cheap electrification in the 1960s. Our current understanding of these systems lacks knowledge about the functional state of these ancient traditional water systems. This is especially critical information that is needed to rehabilitate tank structures and support water management. Previous studies suggest that functioning tanks have the potential to increase both the current water supply and support groundwater recharge. But there is little quantitative evidence to support this assertion. To understand tank functionality, spatially explicit and temporally dynamic frequent high-resolution surface water (SW) estimates developed in a synoptic and detailed way are needed. The increased availability of high-resolution satellite imagery provides a substantial opportunity to fulfill this need through the monitoring of small inland water bodies. Monitoring tank SW from earth observation (EO) sources is constrained by their small size (5-50 ha) and rapid water drainage. To support tank monitoring during cloud-covered monsoon seasons, synthetic aperture radar (SAR) observations used in synergy with high temporal resolution visible infra-red observations is desirable. Building from an existing surface water monitoring approach (Vanthof and Kelly, 2019), the primary aim here is to assess large-scale dynamics of tank water storage state at a basin scale. This is achieved by using multi-date and multi-sensor satellite images (Landsat-8, Sentinel-1, Sentinel-2, PlanetScope) for three years covering the northeast monsoon (Sept. – Dec.). SW observations from optical-infrared and radar observations are used to estimate tank SW areas for three monsoon seasons and converted to volumes using empirical rating curves developed for the region from Vanthof and Kelly (2019). Annually tanks were categorized by ‘tanks with water’ or ‘tanks without water’. For the ‘tanks with water’ category, an analysis was performed annually to identify spatial and temporal patterns in two indicators: temporal period of water storage and the rate of storage loss. Results show that hundreds of tanks are not able to store water despite precipitation inputs to the system. For tanks with water, further analysis reveals great variability among tanks for both indicators. As shown, this decade of EO offers exciting opportunities to apply data-driven approaches to complement more traditional physically-based hydrological understanding. Vanthof, V., & Kelly, R. (2019). Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations. Remote Sens. of Environ., 235, 111437.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".