Leveraging SWOT observations for global reservoir monitoring
Bibliographic record
Abstract
Global evaluation of historical and future water security requires models that can accurately simulate reservoir operations, including reservoir storage and release. Reservoir models rely on time series of reservoir operation patterns (inflow, storage, and release) as inputs to estimate key model parameters; however, such in-situ data is limited and therefore spatially scarce. Satellite remote sensing and hydrological modeling offer another avenue for determining reservoir operation patterns. Recently, advances in satellite technology for the Surface Water and Ocean Topography (SWOT) satellite mission have substantially increased the spatial coverage of globally observed reservoirs. Here, we leverage water surface elevation (WSE) observations from SWOT and simulated flows from the Hydrological Modeling and Analysis Platform (HyMAP) to evaluate a globally applicable framework for simulating reservoir operations. In a case study focused on reservoirs in Brazil, we perform a set of experiments to characterize the error of simulated reservoir operations when using different combinations of in-situ, altimeter, and modeled data sources to optimize reservoir model parameters. To have a baseline, we also compare results using SWOT for parameter optimization to results using the global water measurements (GWM) satellite altimeter dataset, which has less spatial coverage but longer WSE time series. Our results indicate that incorporating satellite observations into reservoir parameterization improves simulated releases compared to using HyMAP naturalized flow alone for both SWOT and GWM. We find that using GWM for parameter optimization results in better performance compared to SWOT; however, further analysis suggests that the performance of reservoir operations using SWOT may increase with a longer observation record. Overall, our study suggests that incorporating satellite observations into hydrological models enhances simulated surface water dynamics, and that SWOT holds promise for the global assessments of reservoir operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".