Monitoring reservoir storage using SWOT satellite observations and reservoir operation models
Bibliographic record
Abstract
Reservoirs play a critical role in water management, yet comprehensive and real-time observations of reservoir storage change remain limited, especially outside the U.S. Observations of two reservoir-related attributes, Water Surface Elevation (WSE) and Surface Area (SA), which can be used to calculate reservoir storage change, are often desynchronized, hindering precise estimation. The recently launched (December 2022) Surface Water and Ocean Topography (SWOT) satellite mission (science observations began August 2023) uses a cutting-edge interferometer to provide global, simultaneous WSE and SA maps of Earth’s water bodies, which can be leveraged to estimate reservoir storage change. We evaluate the accuracy of SWOT-based estimates of reservoir storage change in comparison with in-situ-based observations for 12 reservoirs in the Western U.S., of which four are in California, four are in the Upper Colorado River Basin (UCRB), and four are in the Columbia River Basin (CRB). Our results show that SWOT produces WSE measurements with less than 20 cm MAE (taken across all 12 reservoirs and 19 months of observations) and storage estimates with MAE less than 10%. Model-based reservoir storage estimates (constrained by SWOT observations) can fill temporal gaps accurately and efficiently, even if fewer than one-quarter of SWOT observations are valid. Our results motivate further study of the potential for estimation of reservoir storage change where few or no in-situ observations are available via assimilation of SWOT observations into a reservoir simulation model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".