Lakes and reservoirs storage changes from SWOT and ancillary database in Quebec (Canada)
Bibliographic record
Abstract
Authors: Axel CHUETTTE, Mélanie TRUDEL, Sylvain BIANCAMARIA, Manon DELHOUME, Mathilde DE FLEURY, Gabriela SILES Estimating lakes and reservoirs storage time dynamics is extremely important for multiple aspect of the water and carbon cycle. This study aims to compute and assess accuracy of lake storage change from SWOT data, for some lakes and reservoirs in Quebec, Canada (especially, the Aylmer, Grand Lac Saint-François, and Louise lakes). SWOT simultaneous measurements of lake extent and water surface elevations (WSE) are unique and can be used to estimate lakes/reservoirs storage change at global, continental, basin and local scales. Validation against in situ measurements in Quebec (Canada) showed SWOT meets the requirements on WSE and at some locations outperforms them. If SWOT also meets its requirements of 15% accuracy on lake extent measurements (impact of phenology set aside), for some lakes with extent variations smaller than 15% it is not accurate enough. Besides, lake extent from SWOT is affected by different source of errors (dark water, specular ringing, wetland near lakes, vegetation, layover…). It is therefore needed to combine SWOT data with other satellite data to improve lake extent time series. It is done in this study using Sentinel-1 and Sentinel-2 satellites data. The radar and optical images are selected if they are within + or – three days from SWOT observations. Once lake extent time series from SWOT/Sentinel-1/Sentinel-2 and WSE time series from SWOT have been computed, an hypsographic curve (lake extent versus WSE) is computed. It will allow to get consistent lake extent and WSE at the same measurement times. Then, lake storage change is computed using the incremental approach from the L2_HR_LakeSP product Algorithm Theoretical Basis Document (ATBD; Pottier and Stuurman, 2023). Accuracy of these estimates are evaluated using lake storage change computed using the lake bathymetry and in situ WSE, that are available for the studied lakes. These lakes are also ice-covered during winter. SWOT data and an ice-flagging algorithm developed at Université de Sherbrooke are used to handle ice on lakes. Lake/reservoir storage dynamic can be investigated using the lake water mass balance equation. Lake storage change is equal to the water mass flowing to it (from incoming rivers, direct water runoff from rain and snow melt, and potentially from the connected aquifer) minus water mass leaving it (water flowing out through downstream river network, evaporation, and potentially from the connected aquifer). SWOT estimates of lake storage change provides one part of this mass balance equation and will help to estimate inputs and outputs fluxes. For the studied lakes, information on water levels and outflow from the lakes/reservoirs are available freely through Quebec Government. It is therefore a perfect test case for lake/reservoir mass balance study. Assuming that contribution of connected aquifer is negligible, it will be possible to assess water mass lost via evaporation and quantify this mass loss compared to other term of the equation. This component (evaporation) is largely unknown and currently is only estimated as a byproduct of hydrological models. Deriving water balance components from in-situ and satellite observations will help to obtain a better knowledge of the water fluxes at high latitudes. For these lakes, quality of the SWOT discharge product may be assessed and water budget for other lakes/reservoirs without or partial in situ monitoring possibly computed. The approach will be tested on the studied lakes and estimate the benefits from SWOT data for mass balance computation. This study is done at Université of Sherbrooke (Canada), LEGOS (France), CNES (France) and C-S Group (France) within the context of the SNORKS2 project, funded by the CNES TOSCA program and ASC/CSA.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".