Lakes and reservoirs storage changes from SWOT and ancillary database in Quebec (Canada)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».