Contribution of SWOT Data to Wetland Hydrological Modelling with HYDROTEL: The Oromocto Watershed Case Study
Notice bibliographique
Résumé
The SWOT (Surface Water and Ocean Topography) mission offers significant potential for hydrological modelling in ungauged or sparsely monitored watersheds. It is expected to enhance the representation of wetlands, whose hydrological dynamics are often poorly parameterized in current models. One of the major challenges that remains is the accurate simulation of their storage, connectivity, and water release mechanisms, which are still not understood well. This study explores the use of SWOT-derived products to inform the wetland modules of HYDROTEL, a semi-distributed, deterministic hydrological model. The modules govern the interactions between isolated and riparian wetlands and other hydrological components, using surface variables such as water-covered area as well as maximum and average storage estimates. The Oromocto River watershed (New Brunswick, Canada) was selected for its high density of wetlands and the presence of a stream gauge located near the outlet, making it a suitable site for assessing the added value of SWOT satellite observations. The watershed presents a favourable hydrological context for studying the buffering effect of wetlands on flood regimes and offers reliable calibration data for a strategic portion of the network. To this end, the HYDROTEL model was first calibrated and validated using interpolated meteorological data from the Daymet database, along with observed streamflow records. The calibration focused on key model parameters over the period 2000–2009, while the validation was conducted for 2010–2019. Standard performance metrics, including the Nash–Sutcliffe Efficiency (NSE) and the Kling–Gupta Efficiency (KGE), were used to evaluate the goodness-of-fit of the simulations. Following this phase, three simulation scenarios were developed to analyze the differential impacts of how wetlands are represented in the model: 1. A baseline scenario that incorporates wetland data from the provincial GeoNB geospatial database; 2. A scenario using a wetland map derived from SWOT imagery, combining spatial and altimetric information; and 3. A scenario that does not explicitly include wetlands. These scenarios allow the isolation of the specific influence of different wetland data sources on the simulated hydrological dynamics, particularly regarding peak flow regulation and the gradual release of water. The comparative approach is designed to assess the contribution of SWOT-derived information under real-world conditions, focusing on a partially gauged sub-watershed where wetland-related temporary storage processes play a key role in flow regulation. Preliminary results suggest that SWOT imagery can improve hydrological modelling in ungauged watersheds, particularly by enhancing the representation of wetlands. Comparisons of peak flows, low flows, and flow duration curves indicate that the model incorporating SWOT data slightly attenuates high flows and alters the simulation of low flows. These effects suggest enhanced water retention and delayed release mechanisms when wetlands are characterized using SWOT-derived variables. However, the accuracy of low flow simulations remains uncertain and warrants further investigation to ensure a reliable representation of dry-season dynamics. In addition, a partial validation was carried out using SWOT-derived water level measurements in lakes and wetlands within the Oromocto River watershed. SWOT data provide new insights into the dynamics of water storage and release within the basin, offering an opportunity to advance our understanding of the interactions between isolated and riparian wetlands and other hydrological components. These findings underscore the need for further analysis to fully assess the contribution of SWOT data to hydrological modelling in ungauged basins. Overall, this study presents an operational framework for using SWOT products within a semi-distributed hydrological model. It explores how SWOT data support hydrological modelling in wetland-rich watersheds, particularly where observational data are limited. It contributes to the core objectives of the SWOT mission and aligns with the scientific priorities of the SWOT Science Team by advancing data-driven approaches to representing wetlands in semi-distributed, deterministic hydrological models.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».