Using SWOT Observations to Characterize Hydrodynamics and Hydroperiod in River Deltas and Estuaries
Notice bibliographique
Résumé
River deltas and estuaries are dynamic transitional zones at the land-ocean interface where freshwater, tides, sediment, and nutrients interact, shaping ecosystems that are both ecologically rich and highly vulnerable to climate change and anthropogenic disturbance. The Surface Water and Ocean Topography (SWOT) satellite mission offers unprecedented opportunities to monitor water surface elevation and hydrodynamics in these complex environments. However, extracting actionable information from SWOT’s novel Ka-band radar interferometric measurements—especially in low-slope, tidally influenced coastal regions—requires new approaches tailored to the complex geomorphology and hydrology of deltas and estuaries. In this study, we developed and applied a framework that aggregates SWOT measurements onto a delta-specific river network graphs to extract hydrodynamic parameters and validate numerical hydrodynamic models in global river deltas and estuaries. The graphs organize SWOT’s pixel cloud (PixC) data along channels to allow for a robust non-stationary harmonic analysis (i.e. NS_Tide), which reconstructs hourly tidal constituents from the sparse SWOT measurements. The tidal reconstruction is then used to validate numerical models that are further leveraged to quantify channel-wetland connectivity, which is essential for understanding how water and salinity move across deltaic landscapes. This approach captures temporal variability in tidal dynamics and enables estimation of tidal range and hydroperiod—the duration and frequency of inundation in adjacent wetlands. These parameters are critical for assessing habitat viability, salinity gradients and biogeochemical fluxes. We applied this methodology across a range of representative coastal systems, including: the Guayas River delta (Ecuador), the Komo River estuary (Gabon), the St. Lawrence estuary (Canada), the Langebaan Lagoon (South Africa), and the Mississippi River Delta (USA). These sites span a spectrum of tidal regimes, channel geometries, and wetland types, offering a comparative view of SWOT’s performance across diverse hydro-geomorphological contexts. We evaluated SWOT-derived water levels against in-situ gauges, model outputs, and ancillary remote sensing data, and we assessed the coherence of tidal signals along river-to-ocean transects. Our results demonstrate that SWOT reliably resolves tidal amplitudes and phases in most estuarine channels, enabling consistent estimation of hydroperiod in adjacent wetlands. SWOT’s performance is modulated by regional morphology, including tidal range, channel geometry, vegetation, and bathymetric complexity. SWOT’s capacity to detect water surface slopes and variations diminishes in areas with dense vegetation cover, highly fragmented marshes and narrow tidal creeks. Nevertheless, the aggregated PixC-based approach significantly enhances signal quality by reducing noise and increasing spatial-temporal sampling density. The tidal constituents extracted with NS_Tide show strong agreement with in-situ tidal observations, mostly when SWOT accuracy is significantly larger than tidal range. The approach developed here provides a robust framework for leveraging SWOT data in coastal settings, opening new possibilities for long-term monitoring productivity of coastal wetlands, and their vulnerability to sea-level rise, land subsidence, and reduced freshwater discharge, as well as for identifying thresholds where salinity intrusion may begin to impact ecosystem health and function.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».