Contribution of SWOT Data to Wetland Hydrological Modelling with HYDROTEL: The Oromocto Watershed Case Study
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".