Bibliographic record
Abstract
The SAMBA (SWOT for the AMazon BAsin) project builds upon the precursor project “SWOT for SOUTH AMERICA”, which was part of the 2020-2023 SWOT Science Team, and covered several thematic areas dealing with the water cycle, hydrology and hydroclimatology of South America. These projects have the overall objectives of advancing water sciences in the context of SWOT and connecting the community working on “hydrology from space” across South America. SAMBA builds upon these past efforts (Fassoni et al., 2021, Cretaux et al., 2023) and achievements (Frappart et al., 2019; Fleischmann et al., 2022; 2023), now with SWOT observations, at the scale of the Amazon basin. Its scientific objectives are twofold: 1) make use of SWOT measurements to improve the benefits of SWOT products for hydrology; 2) work towards answering cutting edge research questions with SWOT observations. As the largest watershed on Earth, the Amazon indeed plays a key role at a range of scales on the water, energy and carbon cycles, while also supporting an incredible biodiversity and many human communities across the basin. It contributes to about 20% of the total freshwater input from the continents to the global oceans annually. Now affected by multiple cumulative stressors, including changes in regional and global climate superimposed on human disturbances, observing and understanding the Amazon hydrology and its changes is of primary importance. SAMBA explores SWOT measurements, along with a variety of other satellites, in situ and numerical modeling contributions to progress our understanding of the hydrology and water cycle of the region at different spatio-temporal scales, from basin to regional/local scales at several regions of interest (Mamiraua floodplains, Rio Negro, Curuai-Obidos-Tapajos) to address key scientific questions regarding river science, Amazon floodplains connectivity, hydrological processes during extreme events and the development of new tools such as the assimilations of SWOT data in hydrological models (Wongchuig et al., 2024). Here, we present recent SAMBA advances and results, including the first characterisation of the widespread and exceptional reduction in river water levels across the Amazon Basin during the 2023 extreme drought revealed by satellite altimetry and SWOT (Moreira et al., 2025). In late 2023, the Amazon River Basin indeed experienced one of its most severe drought and gauges that were still functioning recorded the lowest river water levels (RWL) ever. We use satellite observations, especially from nadir altimetry and SWOT to reveal the spread and timing of extremely low RWL across the entire river system. Nadir altimeter observations show that the 2023 minimum RWL in the Central Amazon were 3 m or more below their annual average, representing two to three times its mean variability. SWOT also clearly captures the basin-scale reduction in RWL with a spatial resolution of 200 m and how the riverdrought propagates with time. Large-scale evaluation with gauges suggests that SWOT outperforms classical altimetry in estimating RWL, despites differences that need further investigations. Our study shows that SWOT offers a new opportunity to understand hydroclimatic extremes and their broad impacts on the environment of the Amazon. It represents a first and important step to other planned activities in the SAMBA various WorkPackages. References: Cretaux, et al (2023), Inland Surface Waters Quantity Monitored from Remote Sensing, Surv. Geophys., https://doi.org/10.1007/s10712-023-09803-x Fassoni-Andrade et al. (2021), Amazon hydrology from space: scientific advances and future challenges, Reviews of Geophysics, 59, e2020RG000728. https://doi.org/10.1029/2020RG000728 Fleischmann et al. (2022), How much inundation occurs in the Amazon River Basin?. Remote Sens. of Environ, 278,113099, https://doi.org/10.1016/j.rse.2022.113099. Fleischmann et al. (2023), Increased floodplain inundation in the Amazon since 1980, Environ. Res. Lett., 18 (3), 034024, doi:10.1088/1748-9326/acb9a7 Frappart et al. (2019), The spatio-temporal variability of groundwater storage in the Amazon River Basin, Adv. Wat. Res., 124, 41-52 ; doi : 10.1016/j.advwatres.2018.12.005 Moreira et al. (2025), Widespread and Exceptional Reduction in River Water Levels Across the Amazon Basin during the 2023 Extreme Drought Revealed by Satellite Altimetry and SWOT, Geophys. Res. Lett., in press Wongchuig et al (2024), Multi-satellite data assimilation for large-scale hydrological-hydrodynamic prediction: Proof of concept in the Amazon basin, Water Resour. Res., 60, e2024WR037155, https://doi.org/10.1029/2024WR037155
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.025 |
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".