Toward a global scale runoff estimation through satellite observations: the STREAM model 
Bibliographic record
Abstract
Climate change is significantly transforming familiar environments and affecting daily life. In this context, continuous monitoring of river discharge in space and time is crucial for planning human activities related to water use, preventing or mitigating losses due to extreme flood events, and reducing the effects of water scarcity. Conventional in-situ monitoring stations have limitations such as low spatial density, incomplete time coverage and delays in data availability. These challenges hinder continuous spatio-temporal monitoring of river discharge. In response, researchers and space agencies have developed innovative satellite-based approaches to estimate runoff and river discharge using only satellite observations. In this perspective, the European Space Agency (ESA) has supported the STREAM (SaTellite-based Runoff Evaluation And Mapping) and STREAM-NEXT projects, which integrate satellite data on precipitation, soil moisture, terrestrial water storage anomalies, altimetric water levels, and snow cover into a simplified hydrological model, STREAM, to provide long-term independent global-scale gridded runoff and river discharge time series. The STREAM model has been applied to over 40 river basins globally, including some of the largest such as the Mississippi-Missouri, Amazon, Danube, Murray-Darling, and Niger. It has demonstrated a strong capability to replicate observed river discharge even in heavily human-impacted basins where flow is regulated by dams and reservoirs. In addition, the model has shown its efficiency in simulating runoff and river discharge in Arctic basins (e.g. Lena, Mackenzie, Ob, Yenisey, and Yukon) where flows are controlled by glacier melt, and in small basins where the spatial resolution is still too coarse to describe the characteristics of the basins accurately. The positive results obtained have paved the way for regionalizing the parameters of the STREAM model to make it applicable on a global scale. Through the calibration of the STREAM model across the 40 pilot catchments, it was possible to obtain a large set of parameters that were linked, through specific relationships, to various features including climate, soil characteristics, vegetation and topographic attributes. This approach yielded regionalized STREAM parameters. This study aims to evaluate the efficacy of the STREAM runoff and river discharge estimates, derived from regionalized parameters, across a diverse range of basins. To this end, a comparative analysis will be conducted between observed and simulated river discharge, as well as between simulated and modeled land surface runoff estimates. This work aims to highlight how the use of readily available data, analyzed using a conceptual regionalized hydrological model, can improve the estimation of river discharge and the development of runoff maps, even in basins where complex interactions between natural processes and human activities prevail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".