Toward a global scale runoff estimation through satellite observations: the STREAM model 
Notice bibliographique
Résumé
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.
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 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».