Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Vazken Andréassian"'
Autor:
Paul Royer-Gaspard, François Bourgin, Charles Perrin, Vazken Andréassian, Alban De Lavenne, Guillaume Thirel, François Tilmant
Publikováno v:
LHB Hydroscience Journal (2024)
An hourly hydrological forecasting model (GRP), used for flood forecasting in France, has been enhanced by developing a semi-distributed version (GRPS). This new model addresses some limitations of the original lumped approach by integrating flow obs
Externí odkaz:
https://doaj.org/article/9d965f6525b64432bb7144d76bceecbb
Publikováno v:
LHB Hydroscience Journal (2024)
ABSTRACTWe compared the flood forecasts issued by a model used by operational services in France (GRP) and by a model developed to improve the simulation of floods resulting from intense rainfall (GR5H_RI). We selected 10,652 flood events from 19 yea
Externí odkaz:
https://doaj.org/article/1c9493da3e294fe391d4ade5eb69b2cc
Publikováno v:
Sciences, Eaux & Territoires, Iss 42 (2023)
Cette note présente l’outil de visualisation cartographique ProfHyl, qui propose une représentation originale du débit de référence des cours d’eau sous forme de profils en long, assurant une cohérence amont-aval explicite des valeurs de d
Externí odkaz:
https://doaj.org/article/7e466f71a77644a6a4a103033ac3d645
Publikováno v:
LHB Hydroscience Journal (2022)
Le secteur hydroélectrique est sensible aux variables climatiques qui affectent la production et la consommation d'énergie. Des services climatiques se développent et fournissent des renseignements sur les variations hydroclimatiques pour différe
Externí odkaz:
https://doaj.org/article/cdc9db7c7fcf4afaa3fa360f67015840
Publikováno v:
Comptes Rendus. Géoscience. 355:1-25
Autor:
Antoine Pelletier, Vazken Andréassian
Publikováno v:
Comptes Rendus. Géoscience. 355:1-11
Autor:
Vazken Andréassian
Publikováno v:
Comptes Rendus. Géoscience. 355:1-9
Autor:
Nicolas Weaver, Taha-Abderrahman El-Ouahabi, Thibault Hallouin, François Bourgin, Charles Perrin, Vazken Andréassian
Machine learning models have recently gained popularity in hydrological modelling at the catchment scale, fuelled by the increasing availability of large-sample data sets and the increasing accessibility of deep learning frameworks, computing environ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::93cfd693c97401e6237c2e64a1610450
https://doi.org/10.5194/egusphere-egu23-5199
https://doi.org/10.5194/egusphere-egu23-5199
Whether they refer to it as validation, verification, or evaluation, hydrological practitioners regularly need to compute performance metrics to measure the differences between observed and simulated/predicted streamflow time series. While the metric
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9b6783cbf957ca0b9a03912cb13ec70
https://doi.org/10.5194/egusphere-egu23-5735
https://doi.org/10.5194/egusphere-egu23-5735
Models based on StorAge Selection (SAS) functions are useful tools for understanding of factors controlling transit time distribution (TTD) and catchment-scale solute export. SAS functions describe the sampling of different ages in catchment storage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b64280fca8f7883cbf9462ed8af1c27b
https://doi.org/10.5194/egusphere-egu23-14684
https://doi.org/10.5194/egusphere-egu23-14684