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Renaud Hostache

Dr. Renaud Hostache

Department 'Environmental Research and Innovation' (ERIN), Luxembourg Institute ...

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Renaud Hostache received his M.Sc. in mechanics of geophysical media and environment from Grenoble Alpes Université, France, and his M.E. degree in hydraulics and environmental sciences from the ENSE3, France. He received his Ph.D. in Water Sciences from AgroParisTec, Montpellier, France in 2006. From 2007 to 2014, he was a researcher at the Centre de Recherche Public Gabriel Lippman, Belvaux, Luxembourg. From 2015 to 2021, he was a Senior Research and Technology Associate with the Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg. Since 2022, he has been a senior research fellow with the French National Research Institute for Sustainable Development (IRD). His main research interests include the exploitation of satellite Earth observation for flood, drought, and surface water monitoring and the assimilation of remote sensing observations into flood and drought models for reducing prediction uncertainty.

Research Keywords & Expertise

Data Assimilation
Remote Sensing
SAR
Hydrological Modelling
Flood Mapping

Short Biography

Renaud Hostache received his M.Sc. in mechanics of geophysical media and environment from Grenoble Alpes Université, France, and his M.E. degree in hydraulics and environmental sciences from the ENSE3, France. He received his Ph.D. in Water Sciences from AgroParisTec, Montpellier, France in 2006. From 2007 to 2014, he was a researcher at the Centre de Recherche Public Gabriel Lippman, Belvaux, Luxembourg. From 2015 to 2021, he was a Senior Research and Technology Associate with the Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg. Since 2022, he has been a senior research fellow with the French National Research Institute for Sustainable Development (IRD). His main research interests include the exploitation of satellite Earth observation for flood, drought, and surface water monitoring and the assimilation of remote sensing observations into flood and drought models for reducing prediction uncertainty.