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R is an open-source, versatile programming language that is suitable for multi-scale analyses from just a few observations to big data and high-performance computing. It has a growing, enthusiastic user-base (including hydrologists) that is responsible for a continuous stream of ever more efficient and useful packages and workflows.

Running for its fourth consecutive year, this EGU short course, co-organised by the Young Hydrologic Society (younghs.com), will introduce and showcase a selection of both core and recently developed R packages that can be applied to data analyses in hydrology, as well as other scientific disciplines.

The course will be delivered by hydrologists with wide experience in subjects including: hydrological modelling (including flood and drought analysis), forecasting, statistics, and eco-hydrology. Topics covered in this years’ course include:
• Data retrieval
• Extremes modelling
• Hydrological modelling
• Hydrological forecasting
• Machine learning
• Open discussion and QA

This course contributes new topics to those delivered in previous years, building upon the openly accessible Github repository for hydrologists using R in their work (https://github.com/hydrosoc).

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Co-organized by HS11
Convener: Katie SmithECSECS | Co-conveners: Lucy Barker, Ilaria Prosdocimi, Louise SlaterECSECS, Guillaume Thirel
R is an open-source, versatile programming language that is suitable for multi-scale analyses from just a few observations to big data and high-performance computing. It has a growing, enthusiastic user-base (including hydrologists) that is responsible for a continuous stream of ever more efficient and useful packages and workflows.

Running for its fourth consecutive year, this EGU short course, co-organised by the Young Hydrologic Society (younghs.com), will introduce and showcase a selection of both core and recently developed R packages that can be applied to data analyses in hydrology, as well as other scientific disciplines.

The course will be delivered by hydrologists with wide experience in subjects including: hydrological modelling (including flood and drought analysis), forecasting, statistics, and eco-hydrology. Topics covered in this years’ course include:
• Data retrieval
• Extremes modelling
• Hydrological modelling
• Hydrological forecasting
• Machine learning
• Open discussion and QA

This course contributes new topics to those delivered in previous years, building upon the openly accessible Github repository for hydrologists using R in their work (https://github.com/hydrosoc).