With R Hydrology, organizations can, for example, analyze precipitation time series, evaluate runoff models, calculate drought indicators, and use climate data for water management and climate adaptation. In this blended learning course, you will learn how to process and analyze hydrological time series, gridded data, and climate indicators.
R Hydrology focuses on analyzing, modeling, and visualizing hydrological and climatological data using the R programming language. Within Geo-ICT, this is important for issues related to water management, drought, precipitation, runoff, climate adaptation, and spatial water analyses.
With R, hydrological time series, climate data, and raster data can be processed and combined with geographic datasets. Examples include analyzing precipitation series, evaluating runoff models, calculating drought indicators, and visualizing spatial climate patterns. This creates a powerful environment for water management, GIS, and climate-focused Geo-ICT workflows.
What makes R so powerful is the combination of statistics, time series analysis, raster processing, and reproducible workflows. This allows hydrological analyses to be not only performed manually but also automated and repeated for different areas, measurement series, and climate scenarios. Within Geo-ICT, R is increasingly being used for water data, climate data, and spatial analyses related to drought and precipitation.
In this blended learning course, you will work with key packages such as hydroGOF, hydroTSM, rasterVis, climatrends, climate, and SPEI. You will learn to evaluate hydrological models, analyze time series, visualize raster data, process climate indicators, and perform drought analyses.
In addition, R offers extensive capabilities for combining hydrological analyses with GIS data, visualization, statistics, and reporting. This makes this blended learning course particularly relevant for GIS specialists, hydrologists, water managers, climate advisors, researchers, and Geo-ICT professionals who wish to analyze water and climate data in a reproducible manner.
In this blended learning course, you will be introduced to the key capabilities of R for hydrological analysis. You will learn how to process, analyze, and visualize precipitation, runoff, and climate data within reproducible workflows. You will work with packages such as hydroGOF, hydroTSM, rasterVis, climatrends, climate, and SPEI.
Attention is given to hydrological time series, model validation, drought indicators, climate data, and spatial visualization. You will learn how to evaluate runoff models, investigate trends in time series, and analyze drought or precipitation patterns within Geo-ICT projects.
You will also learn how R can be used for climate adaptation and water management. This includes applications for drought monitoring, precipitation analysis, runoff series, area comparisons, and spatial maps of hydrological indicators. You will also discover how raster data and time series can be used together for area-specific analyses.
During the blended learning program, you will work with practical datasets and learn how to set up reproducible hydrological workflows in R. Upon completion, you will be able to independently process and analyze hydrological data, climate data, and drought indicators for GIS, water management, and climate adaptation projects.
Do you already have experience with R Spatial Basics, R Visualization, or R Data Science? Then this blended learning course is a logical next step toward deepening your knowledge of hydrology, climate data, time series analysis, and spatial water analysis within R.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the hydrological fundamentals and their practical application in R. In the online modules, you’ll learn how to process precipitation data, runoff series, climate data, and drought indicators using modern R packages.
You’ll discover how to evaluate hydrological models, analyze time series, and visualize spatial climate and water data. You’ll also learn how to set up reproducible analyses so that results are transparent and repeatable across different areas and projects. Thanks to unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll apply the theory directly to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute hydrological workflows using packages such as hydroGOF, hydroTSM, rasterVis, climatrends, climate, and SPEI.
The combination of online learning and interactive hands-on experience ensures that you not only learn how hydrological data is technically processed, but also how to translate this data into actionable insights for water management and climate adaptation. After completing the blended learning program, you will be able to use R professionally for hydrological analyses, drought monitoring, and spatial climate issues.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of GIS and data analysis is helpful, but extensive experience in hydrology is not required. During the blended learning course, hydrological analyses, climate data, and time series are explained step by step using practical Geo-ICT examples.
During the blended learning program, you will work with precipitation series, discharge data, drought indicators, climate data, hydrological time series, model validation, and spatial hydrological analyses within Geo-ICT workflows.
R is used for drought monitoring, climate adaptation, water management, precipitation analysis, runoff modeling, hydrological modeling, time series analysis, and spatial analysis of water and climate data.
Yes. R integrates seamlessly with hydrological workflows within QGIS. This includes combining R with hydrological analyses, raster processing, digital elevation models, and QGIS plugins for water management, runoff, precipitation, and spatial hydrological modeling. This allows analyses to be better automated and performed in a reproducible manner.
During the blended learning program, you will work with tools such as hydroGOF, hydroTSM, rasterVis, climatrends, climate, and SPEI for hydrological analysis, climate data, time series, model validation, and drought indicators in R.
