Statistical Downscaling

KrigR - A Tool for Statistically Downscaling Climate Reanalysis Data for Ecological Applications

An R Package aimed at end-users of state-of-the-art climate reanalysis data to streamline retrieval, pre-processing, and statistical interpolation of ERA5(-Land) data.

Reconciling high resolution climate datasets using KrigR

Exploration of the usage of KrigR and implications for the wider field of climate data products for the use in Life Science research.

KrigR Workshop - Introduction to the R Package

A short introduction to the KrigR R Package designed to download, aggregate, and statistically downscale state-of-the-art climate data.

Causes and Processes of Dryland Vegetation Memory

Throughout this project, I aim to identify underlying causes - biological and abiotic - to the striking patterns of vegetation memory I identified in a previous project.

KrigR - Downloading and Downscaling of ERA5(-Land) data using R

An `R` package designed for intuitive downloading, aggregating, and statistical downscaling of ERA5(-Land) data.

Vegetation Memory across Global Dryland Regions

Vegetation memory has been proposed as a proxy for ecosystem resilience. Here, I investigate how well this proxy captures processes of vegetation performance.

KrigR - Downscaling State-of-the-Art Climate Data for Macroecologists

A web-based workshop to introduce the `R`-Package `KrigR` which introduces intuitive downloading and downscaling methods for ERA5(-Land) climate reanalysis data to `R`-users.

Intrinsic vegetation memory as a proxy of engineering resilience may be an oversimplification.

A poster about my recent vegetation memory in global dryland research I presented at ISEC 2020.

KrigR - Climate Data for Your Spatial Study

A short introduction talk to my `R`-Package `KrigR` in its early development.

Identifying Ecological Memory Patterns in Drylands Using Remote Sensing and State-of-the-art Climate Reanalysis Products

A presentation about my recent vegetation memory in global dryland research I presented and won some awards for at ISEM 2019.