Autonomous Robots for a Green and Sustainable Nordic Region (AUTONOMIC)

Current methods for monitoring land use and its environmental impacts are largely based on approaches developed decades ago. They are often highly labor-intensive and costly, and compromises must frequently be made regarding spatial and temporal resolution as well as available resources, which reduces the overall quality of the data. Such methods include, for example, water quality and soil monitoring.

Digitalization is creating fundamental opportunities to modernize monitoring practices, improve cost-effectiveness, and enable novel ways of utilizing and integrating data. This project aims to investigate the feasibility of new robotics-based automated sampling methods in headwater catchments in northern Finland and northern Sweden. The focus is on quantifying nutrient and heavy metal transport into soils and waters using automated sampling - together with project partners - with results compared against standard samples collected from the same sites.

In Finland, the study areas include two headwater catchments in Enontekiö and Kittilä, which form part of the national monitoring network for forestry-related water impacts. Existing water quality and runoff data from these sites will be used as reference information in the project.

The project will also produce so-called digital twins of the catchments—digital models that allow improved prediction of events such as droughts and floods, forest fire risk, greenhouse gas emissions, waterborne nutrient and sediment loads, and the impacts of climate change. The development of the digital twins will build on catchment-scale models of greenhouse gas emissions and waterborne loading generated with the hydrological modelling under different climate scenarios.