Harvester Season_2

A service supporting the forestry sector for climate-smart operations planning.

Sponsor	Project	Solution provider	User

Forestry
Land
Forests, Environment & Climate
Atmosphere & Climate

Metsäteho Oy is a limited company owned by the leading forest industry organisations and companies of Finland and is specialised in research and development (R&D) work and projects.

Metsäteho supports the development of its shareholders’ wood procurement and wood production operations and improves the operating preconditions for wood supply.

With this background, Metsäteho provides for Harvester Seasons contacts to leading forest companies and customers in Finland and Europe.

Harvester Seasons, developed by the Finnish Meteorological Institute, is a web application supporting the Finnish forestry sector by offering high-resolution soil trafficability, seasonal forecast, forest fire index and carbon emission information system. Harvester Seasons has been co-designed with the stakeholders from the Finnish forestry sector and tailored especially to their needs.

The trafficability service combines ALS Airborne Laser Scanning data by Finnish Forest Centre with FMI’s weather forecast as well as seasonal forecast and climatological conditions from Copernicus. The service provides information on forest fire risks. Giving guidelines to the forestry sector about the impacts of deforestation, clear-cutting and optimized forest management with respect to the forest’s carbon cycle helps additionally for sustainable operation planning.

Decreasing the vulnerability of energy companies to variations in meteorological and hydrological conditions through improved seasonal forecast products is the main expected impact of the project.
End-user tailored products on hydrological conditions will be disseminated to the key end user; Kemijoki Oy.

Currently modelling and forecasting of snowmelt timing and melt rate uncertainties stem from uncertainties in model forcing data. The lack of widely available and reliable forcing data restricts wide spread application of more complex models, particularly in operational stream flow prediction systems. EO based snow state ingestion and communication with end users will be used to address these limitations.
The methodology for determining snow conditions using coarse resolution EO data (for hydropower optimization) is already available. The main objective is to derive higher resolution and higher quality products, to improve timeliness and information content.
Providing accurate snow cover information from satellite observations and forecast products requires well-coordinated collaboration between the developers and the end users. All information needs to be disseminated in plain language, with special focus on communicating uncertainties to ensure that no information is "lost in translation".

Successful assimilation of EO-based SWE data in improving spring snowmelt driven runoff peak timing and volume forecasts.
Successful communication of uncertainties relating to long term seasonal ensemble forecasting.
Successful implementation of machine learning based new streamflow forecasting methods.
Successful integration of hydrometeorological data from multiple independent sources.
Useful overall reduction of official forecast uncertainties through auxiliary information dissemination.

Learn more about the service: https://hops.fmi.fi/

Learn more about e-shape: www.e-shape.eu

A question? Contact the Helpdesk: https://helpdesk.e-shape.eu

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