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id	353386303

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id	353386302

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name	Summary
id	18566975

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

Sponsor	Project	Solution provider	User

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name	Taxonomy
id	353386233

Forestry
Land
Forests, Environment & Climate
Atmosphere & Climate

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name	User profile
id	353386247

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.

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name	Service description
id	353386263

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.

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name	Customer experience
id	353386277

The data presented in the webservice is used for hydropower production optimization and assessment of modelling and monitoring uncertainties and as such has been seen as useful auxiliary information. Since FMI is not at this stage legally able to provide direct guidance on operational activities, the pilot is considered as an auxiliary service for new technologies and methods for forecast production and for combining relevant data from multiple sources into a single web service. Combining and displaying data from different sources, is seen as very significant in assessing forecasting uncertainties. Also, new promising methods for streamflow forecasting such as the operational usage of machine learning techniques has been added to the service during 2021.Image Added

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name	Need
id	625990301

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.

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name	Challenges
id	1956087046

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".

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name	Results
id	1956106819

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.

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name	References
id	1956123861

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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