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MaLeFiX - Machine Learning aided ForecastIng of drought related eXtremes


Due to ongoing climate change, drought related extremes will lead to significantly negative ecologically, economically and human health related effects within many different areas, but have been mostly investigated separately within different research units at WSL. Focusing on the hydro-meteorological aspects the platform has been developed for informing end-users about the ongoing dryness situation in Switzerland. Recently this product has been enhanced by implementing sub-seasonal forecasts. Despite the thorough experience of probabilistic drought forecasting as well as expertise in modelling impacts like forest fire, glacier alterations, bark beetle developments and biodiversity at WSL, there is a lack of understanding of the benefits forcing such impact models with sub-seasonal (monthly) range weather and hydrological forecasts. WSL-MaLeFiX should help to close this gap between different disciplines by combining diverse models in a user friendly and optimal way using state-of-the-art and novel machine learning methods. The outcome should provide end users with a decision tool, explained in plain language with the help of social sciences (e.g. the advantages of using probabilistic forecasts), which allow potential stakeholders to be aware of several drought related extremes some weeks in advance and to gain valuable time for avoiding and mitigating severe environmental and socio-economic impacts.