MaLeFiX - Machine-Learning-aided ForecastIng of drought-related eXtremes

Drought-related extremes are, with ongoing climate change, having severe impacts on the environment, economy and human health in many different parts of the world. Up until now, these impacts have mostly been investigated separately in different research units at WSL. The www.drought.ch platform was developed to inform end-users about the current dryness situation in Switzerland, with a focus on hydro-meteorological aspects. It has recently been supplemented with sub-seasonal forecasts. Researchers at WSL have considerable experience in probabilistic drought forecasting as well as expertise in modelling impacts such as forest fires, glacier alterations, bark beetle infestations and biodiversity, but are less familiar with  the benefits of forcing such impact models with sub-seasonal (monthly) range weather and hydrological forecasts.

WSL’s MaLeFiX (Machine-Learning-aided ForecastIng of drought-related eXtremes) should help to bring together the different disciplines involved by combining diverse models in a user-friendly way using state-of-the-art as well as novel machine-learning methods. It should provide end users with a decision tool that explains in plain language, for example, the advantages of using probabilistic forecasts. 

Social scientists will also be involved in ensuring the tool is user-friendly. It should enable potential stakeholders to find out about drought-related extremes some weeks in advance and thus gain valuable time for preventing or mitigating severe environmental and socio-economic impacts.