Malefix ¶

Climate change is intensifying droughts in Switzerland, creating interconnected risks for forests, water resources, public health, and biodiversity. Prior to MaLeFiX, drought forecasting was fragmented: environmental impact models operated in isolation, valuable short- to medium-range forecasts were underutilized, and end-users had little influence on system design. A unified, user-oriented early-warning system was needed to translate complex environmental data into actionable information for decision-makers.

Project Description

MaLeFiX addressed this gap by developing an integrated early-warning system that links models for wildfires, glaciers, water temperature, aquatic biodiversity, bark beetle outbreaks and groundwater levels. By combining advanced machine-learning methods with long-term meteorological data, the system delivers reliable, probabilistic forecasts. Crucially, close collaboration with stakeholders from the outset ensured the resulting platform is practical, intuitive, and directly relevant for managing water, forests, and natural hazards.

Key Findings

1. Accurate, reliable and timely information to report on actual and forecasted drought conditions and impacts provides actionable lead time for proactive decision-making.
2. State-of-the-art AI methods and probabilistic forecasting deliver more accurate predictions. Together with the estimates of their uncertainty, they allow for better risk assessment.
3. Easy access to understandable and actionable drought information with a user-centered design ensures that complex data is translated into intuitive tools for authorities and stakeholders.