Operational machine learning aided sub-seasonal forecasting of drought related extremes

As a result of climate change, the frequency and severity of droughts in Switzerland is set to increase, with potentially devastating impacts on the environment, economy, and human health. To help mitigate these risks, the MaLeFiX project is developing interdisciplinary extension to the established www.drought.ch platform that will provide comprehensive four-week multi-hazards forecasts of drought-related extremes (https://www.drought.ch/de/impakt-vorhersagen-malefix/).

Droughts are complex phenomena that have significant implications for many aspects of the environment and human life. Understanding droughts and predicting their impacts is crucial for effective preparation and mitigation. The MaLeFiX project is therefore extending the portfolio of drought predictions to a set of relevant impacts across disciplines.  The newely developed tools provide comprehensive four-week drought forecasts for the whole of Switzerland, integrating advanced models across hydrology, forest fires, glacier balance, aquatic biodiversity, groundwater, and bark beetle dynamics. Utilizing hybrid AI and meteorological data, the platform will deliver accurate and user-friendly information to help policymakers, stakeholders, scientists, and the public make informed decisions.

The reliability of single forecasts decreases significantly the further they look into the future, making accurate predictions beyond one to two weeks challenging. To overcome this, the MaLeFiX platform uses ensemble forecasts. Its advanced models are fed with meteorological data from MeteoSwiss, which provides monthly forecasts with daily temporal resolution twice weekly. Each forecast is repeated 51 times with slight variations in initial conditions, allowing the MaLeFiX platform to estimate the probability of extreme events up to three to four weeks in advance.

Key recent developments:

• AI-Based Models: Two new AI models have been created to assess forest fire risks and calculate water temperature to evaluate the danger of stress to aquatic life forms, enhancing the accuracy of these critical forecasts.
• Model Harmonization: Existing models for hydrology, glacier balance, and bark beetle dynamics have been refined to work seamlessly with the same input data, enabling clear analysis and interpretation of the overall situation and potential exacerbating factors.
• Multi-model ensemble: the traditional distributed hydrological model PREVAH used at WSL model has been complemented with a multi-model system consisting of 11 different lumped models being operated for 87 headwater catchments.

After the harmonization of models the team is currently working on provide users with a comprehensive overview of the overall drought situation by displaying the possible combined impacts of various drought-related processes (e.g., low runoff and high water temperature).