Using Temporal Fusion Transformer (TFT) to enhance sub-seasonal drought predictions in the European Alps

In recent years, the European Alpine space has witnessed unprecedented low-flow conditions and drought events, affecting various economic sectors reliant on sufficient water availability, including hydropower production, navigation and transportation, agriculture, and tourism. As a result, there is an increasing need for decision-makers to have early warnings tailored to local low-flow conditions.The EU Copernicus Emergency Management Service (CEMS) European Flood Awareness System (EFAS) has been instrumental in providing flood risk assessments across Europe with up to 15 days of lead time since 2012. Expanding its capabilities, the EFAS also generates long-range hydrological outlooks from sub-seasonal to seasonal horizons. Despite its original flood-centric design, previous investigations have revealed EFASs potential for simulating low-flow events. Building upon this finding, this study aims to leverage EFAS's anticipation capability to enhance the predictability of drought events in Alpine catchments, while providing support to trans-national operational services.In this study, we integrate the 46-day extended-range EFAS forecasts into a hybrid setup for 106 catchments in the European Alps. Many studies have demonstrated Long Short-Term Memory (LSTM)s capacity to produce skillful hydrological forecasts at various time scales. Here we employ the deep learning algorithm Temporal Fusion Transformer (TFT), an algorithm that combines aspects of LSTM networks with the Transformer architecture. The Transformer's attention mechanisms can focus on relevant time steps across longer sequences enabling TFT to capture both local temporal patterns as well as global dependencies. The role of the TFT is to improve the accuracy of low-flow predictions and to understand their spatio-temporal evolution. In addition to EFAS data, we incorporate features such as European weather regime data, streamflow climatology, and hydropower proxies. We also consider catchment characteristic information including glacier coverage and lake proximity. By incorporating its various attention mechanisms, makes TFT a more explainable algorithm than LSTMs, which helps us understand the driving factor for the forecast skill. Our evaluation uses EFAS re-forecast data as the benchmark and measures the reliability of ensemble forecasts using metrics like the Continuous Ranked Probability Skill Score (CRPSS).Preliminary results show that a hybrid approach using the TFT algorithm can reduce the flashiness of EFAS during drought periods in some catchments, thereby improving drought predictability. Our findings will contribute to evaluating the potential of these forecasts for providing valuable information for skillful early warnings and assist in informing regional and local water resource management efforts in their decision-making.