Advancing the UK Hydrological Outlook using skill-based forecast blending

The UK Hydrological Outlook (UKHO) provides sub-seasonal to seasonal forecasts of river flows and groundwater levels, providing insight into possible future hydrological conditions over the UK (https://ukho.ceh.ac.uk/). In 2012 the extreme drought–flood transition highlighted the need for a proactive anticipatory system to support better water resource management, and led to development of the UKHO. Since then the UKHO has evolved to provide ensemble forecasts encompassing multiple methods, including Ensemble Streamflow Prediction (ESP) and Historical Weather Analogues (HWA), applied at a daily time-step for multiple lead-times using the catchment-based airGR model (GR6J) and the grid-based Grid-to-Grid/Water Balance Model (G2G-WBM). Work is ongoing to extend the suite of models to include additional catchment models that have previously been successfully applied to UK catchments (e.g., Hydrologiska Byråns Vattenbalansavdelning, HBV and the Probability Distributed Model, PDM). However, use of multiple ensemble methods and models can make it challenging for users and decision-makers to interpret their probabilistic forecasts effectively, especially when compared to the simplicity of a single deterministic forecast. To address this challenge, it is essential to integrate these diverse procedures to deliver skilful, standardized, and easy-to-interpret forecasts. Here, we aim to advance the UKHO by first applying bias correction and then blending ensemble forecasts based on the skill of each method and model for individual catchments at different lead times, to produce consolidated probabilistic predictions that can be utilised more simply. We evaluate several blending techniques designed for probabilistic forecasts, combining the individual strengths of different methods and models while preserving the ensemble spread, which is essential for representing forecast uncertainty. This approach will inform water resource management and support hydrological hazard mitigation by delivering forecasts that are both comprehensive and easy to understand and use for operational decision-making.