Blending Subseasonal-to-Seasonal Hydrological Predictions from Multiple Forecasting Systems

Given the increasing vulnerability due to more frequent and severe hydrological hazards under a changing climate, it is imperative to develop accurate and reliable global and local hydrological prediction systems at subseasonal-to-seasonal (S2S) timescales. However, operational forecasts tailored to specific local regions remain limited due to lack of both local observations and regional hydrological models, while global hydrological models often lack calibration for local conditions, making it challenging for them to capture local-scale dynamics. Additionally, users and decision-makers face difficulties in effectively interpreting ensemble forecasts from multiple hydrological models in operational settings especially when compared to the simplicity and clarity of a single model approach. To bridge this gap, it is essential to integrate existing hydrological forecasting systems across global, regional, and local scales, with the goal of delivering skilful, standardized, and comprehensible predictions. As part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we are exploring various approaches to: (i) validate and enhance the skill of current hydrological probabilistic forecasts, and (ii) blend multi-model ensemble simulations to develop integrated and reliable operational forecasts. Here, we aim to develop a framework for bias-correcting and blending global multi-model ensemble forecasts, based on the skill of each modelling system for each catchment, to deliver unique probabilistic forecasts. Our research, using global hindcasts from various modelling systems, has demonstrated that applying this framework to post-process raw model simulations can deliver reliable S2S hydrological forecasts across diverse global catchments operationally. This approach has the potential for improved water resource management and hydrological hazard mitigation, particularly in data-sparse regions.