Streamlining Operational Hydrological Forecasting with pyFlow

Operational hydrological forecasting involves retrieving, accessing, and processing large volumes of data, alongside managing complex workflows with numerous task dependencies. These challenges are amplified in applications such as flood forecasting, where timely and accurate forecasts are critical for disaster preparedness. Without an efficient workflow manager, significant time is spent diagnosing errors and identifying broken links in the forecasting chain. This inefficiency is particularly problematic in flood forecasting, which demands continuous monitoring and frequent forecast updates—sometimes on an hourly basis—to enable prompt decision-making.

Building on insights from projects such as ULYSSES, we have developed operational hydrological forecasting workflows using pyFlow, a high-level language designed for creating object-oriented suites with ecFlow - the workflow management tool developed by ECMWF. The pyFlow allows users to design, maintain, and execute workflows as software, enhancing efficiency and usability.

In this study, we present the application of pyFlow to develop hydrological forecasting chains that generate ensemble hydrological forecasts on a subseasonal timescale. A key example is the HS2S system, operational since 2021, which provides soil moisture forecasts for Germany using ECMWF ensemble extended forecasts and the mesoscale hydrlogic model (mHM). We detail the transition of workflows from traditional cronjobs to pyFlow on the cluster, showcasing the advantages of this approach.

ecFlow offers a powerful combination of features, including a user-friendly graphical interface, the flexibility to run locally, and open-access customization options. These attributes make PyFlow a versatile tool for both research and operational hydrological forecasting applications. By streamlining workflow management, pyFlow enhances user experiences and supports more effective forecasting and decision-making.