Workflow composition for compound flooding events and adaptation measures

Modelling of compound flood events, the assessment of their impact, and assessing mitigation and adaptation measures is in increasing demand for local authorities and stakeholders to support their decision making. Additionally, the severity of extreme events driving compound flooding, including storms and heavy rainfall, is projected to increase under climate change. To support local communities in flood risk management, complex modelling systems involving multiple cross-disciplinary models need to be orchestrated in order to effectively and efficiently run a wide range of what-if scenarios or historical events to understand the drivers and impacts of compound floods. The large volume and variety of data needed to configure the necessary models and simulate events strain the reproducibility of modelling frameworks, while the number of events and scenarios demand increasingly powerful computing resources. Here we present a solution to these challenges using automated workflows, leveraging the Common Workflow Language standard. The presented workflows update a base model configuration for a user-specified event or scenario, and automatically reruns multiple defined scenarios. The models are executed in containers and dispatched using the StreamFlow workflow manager designed for hybrid computing infrastructures. This solution offers a single, uniform interface for configuring all models involved in the model train, while also offering a single interface for running the model chain locally or on high performance computing infrastructures. The allows researchers to leverage data and computing resources more efficiently and provide them with a larger and more accurate range of compound flood events to support local authorities and stakeholders in their decision making.