New Urban Flash Flood Warning System Combines Radar-Based Nowcasts and Surface Flow Model

With the changing climate, urban flash floods are likely to become more common and societies should be prepared for these events more than ever before. Heavy rainfall and insufficient drainage systems are the main contributors to the overflow of water on streets. Thus, information of meteorological and hydrological conditions are needed to be able to alert the public of urban flood events beforehand.

A new urban flash flood warning system was developed and piloted real-time in the framework of project HULEHENRI. The system consists of three main parts: 1) Weather radar based precipitation nowcasts are used as an input to 2) urban rainfall-runoff model, which simulates the changing water level on city streets. These flood forecasts are then 3) delivered to customers via web interface.

Good accuracy of radar based precipitation nowcasts is a key to accurate flood forecasts. To achieve best possible results multiple improvements were made to algorithms used to calculate the radar-based quantitative precipitation estimation (QPE) during this project. These improved both the reliability and time resolution of the estimates and included for example rain-gauge correction, attenuation correction and advection correction. For nowcasting we chose a model specifically tailored for urban scale precipitation nowcasting, the LINDA model, which is available via the Pysteps Python library.

The flood caused by rain has been calculated on a 2-meter grid using a surface flow model. Rain observations and forecasts based on weather radar data are available every five minutes in a 500 meter grid. This rainfall data is converted to a 2 m grid for use in the surface flow model. In addition to the digital elevation model (DEM), land use and soil data are used in the calculation. Infiltration into the soil is calculated from the soil data using the Green-Ampt method, and accurate land use data (2x2m grid) is used to determine the friction coefficient (Manning n). The main water drainage pipes are included in the model, but not the entire drainage system. Road network data is used to define road sections that may be flooded during the forecast period.

Development of the urban flood warning system was user-centered. Pilot customers, including for example public emergency services and logistics companies, were involved in the development process throughout the project. In this work we discuss the customers’ input and needs and the way we addressed them and the development that was made to each three parts of the system. We also show verification results during urban flash flood events that occurred during the pilot period and discuss possible changes that could be made to improve the warning system even further.