In the face of increasing uncertainties and the expected rise in flood events worldwide, this technical note presents an innovative approach for enhancing rapid response capabilities, exemplified during the Kakhovka Dam breach in Ukraine. Utilizing the Tygron Model, our methodology combines open-source data with high performance computing for swift hydrodynamic simulations—a departure from traditional flood risk management techniques. Central to this approach is the strategic use of social media to gather crowd-sourced feedback during the emergency, enhancing the precision and relevance of flood risk information.

During the Kakhovka Dam event, our model processed extensive datasets, enabling effective predictions of flood impacts, including extents, velocities, depths, and arrival times. The near real-time modeling capability allowed for dynamic updates using social media inputs, which were of value for emergency responders to optimize response strategies for relief coordination.

While the underlying technology is used for flood simulations, its application in emergency response is novel and promising for more adequate disaster response coordination. However, further research and applications are necessary to refine the approach that can ensure real-time flood risk information during a emergency situations.@en

The accuracy, stability and computational efficiency of numerical methods on central processing units (CPUs) for the depth-averaged shallow water equations were well covered in the literature. A large number of these methods were already developed and compared. However, on graphics processing units (GPUs), such comparisons are relatively scarce. In this paper, we present the results of comparing two time-integration methods for the shallow water equations on structured grids. An explicit and a semi-implicit time integration method were considered. For the semi-implicit method, the performance of several iterative solvers was compared. The implementation of the semi-implicit method on a GPU in this study was a novel approach for the shallow water equations. This also holds for the repeated red black (RRB) solver that was found to be very efficient on a GPU. Additionally, the results of both methods were compared with several CPU-based software systems for the shallow water flows on structured grids. On a GPU, the simulations were 25 to 75 times faster than on a CPU. Theory predicts an explicit method to be best suited for a GPU due to the higher level of inherent parallelism. It was found that both the explicit and the semi-implicit methods ran efficiently on a GPU. For very shallow applications, the explicit method was preferred because the stability condition on the time step was not very restrictive. However, for deep water applications, we expect the semi-implicit method to be preferred.@en