IFM: A Scalable High Resolution Flood Modeling Framework

Accurate and timely flood forecasts are essential for effective management of flood disasters, which has become increasingly frequent over the last decade. Obtaining such forecasts requires high resolution integrated weather and flood models with computational costs optimized to provide sufficient lead time. Existing overland flood modeling software packages do not readily scale to topography grids of large size and only permit coarse resolution modeling of large regions. In this paper, we present a highly scalable, integrated flood forecasting system called IFM that runs on both shared and distributed memory architectures, effectively allowing the computation of domains with billions of cells. In order to optimize IFM for large areas, we focus on the computationally expensive overland routing engine. We describe a parallelization scheme and novel strategies to partition irregular domains to minimize load imbalance in the presence of memory constraints that results in 40% reduction in time compared to best uniform partitioning. We demonstrate the scalability of the proposed approach for up to 8192 processors on large scale real-world domains. Our model can provide a 48-hour flood forecast on a watershed of 656 million cells in under 5 minutes.