Tj Fewtrell

A comparison of three parallelisation methods for 2D flood inundation models

For many applications two-dimensional hydraulic models are time intensive to run due to their computational requirements, which can adversely affect the progress of both research and industry modelling projects. Computational time can be reduced by running a model in parallel over multiple cores. However, there are many parallelisation methods and these differ in terms of difficulty of implementation, suitability for particular codes and parallel efficiency. This study compares three parallelisation methods based on OpenMP, message passing and specialised accelerator cards. The parallel implementations of the codes were required to produce near identical results to a serial version for two urban inundation test cases. OpenMP was considered the easiest method to develop and produced similar speedups (of ~3.9x) to the message passing code on up to four cores for a fully wet domain. The message passing code was more efficient than OpenMP, and remained over 90% efficient on up to 50 cores for a completely wet domain. All parallel codes were less efficient for a partially wet domain test case. The accelerator card code was faster and more power efficient than the standard code on a single core for a fully wet domain, but was subject to longer development time (2 months compared to <2 week for the other methods).

The credibility challenge for global fluvial flood risk analysis

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30%–40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections.