Computationally Efficient, Regional Scale Flood Inundation Modelling

The Rapid Flood Spreading Method (RFSM) has been developed and refined over the past decade to facilitate the type of large-scale and regional probabilistic simulations required by Governments and Industry for strategic planning purposes. The latest version of the model (RFSM-EDA) provides a step change in performance over current techniques, and has previously been shown to provide comparable accuracy relative to conventional 2D models, at significantly faster computational speeds, for both small and medium-large scale applications. This paper illustrates the use of RDSM-EDA to a regional scale flood event, namely the northern avulsion element of the 2010 River Indus flooding in Pakistan. The 2010 Indus floods led to ~2000 fatalities and displaced some 20 million people from their homes, with the northern avulsion alone inundating ~8,000 km2 of agricultural land to depths of 1–3 m. Although, the extent of the flooding means that there is a wealth of, primarily remote sensed, data related to the floods, the accuracy of some of the key data is shown to be questionable. The results from a range of different flood inundation models are used in conjunction with observed flood extents, to determine the performance of RFSM-EDA. The overall results again highlight the importance of both accurate topographic data and appropriate DEM sampling techniques, with all model types producing poor results when used in conjunction with “noisy” DEM data and simplistic sampling strategies. Notwithstanding, RFSM-EDA is shown to provide comparable accuracy relative to a selection of conventional 2D models, at a fraction of the computational runtime. This indicates that, given appropriate topographic data, RFSM-EDA is suitable for regional scale flood inundation modeling, and thus has clear application to national flood risk assessment and real-time flood prediction. Recommendations for future work include further simulations of the River Indus flooding, including the use of alternative topographical data and follow-up sensitivity analysis, as well as refinements to the RFSM-EDA pre-processing to help deal with poor topographic data inputs