Numerical modeling of the intensity and evolution of flood events are
affected by multiple sources of uncertainty such as precipitation and land
surface conditions. To quantify and curb these uncertainties, an ensemble-based
simulation and data assimilation model for pluvial flood inundation is
constructed. The shallow water equation is decoupled in the x and y directions,
and the inertial form of the Saint-Venant equation is chosen to realize fast
computation. The probability distribution of the input and output factors is
described using Monte Carlo samples. Subsequently, a particle filter is
incorporated to enable the assimilation of hydrological observations and
improve prediction accuracy. To achieve high-resolution, real-time ensemble
simulation, heterogeneous computing technologies based on CUDA (compute unified
device architecture) and a distributed storage multi-GPU (graphics processing
unit) system are used. Multiple optimization skills are employed to ensure the
parallel efficiency and scalability of the simulation program. Taking an urban
area of Fuzhou, China as an example, a model with a 3-m spatial resolution and
4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the
parallel calculation of 96 model instances. Under these settings, the ensemble
simulation of a 1-hour hydraulic process takes 2.0 minutes, which achieves a
2680 estimated speedup compared with a single-thread run on CPU. The
calculation results indicate that the particle filter method effectively
constrains simulation uncertainty while providing the confidence intervals of
key hydrological elements such as streamflow, submerged area, and submerged
water depth. The presented approaches show promising capabilities in handling
the uncertainties in flood modeling as well as enhancing prediction efficiency