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.

翻译：洪水事件的强度与演化数值模拟受降水、陆面条件等多重不确定性因素的影响。为量化并降低这些不确定性，构建了基于集合模拟与数据同化的暴雨洪水淹没模型。通过将浅水方程在x与y方向解耦，选取惯性形式的圣维南方程以实现快速计算。采用蒙特卡洛样本描述输入与输出因素的概率分布，并引入粒子滤波同化水文观测数据以提升预测精度。为实现高分辨率实时集合模拟，基于CUDA（统一计算设备架构）与分布式存储多GPU（图形处理器）系统采用异构计算技术，并通过多种优化策略确保模拟程序的并行效率与可扩展性。以中国福州市城区为例，构建了空间分辨率3米、包含400万单元的模型，利用8块Tesla P100 GPU并行计算96个模型实例。在此设置下，1小时水力过程的集合模拟耗时2.0分钟，相较于单线程CPU运行实现约2680倍加速。计算结果表明，粒子滤波方法在提供流量、淹没面积及淹没水深等关键水文要素置信区间的同时，有效约束了模拟不确定性。所提方法在应对洪水模拟不确定性及提升预测效率方面展现出良好潜力。