A computationally efficient early warning technique is developed for forecasting flash floods during the pre-monsoon season that are associated with a complex topography and transboundary runoff in northeastern Bangladesh. Locally conditioned topographic and hydrometeorological observations are key forcings to the modeling system that simulate the hydrology and hydraulic processes. The hydrologic model is calibrated and validated using satellite-based observations to estimate the correct amount of transboundary and mountainous inflow into the flash flood-prone plains. Inflow is then forecasted using precipitation forecast from a global numerical weather prediction (NWP) system called the Global Forecasting System (GFS). The forecasted inflows serve as the upstream boundary conditions for the hydrodynamic model to forecast the water stage and inundation downstream in the floodplains. A real-time in-situ data-based error correction methodology is applied to maintain the skill of the system. The simulation grid size and time-step of the hydrodynamic model are also optimized for computational efficiency. Historical performance of the framework revealed at least 60% accuracy at 5-day lead-time in delineating flood inundation when compared against Sentinel-1 synthetic aperture radar (SAR) imagery. The study suggests that higher resolution topographic information and dynamically downscaled meteorological observations can lead to significant improvement in flash flood forecasting skills.

开发了一种计算有效的预警技术，用于预测季风前季节期间的暴洪，这与孟加拉国东北部复杂的地形和跨界径流有关。局部条件的地形和水文气象观测是模拟水文和水力过程的建模系统的关键要素。使用基于卫星的观测数据对水文模型进行校准和验证，以估算向易发洪灾平原提供的跨界和山区入流的正确数量。然后，使用来自称为全球预报系统（GFS）的全球数值天气预报（NWP）系统的降水预报来预测流入量。预测的入水量是水动力模型的上游边界条件，用于预测洪泛区下游的水位和淹没。实时的基于现场数据的纠错方法适用于维护系统的技能。流体动力学模型的仿真网格大小和时间步长也进行了优化，以提高计算效率。与Sentinel-1合成孔径雷达（SAR）图像相比，该框架的历史性能表明，在5天的交货周期内至少有60％的精度可用于描述洪水泛滥。研究表明，更高分辨率的地形信息和动态缩小的气象观测值可以显着提高山洪预报技能。