资源环境科技发展态势分析平台

Recent unprecedented events have highlighted that the existing approach to managing flood risk is inadequate for complex urban systems because of its overreliance on simplistic methods at coarse-resolution large scales, lack of model physicality using loose hydrologic-hydraulic coupling, and absence of urban water infrastructure at large scales. Distributed models are a potential alternative as they can capture the complex nature of these events through simultaneous tracking of hydrologic and hydrodynamic processes. However, their application to large-scale flood mapping and forecasting remains challenging without compromising on spatiotemporal resolution, spatial scale, model accuracy, and local-scale hydrodynamics. Therefore, it is essential to develop techniques that can address these issues in urban systems while maximizing computational efficiency and maintaining accuracy at large scales. This study presents a physically based but computationally efficient approach for large-scale (area > 10(3) km(2)) flood modeling of extreme events using a distributed model called Interconnected Channel and Pond Routing. The performance of the proposed approach is compared with a hyperresolution-fixed-mesh model at 60-m resolution. Application of the proposed approach reduces the number of computational elements by 80% and the simulation time for Hurricane Harvey by approximately 4.5 times when compared to the fixed-resolution model. The results show that the proposed approach can simulate the flood stages and depths across multiple gages with a high accuracy (R-2 > 0.8). Comparison with Federal Emergency Management Agency building damage assessment data shows a correlation greater than 95% in predicting spatially distributed flooded locations. Finally, the proposed approach can estimate flood stages directly from rainfall for ungaged streams.

Plain Language Summary Climate change and land development or urbanization is expected to exacerbate both the intensity and frequency of extreme flooding worldwide. As the flood severity rises, there is a growing need to develop flood prediction and alert systems that provide fast and reliable forecasts. Currently, it is extremely difficult to identify how much, when, and where the flooding will occur, which can create uncertainty in evacuation planning and preparation. This study proposes a method to improve the urban flood prediction by incorporating more physicality into the numerical flood models, which enables a better estimation of the depth, location, and arrival time of flooding. The graphical elements used to construct the models result in a better representation of the real-world physical features, thereby improving the accuracy of flood simulation. Moreover, the approach presented here also decreases the computation time required to simulate flooding, which is vital for providing timely forecasts. The proposed methods are tested across a large and complex urban system using the rainfall from Hurricane Harvey (2017) and validated using three additional flood events in Texas, United States.