L2M NSERC - Advancing urban flood prediction under heavy rainfall

提高城市在气候变化下抵御洪水的能力已成为世界各地决策者、城市规划者和工程实践者面临的主要挑战之一。对强降水下城市洪水的准确预测对于应对这一挑战至关重要，因为它可以帮助了解城市对未来气候变化的脆弱性，并模拟各种可持续工程技术在实际城市环境中降低城市洪水风险的有效性。人们注意到，随着全国和全球洪灾事件的增加，金融部门正在对洪灾预测工作进行大量投资。有几个常规的水文模型可用，但它们不能直接用于洪水模拟，因为它们不能估计洪水的范围和深度。提出了一种新的强降水条件下城市洪涝灾害预报模型“洪水映射器”。在我们的模型中，我们将研究区域划分为许多网格单元，在建模的每一步中都涉及到水平和垂直流动。我们使用地表高度和瞬时地表水深来确定每个网格单元在建模过程中的水平流入和流出方向，这使我们能够捕捉到逆流现象。虽然我们的机型比较新，但我们已经在2018年测试并在市场上发布了它的免费版本。我们使用该模型成功地再现了路易斯安那州拉斐特教区2016年的洪水，以展示其在真实洪水事件中预测洪水范围和深度的有效性。通过L2M的前沿市场培训计划，我们希望能够更好地了解我们的市场，并为未来的步骤做好准备。

Increasing city resilience to floods under climate change has become one of the major challenges for decision-makers, urban planners, and engineering practitioners around the world. Accurate prediction of urban floods under heavy precipitation is critically important to address such a challenge as it can help understand the vulnerability of a city to future climate change and simulate the effectiveness of various sustainable engineering techniques in reducing urban flooding risks in real urban settings. It has been observed that the financial sectors are investing significantly in flood prediction efforts with the increase in flood events nationally and globally. Several conventional hydrological models are available, but they cannot be applied directly for flood simulation because they are incapable of estimating flood extent and depth. We proposed a new model named 'FloodMapper' for accurate urban flooding under heavy precipitation events. In our model, we divide the study area into many grid cells, involving both horizontal and vertical flow together in each step of the modelling. We use both the surface height and instantaneous surface water depth to determine the directions of horizontal inflow and outflow for each grid cell during each time step of modelling; this enables us to capture the reverse-flow phenomenon. Although our model is comparatively new, we tested and released its free version in the market already in 2018. We used the model successfully to reproduce the 2016 flood in Lafayette Parish in Louisiana to demonstrate its effectiveness in predicting both flood extent and depth during real-world flooding events. With the cutting-edge market training by L2M program, we hope to achieve a better understanding of our market and the preparations needed for the future steps.