L2M NSERC - Advancing urban flood prediction under heavy rainfall

在气候变化的背景下，提高城市抗洪能力已成为全球决策者、城市规划者和工程从业者面临的主要挑战之一。强降水条件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。据观察，随着国家和全球洪水事件的增加，金融部门正在大力投资于洪水预测工作。虽然有几种传统的水文模型，但由于无法估计洪水的范围和深度，它们不能直接应用于洪水模拟。我们提出了一个名为“FloodMapper”的新模型，用于在强降水事件下精确计算城市洪水。在我们的模型中，我们将研究区域划分为许多网格单元，在建模的每个步骤中都涉及水平和垂直流动。我们利用地表高度和瞬时地表水深来确定每个网格单元在建模的每个时间步长的水平入流和流出方向；这使我们能够捕捉到逆流现象。虽然我们的模型相对较新，但我们已经在2018年测试并在市场上发布了免费版本。我们成功地使用该模型重现了路易斯安那州拉斐特教区2016年的洪水，以证明其在预测现实世界洪水事件中洪水范围和深度方面的有效性。通过L2M项目的前沿市场培训，我们希望对我们的市场有更好的了解，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.