Projects

At the HydroClimEx lab and in collaboration with Environment and Climate Change Canada (ECCC) and the National Research Council (NRC), I am conducting an analysis of precipitation and storm surge in a combined manner. To accomplish this, I utilize reanalysis data (ERA5) and an ensemble of downscaled Global Climate Models (GCMs).

During my postdoctoral work at the HydroClimEx lab, my primary focus has been on investigating various causes of inland flooding in the context of climate change. One of my main tasks involves evaluating the role of internal climate variability in shaping future changes. To achieve this, I employ a Large Ensemble Regional Climate Model (CanRCM4-LE).

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 This research project focuses on the design and resilience of dams and levees in the face of changing climate conditions. It emphasizes the need to consider potential failures and the exacerbation of flooding risks, as well as the importance of incorporating climate change data into flood estimation methods. We proposed a framework that aims to enhance the resilience of dams and levees by providing effective strategies for design and assessment across Canada.

This paper focuses on the impact of different types and resolutions of Digital Elevation Models (DEMs) on the accuracy of flood inundation prediction using a deep learning method. The study uses a 1D convolutional neural network (CNN) trained with synthetic hydrographs and target data obtained from a 2D hydrodynamic model. The study shows that although using coarser resolution data can affect the accuracy of the CNN model, it still offers a practical and effective method for quickly predicting floods compared to traditional hydrodynamic modeling approaches.