Wenyue Zou (邹文月)

PhD student
Hydrometeorology & Storm and flood risk & Climate extreme
University of Lausanne (UNIL)

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I am a 4th-year PhD student at the Hydrometeorology and Surface Processes group , University of Lausanne (UNIL), supervised by Nadav Peleg. During the final year of my PhD, I am a visiting researcher at the Chair of Hydrology and Water Resources Management, ETH Zurich. My research work focuses on spatial interpolation and downscaling of high-resolution precipitation datasets, future changes in storm fields and regional rainfall extremes (e.g., intensity-duration-frequency curves), and assessing how alterd rainfall extremes influence future urban and regional flooding. The aims of my research are to contribute to our understanding of how storms evolve under climate warming and to predict future urban and regional flood hazards, by integrating both statistical/machine learning and physical modelling approaches. This work seeks to provide comprehensive assessment of storm and flood risk and to help mitigate the damages associated with climate change in urban environments.

Can we project how observed rainfall fields will change with climate warming? A novel method - the gamma-based spatial quantile mapping (GSQM) - has been introduced to morph observed gridded rainfall fileds based solely on temperature shift, without relying on high-resolution climate models. A case study demonstrates how sub-daily storm in Beijing, China, may become more intense and localized with climate warming.

By combining the GSQM approach with a storm stochastic transposition method (SST) (Daniel B. Wright, 2020 ), future changes in regional rainfall extremes (rainfall intensity-duration-frequency curves) can be efficiently estimated. The GSQM-SST aproach provides a computationally effcient and physically informed tool for climate adaptation and flood risk assessment.

A 2-dimensional precipitation downscaling method is suggested by using the multiple-point geostatistics (MPS) model ( Gravey and Mariethoz, 2020 ) with a precipitation coefficient of variation adjustment. We demonstrate how the MPS framework can be used to downscale the 20 years of satellite CMORPH rainfall from 8 to 1 km resolution, taking the city of Beijing as a case study, by using 6 years of CMPAS radar data. The MPS-based downscaling approach can be easily modified and applied to other rainfall products and regions.

Six interpolation methods including Inverse Distance Weighted (IDW), Ordinary Kriging (OK), Kriging with External Drift (KED) assisted by different covariables and Empirical Bayesian Kriging (EBK) have been evaluated, by using hourly precipitation at 232 stations in Haihe River basin in China. We found that KED with annual precipitation outperformed for interpolation of hourly extremes, and the return level maps of 2- to 100-year were generated.

• Excellent Master Thesis in Beijing Normal University, 2021
• Academic Scholarship in Beijing Normal University (first class, twice), 2018-2019

Last updated: June 2025

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