Tokyo Institute of Technology · Environmental Science
Professor Wenpeng Zhao's research lab specializes in climate change impacts on extreme hydrological events, with a focus on high-resolution modeling and analysis of extreme rainfall, flood risk, and water quality dynamics. The lab integrates advanced climate reanalysis data, deep learning techniques, and in-situ monitoring to improve the understanding of temporal and spatial variability of precipitation under global warming. Key research directions include intensity-duration-area-frequency (IDAF) curve development, rainfall disaggregation for sub-daily extremes, and real-time water quality monitoring using UV-Vis spectroscopy.
Figures are computed from collected data and may differ slightly.
Abstract Using potentially best available rainfall data sets for the entire country of Japan (spatial scales of 1‐ and 20‐km), we analyze the 1–24 hr and city‐scale (1–400 km 2 ) extreme rainfalls for both current (2006–2020) and future periods (2081–2109) at 1.5 K global warming scenario, complementing previous work that focuses on either coarse spatial and temporal scales or other warming scenarios (e.g., RCP and 2 K warming scenarios). A peak‐over‐threshold (POT)‐based approach is applied to
River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet-visible (UV-Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this,
Extreme rainfall-induced events adversely affect agriculture, infrastructure, and socioeconomic development in a region. Therefore, a comprehensive understanding of their occurrences and past and future variability in the context of global warming is imperative, especially at the fine temporal (sub-daily) and spatial (local to regional) scales for better contextualizing inferences from a policymaking perspective. This study provides a detailed analysis of global warming’s impacts on extreme rain
This study investigates anomalous precipitation patterns in the Taihu Basin, located in the Yangtze River Delta of eastern China, using high-resolution daily data from 1960 to 2019. Leveraging a deep learning autoencoder and self-organizing map, three spatially distinct types are identified—north type (72%), south type (19.7%), and center type (8.3%). The north type exhibits a pronounced upward trend (+0.11 days/year, p < 0.05), indicating intensifying extreme rainfall under climate warming,
Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]A Rainfall Disaggregation Scheme for Generating Fine Time-scale Extreme Rainfall under Climate ChangeAuthorsWenpengZhaoiDTsuyoshiKinouchiiDSee all authors Wenpeng ZhaoiDCorresponding Author• Submitting AuthorTokyo Institute of TechnologyiDhttps://orcid.org/0000-0002-4850-2291view email addressThe email was not providedcopy email addressTsuyoshi KinouchiiDTokyo Institute of TechnologyiD
Abstract Anomalous precipitation, with unexpected intensity and/or spatiotemporal structures, makes flood risk management in the East Asian monsoon regions challenging, where interacting circulation systems generate highly variable precipitation events. We develop an event‐level quantitative framework based on a novel lightweight Trans‐Unet model and apply it to the Hanjiang River Basin as proof‐of‐concept using a 59‐year, 1‐km daily precipitation data set. The framework reveals four patterns: W
Attached are the rainfall return levels based on peaks-over-threshold.
Attached are the rainfall return levels based on peaks-over-threshold.
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