Tokyo Institute of Technology · 환경과학
조문팽 교수의 연구실은 기후변화에 따른 극단적 강수의 공간적·시간적 변동성과 그 영향을 고해상도 기상 데이터 및 첨단 분석 기법을 기반으로 연구합니다. 주요 연구 방향은 극한 강우의 빈도·강도 변화 분석, 기후 변화 시나리오 하에서의 강수 분해 기법 개발, 그리고 강수 패턴의 고유한 구조를 식별하기 위한 딥러닝 기반 정량적 프레임워크 구축입니다. 특히 동아시아 monsoon 지역의 복잡한 강수 시스템과 수자원 환경 보존 정책 연계를 위한 실시간 수질 모니터링 기술 개발도 함께 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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.
