“Global Climate Change and Water System Impacts” 系列主题学术讲座（讲座议程）

报告题目：A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions with Sparse Ground Data Networks
报  告 人：Prof Hyun-Han Kwon

邀  请 人：金钟硕教授，陈杰 教授

时      间：2021年11月19日（星期五）下午3:00-4:00

地      点：国家重点实验室学术报告厅（农水楼一楼）

              Zoom会议（ID:  873 912 5358）

              密码：123456

报告人简介：  

     Hyun-Han Kwon obtained his PhD from University of Seoul, Korea. Now Dr.Kwon is a professor of Sejong University, an adjunct research scientist of Columbia University and also the technical committee member of ICOLD(International Commission on Land Dams). His research aims to bridge between the disciplines of hydrology, climatology, and data mining to address critical water resource issues, with a particular interest in combining data mining techniques (Artificial Information) and physically-based and statistical approaches in order to develop more reliable models of multiscale hydrologic systems. His long-term research objective is to utilize continuously growing hydro-meteorological data along with satellite data to develop/improve integrated drought, flood and landslide modeling, prediction and decision support systems. He has published almost 230 papers. Also he is an associate editor of Forecasting Journal, Journal of Hydro-Environment Research.

报告简介：

     This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM-spatial bias-correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross-validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias-corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered.

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