Q
Qingyun Duan
Researcher at Hohai University
Publications - 213
Citations - 26241
Qingyun Duan is an academic researcher from Hohai University. The author has contributed to research in topics: Climate change & Precipitation. The author has an hindex of 62, co-authored 196 publications receiving 21640 citations. Previous affiliations of Qingyun Duan include University of Arizona & Beijing Normal University.
Papers
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Journal ArticleDOI
Projected changes in temperature and precipitation in ten river basins over China in 21st century
TL;DR: This paper assessed the simulated surface air temperature and precipitation over China from 24 models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and applied the reliability ensemble average (REA) to project the SAT and precipitation change under representative concentration pathway (RCP) scenarios over China in the 21st century.
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Sensitivity Analysis-Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China
Jiaojiao Gou,Chiyuan Miao,Qingyun Duan,Qiuhong Tang,Zhenhua Di,Weihong Liao,Jingwen Wu,Rui Zhou +7 more
TL;DR: Wang et al. as discussed by the authors proposed an adaptive surrogate modeling-based optimization (ASMO) algorithm to calibrate catchment-specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979.
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An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS)
John Schaake,Qingyun Duan,Victor Koren,Kenneth E. Mitchell,Paul R. Houser,Eric F. Wood,Alan Robock,Dennis P. Lettenmaier,Dag Lohmann,Brian Cosgrove,Justin Sheffield,Lifeng Luo,Lifeng Luo,R. Wayne Higgins,Rachel T. Pinker,J. Dan Tarpley +15 more
TL;DR: In this article, the authors compared four land surface models (LSMs) running in NLDAS both in retrospective mode and in real-time mode, and compared the mean statistical properties and the spatial variation of these soil moisture fields along with their temporal change.
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Ensemble flood forecasting: Current status and future opportunities
TL;DR: There is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real‐world flood management using probabilistic ensemble forecasts, especially through effective communication.