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The implication of spatial interpolated climate data on biophysical modelling in agricultural systems 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019
作者:  Liu, De Li;  Ji, Fei;  Wang, Bin;  Waters, Cathy;  Feng, Puyu;  Darbyshire, Rebecca
收藏  |  浏览/下载：10/0  |  提交时间：2020/02/17
APSIM  bias  gridded climate  interpolation  rainfall  rainfall intensity  rainfall probability  SILO  soil water balance  wheat  
The 30-60-day northward-propagating intraseasonal oscillation over South China Sea during pre-monsoon period in a coupled model 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (12) : 4811-4824
作者:  Zheng, Bin;  Huang, Yanyan;  Li, Chunimi
收藏  |  浏览/下载：5/0  |  提交时间：2019/11/27
coupled general circulation model  intraseasonal oscillation  northward propagation  pre-monsoon  
Projected changes in drought across the wheat belt of southeastern Australia using a downscaled climate ensemble 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (2) : 1041-1053
作者:  Feng, Puyu;  Liu, De Li;  Wang, Bin;  Waters, Cathy;  Zhang, Mingxi;  Yu, Qiang
收藏  |  浏览/下载：6/0  |  提交时间：2019/04/09
climate change  drought  rSPEI  southeastern Australia  spatio-temporal variations  
Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (1) : 424-444
作者:  Liu, De Li;  Wang, Bin;  Evans, Jason;  Ji, Fei;  Waters, Cathy;  Macadam, Ian;  Yang, Xihua;  Beyer, Kathleen
收藏  |  浏览/下载：8/0  |  提交时间：2019/04/09
APSIM  bias correction  bias propagation  bio-physical crop model  NARCliM  rainfall intensity  rainfall probability  RCMs  wheat cropping system  
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 38 (13) : 4891-4902
作者:  Wang, Bin;  Zheng, Lihong;  Liu, De Li;  Ji, Fei;  Clark, Anthony;  Yu, Qiang
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/09
GCMs  machine learning  multi-model ensemble  random forest  support vector machine