CLC number: TV125
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-02-21
Cited: 2
Clicked: 6315
Yue-ping Xu, Chong Ma, Su-li Pan, Qian Zhu, Qi-hua Ran. Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China[J]. Journal of Zhejiang University Science A, 2014, 15(3): 219-230.
@article{title="Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China",
author="Yue-ping Xu, Chong Ma, Su-li Pan, Qian Zhu, Qi-hua Ran",
journal="Journal of Zhejiang University Science A",
volume="15",
number="3",
pages="219-230",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1300267"
}
%0 Journal Article
%T Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China
%A Yue-ping Xu
%A Chong Ma
%A Su-li Pan
%A Qian Zhu
%A Qi-hua Ran
%J Journal of Zhejiang University SCIENCE A
%V 15
%N 3
%P 219-230
%@ 1673-565X
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1300267
TY - JOUR
T1 - Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China
A1 - Yue-ping Xu
A1 - Chong Ma
A1 - Su-li Pan
A1 - Qian Zhu
A1 - Qi-hua Ran
J0 - Journal of Zhejiang University Science A
VL - 15
IS - 3
SP - 219
EP - 230
%@ 1673-565X
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1300267
Abstract: Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model (SDSM) and long Ashton research station weather generator (LARS-WG) are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator (GiST), in simulating precipitation in the qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method (CFM) to investigate future changes of precipitation (2071–2100) in the study area using one global climate model, Hadgem2_ES, and an extreme emission scenario RCP 8.5. The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and historical precipitations show good consistence as well. Compared with the baseline period (1961–1990), precipitation in the future time period (2071–2100) at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.
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