TY - JOUR
T1 - Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change
T2 - A Case Study in Southern Taiwan
AU - Tseng, Hung Wei
AU - Yang, Tao Chang
AU - Kuo, Chen Min
AU - Yu, Pao Shan
N1 - Funding Information:
Acknowledgements The authors would like to thank the National Science Council of the Republic of China (Taiwan) for financially supporting this research under Contract No. NSC 97-2221-E-006-015-MY3 and Taiwan Climate Change Projection and Information Platform Project (TCCIP) (Contract No. NSC 100-2621-M-492-001) for offering future rainfall projections.
PY - 2012/10
Y1 - 2012/10
N2 - The study compared the performances of three weather generators (WGs), including a parametric model and two non-parametric models, in producing synthetic daily rainfall time series for multiple sites. The observed daily rainfalls of six raingauges during 1979~2008 in the catchment of Tseng-Wen Reservoir in Southern Taiwan were used as the data set. The generated results reveal that the k-nearest neighbor WG with a fixed window (i. e., a non-parametric model) is the best for daily rainfall generation at each site and performs well in preserving spatial correlation of rainfall among sites. The best WG was further applied to assess the impact of climate change on rainfall temporal characteristics (i. e., annual number of wet day, annual maximum number of continuous wet days and annual maximum number of continuous dry days) by using the downscaling results of 24 GCMs under the A1B emission scenario during 2020~2039. It is found that the rainfall temporal characteristics will change in the future which may make Southern Taiwan tend to face a longer period with no rain.
AB - The study compared the performances of three weather generators (WGs), including a parametric model and two non-parametric models, in producing synthetic daily rainfall time series for multiple sites. The observed daily rainfalls of six raingauges during 1979~2008 in the catchment of Tseng-Wen Reservoir in Southern Taiwan were used as the data set. The generated results reveal that the k-nearest neighbor WG with a fixed window (i. e., a non-parametric model) is the best for daily rainfall generation at each site and performs well in preserving spatial correlation of rainfall among sites. The best WG was further applied to assess the impact of climate change on rainfall temporal characteristics (i. e., annual number of wet day, annual maximum number of continuous wet days and annual maximum number of continuous dry days) by using the downscaling results of 24 GCMs under the A1B emission scenario during 2020~2039. It is found that the rainfall temporal characteristics will change in the future which may make Southern Taiwan tend to face a longer period with no rain.
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U2 - 10.1007/s11269-012-0146-6
DO - 10.1007/s11269-012-0146-6
M3 - Article
AN - SCOPUS:84868207357
SN - 0920-4741
VL - 26
SP - 4311
EP - 4326
JO - Water Resources Management
JF - Water Resources Management
IS - 15
ER -
