DOI: 10.1007/s00382-017-3731-9
Scopus记录号: 2-s2.0-85019229760
论文题名: Multi-site precipitation downscaling using a stochastic weather generator
作者: Chen J. ; Chen H. ; Guo S.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2018
卷: 50, 期: 2018-05-06 起始页码: 1975
结束页码: 1992
语种: 英语
英文关键词: Climate change impacts
; Inter-station correlation
; Statistical downscaling
; Weather generator
Scopus关键词: algorithm
; climate change
; climate effect
; correlation
; downscaling
; precipitation assessment
; spatial variation
; stochasticity
; time series analysis
英文摘要: Statistical downscaling is an efficient way to solve the spatiotemporal mismatch between climate model outputs and the data requirements of hydrological models. However, the most commonly-used downscaling method only produces climate change scenarios for a specific site or watershed average, which is unable to drive distributed hydrological models to study the spatial variability of climate change impacts. By coupling a single-site downscaling method and a multi-site weather generator, this study proposes a multi-site downscaling approach for hydrological climate change impact studies. Multi-site downscaling is done in two stages. The first stage involves spatially downscaling climate model-simulated monthly precipitation from grid scale to a specific site using a quantile mapping method, and the second stage involves the temporal disaggregating of monthly precipitation to daily values by adjusting the parameters of a multi-site weather generator. The inter-station correlation is specifically considered using a distribution-free approach along with an iterative algorithm. The performance of the downscaling approach is illustrated using a 10-station watershed as an example. The precipitation time series derived from the National Centers for Environment Prediction (NCEP) reanalysis dataset is used as the climate model simulation. The precipitation time series of each station is divided into 30 odd years for calibration and 29 even years for validation. Several metrics, including the frequencies of wet and dry spells and statistics of the daily, monthly and annual precipitation are used as criteria to evaluate the multi-site downscaling approach. The results show that the frequencies of wet and dry spells are well reproduced for all stations. In addition, the multi-site downscaling approach performs well with respect to reproducing precipitation statistics, especially at monthly and annual timescales. The remaining biases mainly result from the non-stationarity of NCEP precipitation. Overall, the proposed approach is efficient for generating multi-site climate change scenarios that can be used to investigate the spatial variability of climate change impacts on hydrology. © 2017, Springer-Verlag Berlin Heidelberg. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109430
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
There are no files associated with this item.
作者单位: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 299 Bayi Road, Wuchang Distinct, Wuhan, Hubei 430072, China
Recommended Citation:
Chen J.,Chen H.,Guo S.. Multi-site precipitation downscaling using a stochastic weather generator[J]. Climate Dynamics,2018-01-01,50(2018-05-06)