GSTDTAP  > 气候变化
DOI10.1007/s00382-019-04702-7
Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations
Wang, S.1; Wang, Y.2
2019-08-01
发表期刊CLIMATE DYNAMICS
ISSN0930-7575
EISSN1432-0894
出版年2019
卷号53页码:1613-1636
文章类型Article
语种英语
国家Peoples R China; USA
英文摘要

Understanding future changes in hydroclimatic variables plays a crucial role in improving resilience and adaptation to extreme weather events such as floods and droughts. In this study, we develop high-resolution climate projections over Texas by using the convection-permitting Weather Research and Forecasting (WRF) model with 4km horizontal grid spacing, and then produce the Markov chain Monte Carlo (MCMC)-based hydrologic forecasts in the Guadalupe River basin which is the primary concern of the Texas Water Development Board and the Guadalupe-Blanco River Authority. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset is used to verify the WRF climate simulations. The Model Parameter Estimation Experiment (MOPEX) dataset is used to validate probabilistic hydrologic predictions. Projected changes in precipitation, potential evapotranspiration (PET) and streamflow at different temporal scales are examined by dynamically downscaling climate projections derived from 15 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). Our findings reveal that the Upper Coast Climate Division of Texas is projected to experience the most remarkable wetting caused by precipitation and PET changes, whereas the most significant drying is expected to occur for the North Central Texas Climate Division. The dry Guadalupe River basin is projected to become drier with a substantial increase in future drought risks, especially for the summer season. And the extreme precipitation events are projected to increase in frequency and intensity with a reduction in overall precipitation frequency, which may result in more frequent occurrences of flash floods and drought episodes in the Guadalupe River basin.


英文关键词Convection permitting High-resolution climate projection Hydroclimatic changes Markov chain Monte Carlo Pseudo global warming
领域气候变化
收录类别SCI-E
WOS记录号WOS:000475558800022
WOS关键词REGIONAL CLIMATE ; WINTER PRECIPITATION ; CHANGE IMPACTS ; FUTURE CHANGES ; WATER-BALANCE ; FORECASTS ; SENSITIVITY ; ROBUST ; MULTIMODEL ; INFERENCE
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/185416
专题气候变化
作者单位1.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China;
2.Texas Tech Univ, Dept Geosci, Lubbock, TX 79409 USA
推荐引用方式
GB/T 7714
Wang, S.,Wang, Y.. Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations[J]. CLIMATE DYNAMICS,2019,53:1613-1636.
APA Wang, S.,&Wang, Y..(2019).Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations.CLIMATE DYNAMICS,53,1613-1636.
MLA Wang, S.,et al."Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations".CLIMATE DYNAMICS 53(2019):1613-1636.
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