气候变化领域集成服务门户(CCPortal): Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations

CCPortal

DOI	10.1007/s00382-019-04702-7
	Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations
	Wang S.; Wang Y.
发表日期	2019
ISSN	0930-7575
起始页码	1613
结束页码	1636
卷号	53 期号:2020-03-04
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 4 km 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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Convection permitting; High-resolution climate projection; Hydroclimatic changes; Markov chain Monte Carlo; Pseudo global warming
语种	英语
scopus关键词	climate change; climate modeling; climatology; computer simulation; convective system; global warming; hydrometeorology; Markov chain; Monte Carlo analysis; numerical model; probability; weather forecasting; Blanco River; Guadalupe Basin [Texas]; Texas; United States
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146136
作者单位	Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong; Department of Geosciences, Texas Tech University, Lubbock, TX, United States
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],2019,53(2020-03-04).
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(2020-03-04).
MLA	Wang S.,et al."Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations".Climate Dynamics 53.2020-03-04(2019).