Global S&T Development Trend Analysis Platform of Resources and Environment
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.1; Wang, Y.2 | |
2019-08-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
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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