Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2017WR022185 |
Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model | |
Qin, Yue1,2; Chen, Jinsong2; Yang, Dawen1; Wang, Taihua1 | |
2018-07-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:7页码:4361-4375 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root-mean-square errors = 0.13-0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai-Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data. |
英文关键词 | Stefan solution Bayesian model Markov chain Monte Carlo (MCMC) seasonally frozen ground climate change Yellow River |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000442502100011 |
WOS关键词 | ACTIVE-LAYER THICKNESS ; YELLOW-RIVER BASIN ; HEIHE RIVER ; SOIL PARAMETERIZATION ; SURFACE TEMPERATURES ; TIBETAN PLATEAU ; CHINA ; PERMAFROST ; HYDROLOGY ; THAW |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
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/20079 |
专题 | 资源环境科学 |
作者单位 | 1.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China; 2.Lawrence Berkeley Natl Lab, Earth & Environm Sci Area, Berkeley, CA USA |
推荐引用方式 GB/T 7714 | Qin, Yue,Chen, Jinsong,Yang, Dawen,et al. Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model[J]. WATER RESOURCES RESEARCH,2018,54(7):4361-4375. |
APA | Qin, Yue,Chen, Jinsong,Yang, Dawen,&Wang, Taihua.(2018).Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model.WATER RESOURCES RESEARCH,54(7),4361-4375. |
MLA | Qin, Yue,et al."Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model".WATER RESOURCES RESEARCH 54.7(2018):4361-4375. |
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