PMID- 31018435
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20191120
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 670
DP  - 2019 Jun 20
TI  - Hydro-thermal boundary conditions at different underlying surfaces in a 
      permafrost region of the Qinghai-Tibet Plateau.
PG  - 1190-1203
LID - S0048-9697(19)31072-1 [pii]
LID - 10.1016/j.scitotenv.2019.03.090 [doi]
AB  - Hydro-thermal properties of permafrost and its distribution are sensitive to 
      climate changes and human activities. Accurate and reasonable prediction on 
      aforementioned information is important for eco-environment construction and 
      vital infrastructures development. To model the current and future states of 
      permafrost, it is a key challenge to effectively determine the upper 
      hydro-thermal boundary conditions for permafrost models under changing climate 
      and different underlying surfaces at proper spatial and temporal scales. An 
      approach, combined regional climate downscaling method with model output 
      statistics method, was developed to produce a time series of air temperature, 
      surface temperatures, and surface unfrozen water contents for different 
      underlying surfaces. It provided various climate and surface parameters at a 
      spatial scale on the order of 10(2) m(2) for engineering designs, which was used 
      to predict boundary conditions under possible climate scenarios. The predicted 
      and simulated models were calibrated and validated by the monitored data at an 
      experimental site in Chumar, China, close to the Qinghai-Tibet Railway and the 
      Qinghai-Tibet Highway. Results show that the multiple linear regression model 
      (MLRM) can predict the current states and future changes of upper hydro-thermal 
      boundary conditions for permafrost while the original states of natural surface 
      are modified by natural or human factors on the condition of complicated climatic 
      and complex topography regions. The statistical regression model (SRM) based on 
      the outputs of regional climate model (RCM) and MLRM provides a simple method for 
      the convenience of numerical calculation. These results also indicate the 
      possible applications to other areas and situations.
CI  - Copyright (c) 2019 Elsevier B.V. All rights reserved.
FAU - Zhang, Mingyi
AU  - Zhang M
AD  - State Key Laboratory of Frozen Soil Engineering, Northwest Institute of 
      Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, 
      China; University of Chinese Academy of Sciences, Beijing 100049, China. 
      Electronic address: myzhang@lzb.ac.cn.
FAU - Wang, Jiwei
AU  - Wang J
AD  - State Key Laboratory of Frozen Soil Engineering, Northwest Institute of 
      Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, 
      China; University of Chinese Academy of Sciences, Beijing 100049, China.
FAU - Lai, Yuanming
AU  - Lai Y
AD  - State Key Laboratory of Frozen Soil Engineering, Northwest Institute of 
      Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, 
      China; University of Chinese Academy of Sciences, Beijing 100049, China.
LA  - eng
PT  - Journal Article
DEP - 20190307
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
OTO - NOTNLM
OT  - Hydro-thermal boundary condition
OT  - Multiple linear regression
OT  - Permafrost region
OT  - Qinghai-Tibet Plateau
OT  - Regional climate model
OT  - WRF
EDAT- 2019/04/26 06:00
MHDA- 2019/04/26 06:01
CRDT- 2019/04/26 06:00
PHST- 2018/10/09 00:00 [received]
PHST- 2019/03/06 00:00 [revised]
PHST- 2019/03/07 00:00 [accepted]
PHST- 2019/04/26 06:00 [entrez]
PHST- 2019/04/26 06:00 [pubmed]
PHST- 2019/04/26 06:01 [medline]
AID - S0048-9697(19)31072-1 [pii]
AID - 10.1016/j.scitotenv.2019.03.090 [doi]
PST - ppublish
SO  - Sci Total Environ. 2019 Jun 20;670:1190-1203. doi: 
      10.1016/j.scitotenv.2019.03.090. Epub 2019 Mar 7.