Simulation and Evaluation of Soil Temperature and Moisture during Freeze-thaw Process in Xizang Plateau by CLM5.0
Received date: 2023-08-23
Revised date: 2024-04-07
Online published: 2024-04-07
The China Meteorological Forcing Dataset(0.1°×0.1°) from 1979 -2018 was used as atmospheric forcing data to drive CLM5.0 (Community Land Model version 5.0) to simulate soil temperature and moisture changes in the Qinghai-Xizang Plateau region from 1979 to 2018.Divide the soil freeze-thaw process into two stages: freezing period and thawing period.By comparing and validating CLM5.0 simulation with site observation data, assimilation data (GLDAS-Noah), and satellite remote sensing data (MODIS soil temperature data and ESA CCI-COMBINED soil moisture data) in two stages, this study explores the applicability of CLM5.0 simulation of soil temperature and moisture in the Qinghai-Xizang Plateau.The results indicate that: (1) CLM5.0 can accurately describe the dynamic changes in soil temperature and moisture at stations on the Qinghai-Xizang Plateau.The soil temperature and moisture simulated by CLM5.0 have consistent variation characteristics with the observed data and are numerically close.The accuracy of CLM5.0 simulation is higher than that of GLDAS Noah.CLM5.0 provides a more accurate description of soil temperature at the stations.(2) CLM5.0 can accurately describe the soil temperature and moisture characteristics during the freeze-thaw process in the Qinghai-Xizang Plateau.CLM5.0 simulated soil temperature and moisture show a significant positive correlation with MODIS and ESA CCI-COMBINED remote sensing data on the Qinghai-Xizang Plateau, with correlation coefficients mostly above 0.9.CLM5.0 has relatively better simulation ability for soil temperature in Qinghai-Xizang Plateau areas.CLM5.0 has better simulation ability for soil moisture during thawing periods than during freezing periods.CLM5.0 overestimates the soil temperature of the Qinghai-Xizang Plateau as a whole, with an average deviation mostly between 0~4 ℃.The average deviation of soil moisture simulated by CLM5.0 is mostly between -0.1~0.1 m3·m-3, and the average deviation of soil moisture during thawing period is relatively small.(3) The soil temperature and moisture data from CLM5.0 simulation, GLDAS-Noah, MODIS, and ESA CCI-COMBINED remote sensing all have similar spatial distribution characteristics, with higher similarity in the spatial distribution characteristics of soil temperature.CLM5.0 has higher spatial resolution and more precise soil stratification, which can better describe the details of soil temperature and moisture.(4) The CLM5.0 simulation data shows an overall warming and drying trend in the Qinghai-Xizang Plateau, while the MODIS and ESA CCI-COMBINED remote sensing data show an overall warming and moistening trend.The trend of soil temperature changes simulated by CLM5.0 is relatively accurate, while there is a greater deviation in the trend of soil moisture changes.
Zhehao ZHANG , Xin LAI , Ge ZHANG , Siyuan YAO , Suyu ZHANG . Simulation and Evaluation of Soil Temperature and Moisture during Freeze-thaw Process in Xizang Plateau by CLM5.0[J]. Plateau Meteorology, 2025 , 44(1) : 32 -45 . DOI: 10.7522/j.issn.1000-0534.2024.00057
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null | 陈渤黎, 罗斯琼, 吕世华, 等, 2017.基于CLM模式的青藏高原土壤冻融过程陆面特征研究[J].冰川冻土, 39(4): 760-770.DOI: 10.7522/j.issn.1000-0240.2017.0086.Chen B L , |
null | |
null | |
null | |
null | 符晴, 阳坤, 郑东海, 等, 2022.青藏高原中部土壤有机质含量对不同深度土壤温湿度的影响研究[J].高原气象, 41(5): 1097-1108.DOI: 10.7522/j.issn.1000-0534.2021.00039.Fu Q , |
null | |
null | 李蓉蓉, 赵平, 2023.青藏高原非冻结期观测的土壤湿度和模式产品的对比分析[J].气象与环境科学, 46(1): 39-47.Doi: 10.16765j.cnki.1673-7148.2023.01.006.Li R R , |
null | |
null | 李时越, 杨凯, 王澄海, 2018.陆面模式CLM4.5在青藏高原土壤冻融期的偏差特征及其原因[J].冰川冻土, 40(2): 322-334.DOI: 10.7522/j.issn.1000-0240.2018.0037.Li S Y , |
null | |
null | 刘川, 余晔, 解晋, 等, 2015.多套土壤温湿度资料在青藏高原的适用性[J].高原气象, 34(3): 653-665.DOI: 10.7522/j.issn.1000-0534.2015.00034.Liu C , |
null | |
null | 刘闻慧, 文军, 陈金雷, 等, 2022.青藏高原土壤冻融过程关键参量时空分布特征分析[J].高原气象, 41(1): 11-23.DOI: 10.7522/j.issn.1000-0534.2021.00024.Liu W H , |
null | |
null | 罗红羽, 于海鹏, 胡泽勇, 等, 2023.青藏高原热源对我国旱区气候异常影响研究进展[J].高原气象, 42(2): 257-271.DOI: 10.7522/j.issn.1000-0534.2022.00070.Luo H Y , |
null | |
null | 满子豪, 翁白莎, 杨裕恒, 等, 2020.青藏高原冻融过程期划分及发展趋势研究[J].水电能源科学, 38(7): 16-19+29.DOI: 1000-7709(2020)07-0016-04.Man Z H , |
null | |
null | 王静, 祁莉, 何金海, 等, 2016.青藏高原春季土壤湿度与我国长江流域夏季降水的联系及其可能机理[J].地球物理学报, 59(11): 3985-3995.DOI: 10.6038/cjg20161105.Wang J , |
null | |
null | 夏坤, 罗勇, 李伟平, 2011.青藏高原东北部土壤冻融过程的数值模拟[J].科学通报, 56(22): 1828-1838.DOI: 10.1007/sl1434-011-4542-8.Xia K , |
null | |
null | 徐洪亮, 常娟, 郭林茂, 等, 2021.青藏高原腹地多年冻土区活动层水热过程对气候变化的响应[J].高原气象, 40(2): 229-243.DOI: 10.7522/j.issn.1000-0534.2020.00071.Xu H L , |
null | |
null | 杨淑华, 吴通华, 李韧, 等, 2018.青藏高原近地表土壤冻融状况的时空变化特征[J].高原气象, 37(1): 43-53.DOI: 10.7522/j.issn.1000-0534.2017.00043.Yang S H , |
null | |
null | 袁源, 赖欣, 巩远发, 等, 2019.CLM4.5模式对青藏高原土壤湿度的数值模拟及评估[J].大气科学, 43(3): 676-690.DOI: 10.3878/j.issn.1006-9895.180&18143.Yuan Y , |
null | |
null | 张戈, 赖欣, 刘康, 2023.黄河源区玛曲土壤冻融过程中地表水热交换特征分析[J].高原气象, 42(3): 575-589.DOI: 10.7522/j.issn.1000-0534.2022.00083.Zhang G , |
null | |
null | 赵林, 胡国杰, 邹德富, 等, 2019.青藏高原多年冻土变化对水文过程的影响[J].中国科学院院刊, 34(11): 1233-1246.DOI: 10.16418/j.issn.1000-3045.2019.11.006.Zhao L , |
null | |
null | 周志雄, 周凤玺, 张明礼, 等, 2023.季节降雨特征对青藏高原中部冻土活动层的水热影响[J].高原气象, 42(5): 1172-1181.DOI: 10.7522/j.issn.1000-0534.2023.00017.Zhou Z X , |
null |
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