Impact of High-Resolution Atmospheric Forcing and Plant Functional Types Datasets on Soil Temperature Simulation in the Qinghai-Tibetan Plateau

  • SHEN Runping ,
  • GUO Qian ,
  • CHEN Pingping ,
  • LI Xinhui ,
  • WANG Shaowu ,
  • SHI Chunxiang
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  • School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China

Received date: 2018-10-15

  Online published: 2019-12-28

Abstract

Soil temperature is a key physical quantity in the land-atmosphere interaction, and also an important parameter in the research of land surface model simulation. However, it is very difficult to obtain the spatiotemporal continuous data in high resolution, especially in the Qinghai-Tibetan Plateau of China with high altitude and harsh environment. Land surface model simulation with fusion of remote sensing data sets provides a feasible and effective way to obtain soil temperature information with high spatial and temporal resolution. Meanwhile, atmospheric forcing data and plant functional type's data are both important input data of Community Land Model (CLM), and their quality affects the accuracy and reliability of simulation results. In this paper, a new surface plant functional types data (MVEG) was produced, based on multi-source remote sensing data fusion. And then several simulation experiments were carried out which focused on the simulation study of soil temperature (0~10 cm) on the Tibetan Plateau in 2015. These experiments were all about Community Land Model, but different atmospheric forcing data and plant functional type's data were inputted. The latest High Resolution China Meteorological Administration Land Data Assimilation System Version1.0 (HRCLDAS V1.0, 1 km, 1 h) was used to improve the spatial and temporal resolution of model simulation. And its influence on soil temperature simulation was explored, as well as its synergistic effect with MVEG. The results showed that HRCLDAS-V1.0 atmospheric forcing data (1 km, 1 h) had a great improvement effect, it could significantly reduce the error of model simulation. MVEG could improve the simulation of extremes and at the same time, the spatial distribution of soil temperature turned out to be more reasonable. The simulated value of CLDAS/CLM (6 km, 1 h) was larger than observation overall(about 1℃). In contrast, HRCLDAS/CLM (1 km, 1 h) could improve the simulation results. The absolute value of annual mean bias and RMSE of soil temperature (0~10 cm) in the HRCLDAS/CLM simulation experiment were reduced by 0.82℃, 0.18℃, respectively. The simulated value by HR-MVEG/CLM (1 km, 1 h, improved plant functional types) was the most close to the observed value with the RMSE of HR-MVEG/CLM decreased by 0.27℃. Moreover, HR-MVEG/CLM could reflect the detailed characteristics of soil temperature spatial distribution.

Cite this article

SHEN Runping , GUO Qian , CHEN Pingping , LI Xinhui , WANG Shaowu , SHI Chunxiang . Impact of High-Resolution Atmospheric Forcing and Plant Functional Types Datasets on Soil Temperature Simulation in the Qinghai-Tibetan Plateau[J]. Plateau Meteorology, 2019 , 38(6) : 1129 -1139 . DOI: 10.7522/j.issn.1000-0534.2018.00159

References

[1]Benítez P, Mccallum I, Obersteiner M, et al, 2007. Global potential for carbon sequestration:Geographical distribution, country risk and policy implications[J]. Ecological Economics, 60(3):572-583. DOI:10.1016/j.ecolecon. 2005.12.015.
[2]Bonan G B, Oleson K W, Vertenstein M, et al, 2002. The land surface climatology of the community land model coupled to the NCAR community climate model[J]. Journal of Climate, 15(15):3123-3149. DOI:10.1175/1520-0442(2002)0152.0. CO; 2.
[3]Chen Y, Yang K, Jie H, et al, 2011. Improving land surface temperature modeling for dry land of China[J]. Journal of Geophysical Research Atmospheres, 116(D20):D20104. DOI:10.1029/2011JD015921.
[4]Fang X W, Luo S Q, Lyu S H, et al, 2016. A Simulation and Validation of CLM during Freeze-Thaw on the Tibetan Plateau[J]. Advances in Meteorology (6):9476098. DOI:10.1155/2016/9476098.
[5]Gibbard S, Caldeira K, Bala G, et al, 2005.Climate effects of global land cover change[J]. Geophysical Research Letters, 32(23):308-324. DOI:10.1029/2005GL024550.
[6]Hoppe C M, Elbern H, Schwinger J, 2014. A variational data assimilation system for soil-atmosphere flux estimates for the Community Land Model (CLM3.5)[J]. Geoscientific Model Development, 7(3):1025-1036. DOI:10.5194/gmd-7-1025-2014.
[7]Huang F, Ma W, 2016. Analysis of Long-Term Meteorological Observation for Weather and Climate Fundamental Data over the Northern TibetanPlateau[J]. Advances in Meteorology (3):4878353. DOI:10.1155/2016/4878353.
[8]Kumar S, Merwade V, 2011. Evaluation of NARR and CLM3.5 outputs for surface water and energy budgets in the Mississippi River Basin[J]. Journal of Geophysical Research Atmospheres, 116(D8). DOI:10.1029/2010JD014909.
[9]Lawrence P J, Chase T N, 2015. Representing a new MODIS consistent land surface in the Community LandModel(CLM3.0)[J]. Journal of Geophysical Research Biogeosciences, 112(G1):252-257. DOI:10.1029/2006JG000168.
[10]Li H, Wolter M, Wang X, et al, 2017. Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM[J]. Theoretical & Applied Climatology, 134:67-81. DOI:10.1007/s00704-017-2253-z.
[11]Moiwo J P, Lu W, Tao F, 2012. GRACE, GLDAS and measured groundwater data products show water storage loss in Western Jilin, China[J]. Water Science & Technology, 65(9):1606-1614. DOI:10.2166/wst. 2012.053.
[12]Oleson K W, Dai Y J, Bonan G, et al, 2004. Technical description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR. DOI: <a href="http://dx.doi.org/10.5065/D6N877R0" target="_blank">10.5065/D6N877R0</a>.
[13]Qin Y, Wu T, Wu X, et al, 2017. Assessment of reanalysis soil moisture products in the permafrost regions of the central of the Qinghai-Tibet Plateau[J]. Hydrological Processes, 31(26):4647-4659. DOI:10.1002/hyp. 11383.
[14]Ran Y H, Li X, Lu L, et al, 2012. Large-scale land cover mapping with the integration of multi-source information based on theDempster-Shafer theory[J]. International Journal of Geographical Information Science, 26(1):169-191. DOI:10.1080/13658816.2011.577745.
[15]Sr R A P, Pitman A, Niyogi D, et al, 2011. Land use/land cover changes and climate:modeling analysis and observational evidence[J]. Wiley Interdisciplinary Reviews Climate Change, 2(6):828-850. DOI:10.1002/wcc. 144.
[16]Wang A, Zeng X, 2011. Sensitivities of terrestrial water cycle simulations to the variations of precipitation and air temperature inChina[J]. Journal of Geophysical Research Atmospheres, 116(D2). DOI:10.1029/2010JD014659.
[17]Yang F, Lu H, Yang K, et al, 2017. Evaluation and comparison among multiple forcing data sets for precipitation and shortwave radiation over Mainland China[J]. Hydrology &amp; Earth System Sciences Discussions, 21(11):5805-5821. DOI:10.5194/hess-21-5805-2017.
[18]Yin Z, Ottlé C, Ciais P, et al, 2018. Evaluation of ORCHIDEE-MICT simulated soil moisture over China and impacts of different atmospheric forcing data[J]. Hydrology and Earth System Sciences, European Geosciences Union, 22(10):5463-5484. DOI:10.5194/hess-22-5463-2018.
[19]Zhao M, Pitman A J, Chase T N, 2001. Climatic effects of land cover change at different carbon dioxidelevels[J]. Climate Research, 17(1):1-18. DOI:10.3354/cr017001.
[20]Zhou S Q, Zhang C, Wang X N, et al, 2004. Simulation of soil temperature with a multi-layer model and its verification[J]. Journal of Nanjing Institute of Meteorology, 27(2):200-209. DOI:10.1117/12.528072.
[21]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.
[22]陈海存, 李晓东, 李凤霞, 等, 2013.黄河源玛多县退化草地土壤温湿度变化特征[J].干旱区研究, 30(1):35-40. DOI:10.13866/j.azr.2013.01.007.
[23]陈海山, 熊明明, 沙文钰, 2010. CLM3.0对中国区域陆面过程的模拟试验及评估I:土壤温度[J].气象科学, 30(05):621-630. DOI:10.3969/j.issn.1009-0827.2010.05.008.
[24]陈宇航, 范广洲, 赖欣, 等, 2016.青藏高原复杂下垫面能量和水分循环季节变化特征分析[J].气候与环境研究, 21(5):586-600. DOI:10.3878/j.issn.1006-9585.2016.15068.
[25]崔园园, 敬文琪, 覃军, 2018.基于TIPEX III资料对CLDAS-V2.0和GLDAS-NOAH陆面模式产品在青藏高原地区的适用性评估[J].高原气象, 37(5):1143-1160. DOI:10.7522/j.issn.1000-0534.2018.00020.
[26]郭东林, 杨梅学, 2010. SHAW模式对青藏高原中部季节冻土区土壤温、湿度的模拟[J].高原气象, 29(6):1369-1377.
[27]韩帅, 师春香, 姜志伟, 等, 2018. CMA高分辨率陆面数据同化系统(HRCLDAS-V1.0)研发及进展[J].气象科技进展, 8(1):102-108.
[28]解晋, 余晔, 刘川, 等, 2018.青藏高原地表感热通量变化特征及其对气候变化的响应[J].高原气象, 37(1):28-42. DOI:10.7522/j.issn.1000-0534.2017.00019.
[29]李剑铎, 段青云, 戴永久, 等, 2013. CoLM模拟土壤温度和湿度最敏感参数的研究[J].大气科学, 37(4):841-851. DOI:10.3878/j.issn.1006-9895.2012.12046.
[30]李明星, 马柱国, 牛国跃, 2011.中国区域土壤湿度变化的时空特征模拟研究[J].科学通报, 56(16):1288-1300. DOI:10.1007/s11434-011-4493-0.
[31]马思源, 朱克云, 李明星, 等, 2016.中国区域多源土壤湿度数据的比较研究[J].气候与环境研究, 21(2):121-133. DOI:10.3878/j.issn.1006-9585.2015.15080.
[32]孟现勇, 王浩, 刘志辉, 等, 2017.基于CLDAS强迫CLM3.5模式的新疆区域土壤温度陆面过程模拟及验证[J].生态学报, 37(3):979-995. DOI:10.5846/stxb201508171717.
[33]冉有华, 李新, 卢玲, 2009.基于多源数据融合方法的中国1 km土地覆盖分类制图[J].地球科学进展, 24(2):192-203. DOI:10.3321/j.issn:1001-8166.2009.02.009.
[34]王平, 沈润平, 2013.基于CLM模型的植被覆盖变化对黄土高原气温和降水的影响研究[J].科学技术与工程, 13(20):5754-5760. DOI:10.3969/j.issn.1671-1815.2013.20.004.
[35]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:10.1088/1674-1137/35/2/019.
[36]熊建胜, 张宇, 王少影, 等, 2014. CLM4.0土壤水分传输方案改进在青藏高原陆面过程模拟中的效应[J].高原气象, 33(2):323-336. DOI:10.7522/j.issn.1000-0534.2014.00012.
[37]杨梅学, 姚檀栋, Toshio K, 2000.藏北高原土壤温度的变化特征[J].山地学报, 18(1):13-17. DOI:10.3969/j.issn.1008-2786.2000.01.003.
[38]杨晓春, 师春香, 赵荣, 等, 2015.基于FY-Y2的大气强迫数据构建及模拟检验[J].高原气象, 34(4):1041-1048. DOI:10.7522/j.issn.1000-0534.2014.00087.
[39]杨扬, 杨启东, 孙旭映, 等, 2016.三个陆面过程模式在西北半干旱区的模拟性能对比[J].气候与环境研究, 21(4):405-417. DOI:10.3878/j.issn.1006-9585.2016.15105.
[40]于伯华, 吕昌河, 吕婷婷, 等, 2009.青藏高原植被覆盖变化的地域分异特征[J].地理科学进展, 28(3):391-397. DOI:10.11820/dlkxjz. 2009.03.010.
[41]于海英, 许建初, 2009.气候变化对青藏高原植被影响研究综述[J].生态学杂志, 28(04):747-754.
[42]张伟, 王根绪, 周剑, 等, 2012.基于CoupModel的青藏高原多年冻土区土壤水热过程模拟[J].冰川冻土, 34(5):1099-1109.
[43]朱智, 师春香, 2014.中国气象局陆面同化系统和全球陆面同化系统对中国区域土壤湿度的模拟与评估[J].科学技术与工程, 14(32):138-144.
[44]朱智, 师春香, 梁晓, 等, 2017.基于高时空分辨率驱动数据的中国区域土壤温度模拟与评估[J].江苏农业科学, 45(5):228-233. DOI:10.15889/j.issn.1002-1302.2017.05.063.
[45]朱智, 师春香, 张涛, 等, 2018.四套再分析土壤湿度资料在中国区域的适用性分析[J].高原气象, 37(1):240-252. DOI:10.7522/j.issn.1000-0534.2017.00033.
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