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Volume 27 Issue 6

Nov.  2010

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(6): 1289-1302

Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets

  • 1.

    Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of Chinese Academy of Sciences, Beijing 100049,Key Laboratory of Regional Climate-Environment Research for Temperate East Asia,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/s00376-010-9155-7

  • Based on station observations, The European Centre for Medium-Range Weather Forecasts reanalysis (ERA40), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Princeton University's global meteorological forcing data set (Princeton), four atmospheric forcing fields were constructed for use in driving the Community Land Model version 3.5 (CLM3.5). Simulated soil moisture content throughout the period 1951--2000 in the Yellow River basin was validated via comparison with corresponding observations in the upper, middle, and lower reaches. The results show that CLM3.5 is capable of reproducing not only the characteristics of intra-annual and annual variations of soil moisture, but also long-term variation trends, with different statistical significance in the correlations between the observations and simulations from different forcing fields in various reaches. The simulations modeled with station-based atmospheric forcing fields are the most consistent with observed soil moisture, and the simulations based on the Princeton data set are the second best, on average. The simulations from ERA40 and NCEP/NCAR are close to each other in quality, but comparatively worse to the other sources of forcing information that were evaluated. Regionally, simulations are most consistent with observations in the lower reaches and less so in the upper reaches, with the middle reaches in between. In addition, the soil moisture simulated by CLM3.5 is systematically greater than the observations in the Yellow River basin. Comparisons between the simulations by CLM3.5 and CLM3.0 indicate that simulation errors are primarily caused by deficiencies within CLM3.5 and are also associated with the quality of atmospheric forcing field applied.
  • [1] GUAN Xiaodan, HUANG Jianping, GUO Ni, BI Jianrong, WANG Guoyin, 2009: Variability of Soil Moisture and Its Relationship with Surface Albedo and Soil Thermal Parameters over the Loess Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 692-700.  doi: 10.1007/s00376-009-8198-0
    [2] Minwei Qian, N. Loglisci, C. Cassardo, A. Longhetto, C. Giraud, 2001: Energy and Water Balance at Soil-Air Interface in a Sahelian Region, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 897-909.
    [3] NIE Suping, LUO Yong, ZHU Jiang, 2008: Trends and Scales of Observed Soil Moisture Variations in China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 43-58.  doi: 10.1007/s00376-008-0043-3
    [4] Changyu ZHAO, Haishan CHEN, Shanlei SUN, 2018: Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 445-456.  doi: 10.1007/s00376-017-7006-5
    [5] Fuqiang YANG, Li DAN, Jing PENG, Xiujing YANG, Yueyue LI, Dongdong GAO, 2019: Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 79-92.  doi: 10.1007/s00376-018-8035-4
    [6] DAN Li, JI Jinjun, ZHANG Peiqun, 2005: The Soil Moisture of China in a High Resolution Climate-Vegetation Model, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 720-729.  doi: 10.1007/BF02918715
    [7] ZHANG Shuwen, LI Deqin, QIU Chongjian, 2011: A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 195-206.  doi: 10.1007/s00376-010-9200-6
    [8] LIU Huizhi, WANG Baomin, FU Congbin, 2008: Relationships Between Surface Albedo, Soil Thermal Parameters and Soil Moisture in the Semi-arid Area of Tongyu, Northeastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 757-764.  doi: 10.1007/s00376-008-0757-2
    [9] Binghao JIA, Longhuan WANG, Yan WANG, Ruichao LI, Xin LUO, Jinbo XIE, Zhenghui XIE, Si CHEN, Peihua QIN, Lijuan LI, Kangjun CHEN, 2021: CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 862-874.  doi: 10.1007/s00376-021-0293-x
    [10] ZHANG Jie, LIU Zhenyuan, CHEN Li, 2015: Reduced Soil Moisture Contributes to More Intense and More Frequent Heat Waves in Northern China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1197-1207.  doi: 10.1007/s00376-014-4175-3
    [11] WU Lingyun, ZHANG Jingyong, 2015: The Relationship between Spring Soil Moisture and Summer Hot Extremes over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1660-1668.  doi: 10.1007/s00376-015-5003-0
    [12] DAN Li, JI Jinjun, LIU Huizhi, 2008: Use of a Land Surface Model to Evaluate the Observed Soil Moisture of Grassland at the Tongyu Reference Site, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 1073-1084.  doi: 10.1007/s00376-008-1073-6
    [13] ZHANG Shuwen, LI Haorui, ZHANG Weidong, QIU Chongjian, LI Xin, 2005: Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF), ADVANCES IN ATMOSPHERIC SCIENCES, 22, 936-945.  doi: 10.1007/BF02918692
    [14] Yinshuo Dong, Haishan Chen, Xuan Dong, Wenjian Hua, Wenjun Zhang, 2024: Synergistic Impacts of Indian Ocean SST and Indo-China Peninsula Soil Moisture on the 2020 Record-breaking Meiyu, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3204-0
    [15] Zhiyan ZUO, Renhe ZHANG, 2016: Influence of Soil Moisture in Eastern China on the East Asian Summer Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 151-163.  doi: 10.1007/s00376-015-5024-8
    [16] GUO Weidong, WANG Huijun, 2003: A Case Study of the Improvement of Soil Moisture Initialization in IAP-PSSCA, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 845-848.  doi: 10.1007/BF02915411
    [17] Yinghan SANG, Hong-Li REN, Xueli SHI, Xiaofeng XU, Haishan CHEN, 2021: Improvement of Soil Moisture Simulation in Eurasia by the Beijing Climate Center Climate System Model from CMIP5 to CMIP6, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 237-252.  doi: 10.1007/s00376-020-0167-7
    [18] Chujie GAO, Gen LI, 2023: Enhanced Seasonal Predictability of Spring Soil Moisture over the Indo-China Peninsula for Eastern China Summer Precipitation under Non-ENSO Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1632-1648.  doi: 10.1007/s00376-023-2361-x
    [19] SONG Yaoming, GUO Weidong, ZHANG Yaocun, 2009: Numerical Study of Impacts of Soil Moisture on the Diurnal and Seasonal Cycles of Sensible/Latent Heat Fluxes over Semi-arid Region, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 319-326.  doi: 10.1007/s00376-009-0319-2
    [20] Yizhe HAN, Dabang JIANG, Dong SI, Yaoming MA, Weiqiang MA, 2024: Time-lagged Effects of the Spring Atmospheric Heat Source over the Tibetan Plateau on Summer Precipitation in Northeast China during 1961–2020: Role of Soil Moisture, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-2363-8
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    • Manuscript History

    Manuscript received: 10 November 2010
    Manuscript revised: 10 November 2010
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets

    • 1. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of Chinese Academy of Sciences, Beijing 100049,Key Laboratory of Regional Climate-Environment Research for Temperate East Asia,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2010-11-10
    • Manuscript revised: 2010-11-10

    Abstract: Based on station observations, The European Centre for Medium-Range Weather Forecasts reanalysis (ERA40), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Princeton University's global meteorological forcing data set (Princeton), four atmospheric forcing fields were constructed for use in driving the Community Land Model version 3.5 (CLM3.5). Simulated soil moisture content throughout the period 1951--2000 in the Yellow River basin was validated via comparison with corresponding observations in the upper, middle, and lower reaches. The results show that CLM3.5 is capable of reproducing not only the characteristics of intra-annual and annual variations of soil moisture, but also long-term variation trends, with different statistical significance in the correlations between the observations and simulations from different forcing fields in various reaches. The simulations modeled with station-based atmospheric forcing fields are the most consistent with observed soil moisture, and the simulations based on the Princeton data set are the second best, on average. The simulations from ERA40 and NCEP/NCAR are close to each other in quality, but comparatively worse to the other sources of forcing information that were evaluated. Regionally, simulations are most consistent with observations in the lower reaches and less so in the upper reaches, with the middle reaches in between. In addition, the soil moisture simulated by CLM3.5 is systematically greater than the observations in the Yellow River basin. Comparisons between the simulations by CLM3.5 and CLM3.0 indicate that simulation errors are primarily caused by deficiencies within CLM3.5 and are also associated with the quality of atmospheric forcing field applied.

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