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中国地区土壤湿度记忆性及其与降水特征变化的关系

赵家臻 王爱慧 王会军

赵家臻, 王爱慧, 王会军. 2021. 中国地区土壤湿度记忆性及其与降水特征变化的关系[J]. 大气科学, 45(4): 799−818 doi: 10.3878/j.issn.1006-9895.2007.20149
引用本文: 赵家臻, 王爱慧, 王会军. 2021. 中国地区土壤湿度记忆性及其与降水特征变化的关系[J]. 大气科学, 45(4): 799−818 doi: 10.3878/j.issn.1006-9895.2007.20149
ZHAO Jiazhen, WANG Aihui, WANG Huijun. 2021. Soil Moisture Memory and Its Relationship with Precipitation Characteristics in China Region [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 799−818 doi: 10.3878/j.issn.1006-9895.2007.20149
Citation: ZHAO Jiazhen, WANG Aihui, WANG Huijun. 2021. Soil Moisture Memory and Its Relationship with Precipitation Characteristics in China Region [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 799−818 doi: 10.3878/j.issn.1006-9895.2007.20149

中国地区土壤湿度记忆性及其与降水特征变化的关系

doi: 10.3878/j.issn.1006-9895.2007.20149
基金项目: 国家自然科学基金41875106
详细信息
    作者简介:

    赵家臻,男,1997年出生,硕士研究生,主要从事陆—气相互作用研究。E-mail: 15195901336@163.com

    通讯作者:

    王爱慧,E-mail: wangaihui@mail.iap.ac.cn

  • 中图分类号: P461+.4

Soil Moisture Memory and Its Relationship with Precipitation Characteristics in China Region

Funds: National Natural Science Foundation of China (Grant 41875106)
  • 摘要: 本文首先利用中国气象局国家气象信息中心提供的中国732个站点观测的土壤体积含水量,评估了CLM4.5(Community Land Model version 4.5)在CFSR(Climate Forecast System Reanalysis)近地面大气数据驱动下模拟的逐月土壤湿度(记为CLM4.5-CFSR),然后基于CLM4.5-CFSR比较了皮尔逊相关法和自相关法计算得到的1980~2009年中国地区土壤湿度记忆性的区域及季节分布特征,量化了土壤湿度的记忆能力,研究了降水频率、降水强度和近地表气温分别对土壤湿度记忆性的影响。结果表明:CLM4.5-CFSR能较好地反映出大部分地区月时间尺度上土壤湿度的变化特征。两种方法描述的土壤湿度记忆性的空间分布特征相似,但季节特征不同。不同深度土壤湿度的记忆时长相差不大,在0.85~2.2个月不等,其中内蒙古东北部较大,新疆西南部较小。春季,较湿的土壤记忆性也较强。当降水频率较低时,其对蒸发速率较大的地区土壤湿度的记忆性影响很小,当降水强度较大时,它会迅速补充土壤散失的水分,破坏初始时刻土壤的干湿状态,引起其记忆性减弱。近地表气温变化主要通过影响土壤的蒸发过程减弱土壤湿度的记忆性。未来可利用气候模式开展数值敏感性试验对本文得到的结论进行机理研究,为进一步提高季节和季节内尺度的降水预报提供依据。
  • 图  1  1992~2009年4~9月,模拟和观测得到的中国732个站点平均的0~10 cm逐月土壤湿度距平序列,图中Std1和Std2分别为模拟(CLM4.5-CFSR)和观测(OBS)序列的标准差(单位:mm3 mm−3),R代表两个序列之间的相关系数,*表示通过0.05显著性水平检验

    Figure  1.  Monthly soil moisture anomalies at 0–10 cm depth averaged over 732 stations in China region from CLM4.5-CFSR (Community Land Model version 4.5 (CLM4.5) driven by CFSR near surface meteorological data) and observations from April to September during 1992–2009. In the figure, Std1 and Std2 represent the standard deviations of the simulated and observed time series, respectively, R represents the correlation coefficient between the two time series, and “*” indicates that R is passing 95% significant level test

    图  2  模拟(CLM4.5-CFSR)和观测(OBS)的新疆(XJ)31个站点和西藏(Tibet)4个站点平均的1992~2009年6月0~10 cm土壤湿度距平序列,图中Std1和Std2分别为模拟和观测序列的标准差(单位:mm3 mm−3),R代表两个序列之间的相关系数,*表示通过0.05显著性水平检验

    Figure  2.  Monthly soil moisture anomalies at 0–10 cm depth averaged over 31 stations in Xinjiang (XJ) and 4 stations in Tibet (Tibet) from model simulations and observations in June for 1992–2009. In the figure, Std1 and Std2 represent the standard deviations of the simulated and observed time series, respectively, R represents the correlation coefficient between the two time series, and “*” indicates that R is passing 95% significant level test

    图  3  中国八个气候分区

    Figure  3.  Eight climate zones in China

    图  4  皮尔逊相关法(ρ1)和自相关法(ρ2)计算得到的中国范围内(a1–a3)春季(MAM)、(b1–b3)夏季(JJA)、(c1–c3)秋季(SON)和(d1–d3)冬季(DJF)0~10 cm(左列)、10 cm~1 m(中间列)、1~2 m(右列)土层深度ρ的散点分布,黑色实线为线性拟合线

    Figure  4.  Scatterplots of ρ calculated by Pearson correlation method (ρ1) and autocorrelation method (ρ2) at 0–10 cm (left column), 10 cm–1 m (middle column), and 1–2 m (right column) soil depths in (a1–a3) spring (MAM), (b1–b3) summer (JJA), (c1–c3) autumn (SON) and (d1–d3) winter (DJF) in China. The black solid line is the linear fitting line

    图  5  皮尔逊相关法(黑色)和自相关法(灰色)计算的中国区域不同季节[春季(MAM),夏季(JJA),秋季(SON)和冬季(DJF)]和不同土层深度ρ的(a)平均值(AVG)和(b)变差系数(CV)分布的柱状图

    Figure  5.  Bar chart of ρ’s (a) average (AVG) and (b) coefficient of variation (CV) calculated by Pearson correlation method (black bar) and autocorrelation method (grey bar) in spring (MAM), summer (JJA), autumn (SON) and winter (DJF) at different soil depths in China

    图  6  自相关法计算的中国(a1–a3)春季(MAM)、(b1–b3)夏季(JJA)、(c1–c3)秋季(SON)和(d1–d3)冬季(DJF)0~10 cm(左列)、10 cm~1 m(中间列)、1~2 m(右列)深度土壤湿度时间序列滞后一个月的自相关系数

    Figure  6.  1-month-lag autocorrelation coefficient of soil moisture time series calculated by autocorrelation method at 0–10 cm (left column), 10 cm–1 m (middle column), and 1–2 m (right column) soil depths in (a1–a3) spring (MAM), (b1–b3) summer (JJA), (c1–c3) autumn (SON) and (d1–d3) winter (DJF) in China

    图  7  图6,但为土壤湿度的记忆时长(单位:月)

    Figure  7.  Same as Fig. 6, but for duration (units: months) of soil moisture memory

    图  8  土壤湿度与对应层土壤湿度记忆性的相关系数在不同季节的分布:(a)春季、(b)夏季、(c)秋季和(d)冬季。打点区域为相关系数通过0.05显著性水平检验的区域

    Figure  8.  The distribution of the correlation coefficients between soil moisture and its memory in the corresponding layer in different seasons: (a) Spring, (b) summer, (c) autumn and (d) winter. Stippled area denotes correlation coefficients pass 95% significant level test

    图  9  自相关法计算的春季浅层土壤(0~10 cm)ρ2与土壤湿度的散点分布:(a)东北;(b)华中;(c)华东;(d)华南;(e)西南;(f)华北;(g)青藏高原;(f)西北。图中,R为相关系数,“*”表示相关系数通过0.05显著性水平检验,黑色实线为线性拟合线

    Figure  9.  Scatterplots of ρ2 calculated by autocorrelation method versus soil moisture at the top soil layer (0–10 cm) in spring: (a) Northeast; (b) Central China; (c) East China; (d) South China; (e) Southwest China; (f) North China; (g) Qinghai–Tibet Plateau; and (h) Northwest China. Among them, R is the correlation coefficient, with “*” indicating that the correlation coefficient is passing the 95% significance test, the black solid line is the linear fitting line

    图  10  图9,但为浅层土壤(0~10 cm)ρ2与降水频率(PF)的散点分布

    Figure  10.  Same as Fig. 9, but for the scatterplots of ρ2 at the top soil layer (0–10 cm) versus precipitation frequency (PF)

    图  11  图9,但为浅层土壤(0~10 cm)ρ2与降水强度(PI)的散点分布

    Figure  11.  Same as Fig. 9, but for the scatterplots of ρ2 at the top soil layer (0–10 cm) versus precipitation intensity (PI)

    图  12  图9,但表示浅层土壤(0~10 cm)ρ2与近地表气温(Ta)的散点分布

    Figure  12.  Same as Fig. 9, but for the scatterplots of ρ2 at the top soil layer (0–10 cm) versus near-surface temperature (Ta)

    表  1  皮尔逊相关法和自相关法分别计算的中国地区四个季节和三个深度层$\;\rho_1 $$\;\rho_2 $的空间相关系数。其中,数值右边带有“*”的表示通过0.01显著性水平检验。全国共有3819个格点

    Table  1.   The spatial correlation coefficients between $\;\rho_1 $ calculated by the Pearson correlation method and $\;\rho_2 $ calculated by the autocorrelation method in four seasons and at the layers of three depths in China. Among them, those with “*” on the right of the value indicate that they pass the 99% significance test. There are 3819 grid points across the country

    土层深度
    ρ1ρ2的空间相关系数
    春季夏季秋季冬季
    0~10 cm0.55*0.33*0.34*0.68*
    10 cm~1 m0.61*0.12*0.12*0.55*
    1~2 m0.55*0.19*0.09*0.50*
    下载: 导出CSV

    表  2  中国八个气候分区,四个季节三个深度层土壤湿度的记忆时长(单位:月)

    Table  2.   Duration of soil moisture memory at the layers of three depths in four seasons in China’ s eight climatic zones (units: month)

    地区土壤湿度的记忆时长/月
    春季夏季
    0~10 cm10 cm~1 m1~2 m0~10 cm10 cm~1 m1~2 m
    东北1.511.561.581.921.851.80
    华中1.421.451.471.941.911.86
    华东1.421.471.491.891.871.82
    华南1.461.481.491.901.881.83
    西南1.401.451.481.901.891.85
    华北1.511.611.592.021.901.83
    青藏高原1.461.491.481.921.891.83
    西北1.391.431.441.811.801.79
    地区秋季冬季
    0~10 cm10 cm~1 m1~2 m0~10 cm10 cm~1 m1~2 m
    东北1.541.491.501.221.181.17
    华中1.481.471.461.201.171.16
    华东1.461.461.471.211.171.15
    华南1.591.501.471.181.161.14
    西南1.521.481.481.121.141.14
    华北1.561.471.451.221.171.14
    青藏高原1.491.451.471.191.191.17
    西北1.471.431.441.121.111.11
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-27
  • 录用日期:  2020-07-28
  • 网络出版日期:  2020-11-24
  • 刊出日期:  2021-07-15

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