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

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

赵家臻^1,,
王爱慧^2, ,,
王会军^{1, 2, 3}

1.
南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室，南京 210044
2.
中国科学院大气物理研究所竺可桢—南森国际研究中心，北京 100029
3.
中国科学院气候变化研究中心，北京 100029

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
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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

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

Jiazhen ZHAO^1
,,
Aihui WANG^{2
, ,},
Huijun WANG^{1, 2, 3}

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044
2.
Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
3.
Climate Change Research Center, Chinese Academy of Sciences, Beijing 100029

Funds: National Natural Science Foundation of China (Grant 41875106)

摘要

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。
- 土壤湿度 /
- 记忆性 /
- 降水频率 /
- 降水强度 /
- 近地表气温
Abstract: This study first uses the volumetric soil water content data from 732 stations in China, as provided by the National Meteorological Information Center (NMIC) at the China Meteorological Administration (CMA), to evaluate the simulations of the Community Land Model version 4.5 (CLM4.5) driven by Climate Forecast System Reanalysis (CFSR) near surface meteorological data (referred as to CLM4.5-CFSR). Then the CLM4.5-CFSR is used to investigate the spatial-temporal characteristics of soil moisture memory in China region during 1980–2009. The soil moisture memory is calculated by both Pearson correlation method and autocorrelation method. The effects of precipitation frequency, precipitation intensity and near-surface temperature on soil moisture memory are then explored. The results show that CLM4.5-CFSR can reflect the soil moisture changes on the monthly time scale in China. The spatial distributions of soil moisture memory from two methods are similar, but their seasonal characteristics are different. The soil moisture memory duration does not vary very much with soil depth, ranging from 0.85–2.2 months across China with relatively larger magnitude in the northeast of the Inner Mongolia, while smaller in the southwest of Xinjiang Province. In spring, the wetter the soil, the longer the soil moisture memory. When the precipitation frequency is low, it has little effect on soil moisture memory in areas where the evaporation rate is high. When the precipitation intensity is high, soil moisture memory will be shortened by it because it can replenish the soil water rapidly and destroy the initially dry or wet conditions of the soil. Changes in the near-surface temperature will shorten the soil moisture memory through its effect on the soil evaporation. In the future, it is necessary to carry out climate model sensitivity experiments to gain insights on physical mechanisms associated with the conclusions obtained in the current study, which in turn to provide a basis for further improvements of the seasonal and intraseasonal precipitation prediction.
- Soil moisture /
- Memory /
- Precipitation frequency /
- Precipitation intensity /
- Near-surface temperature

HTML全文

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

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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

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图 3 中国八个气候分区

Figure 3. Eight climate zones in China

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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

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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

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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

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图 7 同图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

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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

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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

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图 10 同图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)

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图 11 同图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)

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图 12 同图9，但表示浅层土壤（0～10 cm）ρ₂与近地表气温（T_a）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 (T_a)

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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 $和$\;\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	ρ₁和ρ₂的空间相关系数
土层深度	春季	夏季	秋季	冬季
0～10 cm	0.55*	0.33*	0.34*	0.68*
10 cm～1 m	0.61*	0.12*	0.12*	0.55*
1～2 m	0.55*	0.19*	0.09*	0.50*

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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 cm	10 cm～1 m	1～2 m	0～10 cm	10 cm～1 m	1～2 m
东北	1.51	1.56	1.58	1.92	1.85	1.80
华中	1.42	1.45	1.47	1.94	1.91	1.86
华东	1.42	1.47	1.49	1.89	1.87	1.82
华南	1.46	1.48	1.49	1.90	1.88	1.83
西南	1.40	1.45	1.48	1.90	1.89	1.85
华北	1.51	1.61	1.59	2.02	1.90	1.83
青藏高原	1.46	1.49	1.48	1.92	1.89	1.83
西北	1.39	1.43	1.44	1.81	1.80	1.79

地区	秋季			冬季
地区	0～10 cm	10 cm～1 m	1～2 m	0～10 cm	10 cm～1 m	1～2 m
东北	1.54	1.49	1.50	1.22	1.18	1.17
华中	1.48	1.47	1.46	1.20	1.17	1.16
华东	1.46	1.46	1.47	1.21	1.17	1.15
华南	1.59	1.50	1.47	1.18	1.16	1.14
西南	1.52	1.48	1.48	1.12	1.14	1.14
华北	1.56	1.47	1.45	1.22	1.17	1.14
青藏高原	1.49	1.45	1.47	1.19	1.19	1.17
西北	1.47	1.43	1.44	1.12	1.11	1.11

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