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气象要素对华北地区夏季植被覆盖度的影响

白慧敏 龚志强 孙桂全 李莉 周莉

白慧敏, 龚志强, 孙桂全, 等. 2022. 气象要素对华北地区夏季植被覆盖度的影响[J]. 大气科学, 46(1): 1−13 doi: 10.3878/j.issn.1006-9895.2102.20233
引用本文: 白慧敏, 龚志强, 孙桂全, 等. 2022. 气象要素对华北地区夏季植被覆盖度的影响[J]. 大气科学, 46(1): 1−13 doi: 10.3878/j.issn.1006-9895.2102.20233
BAI Huimin, GONG Zhiqiang, SUN Guiquan, et al. 2022. Influence of Meteorological Elements on Summer Vegetation Coverage in North China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 1−13 doi: 10.3878/j.issn.1006-9895.2102.20233
Citation: BAI Huimin, GONG Zhiqiang, SUN Guiquan, et al. 2022. Influence of Meteorological Elements on Summer Vegetation Coverage in North China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 1−13 doi: 10.3878/j.issn.1006-9895.2102.20233

气象要素对华北地区夏季植被覆盖度的影响

doi: 10.3878/j.issn.1006-9895.2102.20233
基金项目: 国家重点研发计划项目2018YFA0606301、2018YFE0109600,国家自然科学基金项目41875100、42075057、42075029
详细信息
    作者简介:

    白慧敏,女,1996年出生,硕士,主要从事气候变化对华北生态影响研究。E-mail: bhm0828@126.com

    通讯作者:

    龚志强,男,1981年出生,研究员,主要从事动力—统计预测和区域性低温研究。E-mail: gzq0929@126.com

  • 中图分类号: P467

Influence of Meteorological Elements on Summer Vegetation Coverage in North China

Funds: National Key Research and Development Program of China (Grant Nos. 2018YFA0606301, 2018YFE0109600), National Natural Science Foundation of China (Grant Nos. 41875100, 42075057, 42075029)
  • 摘要: 植被覆盖对气候变化极其敏感,华北区地处我国半干旱—半湿润过渡地区,气象因子对该地区植被覆盖有重要的影响,但缺少有效的数理模型定量刻画气象要素对植被的影响。因此,本文基于华北区2000~2018年中分辨率成像光谱仪的植被覆盖数据和主要气象要素数据,开展了多气象要素影响植被覆盖度的研究,初步建立了华北夏季植被覆盖度与气象要素的关系。研究表明:(1)华北存在向暖干转变的趋势,且夏季植被覆盖度与降水、相对湿度呈正相关,与气温、日照时数和地温呈负相关。(2)影响华北地区植被覆盖度最重要的气象要素是相对湿度,体现了温度和降水的共同作用。(3)基于多元回归法和最小二乘法可以定量描述气象要素变化对植被覆盖度的可能影响。其中,五变量影响模型对植被覆盖度模拟的相关系数略偏高,因此,基于五变量气象要素可以更好的模拟植被覆盖度的变化。研究结果有利于了解气象要素如何影响植被生态系统,进而为国家生态文明建设提供理论参考依据。
  • 图  1  2000~2018年各月份华北地区的植被覆盖度平均值的空间分布

    Figure  1.  Spatial distributions of the average vegetation coverage in North China for each month from 2000 to 2018

    图  2  2000~2018年华北地区夏季(a1–a3)平均植被覆盖度、(b1–b3)平均气温、(c1–c3)累积降水、(d1–d3)平均相对湿度、(e1–e3)平均日照时数、(f1–f3)平均地温的年变化:(a1–f1)原始变化曲线;(a2–f2)去线性趋势后的变化曲线;(a3–f3)累积距平曲线。p<0.01表示通过99%信度水平的显著性检验,p>0.05表示未通过95%信度水平的显著性检验

    Figure  2.  Annual variations of (a1–a3) mean vegetation coverage (CV), (b1–b3) mean temperature (T), (c1–c3) cumulative precipitation (P), (d1–d3) mean relative humidity (RH), (e1–e3) mean sunshine duration (SD), (f1–f3) mean ground surface temperature (GST) in summer in North China from 2000 to 2018: (a1–f1) original change curves; (a2–f2) change curves after de-linear trend regression; (a3–f3) cumulative anomalies curves. p<0.01 indicates passing the test at 99% confidence level, and p>0.05 indicates failing the test at 95% confidence level

    图  3  2000~2018年华北夏季植被覆盖度(a)平均值、(b)线性趋势系数、(c)线性趋势显著性水平、(d)变异系数的空间分布

    Figure  3.  Spatial distributions of (a) mean value, (b) linear trend coefficients, (c) linear trend significance level, and (d) variation coefficient for summer vegetation coverage in North China from 2000 to 2018

    图  4  2000~2018年华北夏季(a)温度(单位:°C)、(b)降水(单位:mm)、(c)相对湿度、(d)日照时数(单位:h)以及(e)地温(单位:°C)的多年气候态空间分布

    Figure  4.  Climatological spatial distributions of (a) temperature (units: °C), (b) precipitation (units: mm), (c) relative humidity, (d) sunshine duration (units: h), and (e) ground surface temperature (units: °C) in summer in North China from 2000 to 2018

    图  5  2000~2018年华北夏季(6~8月)植被覆盖度与(a1–e1)夏季(6~8月)、(a2–e2)夏季超前一个月(5~7月)的气温、降水、相对湿度、日照时数和地温的相关系数。NC(Negative correlation)表示负相关,PC(Positive correlation)表示正相关,p<0.1表示通过90%信度水平的显著性检验,p>0.1表示未通过90%信度水平的显著性检验

    Figure  5.  Correlations between vegetation coverage in summer (June–August) and temperature, precipitation, relative humidity, sunshine duration, and ground surface temperature (a1–e1) in summer (June–August), (a2–e2) in May–July in North China from 2000 to 2018. NC denotes negative correlation; PC denotes positive correlation; p<0.1 indicates passing the test at 90% confidence level; and p>0.1 indicates failing the test at 90% confidence level

    图  6  2000~2018年华北地区去除线性趋势后的植被覆盖度的观测值、8个模型拟合值。2000~2015年为历史拟合值,2016~2018年为独立样本模拟值

    Figure  6.  Observed and fitting values of eight models of VCR (Vegetation Coverage Residual) in North China from 2000 to 2018. 2000–2015: historical fitted values; 2016–2018: independent sample simulated values

    图  7  2016~2018年华北夏季植被覆盖度的(a1–a3)观测值、(b1–b3)多元线性回归(MLR)方法的模拟值、(c1–c3)偏最小二乘回归(PLS)方法的模拟值

    Figure  7.  (a1–a3) Observed values, (b1–b3) fitted values for MLR (Multiple Linear Regression), (c1–c3) fitted values for PLS (Partial Least Squares Regression) of VCR in North China from 2016 to 2018

    表  1  2000~2018年华北夏季(6~8月)植被覆盖度与不同时间段(6~8月、5~7月、4~6月、3~5月)的各气象要素的相关系数

    Table  1.   Correlation coefficients between vegetation coverage in summer (June–August) and meteorological elements in different periods (June–August, May–July, April–June, March–May) in North China from 2000 to 2018

    6~8月植被覆盖度与不同时间段气象要素的相关系数
    6~8月5~7月4~6月3~5月
    T−0.39−0.58*−0.33−0.35
    P0.47*0.450.260.42
    RH0.68**0.57*0.230.23
    SD−0.75***−0.72***−0.23−0.28
    GST−0.61**−0.68**−0.40−0.41
      注:******分别表示通过95%、99%、99.9%信度水平的显著性检验。
    下载: 导出CSV

    表  2  气象要素对植被覆盖度影响的定量化模型的统计数据

    Table  2.   Statistical data of quantitative model for the impact of meteorological elements on vegetation coverage

    自变量多元线性回归偏最小二乘回归
    复相关系数均方根误差符号一致率相关系数均方根误差符号一致率
    五变量模型TP、RH、SD、GST0.76*0.013381%0.85**0.011688%
    四变量模型TP、SD、GST0.76*0.012781%0.84**0.011788%
    T、RH、SD、GST0.75*0.013081%0.83**0.011988%
    P、RH、SD、GST0.73*0.013688%0.85**0.011488%
    TP、RH、GST0.70*0.014275%0.82**0.012475%
    TP、RH、SD0.69*0.014494%0.83**0.012088%
    三变量模型T、SD、GST0.74**0.012681%0.80**0.013094%
    P、SD、GST0.72*0.013188%0.85**0.011488%
    RH、SD、GST0.70*0.013688%0.84**0.011888%
    P、RH、GST0.69*0.013975%0.83**0.012275%
    T、RH、GST0.68*0.014075%0.81**0.012781%
    TP、GST0.68*0.014075%0.80**0.013075%
    T、RH、SD0.68*0.014188%0.82**0.012388%
    TP、SD0.67*0.014294%0.82**0.012394%
    TP、RH0.66*0.014581%0.81**0.012881%
    P、RH、SD0.66*0.014694%0.81**0.012794%
    二变量模型RH、GST0.67**0.013881%0.82**0.012675%
    P、GST0.66**0.013981%0.81**0.012775%
    RH、SD0.65*0.014194%0.81**0.012894%
    SD、GST0.64*0.014388%0.80**0.012981%
    P、RH0.62*0.014888%0.75**0.014681%
    P、SD0.62*0.014894%0.79**0.013394%
    TP0.59*0.015281%0.77**0.013881%
    T、GST0.59*0.015275%0.77**0.013875%
    T、RH0.58*0.015481%0.80**0.013181%
    T、SD0.58*0.015488%0.76**0.013988%
      注:*表示通过99%信度水平的显著性检验,**表示通过99.9%信度水平的显著性检验。
    下载: 导出CSV

    表  3  四组气象要素影响植被模型中的最优模型及其统计数据

    Table  3.   Best model and statistical data of the four groups of meteorological factors affecting vegetation coverage

    方法自变量模型方程相关系数RMSE符号一致率
    2000~20152016~2018
    多元线性回归TP、RH、SD、GSTy=0.0007+0.0383T+0.0001P−0.0002RH−0.0197SD−0.0365GST0.76*0.013381%100%
    TP、SD、GSTy=0.0008+0.0375T+0.0001P−0.0193SD−0.0358GST0.76*0.012781%100%
    T、SD、GSTy=0.0002+0.0500T−0.0235SD−0.0431GST0.74**0.012681%100%
    RH、GSTy=0.0019+0.0057RH−0.0119GST0.67**0.013881%67%
    偏最小二乘回归TP、RH、SD、GSTy=−00047T+0.0001P+0.0028RH−0.0127SD−0.0062GST0.84**0.011688%100%
    P、RH、SD、GSTy=0.0001P+0.0027RH−0.0145SD−0.0096GST0.85**0.011488%100%
    P、SD、GSTy=0.0001P−0.0165SD−0.0126GST0.85**0.011488%100%
    RH、GSTy=0.0057RH−0.0119GST0.82**0.012675%67%
      注:*表示通过99%信度水平的显著性检验,**表示通过99.9%信度水平的显著性检验。
    下载: 导出CSV

    表  4  五变量模型模拟植被覆盖度的统计量

    Table  4.   Statistical value of the five-variable (temperature, precipitation, relative humidity, sunshine duration, ground surface temperature) model simulating vegetation coverage

    方法相关系数均方根误差符号一致率
    2000~20152000~20152000~20152016201720182016~2018
    多元线性回归 0.460.04468%49%59%56%55%
    偏最小二乘回归0.610.03971%51%59%62%57%
      注:2000~2015年为建模拟合值,2016~2018年为独立样本模拟值。
    下载: 导出CSV
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  • 收稿日期:  2020-11-24
  • 录用日期:  2021-07-16
  • 网络出版日期:  2021-08-27

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