Influence of Meteorological Elements on Summer Vegetation Coverage in North China
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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。Abstract: Vegetation coverage is extremely sensitive to climate change. North China is located in the semiarid and subhumid transitional region of China. Meteorological factors have an important impact on the vegetation coverage in this area. However, effective mathematical models to quantitatively describe the influences of meteorological elements on vegetation are lacking. Therefore, based on the vegetation coverage data of the Moderate Resolution Imaging Spectroradiometer and main meteorological element data in North China from 2000 to 2018, the authors analyzed the influence of multiple meteorological elements on vegetation coverage and initially established the relationship between the summer vegetation coverage and meteorological elements in North China. The main study conclusions are summarized as follows: (1) A trend toward warm and dry climate conditions in North China is observed, and summer vegetation coverage is positively correlated with precipitation and relative humidity and negatively correlated with temperature, sunshine hours, and ground temperature. (2) The most important meteorological element affecting the vegetation coverage in North China is relative humidity, which reflects the combined effect of temperature and precipitation. (3) The multiple regression and least squares methods can quantitatively describe the possible impact of the changes in meteorological elements on vegetation coverage. Therefore, the five-variable combination of meteorological elements can better simulate the change in vegetation coverage. Results elucidate how meteorological elements affect vegetation ecosystem and provide a theoretical reference for constructing national ecological civilization.
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Key words:
- North China /
- Vegetation coverage /
- Meteorological elements /
- Evaluation model
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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 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
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 P 0.47* 0.45 0.26 0.42 RH 0.68** 0.57* 0.23 0.23 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。 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 T、P、RH、SD、GST 0.76* 0.0133 81% 0.85** 0.0116 88% 四变量模型 T、P、SD、GST 0.76* 0.0127 81% 0.84** 0.0117 88% T、RH、SD、GST 0.75* 0.0130 81% 0.83** 0.0119 88% P、RH、SD、GST 0.73* 0.0136 88% 0.85** 0.0114 88% T、P、RH、GST 0.70* 0.0142 75% 0.82** 0.0124 75% T、P、RH、SD 0.69* 0.0144 94% 0.83** 0.0120 88% 三变量模型 T、SD、GST 0.74** 0.0126 81% 0.80** 0.0130 94% P、SD、GST 0.72* 0.0131 88% 0.85** 0.0114 88% RH、SD、GST 0.70* 0.0136 88% 0.84** 0.0118 88% P、RH、GST 0.69* 0.0139 75% 0.83** 0.0122 75% T、RH、GST 0.68* 0.0140 75% 0.81** 0.0127 81% T、P、GST 0.68* 0.0140 75% 0.80** 0.0130 75% T、RH、SD 0.68* 0.0141 88% 0.82** 0.0123 88% T、P、SD 0.67* 0.0142 94% 0.82** 0.0123 94% T、P、RH 0.66* 0.0145 81% 0.81** 0.0128 81% P、RH、SD 0.66* 0.0146 94% 0.81** 0.0127 94% 二变量模型 RH、GST 0.67** 0.0138 81% 0.82** 0.0126 75% P、GST 0.66** 0.0139 81% 0.81** 0.0127 75% RH、SD 0.65* 0.0141 94% 0.81** 0.0128 94% SD、GST 0.64* 0.0143 88% 0.80** 0.0129 81% P、RH 0.62* 0.0148 88% 0.75** 0.0146 81% P、SD 0.62* 0.0148 94% 0.79** 0.0133 94% T、P 0.59* 0.0152 81% 0.77** 0.0138 81% T、GST 0.59* 0.0152 75% 0.77** 0.0138 75% T、RH 0.58* 0.0154 81% 0.80** 0.0131 81% T、SD 0.58* 0.0154 88% 0.76** 0.0139 88% 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。 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 2000~2015 2016~2018 多元线性回归 T、P、RH、SD、GST y=0.0007+0.0383T+0.0001P−0.0002RH−0.0197SD−0.0365GST 0.76* 0.0133 81% 100% T、P、SD、GST y=0.0008+0.0375T+0.0001P−0.0193SD−0.0358GST 0.76* 0.0127 81% 100% T、SD、GST y=0.0002+0.0500T−0.0235SD−0.0431GST 0.74** 0.0126 81% 100% RH、GST y=0.0019+0.0057RH−0.0119GST 0.67** 0.0138 81% 67% 偏最小二乘回归 T、P、RH、SD、GST y=−00047T+0.0001P+0.0028RH−0.0127SD−0.0062GST 0.84** 0.0116 88% 100% P、RH、SD、GST y=0.0001P+0.0027RH−0.0145SD−0.0096GST 0.85** 0.0114 88% 100% P、SD、GST y=0.0001P−0.0165SD−0.0126GST 0.85** 0.0114 88% 100% RH、GST y=0.0057RH−0.0119GST 0.82** 0.0126 75% 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。 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 2000~2015 2000~2015 2000~2015 2016 2017 2018 2016~2018 多元线性回归 0.46 0.044 68% 49% 59% 56% 55% 偏最小二乘回归 0.61 0.039 71% 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。 -
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