Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Kaifeng 475004, China.
Henan Collaborative Innovation Center of Urban-Rural Coordinated Development, Zhengzhou 450046, China.
Int J Environ Res Public Health. 2019 Apr 27;16(9):1497. doi: 10.3390/ijerph16091497.
The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%).
中国 400 毫米年等降水量线经过的地区生态环境极为敏感，对人类活动的干扰极其脆弱。然而，在气候变化的背景下，该地区植被净初级生产力（NPP）的响应尚未得到深入的科学研究。NPP 受到多种气候因素的综合影响，如何消除 NPP 与目标变量（温度或降水）相关分析中干扰变量的影响是研究 NPP 影响因素的主要挑战。在忽略对 NPP 有较大影响的其他变量的情况下，计算 NPP 与目标变量之间的相关系数，这增加了研究结果的不确定性。因此，在本研究中，采用二阶偏相关分析方法，通过控制其他变量来分析 NPP 与目标变量之间的相关性。这可以有效地降低分析结果的不确定性。本文采用单变量线性回归、变差系数和赫斯特指数估计来研究 NPP 的时空变化，并分析 NPP 的季节性和年际变化是否会持续到未来。结果表明：（i）NPP 与降水的空间分布相关，呈东南向西北逐渐减少的趋势。2000 年至 2015 年，研究区 NPP 呈总体上升趋势，范围变化较小。（ii）NPP 与降水呈负偏相关系数的区域与水资源较丰富的区域一致。NPP 与地表温度（LST）之间的偏相关系数有 52.64%的区域为正值。最后，对未来 NPP 变化持续的预测在不同的时间尺度上存在显著差异。在年际尺度上，预测研究区 46%的区域 NPP 将持续存在。在季节尺度上，预测秋季 NPP 占 49.92%，其次是春季（25.67%）、夏季（13.40%）和冬季（6.75%）。
