Responses of Forest Net Primary Productivity to Climatic Factors in China during 1982–2015

Abstract

Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie–Ames–Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 1012 g C/a, and the average annual NPP per unit area was 650.73 g C/m2/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.

Spatiotemporal dynamics of net primary productivity and its influencing factors in the middle reaches of the Yellow River from 2000 to 2020

“…The reason was that the enhanced solar radiation made vegetation accept more light, photosynthesis was strengthened, and the accumulation of dry matter in vegetation was increased. The study was found that the effect of solar radiation on different vegetation types were different, and the effect of solar radiation on forests was stronger than that on precipitation and temperature (Du et al, 2022). In addition, it has been found that NPP is negatively correlated with solar radiation in most areas of the Loess Plateau (Xie et al, 2014), which was not consistent with the conclusion that NPP was mainly affected by solar radiation obtained in this paper.…”

“…Therefore, deciduous coniferous forests are widely distributed in this region, and the NPP of deciduous coniferous forests was lower than that of evergreen broad-leaved forests. Therefore, the NPP in the northwestern MRYR was low (Du et al, 2022). At present, there are relatively few research results on the inhibitory effect of precipitation on NPP, mainly on the Qinghai-Tibet Plateau .…”

Time Effects of Global Change on Forest Productivity in China from 2001 to 2017

“…We investigated the spatiotemporal changes in forest NPP in China from 2001 to 2017. NPP was calculated based on the CASA model, although its prediction accuracy is not perfect due to the differences in spatial resolution and the actual and simulated values of variable factors [ 22 ]. Nevertheless, it is widely recognized and applied in numerous macroecological studies [ 23 , 24 , 25 ].…”

“…NPP data from 2000 to 2017 were obtained at a 250 × 250 m resolution from NASA ( ) (accessed on 10 October 2022). The Carnegie-Ames-Stanford Approach (CASA) model was used to estimate NPP as follows [ 22 ]: where APAR ( x , t ) represents the photosynthetically active radiation (PAR, in units of MJ/m 2 ) absorbed at pixel x in month t , and ε ( x , t ) represents the actual light energy utilization at pixel x in month t (g C/MJ).…”

Human activities significantly impact China’s net primary production variation from 2001 to 2020

“…First, the statistical model-based method. This approach analyses the relative contribution depending on the linear correlation between NPP and its influencers (Du et al, 2022; Yang et al, 2017). It is a simple method but cannot portray the complex nonlinear relationship between NPP and its influencers well (Liu et al, 2020; Turner and Carpenter, 2017).…”