Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

Abstract

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.

Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

“…et al showed that GPP increased by 3.9% per decade. The GPP obtained in this experiment increased by 7.82% over the 20-year period, with an average increase of 3.91% per decade [47], which is basically consistent with the experimental results of Bo et al The regions with more significant growth are mainly located in South America, Central Africa, and parts of South Asia. While Zhao et al found that most of the vegetation GPP in the highlands showed a decreasing trend [48], the higher altitude areas in this experiment, such as the Andes in western South America, also showed a decreasing trend in the vegetation GPP; however, the GPP in the plains increased more rapidly, which is hypothesized to be affected by topographic factors.…”

Maximum Gross Primary Productivity Dominates the Trend in Gross Primary Productivity in China’s Deciduous Forest Ecosystems

“…Previous studies also showed a rapid increase in the annual GPP in the areas covered by deciduous forests. For example, the annual GPP of deciduous forests in the Qin Mountains all showed the largest increasing rates (>10 gC/m 2 /yr) [14,43,44]. The increasing rate of the forest GPP in the Greater Khingan Mountains was usually below 5 gC/m 2 /yr, which was consistent with the estimation from the Global Land Surface Satellite (GLASS) GPP product [45].…”

The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018

“…Even though there were many studies investigating GPP [14], it remains challenging to monitor GPP across different scales [15]. Over the past decades, eddy covariance (EC) technologies have become crucial tools for deriving GPP through direct observations of CO 2 exchange at the canopy-atmosphere interface [16].…”