Quantifying the Lagged Effects of Climate Factors on Vegetation Growth in 32 Major Cities of China

Abstract:

Climate change is affecting the growth of vegetation around the world. It has been realized that the effect of climate change on vegetation growth has hysteresis in addition to spatial and temporal variability. However, the hysteresis effects with varying lagged time lengths have largely unexplored. In this study, we analyzed the impacts of climate on vegetation growth in 32 major cities in China from January 2010 to November 2017. Vegetation growth conditions were characterized using enhanced vegetation index (EVI) datasets from Moderate Resolution Imaging Spectrometer (MODIS). The climate data were extracted from the Daily Value Data Set of China Surface Climate Data(V3.0), including the daily 0cm ground surface temperature (GST), precipitation (PRE), atmospheric pressure (PRS), humidity (RHU), air temperature (TEM), sunshine duration (SSD), wind speed (WIN), and evaporation (EVP). We used the vector error correction model (VECM) and the vector autoregressive model (VAR) to analyze the instant and lagged effects of climate factors on EVI, and the models were validated using data from the last two years. Results showed that, among the climatic factors, EVP, GST, TEM, SSD, and WIN had significant effects on the instant and short-term lagged effect (lagged periods<5, each period consisted of 16 days) of vegetation growth, and the effects in the south were stronger than in the north. PRE and SSD had a positive influence on the growth of EVI in the long-term during the first 10 lagged periods, and then the positive impact gradually weakened. The EVI models performed well during validation with the root mean square error (RMSE) for each of the cities less than 0.1, of which the smallest was only 0.01. The methods developed in this study can be used to predict vegetation EVI dynamics and reveal the instant and lagged effects of climatic factors on vegetation growth.