Diverse responses of spring phenology to preseason drought and warming under different biomes in the North China Plain

Abstract

Global-change-type drought, a combination of drought and warmer temperatures, is projected to have severe effects on vegetation growth and ecosystem functions. Spring phenology is an important biological indicator to understand the response of vegetation growth to climate change. However, the differences in the response of spring phenology to global-change-type drought among various vegetation types remain unclear. Here, we extracted the start of growing season (SOS) from NDVI (Normalized Difference Vegetation Index) data using Spline-midpoint, HANTS-Maximum, and Timesat-SG methods in the North China Plain over the period 1982–2015. Then, we investigated the effects of preseason drought on SOS (based on the Standardized Precipitation Evapotranspiration Index, SPEI), and compared responses of SOS to the minimum temperature (Tmin), maximum temperature (Tmax), and mean temperature (Tmean) in different biomes. Results showed a trend of advanced SOS in 81.7% of pixels in the North China Plain, with an average rate of −0.5 days/yr. Negative correlations were found between preseason SPEI and SOS in 72.1% of the study region, and the SOS of grassland showed the least resistance to drought. Interannual variations of SOS were triggered by Tmin more than by Tmax in the North China Plain. Multiple regression analysis exhibited that a 1 °C increase in Tmin would advance SOS by 10.5, 7.6, 2.9, 2.1 days for wheat, other crops, forests, and grasslands, indicating warming displayed greater effects on advancing the SOS of wheat. Considering the coupled effects of preseason drought and warming on spring phenology, future warming would trigger earlier spring green-up, while drought might slow the trend. Besides, nonlinear responses of SOS to preseason SPEI and Tmin along the humidity gradient were discovered. This research provides a new reference for the biome-specific and nonlinear responses in phenology models to promote the understanding of phenology changes, contributing to ecosystem management under future global-change-type drought.