The role of climatic factor timing on grassland net primary productivity in Altay, Xinjiang

Abstract

Grassland, as highly vulnerable ecosystem, requires a comprehensive understanding of its dynamics and response patterns to climate factors in response to climate change challenges. While previous research has primarily centered on the influence of interannual climate variability on grassland Net Primary Productivity (NPP), knowledge of the impacts of seasonal or monthly climate variations on annual net primary productivity (ANPP) remains limited. This study investigated the climatic drivers of grassland NPP dynamics in Xinjiang's Altay region from 2000 to 2022 using the Carnegie-Ames-Stanford approach (CASA) model and the random forest regression model. The study examined the significance and patterns of precipitation, solar radiation, temperature, soil moisture, and snowmelt water on NPP at three temporal scales. The results revealed the following key findings: (1) Grassland NPP declined significantly from 2000 to 2009 but showed a gradual increase from 2009 to 2022. Spatially, higher values were observed in the northern region and lower values were observed in the southern region. (2) Precipitation was the primary influential climate factor affecting grassland NPP, followed by solar radiation, temperature, soil moisture, and snowmelt water. In determining the significance of climate factor timing on ANPP, June played a critical role particularly for precipitation, temperature, and soil moisture, while August was essential for solar radiation. Moreover, the importance of snowmelt water had a bimodal distribution, with peaks in April and October. (3) Grassland NPP exhibited diverse nonlinear response dynamics and spatial heterogeneity to various climate factors at different temporal scales. These findings highlight the importance of considering both the magnitude and the local conditions, as well as the timing of climate variations when studying grassland dynamic responses to climate change and predicting future impacts. These insights enhance the comprehension of the intricate dynamics of grassland ecosystems and enhance predictions of their responses to future climate change.