Spatiotemporal evolution and predictive analysis of vegetation patterns in the Bayanbulak Grassland under climate change

Zunyou Lv and Yimamu Maimaiti

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: Climate change has profound impacts on the distribution and dynamics of vegetation within ecosystems in arid and semi-arid regions. To uncover the feedback mechanisms between climate and vegetation, this study employs a mathematical model based on reaction–diffusion mechanisms to investigate the effects of climate change (precipitation, temperature, and atmospheric CO2 concentration) on vegetation patterns in the Bayanbulak Grassland. To assess the potential and rate of grassland ecosystem restoration under future climate conditions, historical climate data from the Bayanbulak Grassland is analyzed to develop a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) time-series model, which is then used to predict future climate trends to further study the future trends of vegetation cover changes in the Bayanbulak Grassland. Under current climatic conditions, while rising temperatures may reduce vegetation density, increased precipitation and CO2 concentrations collectively offset this effect, leading to an overall positive growth trend in vegetation density. Additionally, the predictions suggest that over the next 30 years, with continuous increases in temperature, precipitation, and atmospheric CO2 concentration, vegetation density is expected to rise further. This study not only reveals the response mechanisms of the Bayanbulak Grassland’s vegetation to climate change but also provides a scientific basis for evaluating the impact of future climate change on the ecosystems of the Bayanbulak Grassland. These findings offer valuable insights for developing effective policies to protect and restore grassland ecosystems.

Keywords: Vegetation pattern; Dynamic model; Observed climate data; SARIMA forecast (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014936

DOI: 10.1016/j.chaos.2025.117480

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