Temporal Climatic Shifts in Henan Province: A 16-decades Perspective Through Regression, SARIMA, and NAR Modeling

Abstract

Global warming is having a significant impact on all aspects of human production and life. This study employs a cross-sectional analysis to investigate the temporal dynamics of average temperature changes in Henan Province, China, from 1851 to 2012. Utilizing the Berkeley Earth Surface Temperature Data and the Daily Meteorological Dataset of China National Surface Weather Station v3.0, we applied regression analysis, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Nonlinear Autoregressive Network (NAR) models to predict temperature trends. Results indicate a significant warming trend over the 160-year period, with the models demonstrating strong predictive performance, albeit with some variability. The study underscores the increasing temperatures' implications for the province's agricultural sustainability and ecological balance. This study highlights the urgency of understanding and mitigating climate change's impacts, particularly in Henan Province, China, for the sake of agricultural sustainability, water resources, and public health. The research findings contribute valuable insights and methodologies to climate data analysis, aiding future predictions and policy-making efforts.

Keywords

• climatic predict
• temperature change
• henan province
• data-driven analysis
• sarima model
• nar network