Temporal trends and future projections: Analysing land surface temperature in the Kumaun Himalayas using spatial time series analysis

Abstract

In this ground-breaking study, we introduced a novel approach for projecting Land Surface Temperature (LST) in the Kumaun Himalayas, an area critical for understanding regional impacts of global warming. The novelty of this study lies in the utilization of spatial time series analysis, a method that not previously applied for future LST prediction. In this study we examined LST trends from 1990 to 2020 and predicted LST for the year 2030 using satellite-based remote sensing data for LST estimation, advanced statistical techniques such as the Simple Moving Average (SMA), Sen's Slope, and z-statistics with excellent statistical power. The application of z-statistics provides a robust framework for assessing temperature changes, with significant findings such as a z-statistics value of −15.04 for spring, indicating a marked shift in temperature patterns. Similarly, for autumn, the z-statistics value of −21.41 underscores a drastic deviation from historical norms i.e., from 1990 to 2020. Pearson's correlation and the coefficient of determination were used to validate the accuracy of satellite-based LST estimates and SMA. A correlation of 0.93 and R² of 0.87 were found between observed and estimated LSTs, while the SMA-based LST showed a correlation of 0.92 with estimated LST with R² of 0.85. The results highlight a future that is significantly warmer than the present, bringing into sharp focus the urgency of climate change mitigation and adaptation strategies in this ecologically sensitive region. The study also suggested differential rate of seasonal warming. The study is not only pivotal for local climate policy but also contribute significantly to the broader understanding of climate dynamics in mountainous terrains is seasonal variation in warming rates. Despite challenges like rugged terrain and variable cloud cover affecting data accuracy, our approach offered a scalable model for similar climatic studies in other regions, marking a significant advancement in the field of climate change.