Spatial Time Series Analysis of Warming Patterns in the Kumaun Himalayas: A Future Perspective

Abstract

Abstract In this groundbreaking study, we introduce a novel approach to forecasting Land Surface Temperature (LST) in the Kumaun Himalayas, an area critical for understanding regional impacts of global warming. The novelty of our research lies in the utilization of spatial time series analysis, a method not previously applied for future LST prediction. Combined adoption of remote sensing and advanced statistical techniques such as the Simple Moving Average (SMA), Sen’s Slope, and z-statistics with excellent statistical power, our study analyses LST trends from 1990 to 2030 using comprehensive Landsat data. Notably, 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. These values 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 suggests 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 offers a scalable model for similar climatic studies in other regions, marking a significant advancement in the field of climate change.