Geomatics, Natural Hazards & Risk (Dec 2024)

Cloud computing-based estimation of Peninsular India’s long-term climate change impacts on rainfall, surface temperature, and geospatial indices

  • Bijay Halder,
  • Biswarup Rana,
  • Liew Juneng,
  • Chaitanya Baliram Pande,
  • Sultan Alshehery,
  • Mohamed Elsahabi,
  • Krishna Kumar Yadav,
  • Saad Sh. Sammen,
  • Sujay Raghavendra Naganna

DOI
https://doi.org/10.1080/19475705.2024.2381635
Journal volume & issue
Vol. 15, no. 1

Abstract

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Realizing the intricate relationships between drought, vegetation dynamics, and climate change is essential for sustainable resource management. Although temperature and rainfall patterns are the primary determinants of these fluctuations, human activity also plays a significant role. Recent decades have witnessed significant climate change events, particularly in peninsular India. Analyzing these year-by-year variations in rainfall and temperature is essential for informed decision-making. This knowledge can guide the development of innovative adaptation strategies to ensure sustainable livelihoods in the region. This study utilizes the Google Earth Engine platform to analyze yearly climate data and relevant geographical indices from 2003 to 2023 across Peninsular India. The analysis reveals a statistically significant increase in mean annual rainfall (0.262 mm/year) alongside a slight rise in regional land surface temperature (LST) trends (0.102 °C/year). However, yearly average anomaly values for LST also show an upward trend, rising from 2.56 in 2003 to 3.23 in 2023. This suggests a potential shift in rainfall patterns, with potential consequences for water availability. Rising temperatures coupled with altered rainfall patterns can lead to water scarcity, especially in regions reliant on rain-fed agriculture. This has a direct impact on crop yields and overall agricultural productivity. Despite rising temperatures, the analysis using drought indices suggests a decline in average annual drought severity across Peninsular India, with values decreasing from 0.33 in 2003 to 2.70 in 2023. Interestingly, we found a strong positive association between rainfall and vegetation indices, while rainfall and LST exhibited a negative correlation. Interestingly, while rainfall and LST exhibited a negative correlation, a strong positive association was found between rainfall and vegetation indices. These comprehensive findings hold significant potential for informing future climate projections and promoting sustainable development in peninsular India through evidence-based applications.

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