AI Article Synopsis

  • Understanding daily precipitation concentration is essential for assessing extreme weather events like floods and soil erosion.
  • The study analyzes precipitation data from Himachal Pradesh, India, using various concentration indices and evaluates trends and correlations with geoclimatic factors over nearly 70 years (1951-2020).
  • Key findings highlight strong correlations among the indices, with the Q Index being the most sensitive to changes, influenced significantly by both physiographic and climatic drivers like the Atlantic Meridian Oscillation and Pacific Decadal Oscillation.

Article Abstract

A sound understanding of daily precipitation concentration provides critical insights about extreme precipitation and associated hazards such as rainstorms, flash floods, cloud bursts, and soil erosion. Therefore, this study examines the spatiotemporal variability and geoclimatic drivers of daily precipitation concentration using the concentration index (CI), precipitation concentration degree (PCD), Q Index (QI), and Theil index (TI) over a Western Himalayan Province, namely Himachal Pradesh, India. For this, the daily precipitation data pertaining to 111 grids (0.25° × 0.25° resolution), encompassing entire province, have been downloaded from India Meteorological Department (IMD), Pune, for the period 1951 to 2020. The trends in concentration indices have been detected using Mann-Kendall (MK) and Sen's slope methods, whereas their relationship with geoclimatic drivers have been assessed using Pearson's correlation coefficient (physiographic) and random forest regression model (climatic) at annual and monsoon season time scales. The results have revealed a high correlation between CI-TI (> 0.95), followed by QI-TI (> 0.84), QI-PCD (> 0.80), TI-PCD (> 0.78), QI-CI (> 0.65), and CI-PCD (> 0.60), representing nearly similar precipitation concentration distribution. Among the concentration indices, QI has shown maximum sensitivity to long-term changes followed by TI, CI, and PCD at both time scales. Similarly, physiographic factors have maximum effect on QI, showing its greatest sensitivity to latitude, longitude and elevation. While considering the impact of climatic drivers, Atlantic Meridian Oscillation (AMO) and Sunspot activity have exerted maximum influence on annual variability of CI, PCD, QI, and TI, whereas monsoon variability of these indices has been modulated by Pacific Decadal Oscillation (PDO), especially over northern parts of Himachal Pradesh. These findings will assist soil and water resource managers, disaster executives, agricultural stakeholders, and urban planners in understanding the unexplored aspects of daily precipitation concentration and making informed decisions over the mountainous regions of western Himalaya.

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Source
http://dx.doi.org/10.1007/s10661-024-13465-6DOI Listing

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