Spatio-temporal trends in precipitation and temperature means/extremes in the Himalayan states of India

Abstract

Covering sixty years (1955–2016), this paper analyzed the spatio-temporal trends of daily annual and seasonal means of precipitation, temperature, and extreme climate indices. The long-term decadal change analysis was carried out in two timeframes for consecutive analysis periods of 1955–1985 and 1986–2016 to capture the relative regional variations. The trends were evaluated using the modified Mann-Kendall (MMK) and Sen's slope (SS) statistical tests. The pattern visualized on maps was assessed for regions and districts located in the four states of the Indian Himalayan Region. The results revealed significant increases in the annual mean precipitation (1.61 mm/year) and temperature (0.07°/year) in Uttarakhand, followed by Himachal Pradesh from 1986 to 2016. The seasonal monsoonal precipitation showed a significantly increasing trend in parts of Himachal Pradesh, followed by Uttarakhand. The maximum and minimum temperature values increased in all the states, increasing temperature extremes. Significant trends in climate indices were observed in the second period of analysis. A rise of 0.02 °C/year in temperature extremes was observed in Uttarakhand. There is a progressive rise in precipitation indices, specifically in Uttarakhand (0.49 mm/year), and a drop in cold extremes with an increase of hot events in most states. The spatio-temporal variations were driven by multiple dominant mechanisms like orography, anthropogenic activities, land-use changes, teleconnections, and the emission of greenhouse gases. This study highlights the influence of climate change in different divisions of the Himalayas.

Identifying links between monsoon variability and rice production in India through machine learning

“…PGF was selected due to its long timespan and relatively high spatial resolution (0.25°x 0.25°), and has been used by other crop-climate modelling studies [49][50][51] . PGF has also been validated for use over India, specifically including the temperature 52 and precipitation 53 variables used in this study. Furthermore, rice yield sensitivity to the PGF weather variables matched that of similar weather datasets in an India-based comparison 54 .…”