Spatio-temporal variability of temperature and precipitation in a Himalayan watershed

Sakiba Nabi (), Manzoor Ahmad Ahanger () and Abdul Qayoom Dar ()
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Sakiba Nabi: National Institute of Technology Srinagar
Manzoor Ahmad Ahanger: National Institute of Technology Srinagar
Abdul Qayoom Dar: National Institute of Technology Srinagar

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 2, No 31, 1743-1760

Abstract: Abstract Precipitation and temperature, the two most important climate variables, must be thoroughly studied in order to comprehend the hydrological response of any watershed to climate variability. The Himalayan watersheds, in particular, are extremely sensitive to even minor climate changes due to their fragile ecosystem, affecting the socioeconomic conditions and the pristine environments. The goal of this study is to investigate the spatial, temporal, and climate variability of a data-sparse Himalayan watershed. The spatial patterns of the two variables are analysed at monthly, seasonal, and annual time scales. The Mann–Kendall (MK) test and Sen’s slope estimator are employed to detect the trends and their respective magnitudes for precipitation and temperature at different time scales. The findings imply that orographic factors influence precipitation patterns, while elevation variations influence temperature trends in the study area. The western disturbances account for around 68% of annual precipitation in the watershed, while the Indian summer monsoon accounts for 32%. The results of the trend analysis show that both temperature and precipitation are increasing on an annual scale, which could lead to glacier melting, decreased snowfall, and the occurrence of extreme events in the future. Because the region is data-scarce, the study will be helpful in better understanding its hydrology. It can also aid in the more effective execution of rainfall-runoff hydrological modelling.

Keywords: Precipitation; Temperature; Spatio-temporal; Spatial patterns; Trend analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05616-0

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