This study investigates the trends of precipitation and temperature extremes for the historical observations (1961–1990) and future period (2061–2090) in the Jhelum River Basin.Future trends are estimated by using ensemble mean of three general circulation models under RCP4.5 and RCP8.5. Therefore, statistical downscaling model has been used to downscale the future precipitation and temperature. A total of 15 precipitation and temperature indices were calculated using the RClimdex package. Man-Kendall and Sen’s slope tests were used to detect the trends in climate extreme indices. Overall, the results of study indicate that there were significant changes in precipitation and temperature patterns as well as in the climate extremes in the basin for both observed as well as projected climate. Generally, more warming and increase in precipitation were observed, which increases from RCP4.5 to RCP8.5. For all the stations, increasing trends were found for both precipitation and temperature for twenty-first century at a 95% significance level. The frequency of warm days (TX90p), warm nights (TN90p), and summer days (SU25) showed significant increasing trends, alternatively the number of cold nights (TN10p) and cold days (TX10p) exhibited opposite behaviors. In addition, an increasing trend of warmest day (TXx) and coldest day (TNn) was observed. Our analysis also reveals that the number of very wet days (R90p) and heavy precipitation days (R10 mm) will likely increase in the future. Meanwhile, the Max 1-day (RX1-day) and 5-day (RX5-day) precipitation indices showed increasing trends at most of the stations of basin. The results of the study is of potential benefit for decision-makers to develop basin wide appropriate mitigation and adaptation measures to combat climate change and its consequences.
CITATION STYLE
Saddique, N., Khaliq, A., & Bernhofer, C. (2020). Trends in temperature and precipitation extremes in historical (1961–1990) and projected (2061–2090) periods in a data scarce mountain basin, northern Pakistan. Stochastic Environmental Research and Risk Assessment, 34(10), 1441–1455. https://doi.org/10.1007/s00477-020-01829-6
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