The economy of Pakistan relies on the agricultural sector which mainly depends on the irrigation water generating from the upper Indus river basin. Mangla watershed is a trans-boundary basin which shares borders of India and Pakistan, it comprises five major sub-basins, i.e., Jhelum, Poonch, Kanshi, Neelum and Kunhar. The runoff production of this basin is largely controlled by snowmelt in combination with the winter precipitation in the upper part of the basin and summer monsoon. The present study focusses on the application of a statistical downscaling method to generate future climatic scenarios of climatic trends (temperature and precipitation) in Mangla watershed. Statistical Downscaling Model (SDSM) was applied to downscale the Hadley Centre Coupled Model, version 3, Global Climate Model (HadCM3-GCM) predictions of the A2 and B2 emission scenarios. The surface water analyst tool (SWAT) hydrological model was used for the future projected streamflows based on developing climate change scenarios by SDSM. The results revealed an increasing trend of annual maximum temperature (A2) at the rates of 0.4, 0.7 and 1.2 °C for the periods of 2020s, 2050s and 2080s, respectively. However, a consistent decreasing trend of temperature was observed at the high-altitude region. Similarly, the annual minimum temperature exhibited an increasing pattern at the rates of 0.3, 0.5 and 0.9 °C for the periods of 2020s, 2050s and 2080s, respectively. Furthermore, similar increases were observed for annual precipitation at the rates of 6%, 10%, and 19% during 2020, 2050 and 2080, respectively, for the whole watershed. Significant increasing precipitation trends in the future (2080) were observed in Kunhar, Neelum, Poonch and Kanshi sub-basins at the rates of 16%, 11%, 13% and 59%, respectively. Consequently, increased annual streamflow in the future at the rate of 15% was observed attributing to an increased temperature for snow melting in Mangla watershed. The similar increasing streamflow trend is consistent with the seasonal trends in terms of winter (16%), spring (19%) and summer (20%); however, autumn exhibited decreasing trend for all periods.
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