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.

中文翻译：

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巴基斯坦跨界 Mangla 流域基于统计降尺度和水文建模的径流模拟

巴基斯坦的经济依赖于农业部门，农业部门主要依赖印度河流域上游产生的灌溉水。曼格拉流域是一个跨界流域，与印度和巴基斯坦接壤，它包括五个主要的子流域，即杰赫勒姆、庞奇、坎什、尼勒姆和昆哈尔。该流域的径流产生主要受融雪结合流域上部冬季降水和夏季风的控制。本研究的重点是应用统计降尺度方法来生成 Mangla 流域气候趋势（温度和降水）的未来气候情景。应用统计降尺度模型 (SDSM) 来降尺度哈德利中心耦合模型，版本 3，全球气候模型 (HadCM3-GCM) 对 A2 和 B2 排放情景的预测。地表水分析工具 (SWAT) 水文模型用于根据 SDSM 开发的气候变化情景预测未来的流量。结果表明，2020年代、2050年代和2080年代，年最高气温（A2）分别以0.4、0.7和1.2℃的速率上升。然而，在高海拔地区观察到温度持续下降的趋势。同样，2020 年代、2050 年代和 2080 年代，年最低气温分别以 0.3、0.5 和 0.9 °C 的速率增加。此外，在 2020 年、2050 年和 2080 年期间，整个流域的年降水量分别以 6%、10% 和 19% 的速度增加。在Kunhar、Neelum、Poonch和Kanshi子流域观察到未来（2080年）降水量显着增加的趋势分别为16%、11%、13%和59%。因此，观察到未来年流量以 15% 的速度增加，这归因于 Mangla 流域融雪的温度升高。类似的流量增加趋势与冬季（16%）、春季（19%）和夏季（20%）的季节性趋势一致；然而，秋季各时期均呈下降趋势。观察到未来年流量以 15% 的速度增加，这归因于 Mangla 流域融雪的温度升高。类似的流量增加趋势与冬季（16%）、春季（19%）和夏季（20%）的季节性趋势一致；然而，秋季各时期均呈下降趋势。观察到未来年流量以 15% 的速度增加，这归因于 Mangla 流域融雪的温度升高。类似的流量增加趋势与冬季（16%）、春季（19%）和夏季（20%）的季节性趋势一致；然而，秋季各时期均呈下降趋势。