Evaluating the impact of climatic change on hydrologic variables is highly important for sustainability of water resources. Precipitation and temperature are the two basic parameters which need to be included in climate change impact studies. Thirty years (1985–2015) climatic data of Astore, a sub-catchment of the Upper Indus River Basin (UIRB), were analyzed for predicting the temperature and precipitation under different climate change scenarios. The station data were compared with the results of two global climate models (GCMs) each with two emission scenarios, including Representative Concentration Pathway (RCP) 2.6 and 8.5. The Mann–Kendall test and Sen's slope were applied to explore various properties of precipitation and temperature data series for a trend analysis. The commonalities and dissimilarities between the results of various GCMs and the trend of the station data were investigated using the functional data analysis. Two cross distances were estimated on the basis of Euclidean distances between the predicted time series; subsequently, the differences in their first derivatives were used to evaluate their mutual dissimilarities. The long-term predictions by GCMs show a decreasing trend in precipitation and a slight increase in temperature in some seasons. The result of GCMs under both the emission scenarios showed almost the same pattern of changes in the two hydrologic variables throughout the century with their values reporting slightly higher for the RCP8.5 scenario as compared to those for RCP2.6. Validation of the GCM results using GCM-CSIRO-Mk3.6 revealed an overall agreement between the different models. The dissimilarity analysis manifested the difference between the results of temperature predicted by various GCMs.

评估气候变化对水文变量的影响对于水资源的可持续性非常重要。降水和温度是气候变化影响研究中需要包括的两个基本参数。分析了印度河上游流域（UIRB）的一个子集水区Astore的三十年（1985-2015年）气候数据，以预测不同气候变化情景下的温度和降水。将该站的数据与两个全球气候模型（GCM）的结果进行了比较，每个模型都具有两种排放情景，包括代表浓度路径（RCP）2.6和8.5。应用Mann-Kendall检验和Sen斜率来探索降水和温度数据系列的各种特性，以进行趋势分析。使用功能数据分析研究了各种GCM结果与测站数据趋势之间的共性和异同。根据预测时间序列之间的欧几里得距离估计两个交叉距离；随后，使用它们一阶导数的差异来评估它们之间的互不相同。GCM的长期预报显示，某些季节降水减少，温度略有上升。两种排放情景下的GCM结果表明，整个世纪两个水文变量的变化模式几乎相同，与RCP2.6相比，RCP8.5情景的GCM值略高。使用GCM-CSIRO-Mk3验证GCM结果。图6显示了不同模型之间的总体协议。差异分析表明，各种GCM预测的温度结果之间存在差异。