The aim of this study was to evaluate the variability of time distribution and contribution of runoff from snowmelt under the influence of climate change in the Beheshtabad Watershed, Iran, using the snowmelt runoff Model (SRM) and twenty Coupled Model Intercomparison Project phase 5 (CMIP5) for Representative Concentration Pathway 6.0 (RCP 6.0) for Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Twenty CMIP5 General Circulation Models (GCMs) for the lowest (RCP 2.6), highest (RCP 8.5) and medium (RCP 6.0) emissions scenarios for the future periods (2011–2030 and 2046–2065) were obtained using MarkSim (http://gismap.ciat.cgiar.org/MarkSimGCM). The results showed that annual average rainfall will decrease 16.15% and 22.94% and mean annual maximum and minimum temperature will increase 1.79 °C and 2.89 °C for two future periods under RCP 6.0. The SRM variables and parameters were prepared from the Shahrekord station, and snow cover areas (SCAs) were obtained by MODIS satellite images. After the calibration and validation of SRM model, the SRM model was then run with the future data under RCP 6.0. and revealed the effects of climate change on snowmelt runoff. The results show the displacement of the monthly peak flow from April to March, and reducing the contribution of snowmelt runoff from 27.2 to 23.2% and 20.13% for two future periods. The present study confirmed the effects of climate change on future climate data and discharge and temporal pattern of snowmelt runoff.

本研究的目的是使用融雪径流模型 (SRM) 和 20 耦合模型比对项目第 5 阶段 (CMIP5)，评估伊朗 Beheshtabad 流域气候变化影响下融雪径流的时间分布变化和贡献。 ) 用于政府间气候变化专门委员会 (IPCC) 第五次评估报告的代表性浓度途径 6.0 (RCP 6.0)。使用 MarkSim (http:/) 获得了未来时期（2011-2030 和 2046-2065）最低（RCP 2.6）、最高（RCP 8.5）和中等（RCP 6.0）排放情景的 20 个 CMIP5 总循环模型（GCM）。 /gismap.ciat.cgiar.org/MarkSimGCM）。结果表明，年平均降雨量将减少16.15%和22.94%，年平均最高和最低气温将分别增加1.79°C和2。在 RCP 6.0 下的两个未来时期内为 89 °C。SRM 变量和参数是从 Shahrekord 站准备的，积雪面积 (SCA) 是通过 MODIS 卫星图像获得的。在 SRM 模型校准和验证之后，然后在 RCP 6.0 下使用未来数据运行 SRM 模型。并揭示了气候变化对融雪径流的影响。结果表明，4-3月的月峰值流量发生位移，使融雪径流对未来两个时期的贡献从27.2%减少到23.2%和20.13%。本研究证实了气候变化对未来气候数据以及融雪径流的排放和时间模式的影响。在 SRM 模型校准和验证之后，然后在 RCP 6.0 下使用未来数据运行 SRM 模型。并揭示了气候变化对融雪径流的影响。结果表明，4-3月的月峰值流量发生位移，使融雪径流对未来两个时期的贡献从27.2%减少到23.2%和20.13%。本研究证实了气候变化对未来气候数据以及融雪径流的排放和时间模式的影响。在 SRM 模型校准和验证之后，然后在 RCP 6.0 下使用未来数据运行 SRM 模型。并揭示了气候变化对融雪径流的影响。结果表明，4-3月的月峰值流量发生位移，使融雪径流对未来两个时期的贡献从27.2%减少到23.2%和20.13%。本研究证实了气候变化对未来气候数据以及融雪径流的排放和时间模式的影响。