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Assessing snowmelt runoff in catchments without conducting intensive observations for water resource management is very important. This paper presents the modeling results of meltwater runoff in the Juntanghu River basin, where the river flow is dominated by glacier and snow meltwater from above the mid-altitude of the north central Tian Shan Mountains in Northwest China. The soil and water assessment tool (SWAT) model is used to assess the effects of potential climate change on the Juntanghu River basin. The model uses meteorological data, which include precipitation, temperature, wind, and solar radiation, to depict the impact of climate change on the river discharge in the Juntanghu watershed. The SWAT model is validated and verified against the observed discharge at the Hongshan reservoir at the outlet of the watershed from 1995 to 2010. The SWAT model can well differentiate between meltwater runoff from snow and that from glaciers in the basin. The R2 and Nash–Sutcliffe simulation efficiency values computed for the daily comparisons are 0.92 and 0.85 for the calibration period and 0.83 and 0.81 for the validation period, respectively. Experiments show that the hydrological cycle in the Juntanghu River basin may be altered under changing climatic scenarios. The mean annual stream flow changes in response to climate change projections are simulated using SWAT model. A rising mean temperature (T + 1 °C) in early spring results in a 2.14 times increase in the average daily stream flow (on February 27), whereas a higher rising temperature (T + 3 °C) results in an earlier snowmelt phenomenon. However, the snowmelt runoff from the preliminary to interim stages has less influence in relation to changes in the precipitation. The average daily flow changes of 1.07 times are predicted for precipitation change scenarios of 10%. The climate change scenarios show a large degree of uncertainty in terms of current climate change forecasts for the Juntanghu basin. The simulated Juntanghu hydrological cycle is also very sensitive to current forecasted climate changes under climate change conditions.