Elevation-dependent spring snowmelt rates and degree-day factors in the Irtysh River Basin, Northern Xinjiang

Under the combined influence of global climate change and regional climate variability, extreme events are occurring with increasing frequency, and the risk of snowmelt flood disasters is rising in Xinjiang, China. This study utilized high-resolution snow water equivalent (SWE) data to analyze the temporal and spatial characteristics of the spring snowmelt process and rate in the Irtysh River Basin in northern Xinjiang from 2011 to 2015. Our results revealed the following: (1) The average spring snowmelt rate in the Irtysh River Basin was 1.59 mm/day. Spatially, the snowmelt rate exhibited an increasing tendency with altitude. Specifically, the lowest rate occurred in low-altitude areas (<500 m, 0.13 mm/day), while peak values appeared at 1000-1500 m in March (2.43 mm/day) and at 2000-2500 m in April (3.60 mm/day). (2) In response to temperature, the snowmelt rate peaks near the transition from below 0℃ to above 0℃ and then gradually declines as the snowpack depletes. At high elevations (>2000 m), rapid snowmelt (maximum rate 8.3 mm/day) occurs mainly within −5 to 0℃, whereas at lower elevations (<2000 m), the snowmelt rate reaches its peak (9.0 mm/day) after melt onset, when temperature is above 0℃. (3) The degree-day factor (DDF) across the entire Irtysh River Basin has an average value of 1.52 mm/℃/day. Spatially, the DDF exhibits a distinct altitudinal pattern, which first increases with elevation and then decreases at higher altitudes. In low altitude regions (<1000 m), the DDF is relatively small, generally below 1.25 mm/℃/day. In middle altitude regions (1000-2000 m), DDF increases markedly, reaching a maximum of 2.19 mm/℃/day. At high altitudes (>2000 m), DDF decreases again to 1.05-1.81 mm/℃/day. Compared with previous studies, the DDF values in the Irtysh River Basin are slightly lower but present more pronounced spatial heterogeneity, which can be attributed to regional differences in climatic and topographic conditions. Our results contribute to a deeper understanding of the rapid snowmelt process in spring and provide important quantitative references for snowmelt model parameterization scheme, so as to better assess and prevent snowmelt floods.