Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

Abstract

Abstract. Spring melt is a significant feature of high latitude snowmelt dominated drainage basins influencing hydrological and ecological processes such as snowmelt runoff and green-up. Melt duration, defined as the transition period from snowmelt onset until the end of the melt refreeze, is characterized by high diurnal amplitude variations (DAV) where the snowpack is melting during the day and refreezing at night, after which the snowpack melts constantly until depletion. Determining trends for this critical period is necessary for understanding how the Arctic is changing with rising temperatures and provides a baseline from which to assess future change. To study this dynamic period, brightness temperature (T b ) data from the Special Sensor Microwave Imager (SSM/I) 37 V-GHz frequency from 1988 to 2010 were used to assess snowmelt timing trends for the Yukon River basin, Alaska/Canada. Annual T b and DAV for 1434 Equal-Area Scalable Earth (EASE)-Grid pixels (25 km resolution) were processed to determine melt onset and melt refreeze dates from T b and DAV thresholds previously established in the region. Temporal and spatial trends in the timing of melt onset and melt refreeze, and the duration of melt were analyzed for the 13 sub-basins of the Yukon River basin with three different time interval approaches. Results show a lengthening of the melt period for the majority of the sub-basins with a significant trend toward later end of melt refreeze after which the snowpack melts day and night leading to snow clearance, peak discharge, and green-up. Earlier melt onset trends were also found in the higher elevations and northernmost subbasins (Porcupine, Chandalar, and Koyukuk rivers). Latitude and elevation displayed the dominant controls on melt timing variability and spring solar flux was highly correlated with melt timing in middle (∼ 600-1600 m) elevations.

Quantifying the Role of Snowmelt in Stream Discharge in an Alaskan Watershed: An Analysis Using a Spatially Distributed Surface Hydrology Model

“…In late spring, because snowmelt gradually expands to higher altitudes than usual, the timing of snowmelt ending has also shifted 5 days later per decade. Our findings are consistent with other studies (Brown & Robinson, ; Semmens & Ramage, ; Stone et al, ; Tan et al, ).…”

“…While most studies have focused on snow metrics, only a few have quantified the relationship between snow dynamics and stream discharge. For example, many studies have suggested that changes in snow accumulation and ablation have changed the seasonal pattern of streamflow (Semmens & Ramage, ; Stone et al, ). For snow metrics, studies have suggested that snow cover, which plays a special role in surface energy balance, has undergone a significant reduction in recent decades in northern high latitudes (Brown & Robinson, ; Male & Granger, ; Stieglitz et al, ).…”

“…Although no consistent long-term trends in stream discharge were observed for global major rivers, stream discharge in spring has been increasing resulting from earlier snowmelt in northern high latitudes (Hartmann et al, 2013;Semmens & Ramage, 2013;St Jacques & Sauchyn, 2009). In the United States, stream discharge from Alaska contributes approximately 36% to the national total annual streamflow and 7 of the 20 largest rivers are located in Alaska (Bowersox, 2002;Krammer, 1990).…”

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

“…Timing of snowmelt onset and of runoff events due to snowmelt affects the availability of water resources and influences flooding and droughts (Semmens and Ramage, 2013). Therefore, it is crucial to simulate and to evaluate the timing of streamflow accurately when comparing snowmelt models.…”

Enhanced Identification of Snow Melt and Refreeze Events From Passive Microwave Brightness Temperature Using Air Temperature

“…Multiple studies used the DAV to diagnose climate change trends in high‐latitude areas, for example, the Yukon basin (Semmens & Ramage, ), northern Canada (Mioduszewski et al, ), Arctic sea ice (Markus et al, ), and the entire Arctic (Wang et al, ). Tedesco et al () presented a modified version of the DAV method, called the dynamic DAV, where the minimum DAV threshold to indicate snow melt was set to the January‐February average DAV plus 10 K, and the T b threshold was determined by fitting a bimodal Gaussian distribution to T b and finding the minimum density between the two peaks of the distribution.…”