A random-forest-derived 35-year snow phenology record reveals climate trends in the Yukon River Basin

Abstract. This study presents a 35-year snow phenology record for the Yukon River Basin (YRB), developed using a random forest (RF) model at a 3.125 km resolution, capturing detailed trends in snowmelt onset and snow-off. The RF model, incorporating dynamic daily variables, improves upon traditional threshold-based methods by reducing sensitivity to transient thaw events and atmospheric noise. Model evaluation against station observations yielded a mean absolute error (MAE) of 10.5 d and a root mean square error (RMSE) of 13.7 d for snowmelt onset. For snow-off, the model achieved an MAE of 18.1 d and an RMSE of 20.7 d. This approach successfully mapped snow phenology across the diverse YRB landscape, providing valuable insight into how variations in snow cover align with regional climate patterns. Challenges such as sample bias due to limited ground-based data coverage highlight the need to expand in situ measurements to improve model performance further. Trend analysis segmented by two timeframes, 1988–2005 and 2006–2023, revealed distinct climate impacts on snow phenology. During 1988–2005, high snowfall and stable temperatures resulted in hastened snowmelt onset and lengthened snowmelt durations, reflecting early-season snow abundance. In contrast, from 2006–2023, warming spring and summer temperatures corresponded to progressively earlier snowmelt onset and snow-off. These shifts in snowmelt patterns align with a lengthened snow-free season, indicating an increasing influence of warmer temperatures on the snowpack. This RF-derived dataset provides an essential tool for tracking climate-driven snow changes, offering insights into hydrologic and ecologic dynamics in the YRB under accelerating climate change.