Snow provides essential resources/services in the form of water for human use, and climate regulation in the form of enhanced cooling of the Earth. In addition, it supports a thriving winter outdoor recreation industry. To date, the financial evaluation of the importance of snow is incomplete and hence the need for accelerated snow research is not as clear as it could be. With snow cover changing worldwide in several worrisome ways, there is pressing need to determine global, regional, and local rates of snow cover change, and to link these to financial analyses that allow for rational decision making, as risks related to those decisions involve trillions of dollars.

Between 2012 and 2018 we mapped near-peak seasonal snow depths across two swaths covering 126 km 2 in Northern Alaska using aerial structure-from-motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground-based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow-water-equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by topographic differences. Using four metrics from the field of image quality analysis, we tested the year-to-year fidelity of these drift patterns, finding overall high year-to-year similarity, but with higher similarity values for filling drifts, and higher similarity values in one swath versus the other, again a function of the topography. These high drift fidelity values are best explained by climatically convergent winter-cumulative windblown snow fluxes interacting with drift traps to produce the same drifts year after year. However, due to the existence of filling versus nonfilling drifts, and a predicted increasing frequency of rain-on-snow events in the Arctic, future snowdrift patterns and drift evolution pathways in the Arctic could diverge from those of today, with direct hydrologic impacts.

Between 2012 and 2018 we mapped near-peak seasonal snow depths across two swaths covering 126 km 2 in Northern Alaska using aerial structure-from-motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground-based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow-water-equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by topographic differences. Using four metrics from the field of image quality analysis, we tested the year-to-year fidelity of these drift patterns, finding overall high year-to-year similarity, but with higher similarity values for filling drifts, and higher similarity values in one swath versus the other, again a function of the topography. These high drift fidelity values are best explained by climatically convergent winter-cumulative windblown snow fluxes interacting with drift traps to produce the same drifts year after year. However, due to the existence of filling versus nonfilling drifts, and a predicted increasing frequency of rain-on-snow events in the Arctic, future snowdrift patterns and drift evolution pathways in the Arctic could diverge from those of today, with direct hydrologic impacts.