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1. Isolating forest process effects on modelled snowpack density and snow water equivalent

   https://doi.org/10.1002/hyp.14475  

   Bonner, Hannah M.; Raleigh, Mark S.; Small, Eric E. (January 2022, Hydrological Processes)

   Abstract Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA model at three sites representing diverse snow climates in Colorado (USA), Oregon (USA), and Alberta (Canada) for 5 years. First, control simulations were run for open and forest sites. Comparisons to observations showed the uncalibrated model with NLDAS‐2 forcing performed reasonably. Then, experiments were completed to isolate how forest processes affected modelled snowpack density and SWE, including: (1) mass reduction due to interception loss, (2) changes in the phase and amount of water delivered from the canopy to the underlying snow, (3) varying new snow density from reduced wind speed, and (4) modification of incoming longwave and shortwave radiation. Delivery effects (2) increased forest snowpack density relative to open areas, often more than 30%. Mass effects (1) and wind effects (3) decreased forest snowpack density, but generally by less than 6%. The radiation experiment (4) yielded negligible to positive effects (i.e., 0%–10%) on snowpack density. Delivery effects on density were greatest at the warmest times in the season and at the warmest site (Oregon): higher temperatures increased interception and melted intercepted snow, which then dripped to the underlying snowpack. In contrast, mass effects and radiation effects were shown to have the greatest impact on forest‐to‐open SWE differences, yielding differences greater than 30%. The study highlights the importance of delivery effects in models and the need for new types of observations to characterize how canopies influence the flux of water to the snow surface. 

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2. Predicted expansion of beaver pond distribution in Arctic Alaska, 1910–2090

   https://doi.org/10.1088/1748-9326/adeba2  

   Tape, Ken D; Speed, James_D M (June 2025, Environmental Research Letters)

   Abstract Ecosystem engineering by beavers is a nascent disturbance in the Arctic tundra, appearing in the 1970s in western Alaska and since expanding deeper into tundra regions. Evidence from modeling and observations indicates that beaver ponds act as biophysical oases, and we anticipate myriad changes as these disturbances are constructed along tundra streams, sloughs, and lake outlets. We used over 11 000 mapped beaver pond locations in Arctic Alaska and their climatic, geographic, and environmental attributes to understand (1) which of those attributes control the distribution of beaver ponds, and, if temperature is a factor, (2) how beaver pond distribution will change under future climate scenarios. Of the variables used in the ensemble modeling approach, mean annual temperature was the most important variable in determining beaver pond locations, with pond occurrences more likely in warmer locales (>−2 °C). The distance to water was also important in determining beaver pond locations, as expected, with higher likelihood of ponds closer to water features. Lowland topographic variables were also relevant in determining the distribution of beaver ponds. Under the current climate, beaver ponds are widespread in most of western Alaska, matching the predicted extent of potential occupancy, with the exception of areas furthest from treeline, implying possible dispersal lags or other factors. By 2050, under future climate scenarios (RCP8.5; 2090 for RCP6.0), the entire North Slope of Alaska, which currently has no beaver ponds, is predicted to be suitable for beaver ponds, comparable to western Alaska in 2016. The vast extent of future beaver engineering in tundra regions will require reenvisioning the typical tundra stream ecosystems of northern Alaska, northern Canada, northern Europe, and northern Asia to include more extensive wetlands, routine disturbances, permafrost thaw, and other features of these nascent oases that are not fully understood. 

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3. A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

   https://doi.org/10.1029/2022WR033006  

   Bonner, Hannah M.; Smyth, Eric; Raleigh, Mark S.; Small, Eric E. (September 2022, Water Resources Research)

   Abstract We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) athttps://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation. 

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4. Sublimation measurements of tundra and taiga snowpack in Alaska

   https://doi.org/10.5194/tc-19-1739-2025  

   Stockert, Kelsey A; Euskirchen, Eugénie S; Stuefer, Svetlana L (May 2025, The Cryosphere)

   na (Ed.)

   Abstract. Snow sublimation plays a fundamental role in the winter water balance. To date, few studies have quantified sublimation in tundra and boreal forest snow by direct measurements. Continuous latent heat data collected with eddy covariance (EC) measurements from 2010–2021 were used to calculate snow sublimation at six locations in northern Alaska: three Arctic tundra sites at distinct topographical and vegetation communities in the Imnavait Creek watershed on the North Slope underlain by continuous permafrost, and three lowland boreal forest/taiga sites in discontinuous permafrost in interior Alaska near Fairbanks. Mean surface sublimation rates range from 0.08–0.15 mm d−1 and 15–27 mm yr−1 at the six sites, representing, on average, 21 % of the measured solid precipitation and 8 %–16 % of the cumulative annual water vapor flux to the atmosphere (evaporation plus sublimation). The mean daily sublimation rates of the lowland boreal forest sites are higher than those of the tundra sites, but the longer snow cover period of the tundra sites leads to greater mean annual sublimation rates. We examined the potential controls, drivers, and trends of the sublimation rates by using meteorological data collected in conjunction with EC measurements. This research improves our understanding of how site conditions affect sublimation rates and highlights the fact that sublimation is a substantial component of the winter hydrologic cycle. In addition, the study contributes to the sparse literature on tundra and boreal sublimation measurements, and the measured rates are comparable to sublimation estimates in other northern climates. 

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5. Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

   https://doi.org/10.5194/tc-15-5261-2021  

   Liu, Yufei; Fang, Yiwen; Margulis, Steven A. (January 2021, The Cryosphere)

   Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region. 

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