NASA's 2017 SnowEx field campaign at Grand Mesa, CO, generated Airborne Laser Scans (ALS), Terrestrial Laser Scans (TLS), and snow‐probe transects, which allowed for a comparison between snow depth measurement techniques. At six locations, comparisons between gridded ALS and TLS observations, at 1‐m resolution, had a median snow depth difference of 5 cm, root‐mean‐square difference of 16 cm, mean‐absolute difference of 10 cm, and 3‐cm difference in standard deviation. ALS generally had greater but similar snow depth values to TLS, and results were not sensitive to the gridded cell size between 0.5 and 5 m. The greatest disagreements were where snow‐off TLS scans had shrubs and high incidence angles, leading to deeper snow depths (>10 cm) from ALS than TLS. The low vegetation and oblique angles caused occlusion in the TLS data and thus produced higher snow‐off bare Earth models relative to the ALS. Furthermore, in subcanopy areas where both ALS and TLS data existed, snow depth differences were comparable to differences in the open. Meanwhile, median values from 52 snow‐probe transects and near‐coincident ALS data had a mean difference of 6 cm, root‐mean‐square difference of 8 cm, mean‐absolute difference of 7 cm, and a mean difference in the standard deviation of 1 cm. Snow depth probes had greater but similar snow depth values to ALS. Therefore, based on comparisons with TLS and snow depth probes, ALS captured snow depth magnitude with better than or equal agreement to what has been reported in previous studies and showed the ability to capture high‐resolution spatial variability.

Methane emissions regulate the near‐term global warming potential of permafrost thaw, particularly where loss of ice‐rich permafrost converts forest and tundra into wetlands. Northern latitudes are expected to get warmer and wetter, and while there is consensus that warming will increase thaw and methane emissions, effects of increased precipitation are uncertain. At a thawing wetland complex in Interior Alaska, we found that interactions between rain and deep soil temperatures controlled methane emissions. In rainy years, recharge from the watershed rapidly altered wetland soil temperatures, warming the top ~80 cm of soil in spring and summer and cooling it in autumn. When soils were warmed by spring rainfall, methane emissions increased by ~30%. The warm, deep soils early in the growing season likely supported both microbial and plant processes that enhanced emissions. Our study identifies an important and unconsidered role of rain in governing the radiative forcing of thawing permafrost landscapes.

Mountain snow has a fundamental role in regional water budgets through its seasonal accumulation, storage, and melt. However, characterizing snow accumulation over large regions remains difficult because of limited observational networks and the inability of available satellite instruments to remotely sense snow depth or water equivalent in mountains. Models offer some ability to estimate snow water storage (SWS) on mountain range to continental scales. Here we compare four commonly used global data sets to understand whether there is a consensus regarding mountain SWS estimates among them. The data sets—European Centre for Medium‐Range Weather Forecasts Reanalysis‐Interim, Global Land Data Assimilation System, Modern‐Era Retrospective Analysis for Research and Applications version 2, and Variable Infiltration Capacity—agree to within ±36% of the four–data set average for total global SWS. When mountain areas are extracted using a new seasonal mountain snow classification data set, the four data products have more agreement, where all are within ±21% of the seasonal SWS for mountain regions. However, when compared to high‐resolution (9 km) simulations of SWS from the Weather Research and Forecasting (WRF) regional model, the four global products differ from WRF‐estimated North American mountain snow accumulation by 40–66%, with a negative bias up to 651 km³, comparable to the annual streamflow of the Mississippi River. If we extend the North America SWS bias to global mountains, the global data sets may miss as much as 1,500 km³ of SWS, equivalent to 4% of the flow in all the world's rivers. The potential difference of SWS suggests more work must be done to characterize water resources in snow‐dominated regions, particularly in mountains.

Snow distribution is controlled by the interaction between local meteorology and static features like topography and vegetation. The resulting spatial pattern of snow in mountainous terrain is often repeatable and can be used to infer snowpack distribution at periods when observations are limited. This study uses a library of airborne lidar surveys (ALS) in California's Tuolumne watershed to analyze snow patterns at extents (1,650 km²), resolutions (25 m), and temporal scales (47 ALS observations over 7 years) unmatched by previous research. Distributed snow depth was inferred from snow depth observations covering a portion of the domain (< 4%) at a period near peak‐snowpack timing and snow distribution patterns from different years. Snow patterns from different years differed as a function of snow extents, variability, and interannual noise (r = 0.30 to 0.90). However, matching criteria like seasonal timing and snow extents were able to identify pairs of snow patterns with increased spatial agreement (median r > 0.84). Distributed snow depth inferred using a strip of observations (<4% coverage), and a well‐correlated snow pattern (r≥ 0.90) had a mean absolute error (MAE) of 0.22 m and snow volume error of −6%. Distributed snow depth inferred using patterns of reduced accuracy (r < 0.80) were often too homogeneous, thereby increasing MAE and decreasing the duration of the simulated snowmelt season. This work has applications in water management, where distributed snow depth observations in watersheds with interannual snow pattern repeatability could decrease the extent of observations necessary in future years.

Snow depth variability between different sides of the forest edge was analyzed using airborne lidar in the Olympic Mountains, WA; Tuolumne River Watershed, CA; Jemez Caldera, NM; and the Boulder Creek Watershed, CO. At Boulder Creek there were statistically significant snow depth differences between leeward and windward forest edges (mean: 38%), which were likely the result of cold winter temperatures (‐5 ‐ ‐10°C), substantial wind speeds (>10‐12 m s^‐1), and the forest architecture. In the Olympic Mountains, wind speeds were similar to Boulder Creek, but snow depth differences between forest edges were not significantly different, likely due to winter temperatures near 0°C and a more continuous forest distribution. At Jemez, forest shading likely caused significant snow depth differences between north and south‐facing forest edges (mean: 32%). Tuolumne had similar incoming solar radiation to Jemez, but only two of the four sites contained significant snow depth differences between north and south‐facing forest edges (14% and 31%). The Tuolumne sites without significant edge differences received less direct solar radiation due to shading by surrounding topography. At Jemez, Boulder Creek, and Tuolumne, snow depth differences between forest edges were greater than or equal to differences between exposed and under canopy areas. Furthermore, at Jemez and Tuolumne, forest‐edge snow depth differences were similar to snow depth differences between different aspects in exposed areas. Therefore, snow depth differences between forest‐edge classifications are of similar magnitude to classifications currently used to represent sub‐element heterogeneity within hydrologic models. Therefore, representation of forest‐edge variability within models should be explored.