Rising temperatures and declining water availability have influenced the ecological function of mountain forests over the past half-century. For instance, warming in spring and summer and shifts towards earlier snowmelt are associated with an increase in wildfire activity and tree mortality in mountain forests in the western United States 1,2 . Temperature increases are expected to continue during the twenty-first century in mountain ecosystems across the globe 3,4 , with uncertain consequences. Here, we examine the influence of interannual variations in snowpack accumulation on forest greenness in the Sierra Nevada Mountains, California, between 1982 and 2006. Using observational records of snow accumulation and satellite data on vegetation greenness we show that vegetation greenness increases with snow accumulation. Indeed, we show that variations in maximum snow accumulation explain over 50% of the interannual variability in peak forest greenness across the Sierra Nevada region. The extent to which snow accumulation can explain variations in greenness varies with elevation, reaching a maximum in the water-limited midelevations, between 2,000 and 2,600 m. In situ measurements of carbon uptake and snow accumulation along an elevational transect in the region confirm the elevation dependence of this relationship. We suggest that mid-elevation mountain forest ecosystems could prove particularly sensitive to future increases in temperature and concurrent changes in snow accumulation and melt.Recent studies have documented a shift from energy to water limitation across forested ecosystems of western North America 5,6 . This transformation has reversed the response of these ecosystems to increases in temperature where before the early 1990s increases in air temperature increased terrestrial carbon uptake. Following the early 1990s an apparent shift from energy to water limitation resulted in reduced carbon uptake with increased temperature and coincident decreases in water availability 5 . In the western United States, increases in regional spring-summer temperatures and earlier snowmelt since the mid-1980s strongly correlate with increases in forest wildfire activity 1 and increases in tree mortality rates 2 . A consistent message has emerged from these studies: the combined effects of increases in temperature and decreases in water availability over the past half-century have impacted the ecological function of mountain forests.The sensitivity of mid-latitude mountain forests to water availability and the associated importance of snowmelt water has been well documented at the plot scale [7][8][9][10] . However, the effects of variations in snowpack accumulation on vegetation activity

[1] In this study, LIDAR snow depths, bare ground elevations (topography), and elevations filtered to the top of vegetation (topography + vegetation) in five 1-km 2 areas are used to determine whether the spatial distribution of snow depth exhibits scale invariance, and the control that vegetation, topography, and winds exert on such behavior. The one-dimensional and mean two-dimensional power spectra of snow depth exhibit power law behavior in two frequency intervals separated by a scale break located between 7 m and 45 m. The spectral exponents for the low-frequency range vary between 0.1 and 1.2 for the one-dimensional spectra, and between 1.3 and 2.2 for the mean twodimensional power spectra. The spectral exponents for the high-frequency range vary between 3.3 and 3.6 for the one-dimensional spectra, and between 4.0 and 4.5 for the mean two-dimensional spectra. Such spectral exponents indicate the existence of two distinct scaling regimes, with significantly larger variations occurring in the larger-scale regime. Similar bilinear power law spectra were obtained for the fields of vegetation height, with crossover wavelengths between 7 m and 14 m. Further analysis of the snow depth and vegetation fields, together with wind data, support the conclusion that the break in the scaling behavior of snow depth is controlled by the scaling characteristics of the spatial distribution of vegetation height when snow redistribution by wind is minimal and canopy interception is dominant, and by the interaction of winds with features such as surface concavities and vegetation when snow redistribution by wind is dominant.Citation: Trujillo, E., J. A. Ramírez, and K. J. Elder (2007), Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields, Water Resour. Res., 43, W07409,

Snow accumulation and melt patterns play a significant role in the water, energy, carbon, and nutrient cycles in the montane environments of the Western United States. Recent studies have illustrated that changes in the snow/rainfall apportionments and snow accumulation and melt patterns may occur as a consequence of changes in climate in the region. In order to understand how these changes may affect the snow regimes of the region, the current characteristics of the snow accumulation and melt patterns must be identified. Here we characterize the snow water equivalent (SWE) curve formed by the daily SWE values at 766 snow pillow stations in the Western United States, focusing on several metrics of the yearly SWE curves and the relationships between the different metrics. The metrics are the initial snow accumulation and snow disappearance dates, the peak snow accumulation and date of peak, the length of the snow accumulation season, the length of the snowmelt season, and the snow accumulation and snowmelt slopes. Three snow regimes emerge from these results: a maritime, an intermountain, and a continental regime. The maritime regime is characterized by higher maximum snow accumulations reaching 300 cm and shorter accumulation periods of less than 220 days. Conversely, the continental regime is characterized by lower maximum accumulations below 200 cm and longer accumulation periods reaching over 260 days. The intermountain regime lies in between. The regions that show the characteristics of the maritime regime include the Cascade Mountains, the Klamath Mountains, and the Sierra Nevada Mountains. The intermountain regime includes the Eastern Cascades slopes and foothills, the Blue Mountains, Northern and Central basins and ranges, the Columbia Mountains/Northern Rockies, the Idaho Batholith, and the Canadian Rockies. Lastly, the continental regime includes the Middle and Southern Rockies, and the Wasatch and Uinta Mountains. The implications of snow regime classification are discussed in the context of possible changes in accumulation and melt patterns associated with regional warming.

Abstract:This paper reports on a study analysing the spatial distribution functions, the correlation structures, and the power spectral densities of high-resolution LIDAR snow depths (¾1 m) in two adjacent 500 m ð 500 m areas in the Colorado Rocky Mountains, one a sub-alpine forest the other an alpine tundra. It is shown how and why differences in the controlling physical processes induced by variations in vegetation cover and wind patterns lead to the observed differences in spatial organization between the snow depth fields of these environments. In the sub-alpine forest area, the mean of snow depth increases with elevation, while its standard deviation remains uniform. In the tundra subarea, the mean of snow depth decreases with elevation, while its standard deviation varies over a wide range. The two-dimensional correlations of snow depth in the forested area indicate little spatial memory and isotropic conditions, while in the tundra they indicate a marked directional bias that is consistent with the predominant wind directions and the location of topographic ridges and depressions. The power spectral densities exhibit a power law behaviour in two frequency intervals separated by a break located at a scale of around 12 m in the forested subarea, and 65 m in the tundra subarea. The spectral exponents obtained indicate that the snow depth fields are highly variable over scales larger than the scale break, while highly correlated below. Based on the observations and on synthetic snow depth fields generated with one-and two-dimensional spectral techniques, it is shown that the scale at which the break occurs increases with the separation distance between snow depth maxima. In addition, the breaks in the forested area coincide with those of the corresponding vegetation height field, while in the tundra subarea they are displaced towards larger scales than those observed in the corresponding vegetation height field.

No abstract

Snow and hydrological modeling in alpine environments remains challenging because of the complexity of the processes affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high‐alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014–2015. Based on recent advances in Alpine3D, we examine how snow distributions and liquid water transport within the snowpack influence runoff dynamics. By combining these results with multiscale observations (snow lysimeter, distributed snow depths, and streamflow), we demonstrate the added value of a more realistic snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the mass balance errors are corrected (R2 = 0.95 versus R2 = 0.61). At the subbasin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originating in the shallower areas while an extended melting period (by a month) is caused by snowmelt from deeper areas. This is a marked improvement over results obtained using a traditional precipitation interpolation method. Hydrological response is also improved by the more realistic snowpack (NSE of 0.85 versus 0.74), even though calibration processes smoothen out the differences. The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at larger scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well‐instrumented area and present a model validation from multiscale experimental data sets.

Future projections of declining snowpack and increasing potential evaporation are predicted to advance the timing of snowmelt in mountain ecosystems globally with unknown implications for snowmelt‐driven forest productivity. Accordingly, this study combined satellite‐ and tower‐based observations to investigate the forest productivity response to snowpack and potential evaporation variability between 1989 and 2012 throughout the Southern Rocky Mountain ecoregion, United States. Our results show that early and late season productivity were significantly and inversely related and that future shifts toward earlier and/or reduced snowmelt could decrease snowmelt water use efficiency and thus restrict productivity despite a longer growing season. This was explained by increasing snow aridity, which incorporated evaporative demand and snow water supply, and was modified by summer precipitation to determine total annual productivity. The combination of low snow accumulation and record high potential evaporation in 2012 resulted in the 34 year minimum ecosystem productivity that could be indicative of future conditions.

Abstract. In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g., LIDAR, terrestrial laser scanning (TLS), and ground-penetrating radar (GPR)). Despite this, objective and quantitative frameworks for the evaluation of errors in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty in average snow depth derived from point measurements over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one- and two-dimensional survey designs that range from a single measurement to an increasing number of regularly spaced measurements. Using high-resolution (~ 1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly spaced measurements required to achieve such an error. On this basis, a series of figures are presented as an aid for snow survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished that are tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g., SWE – snow water equivalent) whose statistical properties are comparable to those of snow depth.