Canopy influence on snow depth distribution in a pine stand determined from terrestrial laser data

Revuelto, Jesús; López‐Moreno, J. I.; Azorín-Molina, César; Vicente‐Serrano, Sergio M.

doi:10.1002/2014wr016496

Cited by 44 publications

(41 citation statements)

References 58 publications

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“…The increased depth hoar development in canopy gaps may have occurred because the snow was deeper in these locations at a time when kinetic metamorphism was dominating. In this regard, previous works have shown that differences in snow depth between gaps and under the canopy are greater when the snowpack is thinner [ López‐Moreno and Latron , 2008; Revuelto et al ., ]. The increased development of depth hoar in canopy gaps is somewhat counter‐intuitive because, mathematically, temperature gradients must decrease when snow depth increases.…”

Section: Discussionmentioning

confidence: 99%

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

Molotch

Barnard

Burns

et al. 2016

Water Resources Research

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The distribution of forest cover exerts strong controls on the spatiotemporal distribution of snow accumulation and snowmelt. The physical processes that govern these controls are poorly understood given a lack of detailed measurements of snow states. In this study, we address one of many measurement gaps by using contact spectroscopy to measure snow optical grain size at high spatial resolution in trenches dug between tree boles in a subalpine forest. Trenches were collocated with continuous measurements of snow depth and vertical profiles of snow temperature and supplemented with manual measurements of snow temperature, geometric grain size, grain type, and density from trench walls. There was a distinct difference in snow optical grain size between winter and spring periods. In winter and early spring, when facetted snow crystal types were dominant, snow optical grain size was 6% larger in canopy gaps versus under canopy positions; a difference that was smaller than the measurement uncertainty. By midspring, the magnitude of snow optical grain size differences increased dramatically and patterns of snow optical grain size became highly directional with 34% larger snow grains in areas south versus north of trees. In winter, snow temperature gradients were up to 5–15°C m−1 greater under the canopy due to shallower snow accumulation. However, in canopy gaps, snow depths were greater in fall and early winter and therefore more significant kinetic growth metamorphism occurred relative to under canopy positions, resulting in larger snow grains in canopy gaps. Our findings illustrate the novelty of our method of measuring snow optical grain size, allowing for future studies to advance the understanding of how forest and meteorological conditions interact to impact snowpack evolution.

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Section: Discussionmentioning

confidence: 99%

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

Molotch

Barnard

Burns

et al. 2016

Water Resources Research

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“…Snow accumulation across the mountains is primarily influenced by orographic processes, involving feedbacks between atmospheric circulation and terrain (Roe, 2005;Roe and Baker, 2006). In most forested regions, snow distribution is highly sensitive to vegetation structure (Anderson, 1963;Revuelto et al, 2015;Musselman et al, 2008), and canopy interception, sublimation and unloading result in less accumulation of snow beneath the forest canopies in comparison with canopy gaps (Berris and Harr, 1987;Golding and Swanson,…”

Section: Introductionmentioning

confidence: 99%

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

2016

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Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km 2 . The percentage of pixels with at least one snowdepth measurement was observed to increase from 65-90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snowaccumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500-3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain-snow transition zone (1500-1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

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“…High spatial resolution information on the distribution of the snowpack on different days was obtained using a TLS (that uses LiDAR technology for measuring distances); these devices have been used extensively for monitoring snow evolution [36,37,41,42] recently reported the use of a TLS (RIEGL LPM-321) for analysis of snowpack dynamics in the same study area as was used in this study. Following the pruning that occurred in summer 2013, the snowpack distribution was assessed on seven additional dates using the same data acquisition protocol [11]; this enabled direct comparison with the data collected under pre-pruning conditions. Based on results presented in this study, we decided to not include data from four TLS surveys collected during the 2011-2012 snow season, as the snow distribution was clearly driven by an anomalous wind redistribution event, and was not comparable to conditions observed during the snow season following pruning.…”

Section: Methodsmentioning

confidence: 99%

“…Varimax rotation [47,48] was applied to distinguish components having physically consistent patterns. The PCA analysis was applied to the dataset of 11,000 grid cells of snow depth distribution data for each of the 23 TLS survey days. With this analysis, the survey dates were classified in the different components considering the highest correlation with the components.…”

Section: Methodsmentioning

confidence: 99%

“…[8]. Consequently, extensive snow-covered areas interact with forest cover, and this has been the basis of various studies investigating snowpack dynamics in Pyrenean forested areas [9][10][11]. Furthermore, since the middle of the 20th century there has been an increase in forest cover at all elevations in the Pyrenees, as a consequence of decreased human pressure [12,13]; the elevation of the tree line is shifting upwards because of decreasing grazing pressure in conjunction with warmer temperatures [14][15][16].…”

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confidence: 99%

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Small-Scale Effect of Pine Stand Pruning on Snowpack Distribution in the Pyrenees Observed with a Terrestrial Laser Scanner

Revuelto

López‐Moreno

Azorín-Molina

et al. 2016

Forests

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Forests in snow-dominated areas have substantial effects on the snowpack and its evolution over time. Such interactions have significant consequences for the hydrological response of mountain rivers. Thus, the impact of forest management actions on the snow distribution, and hence the storage of water in the form of snow during winter and spring, is a major concern. The results of this study provide the first detailed comparison of the small-scale effect of forest characteristics on the snowpack distribution, assessed prior to and following major modification of the structure of the canopy by pruning of the lower branches of the trees to 3 m above the ground. This is a common management practice aimed at reducing the spread of forest fires. The snowpack distribution was determined using terrestrial laser scanning (LiDAR technology) at a high spatial resolution (0.25 m) over a 1000 m 2 study area during 23 survey dates over three snow seasons in a small study area in the central Pyrenees. The pruning was conducted during summer following the snow season in the second year of the study (i.e., the study duration encompassed two seasons prior to canopy pruning and one following). Principal component analysis (PCA) was used to identify recurring spatial patterns of snow distribution. The results showed that pruning reduced the average radius of the canopy of trees by 1.2 m, and increased the clearance around the trunks, as all the branches that formerly contacted the ground were removed. However, the impact on the snowpack was moderate. The PCA revealed that the spatial configuration of the snowpack did not change significantly, as the principal components included survey days from different periods of the snow season, and did not discriminate days surveyed prior to and following pruning. Nevertheless, removal of the lower branches reduced the area beneath the canopy by 36%, and led to an average increase in total snow depth of approximately 14%. Forests 2016, 7, 166 2 of 15The Pyrenees provide a clear example of the significance of the snowpack to water reservoir areas downstream, as snow accounts for approximately 40% of the spring runoff [6]. In this mountain range, substantial accumulation of snow that largely persists over time typically occurs 1600 m above sea level (a.s.l.), which is the average 0˝C isotherm elevation between November and April [7]. The tree line in the Pyrenees ranges east-west from 1900 to 2300 m a.s.l. [8]. Consequently, extensive snow-covered areas interact with forest cover, and this has been the basis of various studies investigating snowpack dynamics in Pyrenean forested areas [9][10][11]. Furthermore, since the middle of the 20th century there has been an increase in forest cover at all elevations in the Pyrenees, as a consequence of decreased human pressure [12,13]; the elevation of the tree line is shifting upwards because of decreasing grazing pressure in conjunction with warmer temperatures [14][15][16]. Thus, increased knowledge on the interactions between the snowpack and forest i...

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Canopy influence on snow depth distribution in a pine stand determined from terrestrial laser data

Cited by 44 publications

References 58 publications

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

Small-Scale Effect of Pine Stand Pruning on Snowpack Distribution in the Pyrenees Observed with a Terrestrial Laser Scanner

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