ALBERT

Description: Airborne light detection and ranging (LiDAR) snow-on and snow-off measurements collected in the southern Sierra Nevada in the 2010 water year were analyzed for orographic and vegetation effects on snow accumulation during the winter season. Combining data from four sites separated by 10 to 64 km and together covering over 106 km2 area, the 1 m elevation-band-averaged snow depth in canopy gaps as a function of elevation increased at a rate of 15 cm per 100 m until reaching the elevation of 3300 m. The averaged snow depth of the same elevation band from different sites matched up with minor deviation, which could be partially attributed to the variation in other topographic features, such as slope and aspect. As vegetation plays a role in the snow accumulation, the distribution of the vegetation was also studied and shows that the canopy coverage consistently decreased along the elevation gradient from 80 % at 1500 m to near 0 % at above 3300 m. Also, the absolute difference of the averaged snow depth between snow found in canopy gaps and under the canopy increased with elevation, and decreased with canopy coverage disregarding the variation of other topographic features. The influence from the forest density on snow accumulation was quantified based on the snow-depth residuals from 1 m elevation-band-averaged snow depth and the attribute penetration fraction, which is the ratio of the number of ground points to the number of total points per pixel of LiDAR data. The residual increases from −25 to 25 cm at the penetration fraction range of 0 to 80 %; and the relationship could be modeled by exponential functions, with minor fluctuations along the gradient fraction of canopy and small deviation between sites.

Description: 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 km2. The percentage of pixels with at least one snow-depth 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 snow-accumulation 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.

Description: We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm per 100 m, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm per 100 m. Departures from the 15 cm per 100 m trend, based on 1 m elevation-band means of regression residuals, showed slightly less steep increases below 2050 m; steeper increases between 2050 and 3300 m; and less steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m, orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a cumulative precipitation lapse rate for the 2100–3300 m range of about 6 cm per 100 m elevation for the accumulation period of 3 December 2009 to 23 March 2010. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snow-cover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly accurate data and highlighs the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

Description: Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.

Description: Laser vision: lidar as a transformative tool to advance critical zone science. Observation and quantification of the Earth surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of Critical Zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and ecosphere shape and maintain the "zone of life", extending from the groundwater to the vegetation canopy. Lidar holds promise as a transdisciplinary CZ research tool by simultaneously allowing for quantification of topographic, vegetative, and hydrological data. Researchers are just beginning to utilize lidar datasets to answer synergistic questions in CZ science, such as how landforms and soils develop in space and time as a function of the local climate, biota, hydrologic properties, and lithology. This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications. A review of 147 peer-reviewed studies utilizing lidar showed that 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % have an interdisciplinary focus. We find that using lidar to its full potential will require numerous advances across CZ applications, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically-based models and complementary in situ and remote-sensing observations. We provide a five-year vision to utilize and advocate for the expanded use of lidar datasets to benefit CZ science applications.

Description: We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53-km2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm 100 m-1, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm 100 m-1. Departures from the 15 cm 100 m-1 trend, based on 1-m elevation-band means of regression residuals, showed slightly less-steep increases below 2050 m; steeper increases between 2050–3300 m; and less-steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less-steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting, and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a precipitation lapse rate for the 2100–3300 m range of about 6 cm 100 m-1 elevation. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snowcover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly-accurate data, as well as the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2943, doi:10.5194/hess-19-2943-2015.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2881-2897, doi:10.5194/hess-19-2881-2015.

Description: Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.

Description: The workshop forming the impetus for this paper was funded by the National Science Foundation (EAR 1406031). Additional funding for the workshop and planning was provided to S. W. Lyon by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT grant no. 2013-5261). A. A. Harpold was supported by an NSF fellowship (EAR 1144894).