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127 results for “Snow depth”
Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales
<p>This is a repository contains </p> <p>1)training data (x_data, y_data, snow_max) </p> <p>2) Developed Deep Learning model (snow_model.py)</p> <p>3) Training weights (*.hdf files)</p> <p>for publication " Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales"</p> <p> </p>
Experimentally increased snow depth affects High Arctic microarthropods inconsistently over two consecutive winters
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Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR
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Western Arctic caribou herd winter lichen, snow depth, and mortality
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Climate Change Across Seasons Experiment (CCASE) Sapling Study at the Hubbard Brook Experimental Forest: Herbivory Damage and Snow Depth
During the winter 2014-15 there was extensive over-winter herbivory damage to sapling stems in the Climate Chaneg Across Seasons (CCASE) sapling experiment in the form of partial or complete girdling from bark consumption. Measurements of the extent of bark damage, the recovery class of the saplings, and the average snow depth during winter were made for each experimental treatment. There were seven treatments for each species of maple. For each species, ten saplings experienced ambient temperatures (reference), ten experienced growing season warming with no induced freeze-thaw cycles in winter (warmed), ten in each of four groups experienced warming in the growing season coupled with two, four, six, or eight soil freeze-thaw cycles in winter (warmed + 2 FTC, warmed + 4 FTC, warmed + 6 FTC, warmed + 8 FTC), and ten experienced snow removal in winter with ambient temperatures in the growing-season (snow removal). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.
<p><strong>snow_depth_20170501_pleiades.tif</strong></p> <p>Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.</p> <p> </p> <p><strong>land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif</strong></p> <p>Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).</p>
Monitoring spatio-temporal snow depth in the Chilean Andes using spaceborne tri-stereo photogrammetry
<p>Data provided for submitted work:</p> <p>Thomas E. Shaw, César Deschamps-Berger, Simon Gascoin, James McPhee</p> <p>Monitoring spatial and temporal differences in Andean snow depth <br> derived from satellite tri-stereo photogrammetry</p> <p>---------</p> <p><strong>Contents:</strong></p> <p> <strong>'SDmap_2017_3m.tif</strong>' = Filtered DEM difference for September 4th 2017.<br> <strong>'SDmap_2019_3m.tif'</strong> = Filtered DEM difference for September 2nd 2019.<br> <strong>'SDmap_gapfilled_2017_3m.tif'</strong> = Filtered DEM difference for September 4th 2017, gap-filled by random forest model. Random forest model initialised by 100 runs, using all available snow depths in 2017 and topographic indices as predictors (Shaw et al., 2020).<br> <strong>'SDmap_gapfilled_2019_3m.tif'</strong> = As above, but for the filtered DEM difference of September 2nd 2019. <br> <strong> 'Slope_3m.tif'</strong> = Slope angle (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'TPI_3m.tif' </strong>= Topographic position index (TPI - Revuelto et al., 2014) derived from the snow-free DEM (6th January 2018) based upon a 60 m search distance.<br> <strong>'Aspect_3m.tif' </strong>= Aspect (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'Exposure_SX_3m.tif'</strong> = Exposure parameter (SX) based upon Winstral et al. (2002) derived from the snow-free DEM (6th January 2018) and dominant ERA5 10 m wind direction for 2017 and 2019 winter average (45°).<br> <strong>'SkyViewFraction_3m.tif' </strong>= Sky view fraction derived from the snow-free DEM (6th January 2018).</p> <p>---------</p> <p><strong>NOTE:</strong></p> <p>Products of the Pléiades DEM processing are provided here, though publication of the raw Pléiades DEMs are restricted by the regulations of the CNES agreement for grant PNTS‐2018‐ 4.</p> <p>---------</p> <p><strong>Cited works:</strong></p> <p><strong>Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., & Vicente-Serrano, S. M. (2014)</strong>. Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: Intra- and inter-annual persistence. The Cryosphere, 8(5), 1989–2006. https://doi.org/10.5194/tc-8-1989-2014<br> <strong>Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., & McPhee, J. (2020)</strong>. Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing Water Resources Research. Water Resources Research, 56, 1–23. https://doi.org/10.1029/2019WR024880<br> <strong>Winstral, A., Elder, K., & Davis, R. E. (2002)</strong>. Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters. Journal of Hydrometeorology, 3(5), 524–538. https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2</p>
Data from: Forest structure and snow depth alter the movement patterns and subsequent expenditures of a forest carnivore, the Pacific marten
<p>Energetic balance is central to the survival and persistence of free-ranging animals. Quantifying expenditures and identifying factors that drive energetics informs our understanding of species' ecology and their responses to shifting environmental conditions. Approaches used to estimate energetic expenditures of free-ranging species, such as doubly-labelled water (DLW), are precise but difficult to implement. Global positioning system (GPS) collars and accelerometers have emerged as alternatives for estimating expenditures, but these techniques have few applications in terrestrial species and no applications in small-bodied (<5kg) terrestrial animals. Here, we estimated movement characteristics and field metabolic rates (FMR) of Pacific martens (Martes caurina), a small-bodied carnivore with relatively high energetic costs, in a heterogeneous landscape to explore the role of movement and landscape characteristics on energetics. We concurrently used DLW and GPS collars to investigate the relationship between movement characteristics and FMR. Movement velocity explained the greatest amount of variation in mass-specific FMR and we used this relationship to predict expenditures of previously collared martens. We found that predicted mass-specific FMR was highest among males and increased in open patches primarily as a result of increased velocity and more erratic movements. Additionally, martens moving through deep snow also exhibited increased FMR. Our work shows movement metrics can effectively explain variation in FMR and identify landscape features, like forest structure and snow depth, that influence movements with cascading effects on energetics for free-ranging mammals in rapidly changing systems.</p>
Data from: Ectomycorrhizal and saprotrophic fungi respond differently to long-term experimentally increased snow depth in the High Arctic
Changing climate is expected to alter precipitation patterns in the Arctic, with consequences for subsurface temperature and moisture conditions, community structure, and nutrient mobilization through microbial belowground processes. Here, we address the effect of increased snow depth on the variation in species richness and community structure of ectomycorrhizal (ECM) and saprotrophic fungi. Soil samples were collected weekly from mid-July to mid-September in both control and deep snow plots. Richness of ECM fungi was lower, while saprotrophic fungi was higher in increased snow depth plots relative to controls. [Correction added on 23 September 2016 after first online publication: In the preceding sentence, the richness of ECM and saprotrophic fungi were wrongly interchanged and have been fixed in this current version.] ECM fungal richness was related to soil NO3-N, NH4-N, and K; and saprotrophic fungi to NO3-N and pH. Small but significant changes in the composition of saprotrophic fungi could be attributed to snow treatment and sampling time, but not so for the ECM fungi. Delayed snow melt did not influence the temporal variation in fungal communities between the treatments. Results suggest that some fungal species are favored, while others are disfavored resulting in their local extinction due to long-term changes in snow amount. Shifts in species composition of fungal functional groups are likely to affect nutrient cycling, ecosystem respiration, and stored permafrost carbon.
Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA
<p>Growing evidence supports the hypothesis that temperate herbivores surf the green wave of emerging plants during spring migration. Despite the importance of autumn migration, few studies have conceptualized resource tracking of temperate herbivores during this critical season. We adapted the Frost Wave Hypothesis (FWH), which posits that animals pace their autumn migration to reduce exposure to snow but increase acquisition of forage. We tested the FWH in a population of mule deer in Wyoming, USA by tracking the autumn migrations of <em>n</em> = 163 mule deer that moved 15–288 km from summer to winter range. Migrating deer experienced similar amounts of snow but 1.4–2.1 times more residual forage than if they had naïve knowledge of when or how fast to migrate. Importantly, deer balanced exposure to snow and forage in a spatial manner. At the fine scale, deer avoided snow near their mountainous summer ranges and became more risk-prone to snow near winter range. Aligning with their higher tolerance of snow and lingering behavior to acquire residual forage, deer increased stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration completed. Our findings support the prediction that mule deer pace their autumn migration with the onset of snow and residual forage but refine the FWH to include movement behavior en route that is spatially dynamic.</p>
Bias Corrected and Gap Filled Sentinel-1 and University of Arizona Snow Depth Data
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The station-based error information of monthly snow depth, precipitation and air temperature for CMIP6 models in mainland China
<p>This dataset contains the data of monthly snow depth in terms of RMSD (cm), spatial correlation (R<sub>s</sub>), temporal correlation (R<sub>t</sub>), consistency index (CI), and Hotspot score (H-score) of the 1415 weather stations (only 342 stations with longterm observations were available for R<sub>t</sub>, CI and H-score) in China used for evaluating the snow depth simulated or estimated from 31 CMIP6 models, MERRA2 reanalysis and a remote sensing snow depth dataset (Che). It also includes the data of errors and accumulated errors of monthly precipitation (mm) and air temperature (℃) from the 342 stations of all the 31 CMIP6 models, which can be used for constructing the regression models for analyzing error sources of snow depth simulations. The NA values of monthly precipitation and temperature indicate that the effects of accumulated errors were ignored for the corresponding month and station.</p>
Dataset underying the publication titled: Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region
<p>LPJ-GUESS model outputs underlying the figures published in the article "Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region".</p> <p> </p>
Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA
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Data from: Compositional and functional shifts in arctic fungal communities in response to experimentally increased snow depth
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Data from: Phylogenetic origin of two Japanese Torreya taxa found in two regions with strongly contrasting snow depth
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Data from: Ectomycorrhizal and saprotrophic fungi respond differently to long-term experimentally increased snow depth in the High Arctic
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Data from: Forest structure and snow depth alter the movement patterns and subsequent expenditures of a forest carnivore, the Pacific marten
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Snow depth fractal behavior at the Izas experimental catchment from 2011 to 2017
<p>All the files included here contain the data produced for the manuscript "Inter-annual and seasonal variability of snow depth scaling behavior in a sub-alpine catchment", submitted for possible publication in Water Resources Research. The raw data used to generate the figures for the manuscript can be found below:</p> <p>Revuelto, J., Azorin-Molina, C., Alonso-González, E., Sanmiguel- Vallelado, A., Navarro-Serrano, F., Rico, I., and López- Moreno, J. I.: Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017 [Data set], Zenodo, https://doi.org/10.5281/zenodo.848277, 2017.</p> <p>The following data files and scripts were used to produce results for the Izas experimental catchment:</p> <p>Variogram_YYYYMMDD_SD_ext.rsav: Omnidirectional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD.</p> <p>Variogram_YYYYMMDD_SD_directional_ext.rsav: Directional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD.</p> <p>Variogram_DEM_Izas1m_ext.rsav: Omnidirectional variogram for bare earth topography.</p> <p>Variogram_DEM_Izas1m_directional_ext.rsav: Directional variogram for bare earth topography (needs to be loaded in R, as any other .rsav file).</p> <p>VarAnalysis_Omnidirectional_Izas.R: Script used to analyze omnidirectional variograms and plot results.</p> <p>VarAnalysis_Directional_Izas.R: Script used to analyze directional variograms and plot results.</p> <p>Variogram_Omnidirectional_Izas.R: Script used to generate directional variograms and save data.</p> <p>Variogram_Directional_Izas.R: Script used to generate directional variograms and save data.</p>
Lidar-derived snow depth maps along the Chilean Extratropical Andes, winter 2018
<p>All the files included here contain the data produced for the manuscript "<strong>Spatial distribution and scaling properties of lidar-derived snow depth in the extratropical Andes</strong>", submitted for possible publication in Water Resources Research. Lidar measurements for snow-covered conditions were conducted on September 4th, August 9th, and October 25th 2018 in Tascadero, Las Bayas and Valle Hermoso, respectively. Each data acquisition was conducted using a Riegl VZ6000 long range scanner on dates with and without snow, using an angular resolution of 0.01°.</p> <p>Lidar-derived maps are contained in the following files:</p> <p>SiteName_SD_TPI.txt (SiteName: Tascadero, Las Bayas, VH East or VH West).</p> <p>Where the file structure is as follows:</p> <p>X,Y,Z,SD,SLP,NOR,TPI04,TPI07,TPI15,TPI20,TPI30,TPI40,TPI50</p> <p>where Z is bare earth elevation (m a.s.l.), SD is snow depth, SLP is slope (°), NOR is northness (°) whereby 180 = north facing and 0 = south facing, TPIXX is the Topographic Position Index (TPI) computed for a search distance of XX m, whereby positive differences are convex landforms, and negative is concave. The magnitude of TPI indicates the scale of the relative concavity/convexity.</p> <p>Variogram results are contained in files .rsav with the following nomenclature:</p> <p>SiteName_X_TypeOfVariogram.rsav</p> <p>Where X can be SD (snow depth) or Z (bare earth topography), and the type of variogram can be omnidirectional or directional.</p>
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