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127 results for “Snow depth”
Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014
<p>We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 ×10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management .</p> <p> </p>
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C). in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C).
Snow depth and density measurements with different snow core samplers in HARMOSNOW Field Campaigns
<p>The data correspond to snow bulk density and snow depth measured with different snow core sampler in three field campaigns carried out in mountains of Turkey, Iceland and Finland in order to assess the uncertainty of using different snow core samplers and different observers. The field campaigns were carried out in the frame of the COST project HARMOSNOW ES1404 <a href="http://harmosnow.eu/">http://harmosnow.eu/</a></p>
Snow depth on Hardangervidda, South Norway 2008-2009
<p>This data set contains 10 m gridded snow depths derived from airborne light detection and ranging, or lidar, and measurements of surface elevation (DTM). The data were collected by the BREMS project of the Norwegian Water Resources and Energy Directorate. The data were collected along six independent flight lines. Each flight line is 80km long and 500m wide and follows a west-east orientation. Each flight line is separated by 10 km in the north/south direction in order to investigate any change from north to south.</p>
Multi-frequency altimetry snow depth product over Arctic sea ice
<p>Satellite altimetry can be used to estimate sea ice thickness, an essential variable to better understand and forecast the dynamic ice cover. Nevertheless, some sources of uncertainty remain, and one of the most important concerns the snow depth, a key parameter to convert the measured ice freeboard into sea ice thickness.</p> <p>Snow depth can be estimated using different altimeter frequencies with different snow penetration capabilities. We have developed a monthly snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters covering the period 2018-2021 with a spatial resolution of 25 km. </p> <p> </p>
Monthly total freeboard and snow depth over Arctic sea ice from AMSR-E&2 and AVHRR measurements (2003-2020)
<p><strong>[Data description]</strong></p> <p>Monthly total freeboard (Ft), snow depth (hs), and snow depth uncertainty over Arctic sea ice data for January-February-March months of the 2003-2020 period produced by Lee and Shi et al. (2021, manuscript under review) are provided.</p> <p>Both variables are derived from satellite passive infrared and microwave measurements: Total freeboard was obtained from AMSR-E&2 measurements and snow depth was estimated from the AMSR and AVHRR measurements.</p> <p>The uploaded file titled "monthly averaged total freeboard and snow depth (JFM 2003-2020).zip" contains two directories: one for total freeboard and the other for snow depth. Naming convention is "variable_yyyymm.bin" and data format is 32-bit floating point array in shape of 304 x 448 (25 km polar stereographic grid).</p> <p>Here we provide an example Python code to read monthly snow depth of January 2003 using numpy.<br> import numpy as np<br> hs = np.fromfile('hs_200301.bin', dtype=np.float32).reshape(448,304)</p> <p>Geocoordinate tools for the 25 km polar stereographic grid are available at NSIDC website (https://nsidc.org/data/polar-stereo/tools_geo_pixel.html)<br> <br> <strong>[Abbreviations]</strong></p> <p>AMSR: Advanced Microwave Scanning Radiometer<br> AVHRR: Advanced Very High Resolution Radiometer<br> NSIDC: National Snow and Ice Data Center</p>
Daily snow water equivalent and snow depth data from the valley Wattental in the Tuxer Alpen, Tyrol, Austria [dataset]
<p>The herein published dataset contains daily snow depth (HS) and daily snow water equivalent (SWE) data from the catchment of the Lizumbach in the valley bottom of Wattental in the Tuxer Alpen, Tyrol, Austria (N47.16820, E11.63858). The measurements are obtained at the tree line in an altitude of 1995m a.s.l. Measurement data are available from January 11 2010 until September 30 of 2022. The repository will be updated during the coming years. </p> <p> </p>
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
Open the record for dataset details and reuse information.
Southern Sierra Critical Zone Observatory (SSCZO), Providence Creek meteorological data, soil moisture and temperature, snow depth and air temperature
Open the record for dataset details and reuse information.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): snow depth sensor data, 2012-2017
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set contains three-hourly measurements of snow depth collected with acoustic sensors for winter warming and control treatment plots.
Eight Mile Lake Research Watershed, Thaw Gradient: Half-hourly snow depth data, 2012-2017
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. This data set provides half-hourly measurements of snow depth collected with an acoustic sensor.
Geodetic snow depth from UAV campaign at Niwot Ridge, 2017
Geodetic snow depth for of Niwot Ridge from UAV campaign in June 2017. Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017, investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle
Winter snow depths for initializing a glacio-hydrological model in high mountain Chile
<p>The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (<em>Finger et al., 2011; Ragettli and Pellicciotti, 2012</em>) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - <em>Burger et al., 2018</em>). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020. </p> <p> </p> <p>Data and file repository for the submitted article:<br> %-------------------------------------------------------------<br> %-------------------------------------------------------------</p> <p> On the utility of optical satellite winter snow depths for modelling the<br> glacio-hydrological behaviour of a high elevation, Andean catchment.</p> <p>Thomas E. Shaw1, Alexis Caro1,2, Pablo Mendoza3, Álvaro Ayala4, Francesca Pellicciotti5,6, Simon Gascoin7, James McPhee1,3</p> <p>1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile<br> 2 Univ. Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France<br> 3 Department of Civil Engineering, Universidad de Chile, Santiago, Chile<br> 4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile<br> 5 Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland<br> 6 Department of Geography, Northumbria University, Newcastle, UK<br> 7 CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France</p> <p>%-------------------------------------------------------------<br> %-------------------------------------------------------------<br> The following sub-folders are separated into forcings, grids, initial model conditions, model files and parameters.</p> <p>This file describes briefly the contents of each sub-folder.</p> <p>%-------------------------------------------------------------<br> FORCINGS:</p> <p>CloudCover_TPK.csv - A timeseries of hourly cloud cover fraction (-) derived NASA POWER archives.<br> Discharge_TPK.csv - A timeseries of hourly discharge (m3 s-1) from the outlet station F_TdP.<br> Master_Data_TPK.mat - a Matlab structure (written in version 2017a) for all data availble to the catchment for the considered model period.<br> Precipitation_TPK.csv - A timeseries of hourly precipitation (mm/hr) from AWS TdP.<br> Temperature_TPK.csv - A timeseries of hourly temperature (degC) from AWS TdP. <br> TemperatureGradient_TPK.csv - A timeseries of calibrated temperature gradients based upon forcing from AWS TdP.</p> <p>%-------------------------------------------------------------<br> GRIDS:</p> <p>42 ascii files for various grids (primary or secondary) use to derive the .TES file (see TOPKAPI-ETH sub-folder) for running the model.<br> Associated projection (.prj) files are given.<br> Naming convention is provided in the manual (see TOPKAPI-ETH sub-folder) except:<br> rdy_SoilDepth.asc - An adjusted top layer soil depth map based upon Ragettli et al. (2012).<br> rdy_debris_v.asc - A debris thickness map for Piramde Glacier and the tongue of Bello Glacier. Values adjusted slightly from Ayala et al. (2016) to account for areas that are not debris, but bedrock (Bello Glacier).</p> <p>%-------------------------------------------------------------<br> INITIAL_CONDITIONS:</p> <p>Sub-folder 'Albedo': <br> Albedo_Pleiades.asc - An albedo map derived from the model spin up and limited to the snow-covered pixels of the Pléiades snow depth map.<br> Sub-folder 'Snow':<br> XXX_snow_mmwe.asc - A snow water equivalent map (mm w.e.) given by the initialisation method 'XXX' (Pléiades, TOPO or DBSM). TPK is derived solely from the model spin up (an input grid not required). <br> XXXeq_snow_mmew.asc - As above, though considering the equal means approach described in the manuscript. TPK included here.<br> Sub-Folder 'SpinUp_State':<br> 201709040000.stt - The system state file that contains information on the equiblibrium state of catchment (as read by the model upon initialisation). Running the model with a spinup shuld call upon this file within the command prompt.</p> <p><br> %-------------------------------------------------------------<br> PARAMETERS:</p> <p>Sub-folder 'Calibration'<br> TPK_ParameterAllocation - A Matlab script for the establishing the Monte Carlo parameter simulation and running the model n times. The current script is considered for soil parameters.<br> Sub-folder 'Sensitivity'<br> TPK_Sensivity_Analysis - A Matlab script for establishing the upper and lower boundaries of parameter/forcing sensitivities for a one-at-a-time analysis.</p> <p> </p> <p>%-------------------------------------------------------------<br> RESULTS:</p> <p>Model_Output_Comparison.mat - A matlab file with output grids and vectors for model intercomparisons (i.e. Pléiades (Pléiades-Uncertainty and Pléiades+Uncertainty), TOPO, TPK, DBSM + equal means equivalents). Files are:<br> All_S - Daily snow mass balance grids (mm w.e.)<br> Bias_Month - Monthly bias (row) of modelled vs measured streamflow at F_aP site for each model run (column).<br> Date_Daily - Numeric date of daily grids<br> DateTPK - Numeric date of hourly model simulations<br> Gla_Map - Daily cumulative glacier modelled mass balance grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> GMB_Bello - Cumulative modelled mass balance (mm w.e.) of Bello Glacier AWS grid cell<br> GMB_Piramide - Cumulative modelled mass balance (mm w.e.) of Piramide Glacier AWS grid cell<br> GMB_Yeso - Cumulative mass modelled balance (mm w.e.) of Yeso Glacier AWS grid cell<br> KGE_Month - KGE values per month (row) and for each model run (column)<br> M3AP - Measured streamflow at F_aP<br> M3TP - Measured streamflow at F_TdP<br> MeltG_Avg_all - Mean hourly ALL-glacier melt rate (mm w.e./hr) for each model run (column)<br> MeltS_Avg_all - Mean hourly catchment-wide melt rate (mm w.e./hr) for each model run (column)<br> MOD_SnowCC - Daily MODIS snow cover fraction<br> Model_Name - .... well, its the name of the model run :=)<br> MODIS_SLE - The calculated Snow Line Elevation (m a.s.l.) for each daily MODIS scene<br> PlanetSLE - As above, but for PlanetScope images (17 days total)<br> Planet_SnowObs - The numeric dates of the equivalent PlanetSLE data<br> Q_Mod_aP - The modelled hourly streamflow at F_aP<br> Q_Mod_TdP - The modelled hourly streamflow at F_TdP<br> Q_Prc_Month - The percentage difference in monthly (row) modelled-measured streamflow by model run (column)<br> R_Month - Correlation values per month (row) and for each model run (column)<br> RelVar_Month - Relative variance per month (row) and for each model run (column)<br> Snow_Map - Daily snow water equivalent grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> SnowCC - The hourly modelled snow cover fraction for the catchment for each model run (column)<br> TPKSLE - The daily modelled TOPKAPI-ETH model SLE from each model run (column)</p> <p> </p> <p>%-------------------------------------------------------------<br> TOPKAPI-ETH:</p> <p>FIUME - A generic file type that is called by the model to ID the name of the study site. I this case 'rdy'.<br> rdy.TES - A vectorised file of all grids required by the model to run.<br> rdy.TPK - The TPK parameter and command file. This is adjusted to change input parameters and forcing files etc. The current file is the optimised version for this catchment.<br> TManual_Aug2013.pdf - A PDF instruction file (semi-complete) for the model written by Stefan Rimkus (2013). The naming conventions and grid names are given here.</p> <p> </p> <p> </p> <p>CITED MATERIAL REGARDING THE MODEL</p> <p><strong>Burger, F., Ayala, A., Farias, D., Shaw, T. E., Macdonell, S., Brock, B., McPhee, J., Pellicciotti, F. (2018a). Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment: understanding the role of debris cover in glacier hydrology. Hydrological Processes, SI-Latin(January), 1–16. <a href="https://doi.org/10.1002/hyp.13354">https://doi.org/10.1002/hyp.13354</a></strong></p> <p><strong>Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., & Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resources Research, 47(7), 1–14. <a href="https://doi.org/10.1029/2010WR009824">https://doi.org/10.1029/2010WR009824</a></strong></p> <p><strong>Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters. Water Resources Research, 48(3), n/a-n/a. <a href="https://doi.org/10.1029/2011WR010559">https://doi.org/10.1029/2011WR010559</a></strong></p> <p> </p>
TVCSnow 2017-2018 tundra snow depth probe measurements
<p>Snow depth measurements were recorded in March 2018 as part of Environment and Climate Change Canada's 2017-2018 Trail Valley Creek Snow Experiment (TVCSnow 17/18). These snow depths were collected to investigate the relationship between snow microstructure and airborne and ground-based passive microwave radiometer measurements of snow in a tundra environment to develop improved methods for retrieving tundra snow water equivalent and atmospheric profiles of temperature and humidity. Snow depths were recorded 50 km north of the town of Inuvik, Northwest Territories. Measurements took place from March 16th to 23rd 2018 in and around the Trail Valley Creek research station. The snow depth measurements were recorded with an automatic snow depth probe (magnaprobe).</p> <p> </p> <p>Open Government Licence - Canada<br> (https://open.canada.ca/en/open-government-licence-canada)</p>
Experimentally increased snow depth affects High Arctic microarthropods inconsistently over two consecutive winters
<p>Climate change induced alterations to winter conditions may affect decomposer organisms controlling the vast carbon stores in northern soils. Soil microarthropods are abundant decomposers in Arctic ecosystems affecting soil carbon release through their activities. We studied whether increased snow depth affected microarthropods, and if effects were consistent over two consecutive winters. We sampled Collembola and soil mites from a snow accumulation experiment at Svalbard in early summer and used soil microclimatic data to explore to which aspects of winter climate change microarthropods are most sensitive. Community densities differed substantially between years and increased snow depth in winter had inconsistent effects. Increased snow depth hardly affected microarthropods in 2015, but decreased overall abundance and altered relative abundances of microarthropod groups and Collembola species after a milder winter in 2016. Although our increased snow depth treatment enhanced soil temperatures by 3.2 ⁰C in the snow cover periods, the only good predictors of microarthropod density changes were soil conditions around snowmelt. Our study underpins that extrapolation of observations of decomposer responses to altered winter climate conditions to future scenarios should be avoided when communities are only sampled on a single occasion, since effects of longer-term gradual changes in winter climate may be obscured by inter-annual weather variability.</p>
Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables -- data set
<p>This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.</p> <p>Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (<a href="https://psl.noaa.gov/data/gridded/data.narr.html">https://psl.noaa.gov/data/gridded/data.narr.html</a>).</p> <p>Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (<a href="https://climatena.ca/">https://climatena.ca/</a>).</p>
Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps
<p>This data set contains model output presented in the following paper: I. Brangers, H. Lievens, A. Getirana, and G. J. M. De Lannoy. (2024). Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps. Water Resources Research. Under review.</p> <div>The model simulations were carried out in NASA's Land Information System (LIS), using the NoahMP v3.6 land surface model, forced with ERA5. The land surface model was coupled to the HyMAP routing algorithm to produce streamflow estimates. The data contains model results for the western European Alps for the period of 2015-2021 for two seperate cases. 1) The OL run: model run without assimilation of external observations; and 2) DA run: model run with the assimilation of Sentinel-1 snow depth observations.</div> <div> </div> <div>The zip-folders contain netcdf files for each day of the simulation period, for 1) the river discharge (_ROUTING), </div> <div>2) land surface model variables such as snow depth and SWE (_SURFACEMODEL_yyyy) grouped per year, and 3) variables related to the data assimilation such as the spread and innovations (_EnKF).</div>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Code and Data for "A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery"
<p>Here we share the data and code for “A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery”</p> <p>Corresponding author: Devon Dunmire devon.dunmire@kuleuven.be</p> <p>‘model_training’ - contains script to train the ML model, and training data sets from (1) in-situ snow measurement sites (training_data.p) and (2) photogrammetry snow depth maps (map_training_data.p)</p> <p>‘Cross_val_predictions’ contains model predictions for our cross-validation of all the in-situ snow measurement sites</p> <p>‘run_model’ contains the trained model (final_model_xg.pkl) and scripts to retrieve snow depth with our ML model.</p> <p>‘SD_*’ zip folders contains daily ML snow depth output over the European Alps for each snow year from Sept. 1 2015 - Apr. 30 2023. Data from multiple orbits is averaged.</p> <p>Naming convention: ‘S1_ml_SD_{yyyymmdd}_.nc’</p>
Snow depth calculation in Lake Winnipeg
<p># Snow_depth_LW<br>This repository is for snow depth calculation model in Lake Winnipeg.</p> <p>The meteorological data (i.e., air temperature, air pressure, wind speed, precipitation, and est) were used to calculate snow depth during winter at three major sites in Lake Winnipeg (STN 500 in South Basin, STN 502 in Narrow, and STN 505 in North Basin).</p> <p>A physical model were designed to simulate snow formation and melting on the ice.</p>
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