scholarly journals Generation of daily high-spatial resolution snow depth maps from in-situ measurement and time-lapse photographs

2020 ◽  
Vol 46 (1) ◽  
pp. 59-79
Author(s):  
J. Revuelto ◽  
E. Alonso-González ◽  
J.I. López-Moreno
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Distribution Patterns ◽  
Time Lapse ◽  
Daily Basis ◽  
Small Scale ◽  
Mountain Areas ◽  
Depth Maps

Acquiring information on snow depth distribution at high spatial and temporal resolution in mountain areas is time consuming and generally these acquisitions are subjected to meteorological constrains. This work presents a simple approach to assess snow depth distribution from automatically observed snow variables and a pre-existing database of snow depth maps. By combining daily observations of in-situ snow depth, georectified time-lapse photography (snow presence or absence) and information on snowpack distribution during annual snow peaks determined with a Terrestrial Laser Scanner (TLS), a method was developed to simulate snow depth distribution on day-by-day basis. This method was tested is Izas Experimental Catchment, in the Central Spanish Pyrenees, a site with a large database of TLS observations, time-lapse images and nivo-meteorological variables for six snow seasons (from 2011 to 2017). The contrasted snow climatic characteristics among the snow seasons allowed analysis of the transferability of snowpack distribution patterns observed during particular seasons to periods without spatialized snow depth observations, by TLS or other procedures. The method i) determines snow depth ratio among the observed maximum snow depths and all other snow map pixels at the TLS yearly snow peak accumulation, ii ) rescales these ratios on a daily basis with time-lapse images information and iii) calculates the snow depth distribution with; the rescaled ratios and the snow depth observed at the automatic weather station. The average of the six TLS observed peaks was the combination showing optimal overall applicability. Despite its simplicity, these simulated values showed encouraging results when compared with snow depth distribution observed on particular dates. This was due primarily to the strong topographic control of small scale snow depth distribution on heterogeneous mountain areas, which has high inter- and intra-annual consistencies.

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

2020 ◽  
Vol 34 (26) ◽  
pp. 5384-5401
Author(s):  
Jesús Revuelto ◽  
Paul Billecocq ◽  
François Tuzet ◽  
Bertrand Cluzet ◽  
Maxim Lamare ◽  
...  
Keyword(s):  
Random Forests ◽  
Snow Depth ◽  
Depth Distribution ◽  
Mountain Areas

A comparison between coincident laser and Ku radar versus S- to C-band 'snow radar' data for airborne retrievals of snow depth on sea ice

2021 ◽  
Author(s):  
Claude de Rijke-Thomas ◽  
Jack Landy ◽  
Joshua King ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Radar Data ◽  
Scanner Data ◽  
Sea Ice Thickness ◽  
Band Frequency ◽  
Radar Waveforms ◽  
Calibration Step

<p>Snow depth estimates remain a large uncertainty for constraining the accuracy of sea ice thickness retrievals from polar altimetry. There have been several recent investigations into methods for estimating snow depth from airborne observations over sea ice; this poster outlines a comparison between two different methods applied to Operation IceBridge data from the Spring 2016 campaign. The first co-locates visible-band laser scanner data from the Airborne Topographic Mapper with Ku-band data from the CReSIS radar, using a fixed threshold first-maximum retracker algorithm for retracking radar waveforms and applying a calibration step to remove the vertical offset between sensors at leads. This method represents an airborne proxy for the newly-aligned ICESat-2 and CryoSat-2 orbits of the Cryo2Ice campaign. The second method uses the conventional CReSIS ultrawide-band frequency‐modulated continuous‐wave ‘snow radar’ system, that ranges between S- and C-band, applying the retracker algorithm described by Newman et al 2014. We evaluate properties of the estimated snow depth distribution, and alignment of air-snow and snow-ice interfaces, along the aircraft track and the scale of correlation between sensors.</p>

scholarly journals Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Douglas M. Hultstrand ◽  
Steven R. Fassnacht ◽  
John D. Stednick ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Generalized Additive Models ◽  
Winter Season ◽  
Regression Tree ◽  
Distribution Patterns ◽  
Time Frame ◽  
Additive Models ◽  
Depth Measurement ◽  
Accumulation Index

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

scholarly journals In situ observations of meteorological variables and snowpack distribution at the Izas Experimental Catchment (Spanish Pyrenees): The importance of high quality data in sub-alpine ambients

2017 ◽  
Author(s):  
Jesús Revuelto ◽  
Cesar Azorin-Molina ◽  
Esteban Alonso-González ◽  
Alba Sanmiguel-Vallelado ◽  
Francisco Navarro-Serrano ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanner ◽  
Soil Surface ◽  
Time Lapse ◽  
Snow Accumulation ◽  
Meteorological Variables ◽  
Quality Data ◽  
Wave Radiation ◽  
Data Set ◽  
Spanish Pyrenees

Abstract. his work describes the snow and meteorological dataset available for the Izas Experimental Catchment, in the Central Spanish Pyrenees, from 2011 to 2016 snow seasons. The experimental site is located in the southern side of the Pyrenees between 2000 and 2300 m above sea level with an extension of 55 ha. The site is a good example of sub-alpine ambient in which snow accumulation and melting dynamics have major importance in many mountain processes. The climatic dataset includes information on different meteorological variables acquired with an Automatic Weather Station (AWS) such as precipitation, air temperature, incoming and reflected short and long-wave radiation, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface) and soil temperature; all of them at 10 minute intervals. Snow depth distribution was measured during 23 field campaigns using a Terrestrial Laser Scanner (TLS), and there is also available daily information of the Snow Covered Area (SCA) retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.579979) is valuable since it provides high spatial resolution information on the snow depth and snow cover distribution, which is particularly useful in combination with meteorological variables to simulate the snow energy and mass balance. This information has already been analyzed in different scientific works studying snow pack dynamics and its interaction with the local climatology or terrain topographic characteristics. However, the database generated till the date has great potential for understanding other environmental processes from a hydrometerological or ecological perspective in which snow dynamics play a determinant role.

scholarly journals Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

2014 ◽  
Vol 8 (2) ◽  
pp. 1937-1972 ◽  
Author(s):  
J. Revuelto ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
S. M. Vicente-Serrano
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Climatic Conditions ◽  
Snow Accumulation ◽  
Small Catchment ◽  
Explanatory Variables ◽  
Spanish Pyrenees ◽  
Snow Distribution ◽  
Variance Explained

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions and binary regression trees were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that better explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and inter-annual time scales. The topographic position index, which has rarely been used in these types of studies, most accurately explained the distribution of snow accumulation. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation, and northing (or potential incoming solar radiation). The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the most explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a~year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snow pack was thinner and more patchily distributed. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, some similarities in snow accumulation patterns were observed.

Characterisation of SiCf/SiC Specimens Using an In-Situ Tensile Stage Within a Scanning Electron Microscope

2019 ◽  
Author(s):  
Steven P. Jordan ◽  
Martin R. Bache ◽  
Christopher D. Newton ◽  
Louise Gale
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Time Lapse ◽  
Image Correlation ◽  
Small Scale ◽  
Initiation And Propagation ◽  
Gauge Section ◽  
Time Lapse Imaging ◽  
Scanning Electron

Abstract The present paper will introduce the use of scanning electron microscope based, in-situ tensile testing as a method of detecting cracking in a SiCf/SiC CMC at room temperature. Small scale tensile specimens were prepared, but still sampling multiple longitudinal and transverse fibre tows. Monotonic loading was applied to initiate cracking, whilst contemporary time lapse imaging and retrospective digital image correlation recorded the development of these cracks at the specimen surface. Examples of strain localization, crack initiation and propagation will be presented for a plain gauge section specimen and single edge notched specimen. The critical combination of SEM imaging together with real time loading, in order to identify microscopic cracking in this CMC system, will be demonstrated.

scholarly journals Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

2013 ◽  
Vol 59 (218) ◽  
pp. 1047-1059 ◽  
Author(s):  
Leo Sold ◽  
Matthias Huss ◽  
Martin Hoelzle ◽  
Hubert Andereggen ◽  
Philip C. Joerg ◽  
...  
Keyword(s):  
Mass Balance ◽  
Snow Depth ◽  
Multiple Linear Regression Model ◽  
Field Measurements ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Small Scale ◽  
Alpine Glaciers ◽  
Mass Balance Models

AbstractSnow accumulation is an important component of the mass balance of alpine glaciers. To improve our understanding of the processes related to accumulation and their representation in state-of-the-art mass-balance models, extensive field measurements are required. We present measurements of snow accumulation distribution on Findelengletscher, Switzerland, for April 2010 using (1) in situ snow probings, (2) airborne ground-penetrating radar (GPR) and (3) differencing of two airborne light detection and ranging (lidar) digital elevation models (DEMs). Calculating high-resolution snow depth from DEM-differencing requires careful correction for vertical ice-flow velocity and densification in the accumulation area. All three methods reveal a general increase in snow depth with elevation, but also a significant small-scale spatial variability. Lidar-differencing and in situ snow probings show good agreement for the mean specific winter balance (0.72 and 0.78 m w.e., respectively). The lidar-derived distributed snow depth reveals significant zonal correlations with elevation, slope and curvature in a multiple linear regression model. Unlike lidar-differencing, GPR-derived snow depth is not affected by glacier dynamics or firn compaction, but to a smaller degree by snow density and liquid water content. It is thus a valuable independent data source for validation. The simultaneous availability of the three datasets facilitates the comparison of the methods and contributes to a better understanding of processes that govern winter accumulation distribution on alpine glaciers.

scholarly journals Effect of small-scale snow surface roughness on snow albedo and reflectance

2020 ◽  
Author(s):  
Terhikki Manninen ◽  
Kati Anttila ◽  
Emmihenna Jääskeläinen ◽  
Aku Riihelä ◽  
Jouni Peltoniemi ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Small Scale ◽  
Snow Surface ◽  
Roughness Effects ◽  
Bidirectional Reflectance Factor ◽  
Reflectance Factor ◽  
Bulk Volume ◽  
The Impact

Abstract. The primary goal of this paper is to present a model of snow surface albedo accounting for small-scale surface roughness effects. The model is based on photon recollision probability and it can be combined with existing bulk volume albedo models, such as TARTES. The model is fed with in situ measurements of surface roughness from plate profile and laser scanner data, and it is evaluated by comparing the computed albedos with observations. It provides closer results to empirical values than volume scattering based albedo simulations alone. The impact of surface roughness on albedo increases with the progress of the melting season and is larger for larger solar zenith angles. In absolute terms, surface roughness can decrease the total albedo by up to about 0.1. As regards the bidirectional reflectance factor (BRF), it is found that surface roughness increases backward scattering especially for large solar zenith angle values.

scholarly journals Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

2017 ◽  
Vol 9 (2) ◽  
pp. 993-1005 ◽  
Author(s):  
Jesús Revuelto ◽  
Cesar Azorin-Molina ◽  
Esteban Alonso-González ◽  
Alba Sanmiguel-Vallelado ◽  
Francisco Navarro-Serrano ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Snow Depth ◽  
Depth Distribution ◽  
Time Lapse ◽  
Snow Accumulation ◽  
Meteorological Variables ◽  
Climatic Data ◽  
Data Set ◽  
Snow Season ◽  
Spanish Pyrenees

Abstract. This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

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