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

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.

scholarly journals A snow-transport model for complex terrain

1998 ◽  
Vol 44 (148) ◽  
pp. 498-516 ◽  
Author(s):  
Glen E. Liston ◽  
Matthew Sturm
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Snow Water Equivalent ◽  
Transport Model ◽  
Vegetation Type ◽  
Cold Season ◽  
Snow Accumulation ◽  
Surface Shear Stress ◽  
Surface Shear ◽  
Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

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>

Prediction of Regolith Depth Using Topographic and Environmental Variables in the Central Mountain Division, Taiwan

2013 ◽  
Vol 284-287 ◽  
pp. 1489-1493
Author(s):  
Pei Ling Li ◽  
Teng To Yu ◽  
Shing Tsz Lee ◽  
Ting Shiuan Wang
Keyword(s):  
Environmental Variables ◽  
Depth Distribution ◽  
Groundwater Resources ◽  
Temporal Distribution ◽  
Underground Water ◽  
High Population Density ◽  
Mountain Areas ◽  
Central Division ◽  
Drilling Data ◽  
Relation Model

Due to several reasons, there is a shortage of water resources in Taiwan, despite abundant rainfall. These reasons include high population density, uneven spatial and temporal distribution of rainfall, and rivers with steep gradients. According to the data collected by Central Geological Survey in the project: Pumping of Groundwater Resources in the Central Division of Mountain Areas of Taiwan, the abundant underground water in mountain areas exists in areas with thick regolith, broken rock or sandstone. Therefore, the investigation of regolith depth distribution would help us to further understand the amount of underground water, and the areas that can be exploited. This study is based on 441 drilling data over the Da-Chia and Choshui river basins, and aims to construct a relation model of topographic and environmental variables and to estimate the regolith depth in the study area. The amount of drilling data will be increased each year so that the model can be improved, developed and converged. Moreover, it can help in developing a model that is most suitable for estimating regolith depth in Taiwan.

scholarly journals Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

2013 ◽  
Vol 7 (5) ◽  
pp. 4633-4680 ◽  
Author(s):  
J. Veitinger ◽  
B. Sovilla ◽  
R. S. Purves
Keyword(s):  
Surface Roughness ◽  
Snow Depth ◽  
Depth Distribution ◽  
Management Strategies ◽  
Snow Accumulation ◽  
Swiss Alps ◽  
Transfer Energy ◽  
Hazard Management ◽  
Terrestrial Lidar ◽  
Basin Scale

Abstract. In alpine terrain, the snow covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation, avalanche dynamics and affects surface heat transfer, energy balance as well as snow depth distribution. To characterize the effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and its corresponding underlying terrain for three alpine basins in the Swiss Alps characterized by low medium and high terrain roughness. To this end, elevation models of winter and summer terrain are derived from high-resolution (1 m) measurements performed by airborne and terrestrial LIDAR. We showed that on basin scale terrain smoothing not only depends on mean snow depth in the basin but also on its variability. Terrain smoothing can be modelled in function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth does not exist on a pixel scale. Further we demonstrated the high persistence of snow surface roughness even in between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can potentially improve avalanche release area definition and in the long term natural hazard management strategies.

scholarly journals On the Use of Telephone Poles for the Observation of Areal Snow-Depth Distribution

1973 ◽  
Vol 12 (66) ◽  
pp. 514-517
Author(s):  
Katsuhiro Kikuchi
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Wide Area ◽  
Zoom Lens ◽  
Lower Edge ◽  
Snow Surface ◽  
Good Coincidence

For the observation of areal snow-depth distribution over a wide area, such as the Ishikari Plain in Hokkaido, Japan, existing telephone poles were used in place of the snow scales. Telephone pole number plates (telephone poles in Japan have number plates) were photographed by a 35 mm camera with zoom lens ranging from 80 mm to 200 mm through the window of the observation vehicle. The snow depth was calculated by the ratios of the length of the number plates to the distance between the lower edge of the number plates and the snow surface. Since measurements by direct sounding using a rod as compared with the values by the above method showed a good coincidence with an accuracy of ± 10 cm, it was considered satisfactory for snow depth observation over a wide area.

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