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 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.

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.

scholarly journals Observation of the process of snow accumulation on the Antarctic Plateau by time lapse laser scanning

2019 ◽  
Vol 13 (7) ◽  
pp. 1983-1999 ◽  
Author(s):  
Ghislain Picard ◽  
Laurent Arnaud ◽  
Romain Caneill ◽  
Eric Lefebvre ◽  
Maxim Lamare
Keyword(s):  
Mass Balance ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Ice Cores ◽  
Surface Heterogeneity ◽  
Time Lapse ◽  
Snow Accumulation ◽  
Surface Energy Budget ◽  
Surface Mass ◽  
Continental Scale

Abstract. Snow accumulation is the main positive component of the mass balance in Antarctica. In contrast to the major efforts deployed to estimate its overall value on a continental scale – to assess the contribution of the ice sheet to sea level rise – knowledge about the accumulation process itself is relatively poor, although many complex phenomena occur between snowfall and the definitive settling of the snow particles on the snowpack. Here we exploit a dataset of near-daily surface elevation maps recorded over 3 years at Dome C using an automatic laser scanner sampling 40–100 m2 in area. We find that the averaged accumulation is relatively regular over the 3 years at a rate of +8.7 cm yr−1. Despite this overall regularity, the surface changes very frequently (every 3 d on average) due to snow erosion and heterogeneous snow deposition that we call accumulation by “patches”. Most of these patches (60 %–85 %) are ephemeral but can survive a few weeks before being eroded. As a result, the surface is continuously rough (6–8 cm root-mean-square height) featuring meter-scale dunes aligned along the wind and larger, decameter-scale undulations. Additionally, we deduce the age of the snow present at a given time on the surface from elevation time series and find that snow age spans over more than a year. Some of the patches ultimately settle, leading to a heterogeneous internal structure which reflects the surface heterogeneity, with many snowfall events missing at a given point, whilst many others are overrepresented. These findings have important consequences for several research topics including surface mass balance, surface energy budget, photochemistry, snowpack evolution, and the interpretation of the signals archived in ice cores.

scholarly journals Surface and subsurface characterisation of salt pans expressing polygonal patterns

2020 ◽  
Vol 12 (4) ◽  
pp. 2881-2898
Author(s):  
Jana Lasser ◽  
Joanna M. Nield ◽  
Lucas Goehring
Keyword(s):  
Environmental Conditions ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Time Lapse ◽  
General Context ◽  
Data Set ◽  
Owens Lake ◽  
Salt Pans ◽  
Salt Crust

Abstract. The data set described here contains information about the surface, subsurface, and environmental conditions of salt pans that express polygonal patterns in their surface salt crust (Lasser et al., 2020b; https://doi.org/10.5880/fidgeo.2020.037). Information stems from 5 field sites at Badwater Basin and 21 field sites at Owens Lake – both in central California. All data were recorded during two field campaigns from between November and December 2016 and in January 2018. Crust surfaces, including the mean diameter and fluctuations in the height of the polygonal patterns, were characterised by a terrestrial laser scanner (TLS). The data contain the resulting three-dimensional point clouds that describe these surfaces. The subsurface is characterised by grain size distributions of samples taken from depths between 5 and 100 cm below the salt crust and measured with a laser particle size analyser. Subsurface salinity profiles were recorded, and the groundwater density was also measured. Additionally, the salts present in the crust and pore water were analysed to determine their composition. To characterise the environmental conditions at Owens Lake, including the differences between nearby crust features, records were made of the temperature and relative humidity during 1 week in November 2016. The field sites are characterised by images showing the general context of each site, such as pictures of selected salt polygons, including any which were sampled, a typical core from each site at which core samples were taken, and close-ups of the salt crust morphology. Finally, two videos of salt crust growth over the course of spring 2018 and reconstructed from time lapse images are included.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2018 ◽  
Vol 12 (6) ◽  
pp. 2123-2145 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Åsmund Bakketun ◽  
Mariken Homleid
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Surface Model ◽  
Observation Data ◽  
Grid Spacing ◽  
Data Set ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data.

scholarly journals Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

2021 ◽  
Vol 13 (22) ◽  
pp. 4691
Author(s):  
Tianwen Feng ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Juan Zhang
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
High Spatial Resolution ◽  
Soil Surface ◽  
Mean Value ◽  
Snow Accumulation ◽  
Cross Ratio ◽  
Retrieval Algorithm ◽  
Observation Data ◽  
The Impact

High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

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 Surface and subsurface characterisation of salt pans expressing polygonal patterns

2020 ◽  
Author(s):  
Jana Lasser ◽  
Joanna M. Nield ◽  
Lucas Goehring
Keyword(s):  
Environmental Conditions ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Time Lapse ◽  
General Context ◽  
Data Set ◽  
Owens Lake ◽  
Salt Pans ◽  
Salt Crust

Abstract. The data set described here contains information about the surface, subsurface and environmental conditions of salt pans that express polygonal patterns in their surface salt crust. Information stems from 5 field sites at Badwater Basin and 21 field sites at Owens Lake – both in central California. All data was recorded during two field campaigns, from between November and December, 2016, and in January 2018. Crust surfaces, including the mean diameter and fluctuations in the height of the polygonal patterns, were characterised by terrestrial laser scanner (Nield et al., 2020b), DOI 10.1594/PANGAEA.911233. The data contains the resulting three dimensional point clouds, which describe these surfaces. The subsurface is characterised by grain size distributions of samples taken from depths between 5 cm and 100 cm below the salt crust, and measured with a laser particle size analyser (Lasser and Goehring, 2020b), DOI 10.1594/PANGAEA.910996. Subsurface salinity profiles were recorded and the ground water density was also measured (Lasser and Goehring, 2020a), DOI 10.1594/PANGAEA.911059. Additionally, the salts present in the crust and pore water were analysed to determine their composition (Lasser and Karius, 2020), DOI 10.1594/PANGAEA.911239. To characterise the environmental conditions at Owens Lake, including the differences between nearby crust features, records were made of the temperature and relative humidity during one week in November 2016 (Nield et al., 2020a), DOI 10.1594/PANGAEA.911139. The field sites are characterised by images (Lasser et al., 2020), DOI 10.1594/PANGAEA.911054, showing the general context of each site, such as pictures of selected salt polygons, including any which were sampled, a typical core from each site at which core samples were taken and close-ups of the salt crust morphology. Finally, two videos of salt crust growth over the course of spring 2018 and reconstructed from time-lapse images are included (Lasser et al., 2020), DOI 10.1594/PANGAEA.911054.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

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