scholarly journals Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

2021 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan I. López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals Subgrid snow depth coefficient of variation within complex mountainous terrain

2016 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan Ignacio López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Abstract. Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets from mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was an important factor of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influences seasonal snow variability. Subgrid CVds was also correlated with topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two simple statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for parameterizing CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2006 ◽  
Vol 3 (6) ◽  
pp. 3655-3673 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

Fractal Distribution of Snow Depth from Lidar Data

2006 ◽  
Vol 7 (2) ◽  
pp. 285-297 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Steven R. Fassnacht ◽  
Kelly J. Elder
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Multiple Scales ◽  
Driving Forces ◽  
Spatial Scales ◽  
Fractal Dimensions ◽  
Airborne Lidar ◽  
Surface Elevation ◽  
Fractal Distribution ◽  
Wide Range

Abstract Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15–40 m, depending on the site, which indicates a process change at that scale. Terrain has a fractal distribution over nearly the entire range of scales available in the data. Directional differences in the fractal dimensions for each parameter are also present at multiple scales, and are related to the wind direction frequency distributions at each site. The results indicate that different sampling resolutions may yield different results and allow rescaling in specific scale ranges. Resolutions of 10 m and finer are consistently self-similar, as are resolutions greater than 30 m, though the coarser resolutions show nearly random distributions.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2007 ◽  
Vol 11 (4) ◽  
pp. 1353-1360 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent (SWE) values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

Sentinel-1 snow depth assimilation improves river discharge simulations in the western Alps

2021 ◽  
Author(s):  
Isis Brangers ◽  
Hans Lievens ◽  
Augusto Getirana ◽  
Sujay Kumar ◽  
Gabrielle De Lannoy
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
River Discharge ◽  
Hydrological Modeling ◽  
Land Surface Model ◽  
Western Alps ◽  
Accurate Information ◽  
Surface Model ◽  
Mountain Regions ◽  
Mountainous Regions

<p>In many of the world’s mountainous regions, river discharge is largely influenced by the seasonal melt of snow. Therefore, accurate information on the amount of water stored as snow is essential for water management and flood forecasting. However, there are large uncertainties in model simulations of snow depth, partly due to uncertain precipitation estimates in mountain regions with complex topography. A study by Lievens et al. (2019) showed the potential of Sentinel-1 (S1) satellite observations to provide snow depth estimates at 1 km spatial and ~weekly temporal resolution in mountain regions. In this study, we assimilated these retrievals into the Noah Multiparameterization (Noah-MP) v3.6 land surface model for the western Alps using an ensemble Kalman filter. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP) routing scheme to also provide estimates of river discharge. With S1 data assimilation, the snow depth estimates improved, reducing the bias from 0.23 m to 0.05 m compared to in situ measurements. Preliminary results also show improved discharge simulations mainly in mountain catchments at high elevations that are less prone to regulations (e.g., by dams). This study demonstrates the capability of the S1 snow depth retrievals to improve not only snow depth estimates, but also the estimation of snow melt water contributions to river discharge.</p>

Research of the activity and viability of spermatozoa at different concentrations and proportions of diluents after incubation

2020 ◽  
pp. 88-95
Author(s):  
Svitlana Lobchenko ◽  
Tetiana Husar ◽  
Viktor Lobchenko
Keyword(s):  
Plasma Concentration ◽  
Incubation Time ◽  
Coefficient Of Variation ◽  
Incubation Medium ◽  
Wide Range ◽  
Series Of Experiments ◽  
The Subject ◽  
Boar Spermatozoa ◽  
Native Plasma ◽  
Better Than

The results of studies of the viability of spermatozoa with different incubation time at different concentrations and using different diluents are highlighted in the article. (Un) concentrated spermatozoa were diluented: 1) with their native plasma; 2) medium 199; 3) a mixture of equal volumes of plasma and medium 199. The experiment was designed to generate experimental samples with spermatozoa concentrations prepared according to the method, namely: 0.2; 0.1; 0.05; 0.025 billion / ml. The sperm was evaluated after 2, 4, 6 and 8 hours. The perspective of such a study is significant and makes it possible to research various aspects of the subject in a wide range. In this regard, a series of experiments were conducted in this area. The data obtained are statistically processed and allow us to highlight the results that relate to each stage of the study. In particular, in this article it was found out some regularities between the viability of sperm, the type of diluent and the rate of rarefaction, as evidenced by the data presented in the tables. As a result of sperm incubation, the viability of spermatozoa remains at least the highest trend when sperm are diluted to a concentration of 0.1 billion / ml, regardless of the type of diluent used. To maintain the viability of sperm using this concentration of medium 199 is not better than its native plasma, and its mixture with an equal volume of plasma through any length of time incubation of such sperm. Most often it is at this concentration of sperm that their viability is characterized by the lowest coefficient of variation, regardless of the type of diluent used, which may indicate the greatest stability of the result under these conditions. The viability of spermatozoa with a concentration of 0.1 billion / ml is statistically significantly reduced only after 6 or even 8 hours of incubation. If the sperm are incubated for only 2 hours, regardless of the type of diluent used, the sperm concentrations tested do not affect the viability of the sperm. Key words: boar, spermatozoa, sperm plasma, concentration, incubation, medium 199, activity, viability, rarefaction.

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

scholarly journals Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO<sub>2</sub> budget in South America

2013 ◽  
Vol 6 (1) ◽  
pp. 453-494 ◽  
Author(s):  
D. S. Moreira ◽  
S. R. Freitas ◽  
J. P. Bonatti ◽  
L. M. Mercado ◽  
N. M. É. Rosário ◽  
...  
Keyword(s):  
South America ◽  
Atmospheric Chemistry ◽  
Land Surface ◽  
Amazon Basin ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Dew Point ◽  
Surface Model ◽  
Co2 Concentrations ◽  
Wide Range

Abstract. This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

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