The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
R. S. Purves - One of the best experts on this subject based on the ideXlab platform.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
The Cryosphere, 2014Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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 and avalanche dynamics, and it affects surface heat transfer, energy balance as well as snow depth distribution. To assess the effect of snow on terrain, we use an adequate roughness definition. We developed a method to quantify terrain smoothing by combining roughness calculations of snow surfaces and their corresponding underlying terrain with snow depth measurements. To this End, Elevation models of winter and summer terrain in three selected alpine basins in the Swiss Alps characterized by low, medium and high terrain roughness were derived from high-resolution measurements performed by airborne and terrestrial lidar. The preliminary results in the selected basins reveal that, at basin scale, terrain smoothing depEnds not only on mean snow depth in the basin but also on its variability. The multi-temporal analysis over three winter seasons in one basin suggests that terrain smoothing can be modelled as a function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth was not found at pixel scale. Further, we show that snow surface roughness is to some extent persistent, 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 for example improve avalanche release area definition and, in the long term, natural hazard management strategies.
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Winter Terrain Roughness as a New Parameter to Define Size and Location of Avalanche Release Areas
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract:Location and size of avalanche release areas are crucial inputs in modelling of ava- lanche dynamics as, together with fracture depth, they determine the initial avalanche volume. One difficulty in estimating avalanche release areas is that they vary in location and size within the same topographical basin due to variation in snow cover distribution. During the snow accumulation season, terrain features successively disappear leading to increasingly homogeneous deposition patterns dur- ing storm events and, thus, to a progressive smoothing of the terrain surface. These changing deposi- tion patterns might therefore explain the differences in release areas. To characterize the smoothing effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and their corresponding underlying terrain. To this End, Elevation models of winter and sum- mer terrain are derived from high-resolution measurements performed by airborne LIDAR. The winter datasets correspond to snow cover scenarios with varying snow depths ranging from 1m to 4m. For one scenario, six avalanches were artificially triggered and an additional laser scan was performed after the releases. We show that for both summer and winter surfaces, low roughness values are or- ganized in clusters. Further, the clusters obtained from the snow scenario with avalanches are able to reproduce location and size of the observed release areas.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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.
J. Veitinger - One of the best experts on this subject based on the ideXlab platform.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
The Cryosphere, 2014Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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 and avalanche dynamics, and it affects surface heat transfer, energy balance as well as snow depth distribution. To assess the effect of snow on terrain, we use an adequate roughness definition. We developed a method to quantify terrain smoothing by combining roughness calculations of snow surfaces and their corresponding underlying terrain with snow depth measurements. To this End, Elevation models of winter and summer terrain in three selected alpine basins in the Swiss Alps characterized by low, medium and high terrain roughness were derived from high-resolution measurements performed by airborne and terrestrial lidar. The preliminary results in the selected basins reveal that, at basin scale, terrain smoothing depEnds not only on mean snow depth in the basin but also on its variability. The multi-temporal analysis over three winter seasons in one basin suggests that terrain smoothing can be modelled as a function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth was not found at pixel scale. Further, we show that snow surface roughness is to some extent persistent, 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 for example improve avalanche release area definition and, in the long term, natural hazard management strategies.
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Winter Terrain Roughness as a New Parameter to Define Size and Location of Avalanche Release Areas
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract:Location and size of avalanche release areas are crucial inputs in modelling of ava- lanche dynamics as, together with fracture depth, they determine the initial avalanche volume. One difficulty in estimating avalanche release areas is that they vary in location and size within the same topographical basin due to variation in snow cover distribution. During the snow accumulation season, terrain features successively disappear leading to increasingly homogeneous deposition patterns dur- ing storm events and, thus, to a progressive smoothing of the terrain surface. These changing deposi- tion patterns might therefore explain the differences in release areas. To characterize the smoothing effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and their corresponding underlying terrain. To this End, Elevation models of winter and sum- mer terrain are derived from high-resolution measurements performed by airborne LIDAR. The winter datasets correspond to snow cover scenarios with varying snow depths ranging from 1m to 4m. For one scenario, six avalanches were artificially triggered and an additional laser scan was performed after the releases. We show that for both summer and winter surfaces, low roughness values are or- ganized in clusters. Further, the clusters obtained from the snow scenario with avalanches are able to reproduce location and size of the observed release areas.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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.
B. Sovilla - One of the best experts on this subject based on the ideXlab platform.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
The Cryosphere, 2014Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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 and avalanche dynamics, and it affects surface heat transfer, energy balance as well as snow depth distribution. To assess the effect of snow on terrain, we use an adequate roughness definition. We developed a method to quantify terrain smoothing by combining roughness calculations of snow surfaces and their corresponding underlying terrain with snow depth measurements. To this End, Elevation models of winter and summer terrain in three selected alpine basins in the Swiss Alps characterized by low, medium and high terrain roughness were derived from high-resolution measurements performed by airborne and terrestrial lidar. The preliminary results in the selected basins reveal that, at basin scale, terrain smoothing depEnds not only on mean snow depth in the basin but also on its variability. The multi-temporal analysis over three winter seasons in one basin suggests that terrain smoothing can be modelled as a function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth was not found at pixel scale. Further, we show that snow surface roughness is to some extent persistent, 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 for example improve avalanche release area definition and, in the long term, natural hazard management strategies.
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Winter Terrain Roughness as a New Parameter to Define Size and Location of Avalanche Release Areas
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract:Location and size of avalanche release areas are crucial inputs in modelling of ava- lanche dynamics as, together with fracture depth, they determine the initial avalanche volume. One difficulty in estimating avalanche release areas is that they vary in location and size within the same topographical basin due to variation in snow cover distribution. During the snow accumulation season, terrain features successively disappear leading to increasingly homogeneous deposition patterns dur- ing storm events and, thus, to a progressive smoothing of the terrain surface. These changing deposi- tion patterns might therefore explain the differences in release areas. To characterize the smoothing effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and their corresponding underlying terrain. To this End, Elevation models of winter and sum- mer terrain are derived from high-resolution measurements performed by airborne LIDAR. The winter datasets correspond to snow cover scenarios with varying snow depths ranging from 1m to 4m. For one scenario, six avalanches were artificially triggered and an additional laser scan was performed after the releases. We show that for both summer and winter surfaces, low roughness values are or- ganized in clusters. Further, the clusters obtained from the snow scenario with avalanches are able to reproduce location and size of the observed release areas.
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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach
2013Co-Authors: J. Veitinger, B. Sovilla, R. S. PurvesAbstract: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.
Leonard Ilkhanoff - One of the best experts on this subject based on the ideXlab platform.
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Abstract 14876: The Association of Baseline Elevation at Various ST Points With Mortality in the Multi-Ethnic Study of Atherosclerosis and Atherosclerosis Risk in Communities Study Cohorts
Circulation, 2016Co-Authors: Rachit M. Vakil, David G. Tian, Yiyi Zhang, Eliseo Guallar, Elsayed Z. Soliman, Susan R. Heckbert, Gordon F. Tomaselli, Wendy S. Post, David A. Bluemke, Leonard IlkhanoffAbstract:Background: Prior studies suggest that ST Elevation at the J-point is associated with elevated risk of death. We sought to examine the prevalence and prognostic importance of Elevation at various ST points in a large multi-ethnic population. Methods and Results: After confirming data harmonization, we combined ECG, demographics, and mortality data for 19,578 participants from the Atherosclerosis Risk in Communities Study (ARIC) and the Multi-ethnic Study of Atherosclerosis (MESA) population-based cohorts. The average age at baseline was 56.1 ± 8.1 years and 56.2% of the participants were female. Participants were stratified by the presence of ≥1 mm inferior (0.5%), lateral (27.8%), inferior or lateral (29.3%), and inferior and lateral (0.2%) ST Elevation (at the J-point, mid-point, 60ms after the J-point, and End-point). We utilized models adjusted for age, gEnder, ethnicity, source cohort, BMI, education, heart rate, hypertension, left ventricular hypertrophy, smoking status, diabetes, LDL, HDL, and aspirin and/or statin therapy. Inferior ST Elevation at any ST point was associated with increased mortality (HR 1.94, 95%CI 1.32 – 2.84). In contrast, lateral Elevation at any ST point was associated with decreased mortality (HR 0.88, 95%CI 0.81 – 0.95). ST-End Elevation was more common and drove the association of lateral Elevation with decreased mortality. The magnitude of association between inferior ST Elevation and increased mortality was strongest when Elevation occurred at the mid-ST or J-points. Although the prevalence of Elevation varied among subgroups, no additive or multiplicative interactions were noted with gEnder or ethnicity. Conclusions: We found that asymptomatic inferior lead ST Elevation is uncommon and is associated with elevated risk of mortality regardless of ethnicity. In contrast, asymptomatic lateral ST Elevation at the ST-End point is common and is associated with lower risk of mortality.
Mostafa Sayed Ahmed Ellitthy - One of the best experts on this subject based on the ideXlab platform.
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IJV collapsibility index vs IVC collapsibility index by point of care ultrasound for estimation of CVP: a comparative study with direct estimation of CVP.
Open access emergency medicine : OAEM, 2019Co-Authors: Haitham M. Jassim, Vamanjore A Naushad, Mohamad Y. Khatib, Prem Chandra, Mohammed Milad Abuhmaira, Sunil Hassan Koya, Mostafa Sayed Ahmed EllitthyAbstract:Purpose To compare the bedside ultrasound estimation of internal jugular vein (IJV)-collapsibility index with inferior vena cava (IVC)-collapsibility index and invasively monitored central venous pressure (CVP) in ICU patients. Design prospective observational study. Setting The study was carried out in the ICU of Al Wakra and Al Khor hospitals of the Hamad Medical Corporation, Qatar. The patients were enrolled from November 2013 to January 2015. Patients Patients admitted to the ICU with central venous catheter were included. Material and methods The A-P diameter, cross-sectional area of the right IJV, and diameter of IVC were measured using bedside USG, and their corresponding collapsibility indices were obtained. The results of the IJV and IVC indices were compared with CVP. The sensitivity, specificity, and positive and negative predictive values were calculated to determine the diagnostic and predictive accuracy of the IJV collapsibility index in predicting the CVP. Results Seventy patients were enrolled, out of which 12 were excluded. The mean age was 54.34±16.61 years. The mean CVP was 9.88 mmHg (range =1-25). The correlations between CVP and IJV-CI (collapsibility index) at 0° were r=-0.484 (P=0.0001), r=-0.416 (P=0.001) for the cross-sectional area (CSA) and the diameter, respectively, and, at 30°, the most significant correlation discovered was (r=-0.583, P=0.0001) for the CSA-CI and r=-0.559 (P=0.0001) for the diameter-CI. In addition, there was a significant and negative correlation between IVC-CI and CVP (r=-0.540, P=0.0001). Conclusion The IJV collapsibility index, especially at 30° head End Elevation, can be used as a first-line approach for the bedside non-invasive assessment of CVP/fluid status in critical patients. IVC-CI can be used either as an adjunct or in conditions where IJV assessment is not possible, such as in the case of a neck trauma/surgery.