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1,568 results for “slope”
Dataset for "Is the linear relationship between the slope and intercept observed in field emission S-K plots an artifact?"
<p>The dataset are in text format, the files to analyze and generate the figures are in jupyter format for python with a copy in pdf format.</p> <p>Description of files and folders :</p> <p><strong>I) Analyzed</strong></p> <p>This folder contains Fowler-Nordheim analysis of the I-V data in the folder Data, that where obtained in the publication "All field emission experiments are noisy, ... are any meaningful ?"</p> <p><strong>II) Data</strong></p> <p>This folder contains the different experimental I-V data</p> <p><strong>III) Numerical data<br></strong></p> <p>This folder contains the different noisy I-V data generated by the file "Simul"</p> <p><strong>IV) Fig1</strong></p> <p>This file generates the figure "MySK4.svg" based on the data in the folders <strong>I) Analyzed</strong> and <strong>III) Numerical data</strong></p> <p><strong>V) Fig2</strong></p> <p>This file analyses the data in the folder <strong>II) Data</strong> and then extract the parameters of their SK plot.</p> <p>It gathers the SK plot parameters of several work in the litterature.</p> <p>It plots "Slopm4.svg" and "Intm4.svg".</p> <p><strong>VI) Simul</strong></p> <p>This file generates the different noisy I-V data necessary fir Fig1</p> <p><strong>VII) FigSuppl</strong></p> <p>This file shows the different experimental Fowler-Nordheim plots obtained from the files in the folder <strong>II) Data</strong></p>
Fig. 5 in A new glassfrog (Centrolenidae: Hyalinobatrachium) from the Topo River Basin, Amazonian slopes of the Andes of Ecuador
Fig. 5. Distribution of Hyalinobatrachium adespinosai sp. nov. in Ecuador.
Fig. 2 in A new glassfrog (Centrolenidae: Hyalinobatrachium) from the Topo River Basin, Amazonian slopes of the Andes of Ecuador
Fig. 2. Hyalinobatrachium adespinosai sp. nov. in life, holotype.
Slope position affects growth and allometry of the endangered conifer Calocedrus macrolepis by mediating soil properties and microbial communities
<p><strong>Premise:</strong><em> </em>The allometric relationships among growth traits are highly relevant for a tree's fitness, however, the mechanism of how the slope position affects the plant growth and allometry remains poorly understood, hindering our understanding of the variation in allometry of trees along slope position in mountainous areas.</p> <p><strong>Methods:</strong> A typical slope of <em>Calocedrus macrolepis</em> plantation in southwest China was chosen to measure growth traits and their allometric relationships. In addition, spatial variations in soil properties and microbial communities were also investigated.</p> <p><strong>Results:</strong><em> </em>Slope position altered the allometric growth pattern with the larger allometric exponents of the tree height, diameter and wood volume relative to the crown size and height under the branch for the downslope. Additionally, most of soil nutrients, microbial diversity and abundances were greater at mesoslope and downslope, especially at the surface soil layer. The relative abundance both in Chloroflexi and Actinobacteria differed significantly among slope positions, while fungal dominant phyla abundances varied little across slope positions, indicating that bacterial community was more sensitive to slope position than fungal community. The growth traits and allometry were affected by the slope position, which is mainly caused by the variations of soil properties and microbial communities, and bacteria were more important than fungi in their relationships to growth traits and allometry.</p> <p><strong>Conclusions:</strong> Together, results emphasized that slope position indirectly influences the growth traits and allometry of <em>C. macrolepis</em> by regulating soil nutrients and microbial communities, which will provide important theoretical basis for the plantation management of <em>C. macrolepis</em>.</p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
Bichromatic wave groups with DIFFerent REPetition periods over a 1:100 sloping bed (DIFFREP-ICL).
<p><strong>DATASET DESCRIPTION</strong></p> <p>DIFFREP-ICL is the dataset that gathers the measurements described in <em>Padilla and Alsina, 2018</em>: <em>Long Wave Generation Induced by Differences in the Wave-Group Structure</em>.</p> <p>For wave cases from MR-01 to MR-10, the provided variables are:</p> <p><strong>i.</strong> Spatial domain measured from the wave paddle (<em>X</em>)</p> <p><strong>ii.</strong> Spatial domain measured from the shoreline at still water conditions (<em>x</em>)</p> <p><strong>iii.</strong> Water depth (<em>d</em>)</p> <p><strong>iv.</strong> Temporal domain (<em>time</em>)</p> <p><strong>v.</strong> Surface elevation series (<em>eta</em>) </p> <p><strong>vi.</strong> Run-up series measured according to the x-coordinate (<em>runup</em>) </p>
Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge
<p><strong>1.Summary</strong></p> <p>This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below. The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes. A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov).</p> <p>This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc.</p> <p><strong>2.Polyline geometry</strong></p> <p>The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018). Each line describes a meander and features the following attributes.</p> <p><strong>3.Attribute description</strong></p> <ul> <li><strong>SegmentID:</strong> identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li><strong>lakeFlag:</strong> 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li><strong>Width:</strong> average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li><strong>Elevation:</strong> mean elevation from SRTM (90m) per river meander in meters. SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach. The average elevation of all valid points per meander is reported here. Locations where no elevation could be produced are marked as -9999.</li> <li><strong>Slope:</strong> water surface slope in centimeter per kilometer. Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate. Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination. Locations where no slope could be produced are marked as -9999.</li> <li><strong>Meandwave:</strong> Meander wavelength in meters. This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines. Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li><strong>Sinuosity:</strong> Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li><strong>catch_area:</strong> Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner<em> et al.</em>, 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location. We translated flow accumulation given in number of pixels into catchment area (in m<sup>2</sup>) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li><strong>QWBM:</strong> mean annual flow estimated with the water balance model WBMsed (Cohen<em> et al.</em>, 2014).</li> <li><strong>Strpwr_len:</strong> stream power normalized by width (W/m).</li> <li><strong>Strpwr_are:</strong> stream power normalized by area (W/m<sup>2</sup>).</li> </ul> <p><strong>Acknowledgements</strong></p> <p>Use of this database should be acknowledged appropriately.</p> <p>The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder.</p> <p><strong>References</strong></p> <p>Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, <em>Geophysical Research Letters</em>, <em>42</em>(2), 395-402, doi: 10.1002/2014gl062764.</p> <p>Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, <em>Science</em>, doi: 10.1126/science.aat0636.</p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.</p> <p>LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, <em>Geophysical Research Letters</em>, <em>32</em>(17), doi: 10.1029/2005gl023836.</p> <p>Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, <em>EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION</em>, <em>89</em>(10), 93-94, doi: doi:10.1029/2008EO100001.</p> <p>Leopold, L. B., and M. G. Wolman (1960), River meanders, <em>Geological Society of America Bulletin</em>, <em>71</em>(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.</p> <p> </p> <p> </p>
From fine sand to boulders: examining the relationship between beach-face slope and sediment size. Dataset and references.
<p>A collection of 2144 pairs of beach-face slope and median sediment size measurements gathered from 78 publications that covers the whole range of coastal sediments.<br> </p> <p>Size-Slope-Data-Points.ods :<br> Size/slope values and their references. The 'Type' column separates beaches from boulder ridges.</p> <p>Size-Slope-References.ods :<br> Details of each reference used.</p> <p> </p>
Prediction and Rehabilitation of Highway Embankment Slope Failures in Changing Climate
<p>Corresponding data set for Tran-SET Project No. 17GTLSU04. Abstract of the final report is stated below for reference:</p> <p>"Highway slopes constructed with clayey soil are prone to desiccation cracks due to wetting and drying weather cycle, which allows greater moisture infiltration into the embankment from precipitation. Fissures formed due to extended wetting and drying cycles allow water to seep deeper into the soil than surficial wetting and increase the water content. This increases the moisture content in the soil and results in reduction in shear strength to the fully softened strength. On the other hand, development of desiccation cracks and reduction of the soil matric suction ultimately result in higher hydraulic conductivity value which causes development of higher pore water pressure. As the moisture content of the clayey soil increases, the strength reduces to a fully softened shear strength that causes frequent shallow and medium slope failures that are oriented approximately parallel to the surface of the embankment. Hence, the fully softened strength of Louisiana and Texas soils need to be quantified to develop a predictive tool for identifying high-risk zones of highway embankments. Given the documented failures in Texas and Louisiana, this research project is focused on investigating past failures to develop lessons learned and guidelines that can be implemented in the predictive framework."</p>
Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech
<p>An interactive user interface and the raw data from the manuscript titled: "Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech". </p>
Figure 6 in Earthworm community structure along altitudinal gradients on the western slopes of Kopaonik Mountain in Serbia
Figure 6. Altitude effect on the abundance of ecological categories.
Figure 4 in Earthworm community structure along altitudinal gradients on the western slopes of Kopaonik Mountain in Serbia
Figure 4. The altitudinal pattern of mean altitudinal range in altitudes.
Figure 2 in Earthworm community structure along altitudinal gradients on the western slopes of Kopaonik Mountain in Serbia
Figure 2. Relationship between earthworm abundance, species richness, and altitudes.
Slope Units delineation for Marche region in Italy
<p>To use this dataset, please cite the article; <em>Ahmed, M., Titti, G., Trevisani, S., Borgatti, L., Francioni, M.: Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction. Submitted to Geoscientific Model Development. 2024. <br></em></p> <p> </p> <p>Using three metrics to assess the segmentation of the terrain into Slope Units (SUs), we choose the partition which has been calculated with the following parameters in r.slopunits software (Alvioli et. al., 2016): flow accumulation threshold of 1,000,000 square meters, clean size of 20,000 square meters with the cleaning method (flag -m), minimum area of 300,000 square meters with circular variance of 0.1. The metrics incorporated the landslide extension, landslide number and the aspect homogenity of each segmentation. This has been done using TINITALY resampled to 30m for the Marche region of Italy.</p> <p> </p> <p> </p>
Unconfined gravity current interactions with oblique slopes: deflection, reflection and combined-flow behaviours
<p><span>Video 1. Annotated video illustrating the behaviour of density currents upon incidence with an oblique topographic slope (Experiment S40°IN75°). </span></p> <p><span> </span><span>Video 2. Annotated video illustrating the behaviour of density currents upon incidence with an oblique topographic slope (S40°IN60°). </span></p> <p><span> </span><span>Video 3. Annotated video illustrating the behaviour of density currents upon incidence with an oblique topographic slope (Experiment S40°IN15°). </span></p> <p><span> </span><span>Video 4. Annotated video illustrating the behaviour of density currents upon incidence with an oblique topographic slope (Experiment S30°IN75°). </span></p> <p><span> </span><span>Video 5. Annotated video illustrating the behaviour of density currents upon incidence with a flow-parallel topographic slope of 10° slope gradient.</span></p>
Fig. 6 in Serratacosa, a new genus of Lycosidae (Araneae) from the southern slopes of the Eastern Himalayas
Fig. 6. Distribution of Serratacosa gen. nov.
Figure 7 in Biology and life cycle of Tmetonyx similis (G. O. Sars, 1891) (Amphipoda, Lysianassidae), a scavenging amphipod from the continental slope of the Mediterranean
Figure 7. Development of the various male and female generations.
Figure 1 in Biology and life cycle of Tmetonyx similis (G. O. Sars, 1891) (Amphipoda, Lysianassidae), a scavenging amphipod from the continental slope of the Mediterranean
Figure 1. Sexual characteristic of a male. Calceoli on antennae 2 of a male.
Figure 5 in Biology and life cycle of Tmetonyx similis (G. O. Sars, 1891) (Amphipoda, Lysianassidae), a scavenging amphipod from the continental slope of the Mediterranean
Figure 5. Growth curve of Tmetonyx similis collected in Toulon Canyon: males and females.
FIG. 3. — Apseudes batillus n in New apseudomorph tanaidaceans (Crustacea, Peracarida, Tanaidacea) from the bathyal slope off New Caledonia
FIG. 3. — Apseudes batillus n. sp.: A-F, pereopods 1-6 respectively. Scale bar: 0.6 mm.
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