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57 results for “depth distribution”
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Terrestrial Laser Scanner observations of snow depth distribution at Col du Lautaret and Col du Lac Blanc mountain sites
<p>This dataset contains snow depth distribution observations obtained in two high mountain experimental sites, Col du Lac Blanc and Col du Lautaret, both located in French Alps. The snow depth distribution maps were generated using a Terrestrial Laser Scanner (TLS) for 10 acquisition dates. Observations obtained in Col du Lac Blanc were acquired in the 2014-15 snow season while Col du Lautaret observations were acquired in 2017-18 snow season. The snow depth maps have a grid cell size of 1x1m. The two study sites have extensions comprised between 17 and 31 ha with elevations ranging from 2000-2100 m a.s.l. (Col du Lautaret)and 2600-2800 m a.s.l. (Col du Lac Blanc)and show a patchy distribution of bare soil and alpine grass. The dataset allows a better understanding of snow related processes in mountain areas.</p>
Pan-European temperature distribution at depth - GeoDH project
<p>The dataset includes two shapefiles showing the temperature distribution at depth in Europe, specifically areas with temperatures exceeding 50°C at 1000m depth and 90°C at 2000m depth.<br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г).
Figure 6 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 6: PCR results using Meloidogyne-specific and M. naasi and M. marylandi-specific primers. DL: DNA Ladder; 1: Meloidogyne spp. (DNA ID:9); 2: M. naasi (DNA ID:9); 3: Meloidogyne spp. (DNA ID:4); 4: M. marylandi (DNA ID:4); and 5: Meloidogyne spp.(DNA ID:4).
Figure 3 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 3: PCR results using Hoplolaimus-specific and H. stephanus, H. columbus and H. galeatus-specific primers. DL: DNA Ladder; 1: Hoplolaimus spp. (DNA ID:10); 2: H. stephanus (DNA ID:10); 3: H. columbus (DNA ID:10); 4 H. galeatus (DNA ID:10); 5: Hoplolaimus spp. (DNA ID:3); 6: H. stephanus (DNA ID:3); 7: H. columbus (DNA ID:3); 8 H. galeatus (DNA ID:3); 9: Hoplolaimus spp. (DNA ID:4); 10: H. stephanus (DNA ID:4); 11: H. columbus (DNA ID:4); and 12 H. galeatus (DNA ID:4).
Figure 2 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 2: Phylogeny of the rDNA ITS region of Hoplolaimus spp. isolated from golf putting greens. Phylogenetic trees were constructed with the neighbor-joining algorithm using the Kimura two-parameter model with Litylenchus spp. (LC383724) as the outgroup. Bootstrap values are based on 1000 resamplings of the data set. DNAID codes correlate to Table 2.
Figure 1 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 1: Distribution of plant-parasitic nematode species sampled from creeping bentgrass putting greens in Missouri and eastern Kansas in 2021 and Indiana in 2022 in two independent pie charts. Samples were collected during the months of April, June, August and October of 2021 and 2022, respectively. "n" indicates total PPNs represented within each chart.
Figure 4 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 4: Scanning-electron micrographs of a lance nematode specimen collected form Site 5. A) four lip annules; B) the presence of an epiptygma; C) 25 longitudinal striae on the basal lip annule; and D) four lateral incisures.
Figure 5 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 5: Phylogeny of molecularly characterized Meloidogyne spp. isolated from golf coursed based on D2/D3 28S genes. phylogenetic trees were constructed with the neighborjoining algorithm using the Kimura two-parameter model with Litylenchus spp. (LC383724) as the outgroup. Bootstrap values are based on 1000 resamplings of the data set and displayed near branch nodes. DNAID codes correlate to Table 2.
Figure 1 in Geographic and depth distributional patterns of western Atlantic Porcellanidae (Crustacea: Decapoda: Anomura), with an updated list of species
Figure 1. Comparison of the tropical porcellanid faunas from different regions of the western Atlantic. Large, simple circles indicate total number of species in Florida and Brazil regions; small, simple circles indicate species shared by regions; double circle indicates species in Caribbean-West Indian region; rectangle indicates species in southern Caribbean; oval indicates species in the Antilles.
Figure 3 in Depth-related gradient of soft-bottom crustacean distribution along the Cilician shelf*
Figure 3. Spatiotemporal (depths and months) and annual (average) changes of crustacean faunistic parameters on the Cilician shelf for number of crustacean species (S), density (N), biomass, species richness (d), evenness (J'), and Shannon–Wiener diversity (H') indexes. Each point represents the value obtained from averaging values of each parameter across all replicates at each depth (n = 9).
Figure 5 in Depth-related gradient of soft-bottom crustacean distribution along the Cilician shelf*
Figure 5. CCA ordination of species (based on abundance data) with superimposed symbols representing bottom depth (see Figure 4), sedimentary parameters, and sea surface temperature of the sampling locations in February (F), May (M), August (A), and November (N). Symbol size is proportional to value of each variable, i.e. the largest symbol corresponds to the maximum value.
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
<p>The number of background earthquakes (<em>M<sub>L</sub></em> ≥ 0) in the southern Sichuan basin, southwest China, has increased thirtyfold as a result of hydraulic fracturing. Background events are originally deep (4-6 <em>km</em>) within the sedimentary section but build into a multimodal distribution both at depth and in the shallow stimulated reservoir (2-4 <em>km</em>) - representing a counterpoint to the usual triggering of seismicity on deep sub-reservoir basement faults. Surprisingly, the largest events (<em>M<sub>L</sub></em> ≥ 3) evolve in the deep sedimentary strata (4-6 <em>km</em>) that are hydraulically isolated from the injection zone (2-4 <em>km</em>) by low permeability layers. We evaluate the friction-stability rheology of the strata within the full stratigraphic section to define the feasibility of nucleation within these shallow and deep strata. These show velocity-neutral to velocity-weakening behavior in the shallow reservoir transitioning to more strongly velocity-weakening with increase in both depth and temperature. Poroelastic stress calculations confirms that stress transfer, rather than transmitted fluid pressures, are capable of directly reactivating critically-stressed faults at depth, with fluid pressures the triggering source within the shallow reservoir.</p>
SOC depth distribution patterns across the US Midwest
<p>Data and codes used in the paper "Root distributions, precipitation, and soil structure converge to govern soil organic carbon depth distributions", <a href="https://doi.org/10.1016/j.geoderma.2023.116569">https://doi.org/10.1016/j.geoderma.2023.116569</a>.</p> <p>v2: Updated codes and figures on May 3, 2023.</p>
A non-equilibrium species distribution model reveals unprecedented depth of time lag responses to past environmental change trajectories
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Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
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