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27 results for “Dynamic topography”
Supplementary data - Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin
<p>This data repository contains the supplementary data for the paper:</p> <p><strong>Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin.</strong></p> <p>Carmen Braz<sup>1</sup>, Sabin Zahirovic<sup>1</sup>, Tristan Salles<sup>1</sup>, Nicolas Flament<sup>2</sup>, Lauren Harrington<sup>1</sup>, R. Dietmar Müller<sup>1</sup></p> <p><sup>1</sup> EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia</p> <p><sup>2</sup> GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia</p> <p><em>Basin Research, https://doi.org/10.1111/bre.12606</em></p> <p>Included in this supplement are:</p> <ul> <li> All input files required for running Badlands models M1-M4</li> <li> Badlands digital output for preferred model M4</li> <li> Animations of topography and erosion-deposition through time for all four models presented in the paper </li> <li> Sediment layers for all time steps for preferred model M4 provided as netcdf grids.</li> </ul>
UAV data of post fire dynamics, Quesenbank, Harz, 2022 (orthomosaics, topography, point clouds)
<p>Unoccupied aerial vehicles (UAVs) were used to investigate a burnt forest site situated in the Harz National Park area near the location Schierke, east of the Brocken, called Quesenbank (<a href="https://www.google.com/maps/place/51%C2%B046'07.6%22N+10%C2%B041'30.2%22E/@51.7682386,10.6905312,489m/data=!3m1!1e3!4m5!3m4!1s0x0:0xd8d7f64a3ff9acf1!8m2!3d51.76877!4d10.69173">51.76877 °N, 10.69173 °E</a>) which is a spruce stand stand severely affected by bark-beetle and windfall. Most of the trees are dead such that they provide high fuel loads for potentially occurring wildfires. The small river Wormke crosses the Quesenbank and separates the survey area between a hiking path and the forest stand.</p> <p>During the 12.08.2022, inhabitants reported a <a href="https://www.ndr.de/nachrichten/niedersachsen/braunschweig_harz_goettingen/Waldbrand-im-Harz-Polizei-geht-von-Brandstiftung-aus,waldbrand882.html">fire</a> near Schierke which was quickly contained by the authorities. Two months after the fire, on 13.10.2022, a team of scientists from GAU Göttingen and TU Berlin was accompanied by a National Park representative for investigation. The site was surveyed by using modern UAVs and by sampling soil strata, ash and vegetation. This report will briefly describe UAV-based surveys and the derived data products.</p> <p>For an overview, see the <strong>report </strong>or download the Maps.zip folder.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>Projekt ”Postfeuerdynamik auf Brandflächen im Nationalpark Harz”</strong></p> <p>Dr. Simon Drollinger, Georg-August Universität Göttingen</p> <p>Marlene Düngelhoef, Nationalparkverwaltung Harz</p> <p>Thomas Glinka, Nationalparkverwaltung Harz</p>
Supplementary data for: "The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia"
<p>This repository contains supplementary information and data for the paper: ""The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia", submitted to Communications Earth & Environment.</p> <p>This version (1.1) was produced to answer comments from reviewers.</p>
The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism
<p>This dataset contains the following figures:</p> <p><em>Table 1, The Radar Chart</em></p> <p><em>Map 1, ROME, 2nd CENTURY BCE</em></p> <p><em>Map 2, ROME, 1st CENTURY BCE</em></p> <p><em>Map 3, ROME, 1st CENTURY ACE</em></p> <p><em>Map 4, ROME, 2nd CENTURY ACE</em></p> <p><em>Map 5, ROME, 3rd CENTURY ACE</em></p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Global Cenozoic paleogeography and paleo-dynamic topography
<p>This repository contains the data and files supporting the research paper titled: "Impact of mantle convection and dynamic topography on the Cenozoic paleogeography of Central Eurasia and the West Siberian Seaway", EPSL 2024. It contains global paleogeography for every millionth year of the Cenozoic and paleo-Dynamic topography from 100 - 0 Ma. </p> <p>### Contents ###</p> <p>- Paleogeography:</p> <p> - This folder contains NetCDF files representing global paleogeographic reconstruction for each millionth year of the Cenozoic era. There is also a folder with global plots for each time step.</p> <p> </p> <p>- Paleo-Dynamic_Topography:</p> <p>- This folder contains NetCDF files from the paleo-dynamic topography models in 1 Myr time interval for the last 100 million years distributed in subfolders containing data computed in different reference frames i.e. paleomagnetic/<strong>pmag</strong> , <strong>mantle</strong> and dynamic topography in the reference frame of simplified individual <strong>plates</strong> where plate motions are from Torsvik et al. 2019 (as for both pmag and mantle), and we distinguish the plates using present-day plate boundaries. Within these subfolders you find the dynamic topography based on various tomography models:</p> <p> - SMEAN2: Dynamic topography results based on the SMEAN2 tomography model (preferred).</p> <p> - Tx2019Slab: Dynamic topography results based on the Tx2019Slab tomography model (preferred).</p> <p> - S40RTS: Dynamic topography results based on the S40RTS tomography model.</p> <p> - SEMUCB-WM1: Dynamic topography results based on the SEMUCB-WM1 tomography model.</p>
Bed topography of the large-scale meandering rivers at dynamic equilibrium state
<p>The simulation results for the bed topography of the 42 large-scale testbed rivers at dynamic equilibrium (along with instantaneous bed shear stress distributions) are presented.</p>
Global annual mean Absolute Dynamic Topography of permanently ice-free Oceans 1993 to 2020
<p>The text, data and figure disclosed are part of the manuscript “Intensification of the South Pacific Subtropical Gyre Circulation (1993–2020) Revealed by Satellite Altimetry” by Wolfgang Schneider, Freddy Hernández-Vaca, José Garcés-Vargas, and Iván Pérez-Santos, which was submitted to the Journal of Geophysical Research: Oceans</p> <p> </p> <p>The time series presented below were generated using E.U. Copernicus Marine Service Information, namely “Absolute Dynamic Topography” which was retrieved from the following Copernicus Marine Environment Monitoring Service (CMEMS) ftp site.</p> <p> </p> <p>ftp://my.cmems-du.eu/Core/SEALEVEL_GLO_PHY_L4_MY_008_047/cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1D/1993/01/</p> <p> </p>
Impacts of bed topography resolution on sea-level rise projections from coupled subglacial hydrology and ice dynamics for Thwaites Glacier, Antarctica
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Data from: Feedbacks between shallow groundwater dynamics and surface topography on runoff generation in flat fields
In winter, saturation excess (SE) ponding is observed regularly in temperate lowland regions. Surface runoff dynamics are controlled by small topographical features that are unaccounted for in hydrological models. To better understand storage and routing effects of small scale topography and their interaction with shallow groundwater under SE conditions, we developed a model of reduced complexity to investigate SE runoff generation, emphasizing feedbacks between shallow groundwater dynamics and mesotopography. The dynamic specific yield affected unsaturated zone water storage, causing rapid switches between negative and positive head and a flatter groundwater mound than predicted by analytical agro-hydrological models. Accordingly, saturated areas were larger and local groundwater fluxes smaller than predicted, leading to surface runoff generation. Mesotopographic features routed water over larger distances, providing a feedback mechanism that amplified changes to the shape of the groundwater mound. This in turn enhanced runoff generation, but whether it also resulted in runoff events depended on the geometry and location of the depressions. Whereas conditions favourable to runoff generation may abound during winter, these feedbacks profoundly reduce the predictability of SE runoff: statistically identical rainfall series may result in completely different runoff generation. The model results indicate that waterlogged areas in any given rainfall event are larger than those predicted by current analytical groundwater models used for drainage design. This change in the groundwater mound extent has implications for crop growth and damage assessments.
Data and code for "Topography and climate drive recent shifts in vegetation productivity dynamics"
<p>This zip file includes data and code for manuscript "<span><span>Topography and climate drive recent shifts in vegetation productivity dynamics</span></span>".</p>
Probing the viscosity of Venus mantle from dynamic topography at Baltis Vallis
<p>The Baltis Vallis channel on Venus preserves a record of long-wavelength deformation generated by a convecting mantle, providing a unique window into the planet's geodynamics. Notably, the observed topography along the channel is not downhill, suggesting complex interactions between surface processes and mantle dynamics. We statistically compare the observed dynamic topography of Baltis Vallis with dynamic topographies generated by a suite of stagnant-lid mantle convection models to constrain Venus' interior dynamics. Baltis Vallis's relatively young age (likely less than 250 Myr) and low root-mean-square relief of 217 m indicate vigorous convection in Venus's mantle, with a Rayleigh number greater than 4x10<sup>8</sup>, implying a mantle viscosity 1-2 orders of magnitude lower than Earth's. This difference may result from either a water-rich, less-degassed interior or a higher-temperature mantle beneath an insulating lid. Additionally, our simulations suggest that melt advection may dominate heat transport on Venus, potentially leading to non-linear temperature profiles in the crust. Upcoming missions such as <em>VERITAS </em>and <em>EnVision </em>will deliver higher-resolution gravity and topographic data, providing further constraints on Venus's present-day internal dynamics and the origin of Baltis Vallis.</p>
Data from: Interactions among herbivory, climate, topography, and plant age shape riparian willow dynamics in northern Yellowstone National Park, USA
Understanding how the environmental context modifies the strength of trophic interactions within food webs forms a central challenge in community ecology. Here, we demonstrate the necessity of considering the influence of climate, landscape heterogeneity and demographics for understanding trophic interactions in a well-studied food web in Yellowstone National Park, USA. We studied riparian willow (Salix spp.) establishment and stem growth reconstructed from tree rings on the northern range of Yellowstone over a 30-year period that included the reintroduction of a top-predator, the gray wolf (Canis lupus). We used climate variables (annual precipitation, stream flow, and growing season length), herbivore abundance, and landscape descriptors (elevation and topographic wetness index) to predict establishment and growth processes through time before and after the introduction of wolves. We fitted Bayesian hierarchical models to establishment data and time series of individual stem heights from 1980 to 2008. Explaining variability in establishment required models with stream flow, annual precipitation, and elk abundance. Climate, trophic, and landscape covariates interacted with stem age to determine stem height and growth rate through time. Growth rates of most stems ages (2+) declined after the reintroduction of wolves. However, stem growth rates naturally declined with age, and the decline we observed was coincident with faster growth rates for the youngest stems. Mean stem heights at age have remained relatively stable through time for most age classes. Estimated effects of landscape topography had approximately the same magnitude of effect on stem growth rate at age as elk abundance. Synthesis: We show that the effects of modification of a food web cannot be predicted by studying trophic dynamics in isolation. No single driver explained patterns of willow establishment and growth over the past three decades in Yellowstone. Instead, interactions among trophic forces, interannual climate variability and landscape topography together shaped how the ecosystem responded to perturbations. Top-down effects of ungulates on riparian woody vegetation must be considered in the context of plant age, and climate and landscape heterogeneity.
Dynamic gravity and topography modeling: the Venus case
<p>This archive contains codes and data files supporting the study "The Mantle Viscosity Structure of Venus" by Julia Maia, Mark Wieczorek and Ana-Catalina Plesa submitted to Geophysical Research Letters.</p> <p>The dynamic loading code is based on the model by Hager and Clayton (1989).</p>
Data from: How topography induces reproductive asynchrony and alters gypsy moth invasion dynamics
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Probing the viscosity of Venus mantle from dynamic topography at Baltis Vallis
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Data from: Feedbacks between shallow groundwater dynamics and surface topography on runoff generation in flat fields
Open the record for dataset details and reuse information.
Data from: Interactions among herbivory, climate, topography, and plant age shape riparian willow dynamics in northern Yellowstone National Park, USA
Open the record for dataset details and reuse information.
The influence of dynamic topography, climate, and tectonics on the Nile River source-to-sink system – Model input data
<p>Input data for Badlands models used in 2020 Honours thesis at the University of Sydney.</p>
The role of subsurface ocean dynamics and phase transitions in forming the topography of icy moons
<p>Dataset used to plot figures for an article that is about to be sent to Icarus.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.