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56 results for “Earth surface”
Dataset of "Inner Edge of Habitable Zone for Earth-sized Planets with Various Surface Water Distribution"
<p>This dataset contains results to create figures in the paper "Inner Edge of Habitable Zone for Earth-sized Planets with Various Surface Water Distribution" by T. Kodama et al.</p> <p>It contains that temperature, ground temperature, ground specific water, evaporation efficiency, cloud fraction, latent heat flux, top radiative flux (long and short), precipitable water, cloud water ratio from our GCM calculations. We used Gtool3-dcl5 which is developed by the GFD-DENNOU Club for analysis (<a href="https://www.gfd-dennou.org/">https://www.gfd-dennou.org</a>). For more </p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
Dataset to manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar" by Wang et al. (2023) submitted to Journal of Geophysical Research-Earth Surface.
<p>The dataset includes the relevant raw chemical element content data and the chemical weathering condition analysis of river sediment samples of Madagascar. The dataset is a supplement to the manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar", by Wang et al. (2023) submitted to the Journal of Geophysical Research-Earth Surface. Commercially sensitive data is hidden but is available by request directly to the corresponding author. The data should <strong>NOT</strong> be used commercially.</p> <p>A MATLAB code for the weathering indices calculation is open-access on GitHub (https://github.com/yanyanwangesd/chemical_weathering). Please contact the corresponding author for more info or technical support on using the code. </p>
Figure data for paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars"
<p>Here we provide the data used for generating Figures 1-4 as published in the paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars" </p>
Data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"
<p>Main data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"</p>
Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
Open the record for dataset details and reuse information.
Data from: The seasonal cycles of tropical sea surface temperature from Earth's axial tilt and orbital eccentricity
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Data from: Biomass consumption by surface fires across Earth's most fire prone continent
Landscape fire is a key but poorly understood component of the global carbon cycle. Predicting biomass consumption by fire at large spatial scales is essential to understanding carbon dynamics and hence how fire management can reduce greenhouse gas emissions and increase ecosystem carbon storage. An Australia‐wide field‐based survey (at 113 locations) across large‐scale macroecological gradients (climate, productivity and fire regimes) enabled estimation of how biomass combustion by surface fire directly affects continental‐scale carbon budgets. In terms of biomass consumption, we found clear trade‐offs between the frequency and severity of surface fires. In temperate southern Australia, characterised by less frequent and more severe fires, biomass consumed per fire was typically very high. In contrast, surface fires in the tropical savannas of northern Australia were very frequent but less severe, with much lower consumption of biomass per fire (about a quarter of that in the far south). When biomass consumption was expressed on an annual basis, biomass consumed was far greater in the tropical savannas (>20 times that of the far south). This trade‐off is also apparent in the ratio of annual carbon consumption to net primary production (NPP). Across Australia's naturally vegetated land area, annual carbon consumption by surface fire is equivalent to about 11% of NPP, with a sharp contrast between temperate southern Australia (6%) and tropical northern Australia (46%). Our results emphasise that fire management to reduce greenhouse gas emissions should focus on fire prone tropical savanna landscapes, where the vast bulk of biomass consumption occurs globally. In these landscapes, grass biomass is a key driver of frequency, intensity and combustion completeness of surface fires, and management actions that increase grass biomass are likely to lead to increases in greenhouse gas emissions from savanna fires.
Data from: Climatologies at high resolution for the earth's land surface areas
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Research Data for Martin and Edmonds, 2022, Earth Surface Dynamics.
<p>Research data for: Martin, H.K and Edmonds, D.A., 2022. The push and pull of abandoned channels: How floodplain processes and healing affect avulsion dynamics and alluvial landscape evolution in foreland basins. Earth Surface Dynamics. Available at: https://doi.org/10.5194/esurf-2021-82.</p> <p>Consists of .gif animations and some .csv files for all model output used to generate figures for the paper.</p> <p> </p>
A Review of Global Bedrock (234U/238U) Disequilibrium and its Controlling factors on Earth's Surface
<p>The <sup>234</sup>U-<sup>238</sup>U disequilibrium system offers a broad spectrum of applications to study various Earth-surface processes, such as the evolution of weathering profile and erosion through time and sediment transport processes across the river catchments. Many of these applications require a critical assumption regarding the initial bedrock (<sup>234</sup>U/<sup>238</sup>U) activity ratios ( ), typically assumed to be in secular equilibrium. However, it was until recently that this assumption was gradually challenged. To address this uncertainty and the controlling factors responsible for disequilibrium, we measured of the uncomminuted bedrock retrieved from Qinghai, China, in which one of the samples (ZC2F) exhibits severe <sup>234</sup>U deficiency, with measured to be 0.691 ± 0.004 (2σ; n = 3). To complement our new data, we compiled the published global values ranging from 0.700 to 1.280, with an average of 0.981 ± 0.070 (2σ; n = 160), indicating that visibly unweathered rocks worldwide could have activity ratios higher or lower than 1. This widely observed <sup>234</sup>U depletion among global bedrock (especially in the near-surface region, < 10 m) cannot result from the direct α-recoil of <sup>234</sup>U alone but is attributed to the preferential release of <sup>234</sup>U from microfissures created during rock diagenesis and probably from hydrothermal alteration processes. Moreover, the bedrock <sup>234</sup>U deficiency corroborates the local lithology, primarily defined by the mineral crystal sizes and abundance of specific minerals (biotite and quartz in granites) and the local precipitation amount where the bedrocks were recovered. In addition, bedrock samples can likewise be enriched with <sup>234</sup>U, as evidenced by the nine chemically treated bedrocks’ compiled in this study, ranging from 1.002 to 1.020 (n = 9), attributed to alpha recoil gain from neighboring grains or surface deposits enriched with uranium. As bedrock can be modified by geologic factors, such as tectonism, lithology, and climate, notably in the near-surface region, should be constrained with an optimal sampling strategy to provide a holistic perspective of evaluating catchment-scale Earth-surface processes. </p>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
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GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Surface Deformations from Glacial Isostatic Adjustment Models with Laterally Homogeneous, Compressible Earth Structure
<p>The zipped file contains global 1-degree grid files of four different surface deformation parameters:</p> <ul> <li>Horizontal velocity, North component (vnorth), in mm/a</li> <li>Horizontal velocity, East component (veast), in mm/a</li> <li>Vertical velocity (vup), in mm/a</li> <li>Geoid change (dgeoid), in mm/a</li> </ul> <p>calculated from a set of 26 different glacial isostatic adjustment models applying 10 different radially varying (=layered) earth structures and 3 different global ice models.</p> <p>The different earth structures and the available combinations with ice models can be found in the readme.pdf.</p> <p>File naming is simply {ice model}_{earth model}_{parameter}.grd. For example, anu-ice_f72_vup.grd is the global vertical velocity grid file calculated with a GIA model with ANU-ICE ice history and 60 km lithospheric thickness, 4E20 Pa s upper mantle viscosity and 2E21 Pa s lower mantle viscosity. Grid files (NetCDF format) were generated with GMT6 (Wessel et al., 2019), thus can be directly used.</p> <p>Ice thickness histories of ICE-6G_C (Argus et al., 20214; Peltier et al., 2015) and ICE-7G_NA (Roy & Peltier, 2017) were downloaded from W. R. Peltier’s data website at the University of Toronto, Canada: <a href="https://www.atmosp.physics.utoronto.ca/~peltier/data.php">https://www.atmosp.physics.utoronto.ca/~peltier/data.php</a></p> <p>Note that the velocity field of ICE-6G_C(VM5a) can be compared to the one available from W. R. Peltier’s data website. The files provided here are not a substitute for the ones by W. R. Peltier and colleagues! Analyzing the difference between the files from this work and the ones available on the data website can help getting an error estimate from the two GIA model implementations (see further below). The user will find minor differences in the uplift component but larger ones in far field areas of the horizontal components.</p> <p>ICE-7G_NA(VM7) results are added as complement for interested users. However, note that ICE-7G_NA contains ice thickness history modifications in North America only and a fully global re-optimization of the ice thickness is warranted. Hence, excessive use and interpretation of these grid files, especially on global scale, should be avoided.</p> <p>ANU-ICE is a global 1-degree ice thickness model merged from several regional models (Lambeck, 1995; Fleming & Lambeck, 2004; Lambeck et al., 2010; 2014; 2017) kindly provided by Anthony Lambert and Kurt Lambeck, ANU, Canberra, Australia. The regional models contain differing spatial and temporal resolutions that were unified to fit the global 1-degree spatial resolution at mainly common time steps (500–1000 years). The Antarctic Ice Sheet part contains changes in the last time steps, thus some larger changes in the velocities can be found there.</p> <p>The software ICEAGE (Kaufmann, 2004) is used for calculating the grids, which applies the viscoelastic normal-mode method (Peltier, 1974; Wu, 1978). The sea-level equation is solved in a pseudo-spectral approach (Mitrovica et al., 1994; Mitrovica & Milne, 1998) in an iterative procedure in the spectral domain. See further details in Kaufmann and Lambeck (2000; 2002). The spherical harmonic expansion in the spectral domain is truncated at degree 192, which corresponds to ~1° spatial resolution. The models are spherically symmetric (1D), compressible, with Maxwell-viscoelasticity, rotational feedback, and time-dependent coastlines. The Earth’s core is, as assumed to be inviscid, incorporated as lower boundary condition. Rheological parameters such as depth-dependent density, Young’s modulus, etc., are taken from PREM (Preliminary Reference Earth Model; Dziewonski & Anderson, 1981).</p> <p><strong>Acknowledgments</strong></p> <p>HS would like to thank Jeff Freymueller for discussions on model selection.</p> <p><strong>References</strong></p> <p>Argus, D. F., Peltier, W., Drummond, R., Moore, A.W. 2014. The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories. Geophysical Journal International 198, 537–563, doi:10.1093/gji/ggu140.</p> <p>Dziewonski, A. M., Anderson, D. L. 1981. Preliminary reference Earth model. Physics of the Earth and Planetary Interiors 25, 297–356, doi:10.1016/0031-9201(81)90046-7.</p> <p>Fleming, K., Lambeck, K. 2004. Constraints on the Greenland ice sheet since the Last Glacial Maximum from sea-level observations and glacial-rebound models. Quaternary Science Reviews 23, 1053–1077, doi:10.1016/j.quascirev.2003.11.001.</p> <p>Kaufmann, G. 2004. Program Package ICEAGE, Version 2004. Manuscript. Institut für Geophysik der Universität Göttingen.</p> <p>Kaufmann, G., Lambeck, K., 2000. Mantle dynamics, postglacial rebound and the radial viscosity profile. Physics of the Earth and Planetary Interiors 121, 301–324, doi:10.1016/S0031-9201(00)00174-6.</p> <p>Kaufmann, G., Lambeck, K., 2002. Glacial isostatic adjustment and the radial viscosity profile from inverse modeling. Journal of Geophysical Research Solid Earth 107, ETG 5-1-ETG 5-15, doi:10.1029/2001JB000941.</p> <p>Lambeck, K. 1995. Late Devensian and Holocene shorelines of the British Isles and North Sea from models of glacio-hydro-isostatic rebound. Journal of the Geological Society London 152, 437–448, doi:10.1144/gsjgs.152.3.0437.</p> <p>Lambeck, K., Purcell, A., Zhao, J., Svensson, N.-O. 2010. The Scandinavian ice sheet: from MIS 4 to the end of the last glacial maximum. Boreas 39 (2), 410–435, doi:10.1111/j.1502-3885.2010.00140.x.</p> <p>Lambeck, K., Rouby, H., Purcell, A., Sun, Y., Sambridge, M. 2014. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences of the United States of America 111 (43), 15296–15303, doi:10.1073/pnas.1411762111.</p> <p>Lambeck, K., Purcell, A., Zhao, J. 2017. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quaternary Science Reviews 158, 172–210, doi:10.1016/j.quascirev.2016.11.033.</p> <p>Mitrovica, J. X., Davis, J. L., Shapiro, I. I. 1994. A spectral formalism for computing three–dimensional deformations due to surface loads: 1. Theory. Journal of Geophysical Research Solid Earth 99(B4), 7057–7073, doi:10.1029/93JB03128.</p> <p>Mitrovica, J. X., Milne, G. A. 1998. Glaciation-induced perturbations in the Earth’s rotation: a new appraisal. Journal of Geophysical Research Solid Earth 103, 985–1005, doi:10.1029/97JB02121.</p> <p>Peltier, W. R. 1974. The impulse response of a Maxwell Earth. Reviews of Geophysics and Space Physics 12(4), 649–669, doi:10.1029/RG012i004p00649.</p> <p>Peltier, W., Argus, D., Drummond, R. 2015. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research Solid Earth 120, 450–487, doi:10.1002/2014JB011176.</p> <p>Roy, K., Peltier, W. R. 2017. Space-geodetic and water level gauge constraints on continental uplift and tilting over North America: regional convergence of the ICE-6G_C (VM5a/VM6) models. Geophysical Journal International 210(2), 1115-1142, doi:10.1093/gji/ggx156.</p> <p>Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., Tian, D. 2019. The Generic Mapping Tools version 6. Geochemistry, Geophysics, Geosystems 20, 5556–5564, doi:10.1029/2019GC008515.</p> <p>Wu P. 1978. The response of a Maxwell earth to applied surface mass loads: glacial isostatic adjustment. MSc thesis, University of Toronto, Toronto, Ontario, Canada.</p>
Data to reproduce the results presented in Sehgal et al. 2022. Journal of Geophysical Research Earth Surface, https://doi.org/10.1029/2022JF006838 ("A Generic Relation Between Turbidity, Suspended Particulate Matter Concentration, and Sediment Characteristics")
<p>This repository consists data to reproduce results as presented in: "A Generic Relation Between Turbidity, Suspended Particulate Matter Concentration, and Sediment Characteristics", Journal of Geophysical Research Earth Surface. Kindly refer to the readme.text file to navigate through the dataset.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
data for the submission in JGR: Earth Surface
<p><strong>Data explanation</strong></p> <p>The dataset includes the major data for the manuscript: ‘Modeling Multi-Decadal Morphological Evolution of the Radial-Shaped Sand Ridges in the Southern Yellow Sea, China’.</p> <p> </p> <p>There are 7 data structures in the mat file:</p> <ol> <li>Elevation_SLRscenarios: this includes the elevation data for the sea level rise scenarios at year 2059, including the base scenario (noSLR), SLR1 (3 mm/yr), and SLR2 (11 mm/yr). Besides, the spatial info for the data and shown as X and Y, and the variable area is the area of the grid.</li> <li>Elevation_tide_ww_2017: this includes the elevation data at 2017 for two cases: tide-driven model (elevation_tide), and the tide, wind and waves driven model (elevation_tww). The spatial info is also included.</li> <li>Grainsize: this includes sand and fine sediment percentages for initial condition (RSR_fine_ini, RSR_sand_ini), at 2017 from the tide-driven model (RSR_fine_tide_2017, RSR_sand_2017), and at 2017 from the tide, wind and waves driven model (RSR_fine_tww_2017, RSR_sand_tww_2017). The spatial info is also included.</li> <li>Residual1979: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 1979. The spatial info is also included.</li> <li>Residual2017: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2017. The spatial info is also included.</li> <li>Residual2059: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2059. The spatial info is also included.</li> <li>Sedimentflux_profiles: this includes the net sediment flux across the profiles between the radial sand ridge system (RSR) and the abandoned yellow river delta (AYRD) (p1), and the RSR and the Yangtze River Delta (YRD) (p2). The variable distance_p1 and distance_p2 show the distance from coastline for P1 and P2 profiles, respectively. And the variables with ‘tide’ show the sediment flux from the tide-driven model at 2017, while the variables with ‘tww’ show the sediment flux from the tide, wind and waves driven model at 2017.</li> </ol>
Dataset and Software for An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms
<p><strong>What is the nature of flow in the mantle?</strong><br> <strong>How fast do waves travel anywhere on Earth?</strong><br> <strong>Where can radial anisotropy be robustly detected?</strong><br> <strong>Can we reconcile a broad spectrum of seismic data? What are the benefits?</strong></p> <p>We use normal-mode splitting functions in addition to surface-wave phase anomalies, body-wave travel times and long-period waveforms to construct a three-dimensional model of anisotropic shear-wave velocity in the Earth's mantle. This is the <strong>first tomographic study</strong> to exploit the sensitivity of mode-splitting data to constrain radial anisotropy in the Earth's mantle jointly with several other types of data. Our modeling approach inverts for mantle velocity and anisotropy as well as transition-zone discontinuity topographies, and incorporates new crustal corrections for the splitting functions that are consistent with the nonlinear corrections we employ for the waveforms. Our preferred anisotropic model, S362ANI+M, is an update to the earlier model S362ANI, which did not include normal-mode splitting functions in its derivation.</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. & Ekström, G., 2014. An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms, <em>Geophys. J. Int.</em>, <strong>199</strong>(3), 1713-1738, doi: <a href="http://dx.doi.org/10.1093/gji/ggu356">10.1093/gji/ggu356</a>. <em><a href="https://rajmoulik.com/Publications/MoulikEkstrom_GJI2014.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. & Ekström, G., 2014. Dataset and Software for An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms. In Geophys. J. Int. (v1.0, Vol. 199, pp. 1713–1738). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8357379">10.5281/zenodo.8357379</a></p> </li> </ul> <p><strong>Data Products:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANIplusM_Figures.tar.gz"><strong>S362ANIplusM_Figures.tar.gz</strong></a> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM_MapViewsCrossSections.pdf"><strong>S362ANI+M_MapViewsCrossSections.pdf</strong></a> - Some cross sections and map views at various depths in the mantle</li> <li><strong><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM">S362ANI+M</a> - </strong>Coefficients of the spline basis functions for each parameter. Refer c<sub>ij</sub> in equation 11. This is our preferred global model of shear-wave velocity. In this model, radial anisotropy is confined to the uppermost mantle (that is, since the anisotropy is parameterized with only the four uppermost splines, it becomes very small below a depth of 250 km, and vanishes at 410 km). This is an updated version of S362ANI (Kustowski et al., 2008) which did not include normal modes in its derivation. Please note the stronger isotropic shear velocity anomalies in the transition zone.</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/STW105"><strong>STW105</strong></a> - reference model used in S362ANI+M. Described in Kustowski et al. (2008)</li> <li><strong><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/setup.cfg">setup.cfg</a> - </strong>Some configuration metadata relevant to this model for reproducibility.</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/epix.tar.gz"><strong>epix.tar.gz</strong></a> - Perturbations in horizontally (vsh) and vertically polarized shear velocity (vsv), Voigt-average isotropic velocity (vs), ansotropy (as) and topography of the internal boundaries. This is calculated from the spline coefficients at every 1 by 1 degree cell-centered pixel and at every ~25 km depth region from Moho to the core-mantle boundary and stored in extended pixel format (.epix) ASCII files. </li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM.BOX25km_PIX1X1.avni.nc4"><strong>S362ANI+M.BOX25km_PIX1X1.avni.nc4</strong></a> - The perturbations in a standard AVNI format that utilizes the NETCDF4 container format. This file can be read in Python using either xarray or AVNI libraries. For example, to plot vs perturbations at 24.4-50 km depth range <ul> <li><em>import xarray as xr</em></li> <li><em>ds = xr.open_dataset('S362ANI+M.BOX25km_PIX1X1.avni.nc4')</em></li> <li><em>ds.vs[0].plot()</em></li> </ul> </li> <li><strong>Fortran Code</strong> <ul> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/readme"><strong>readme</strong></a> - contains a description of all files in the folder below</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/PROGRAMS.tar.gz"><strong>PROGRAMS.tar.gz</strong></a> - tools for obtaining model values at specific locations and some GMT plotting tools</li> </ul> </li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/GRD.tar.gz"><strong>GRD.tar.gz</strong></a> - longitude-latitide-velocity files with vsh, vsv, and Voigt average in km/s evaluated on a grid of points at many depths in the mantle</li> </ul>
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