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We merged various digital elevation models (DEMs) published in the recent years and created an up-to-date composite and global solution for Earth’s topography and bathymetry. Compared to the original geographically limited data sets, the final product is a seamless merged grid which additionally provides high resolution and accuracy topography and depth globally. We provide Earth relief grids w.r.t EIGEN-6C4 global geoid in terms of surface and bedrock elevation, ice thickness, and land-type masks which have been substantially improved w.r.t the global grids found in literature. We assessed the quality of the merged surface elevations w.r.t the heights given for about globally distributed 5000 ITRF stations. The merged surface model shows improvement of a factor of three w.r.t the other commonly used DEMs in terms of standard deviation. In addition to the four grids, GDEMM2024_SUR, GDEMM2024_BED, GDEMM2024_ICE, and GDEMM2024_LTM, we provide two additional files, the surface elevation without water (GDEMM2024_TBI) and the GDEMM2024_GEO file to transform the heights above EIGEN_6C4 geoid to ellipsoidal heights. The final grids are provided both in 30 arcsec and 1arcmin resolution and in GeoTIFF format which is one of the standards that is available in GMT (Generic Mapping Tools), GDAL (Geospatial Data Abstraction Library) and in almost all GIS software systems.
This data set was taken within the Perturbations of Earth Surface Processes by Large Earthquakes PRESSurE Project (https://www.gfz-potsdam.de/en/section/geomorphology/projects/pressure/) of the GFZ Potsdam. This project aims to better understand the role of earthquakes on earth surface processes. Strong earthquakes cause transient perturbations of the near Earth’s surface system. These include the widespread landsliding and subsequent mass movement and the loading of rivers with sediments. In addition, rock mass is shattered during the event, forming cracks that affect rock strength and hydrological conductivity. Often overlooked in the immediate aftermath of an earthquake, these perturbations can represent a major part of the overall disaster with an impact that can last for years before restoring to background conditions. Thus, the relaxation phase is part of the seismically induced change by an earthquake and needs to be monitored in order to understand the full impact of earthquakes on the Earth system. Early June 2015, shortly after the April 2015 Mw7.9 Gorkha earthquake, 6 automatic compact weather station were installed in the upper Bhotekoshi catchment covering an area ~50km2. The weather station network is centered around the Kahule Khola catchment, a small headwater catchment and is part of a wider data acquisition strategy including hydrological monitoring, seismometers, geophones and high resolution optical (RapidEye) as well as radar imagery (TanDEM TerraSAR-X).
The determination of the global gravity field has gained momentum due to high accuracy satellite-derived observations and development of forward gravity modelling. Forward modelling computes the global gravitational field from mass distribution sources instead of actual gravity measurements and helps improving and complementing the medium to high frequency components of the global gravity field models. In this study, we approximate the global gravity potential of the Earth’s upper crust based on ellipsoidal approximation and a mass layer concept. Lateral density variations within a sequence of thin volumetric shells bounded by confocal lower and upper shell ellipsoids are used in the computation of the ellipsoidal harmonic coefficients which are then transformed into spherical harmonic coefficients on the Earth’s surface in the final step. The main outcome of this research is a spectral representation of the Earth’s upper crust’s gravitational potential, computed up to degree and order 3660 in terms spherical harmonic coefficients (ROLI_EllApprox_SphN_3660).
The effects of climate and topography on soil physico-chemical and microbial parameters were studied along an extensive latitudinal climate gradient in the Coastal Cordillera of Chile (26° - 38°S). The study sites encompass arid (Pan de Azúcar), semiarid (Santa Gracia), mediterranean (La Campana) and humid (Nahuelbuta) climates and vegetation, ranging from arid desert, dominated by biological soil crusts (biocrusts), semiarid shrubland and mediterranean sclerophyllous forest, where biocrusts are present but do have a seasonal pattern to temperate-mixed forest, where biocrusts only occur as an early pioneering development stage after disturbance. All soils originate from granitic parent materials and show very strong differences in pedogenesis intensity and soil depth. Most of the investigated physical, chemical and microbiological soil properties showed distinct trends along the climate gradient. Further, abrupt changes between the arid northernmost study site and the other semi-arid to humid sites can be shown, which indicate non-linearity and thresholds along the climate gradient. Clay and total organic carbon contents (TOC) as well as Ah horizons and solum depths increased from arid to humid climates, whereas bulk density (BD), pH values and base saturation (BS) decreased. These properties demonstrate the accumulation of organic matter, clay formation and element leaching as key-pedogenic processes with increasing humidity. However, the soils in the northern arid climate do not follow this overall latitudinal trend, because texture and BD are largely controlled by aeolian input of dust and sea salts spray followed by the formation of secondary evaporate minerals. Total soil DNA concentrations and TOC increased from arid to humid sites, while areal coverage by biocrusts exhibited an opposite trend. Relative bacterial and archaeal abundances were lower in the arid site, but for the other sites the local variability exceeds the variability along the climate gradient. Differences in soil properties between topographic positions were most pronounced at the study sites with the mediterranean and humid climate, whereas microbial abundances were independent on topography across all study sites. In general, the regional climate is the strongest controlling factor for pedogenesis and microbial parameters in soils developed from the same parent material. Topographic position along individual slopes of limited length augmented this effect only under humid conditions, where water erosion likely relocated particles and elements downward. The change from alkaline to neutral soil pH between the arid and the semi-arid site coincided with qualitative differences in soil formation as well as microbial habitats. This also reflects non-linear relationships of pedogenic and microbial processes in soils depending on climate with a sharp threshold between arid and semi-arid conditions. Therefore, the soils on the transition between arid and semi-arid conditions are especially sensitive and may be well used as indicators of long and medium-term climate changes. Concluding, the unique latitudinal precipitation gradient in the Coastal Cordillera of Chile is predestined to investigate the effects of the main soil forming factor – climate – on pedogenic processes. The data presented here is part of the German-Chilean Priority Program “EarthShape” (Earth Surface Shaping by Biota), funded by the German Research Foundation (DFG). We provide the basic background data, which includes investigations into the influence of climate, vegetation and topography on pedogenesis and microbial abundances. The data are supplementary material to Bernhard et al. (2018). All tables are available as one Excel file, as individual tables in .csv format in a zipped archive and as PDF file. The samples are assigned with International Geo Sample Numbers (IGSN) and linked to a comprehensive sample description in the internet. The content of the five data tables is: Table S1: Soil profile field description for the EarthShape study sites Table S2: Soil physico-chemical properties for the depth increment samples in the four study sites Table S3: Soil physico-chemical properties for the horizon samples in the four study sites Table S4: Relative microbial abundances in the four study sites Table S5: Plant species and abundance (% cover) in the four study sites
High-resolution spherical harmonic representation of the Earth's topographic gravitational potential based on a three-layer decomposition of the topography with variable density values. Main features: - Three-layer decomposition of the topography using information of the new 1'x1' Earth2014 topography model - Rigorous separate modeling of rock, water, and ice masses with layer-specific density values: Rock: 2670 kg m-3, Water: 1030 kg m-3 (Ocean), 1000 kg m-3 (Inland), Ice: 917 kg m-3 - Ellipsoidal arrangement of the topography using the GRS80 ellipsoid + geoid undulations as height reference surface - Additional compilation of a consistent rock-equivalent version REQ_TOPO_2015 using condensed DTM-heights Processing: - Forward modelling in the space domain using tesseroid mass bodies - Transformation of global gridded values to the frequency domain by applying harmonic analysis up to degree and order 2190 Model versions: - Spherical harmonic coefficients of the RWI model are provided by two versions (GM = 3.986004415e+14 m3 s-2, a = 6378136.3 m): RWI_TOPO_2015 (topographic potential) REQ_TOPO_2015 (topogr. potential of rock-equivalent heights) - To allow the evaluation of the RWI model by synthesis software that by default subtracts the coefficients of a normal gravity field, two additional versions are available: RWI_TOPO_2015_plusGRS80 (RWI_TOPO_2015 + GRS80) REQ_TOPO_2015_plusGRS80 (REQ_TOPO_2015 + GRS80) where the following zonal harmonic coefficients of the GRS80 normal gravity field are added to the coefficients of the RWI model: C( 0,0) = 0.100000014676351e+01 C( 2,0) = -0.484167032228604e-03 C( 4,0) = 0.790304535833168e-06 C( 6,0) = -0.168725253450154e-08 C( 8,0) = 0.346053594536695e-11 C(10,0) = -0.265006548323563e-14 C(12,0) = -0.410788602320538e-16 C(14,0) = 0.447176931400485e-18 C(16,0) = -0.346362561442980e-20 Note that these coefficients are already rescaled to the above specified parameters GM and a of the RWI model. Details about the used Earth2014 topography model can be found in Hirt and Rexer (2015, https://doi.org/10.1016/j.jag.2015.03.001).
High-resolution spherical harmonic representation of the Earth's topographic, isostatic, and topographic-isostatic gravitational potential based on a three-layer decomposition of the topography with variable density values and a modified Airy-Heiskanen concept incorporating seismic Moho depths. Main features: - Three-layer decomposition of the topography using information of the 5'x5'global topographic database DTM2006.0 - Rigorous separate modeling of rock, water, and ice masses with layer-specific density values (2670, 1000, 920 kg m-3) - Avoidance of geometry changes compared to classical condensation methods (e.g. rock-equivalent heights) - Ellipsoidal arrangement of the topography using the GRS80 ellipsoid as reference surface - Adapted and modified Airy-Heiskanen isostatic concept - Incorporation of seismic Moho depths derived from CRUST2.0 - Location-dependent estimation of the crust-mantle density contrast Processing: - Forward modelling in the space domain using tesseroid mass bodies - Transformation of global gridded values to the frequency domain by applying harmonic analysis up to degree and order 1800 Model versions: - Spherical harmonic coefficients of the RWI model are provided by three versions (GM = 3.986004415e+14 m3 s-2, a = 6378136.3 m): RWI_TOPO_2012 (topographic potential) RWI_ISOS_2012 (isosatic potential) RWI_TOIS_2012 (topographic-isostatic potential) - To allow the evaluation of the RWI model by synthesis software that by default subtracts the coefficients of a normal gravity field, three additional versions are available: RWI_TOPO_2012_plusGRS80 (topographic potential + GRS80) RWI_ISOS_2012_plusGRS80 (isosatic potential + GRS80) RWI_TOIS_2012_plusGRS80 (topogr.-isostatic potential + GRS80) where the following zonal harmonic coefficients of the GRS80 normal gravity field are added to the coefficients of the RWI model: C( 0,0) = 0.100000014676351e+01 C( 2,0) = -0.484167032228604e-03 C( 4,0) = 0.790304535833168e-06 C( 6,0) = -0.168725253450154e-08 C( 8,0) = 0.346053594536695e-11 C(10,0) = -0.265006548323563e-14 C(12,0) = -0.410788602320538e-16 C(14,0) = 0.447176931400485e-18 C(16,0) = -0.346362561442980e-20 Note that these coefficients are already rescaled to the above specified parameters GM and a of the RWI model. Details about the used DTM2006.0 topography model can be found in Pavlis et al. (2012, https://doi.org/10.1029/2011JB008916). Details about the used CRUST2.0 model is available from Laske et al. (2000, https://igppweb.ucsd.edu/~gabi/crust2.html).
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