249 documents found in 409ms
# 1
Dahle, Christoph • Flechtner, Frank • Murböck, Michael • Michalak, Grzegorz • Neumayer, Hans • (et. al.)
Abstract: Spherical harmonic coefficients representing an estimate of Earth's mean gravity field during the specified timespan derived from GRACE mission measurements. These coefficients represent the full magnitude of land hydrology, ice, and solid Earth processes. Further, they represent atmospheric and oceanic processes not captured in the accompanying GAC product.
# 2
Francke, Till • Foerster, Saskia • Brosinsky, Arlena • Sommerer, Erik • López-Tarazón, José A. • (et. al.)
Abstract: Version history: This datased is an updated version of Francke et al. (2017; http://doi.org/10.5880/fidgeo.2017.003) for a revised version of this discussion paper. It contains further data collected, some of which also resulted in the revision of previous data (e.g. updated rating curves). A comprehensive hydro-sedimentological dataset for the Isábena catchment, NE Spain, for the period 2010-2018 is presented to analyse water and sediment fluxes in a Mediterranean meso-scale catchment. The dataset includes rainfall data from twelve rain gauges distributed within the study area complemented by meteorological data of twelve official meteo-stations. It comprises discharge data derived from water stage measurements as well as suspended sediment concentrations (SSC) at six gauging stations of the Isábena river and its sub-catchments. Soil spectroscopic data from 351 suspended sediment samples and 152 soil samples were collected to characterize sediment source regions and sediment properties via fingerprinting analyses. The Isábena catchment (445 km²) is located in the Southern Central Pyrenees ranging from 450 m to 2,720 m in elevation, together with a pronounced topography this leads to distinct temperature and precipitation gradients. The Isábena river shows marked discharge variations and high sediment yields causing severe siltation problems in the downstream Barasona reservoir. Main sediment source are badland areas located on Eocene marls that are well connected to the river network. The dataset features a wide set of parameters in a high spatial and temporal resolution suitable for advanced process understanding of water and sediment fluxes, their origin and connectivity, sediment budgeting and for evaluating and further developing hydro-sedimentological models in Mediterranean meso-scale mountainous catchments. The data have been published with the CUAHSI Water Data Center and is structured according to its guidelines (.csv format). For more detailed information please read the user guide on cloud publications with the CUAHSI Water Dater Center or the ODM guide for uploading data using CUAHSI´s ODM uploader added to the folder CUAHSI_ODM-Guide.zip. The database can be found in the HISCENTRAL catalogue (http://hiscentral.cuahsi.org/pub_network.aspx?n=5622). It is directly accessible via the API (http://hydroportal.cuahsi.org/isabena/cuahsi_1_1.asmx?WSDL) or in zipped archives at this DOI Landing Page (http://doi.org/10.5880/fidgeo.2018.011). For more detailed information, please read the user guide on cloud publications with the CUAHSI Water Dater Center (UserGuide.pdf) or the ODM guide for uploading data using CUAHSI´s ODM uploader in the ODM_Guide.zip archive. The data are available in four thematic zip folders:(1) hydro (hydrological data): water stage (manual readings and automatically recorded), river discharge (meterings and converted from stage)(2) meta (metadata) with the description of the different datafiles relevant for this dataset according to the CUAHSI HIS Standards(3) meteo (meteorological data): rainfall, temperature, radiation, humidity(4) sediment (sedimentological data): turbidity, suspended sediment concentration (from samples and from turbidity), sediment and soil reflectance spectra and are complemented by:(5) CUAHSI_ODM-Guide: User Guide, CUAHSI´s ODM uploader in Excel (.xlsx) and Open Office (.ods) formats(6) scripts: auxiliary R-script templates for data access, data analysis and visualisation(7) supplementary materials: stage-discharge- and turbidimeter rating curves
# 3
Petricca, Patrizio • Trippetta, Fabio • Billi, Andrea • Collettini, Cristiano • Cuffaro, Marco • (et. al.)
Abstract: This data publication includes a grid composed by contiguous 25 x 25 km square elements covering the Italian area and each parametrized by 1) the maximum length of faults included within the cell, 2) the maximum magnitude from instrumental seismic data, 3) the maximum magnitude from historical seismic data, 4) the maximum magnitude calculated from fault length using empirical scaling laws. This collection represents the basis to a work (Trippetta et al., 2019) aiming to test a fast method comparing the geologic (faults) and the seismologic (historical-instrumental seismicity) information available for a specific region. To do so, (1) a comprehensive catalogue of all known faults and (2) a comprehensive catalogue of earthquakes were compiled by merging the most complete available databases; (3) the related possible maximum magnitudes were derived from fault dimensions, upon the assumption of seismic reactivability of any fault; (4) the calculated magnitudes were compared with earthquake magnitudes recorded in historical and instrumental time series. Faults: to build the dataset of faults for Italy, the following databases were merged: (1) the entire faults collection after the Italian geological maps at the 1:100,000 scale (available online at www.isprambiente.it); (2) the faults compilation from the structural model of Italy at the 1:500,000 scale (Bigi et al., 1989); (3) faults provided in the ITHACA-Italian catalogue of capable faults (Michetti et al., 2000); and (4) the inventory of active faults of the GNDT (Gruppo Nazionale per la Difesa dai Terremoti, Galadini et al., 2000). To improve and implement the database, published complementary studies were selected for some specific areas considered to not be exhaustively covered by the aforementioned collection of faults, including Sardinia, SW Alps, Tuscany, the Adriatic front, Puglia, and the Calabrian Arc. For these areas, faults were selected on the grounds of scientific contributions that documented recent fault activity based on seismic, field, and paleoseismological data. In particular, for the southern Sardinia, the fault pattern proposed by Casula et al. (2001) was used. For the SW Alps, the works of Augliera et al. (1994), Courboulex et al. (1998), Larroque et al. (2001), Christophe et al. (2012), Sue et al. (2007), Capponi et al. (2009), Turino et al. (2009) and Sanchez et al. (2010) were followed. For the Tuscany area, Brogi et al. (2003), Brogi et al. (2005), Brogi (2006), Brogi (2008), Brogi (2011), and Brogi and Fabbrini (2009) were consulted. For the buried northern Apennines and Adriatic front, the fault datasets provided by Scrocca (2006), Cuffaro et al. (2010), and Fantoni and Franciosi (2010) were used. For the Puglia region, data from Patacca and Scandone (2004) and Del Gaudio et al. (2007) were used, while for the Calabrian Arc data were obtained from Polonia et al. (2016). Seismicity: to obtain a complete earthquake catalogue for the Italian territory, the following catalogues of instrumental and historical seismicity were integrated: (1) the CSI1.1 database (http://csi.rm.ingv.it; Castello et al., 2006) for the period 1981–2002, (2) the ISIDe database (http://iside.rm.ingv.it/iside/; IsideWorkingGroup, 2016) for the period 2003–2017 (Figure 3) and the CPTI15 (https://emidius.mi.ingv.it/CPTI15-DBMI15/; Rovida et al., 2016) for the period 1000-1981. The CSI 1.1 database (Castello et al., 2006) is a relocated catalogue of Italian earthquakes during the period 1997–2002. This collection derives from the work of Chiarabba et al. (2005). Most seismic events are lower than 4.0 in magnitude and are mostly located in the upper 12 km of the crust. A few earthquakes exceed magnitude 5.0, and the largest event is Mw 6.0. Due to their poorly constrained location, events with Mw < 2.0 were removed. The ISIDe database (IsideWorkingGroup, 2016) provides the parameters of earthquakes obtained by integrating data from real time and Italian Seismic Bulletin earthquakes. The time-span of this compilation begins in 1985. To avoid an overlap with the CSI database, only the time interval 2003–2017 was considered. Mw = 2.0 is the lower limit used for earthquake magnitude. The CPTI15 database integrates the italian macroseismic database version 2015 (DBMI15, Locati et al., 2016) and instrumental data from 26 different catalogues, databases and regional studies starting from the 1000 up to the 2014. To avoid overlapping of data with the utilized instrumental datasets, from the CPTI2015 we took data for the period 1000-1981 in the range of Mw 4-7. Method: starting from the entire faults dataset, the length of each structure was calculated (Lf, in km). Then, the Italian territory was divided into a grid with square cells of 25 x 25 km. The length of the longest fault crossing each cell characterizes the parameter “fault length” (Lf) of the considered cell. In the second step, these lengths were used as the input parameter to empirically derive the magnitude. The equations provided by Leonard (2010), were applied for earthquake magnitude-fault length relationships to infer the Potential Expected Maximum Magnitude as M = a + b ∗ log (Lf), with a=4.24 and b=1.67. The obtained magnitudes were assigned to each single cell. Furthermore, the maximum magnitude recorded/reported in instrumental/historical catalogs is associated to each containing cell. The resulting datasets are presented in txt format and included in the following files: - Grid_Coordinates.txt (contains ID and coordinates of grid's elements)- Grid_Structure.txt (contains geometry and specifications of the used grid)- Table_results (five columns table containing 1=element ID, 2= element max fault length (Lf_max in km), 3=element max Mw from instrumental record (MwInstr_max), 4=element max Mw from historical record (MwHist_max), 5=element max Mw derived by empirical relationship (PEMM).- The full list of references is included in the file Petricca_2018-003_References.txt
# 4
Scherler, Dirk • Wulf, Hendrik • Gorelick, Noel
Abstract: This dataset is supplementary to the article of Scherler et al. (submitted), in which the global distribution of supraglacial debris cover is mapped and analyzed. For mapping supraglacial debris cover, we combined glacier outlines from the Randolph Glacier Inventory (RGI) version 6.0 (RGI consortium, 2017) with remote sensing-based ice and snow identification. Areas that belong to glaciers but that are neither ice nor snow were classified as debris cover. This dataset contains the outlines of the mapped debris-covered glaciers areas, stored in shapefiles (.shp). For creating this dataset, we used optical satellite data from Landsat 8 (for the time period 2013-2017), and from Sentinel-2A/B (2015-2017). For the ice and snow identification, we used three different algorithms: a red to short-wavelength infrared (swir) band ratio (RATIO; Hall et al., 1988), the normalized difference snow index (NDSI; Dozier, 1989), and linear spectral unmixing-derived fractional debris cover (FDC; e.g., Keshava and Mustard, 2002). For a detailed description of the debris-cover mapping and an analysis of the data, please see Scherler et al. (2019) to which these data are supplementary material. This dataset includes debris cover outlines based on either Landsat 8 (LS8; 30-m resolution) or Sentinel 2 (S2; 10-m resolution), and the three algorithms RATIO, NDSI, FDC. In total, there exist six different zip-files that each contain 19 shapefiles. The structure of the shapefiles follows that of the RGI version 6.0 (RGI consortium, 2017), with one shapefile for each RGI region. The original RGI shapefiles provide each glacier as one entry (feature) and include a variety of ancillary information, such as area, slope, aspect (RGI Consortium 2017a, Technical Note p. 12ff). Because the debris-cover outlines are based on the RGI v6.0 glacier outlines, all fields of the original shapefiles, which refer to the glacier, are retained, and expanded with four new fields: - DC_Area: Debris-covered area in m². Note that this unit for area is different from the unit used for reporting the glacier area (km²).- DC_BgnDate: Start of the time period from which satellite imagery was used to map debris cover.- DC_EndDate: End of the time period from which satellite imagery was used to map debris cover.- DC_CTSmean: Mean number of observations (CTS = COUNTS) per pixel and glacier. This number is derived from the number of available satellite images for the respective time period, reduced by filtering pixels due to cloud and snow cover. The dataset has a global extent and covers all of the glaciers in the RGI v. 6.0, but it exhibits poor coverage in the RGI region Subantarctic and Antarctic, where the debris cover extents are based on very few observations.
# 5
Neuper, Malte • Ehret, Uwe
Abstract: The dataset consist of time series of hourly rain rates and mean radar reflectivity factor (herein after referred to as reflectivity) near the ground, 100 meter and 1500 meter above the ground at six locations in the Attert catchment in Luxembourg. The time series cover a time span of 4 years (from the 1st of October 2012 tor the 30th of September 2016). The dataset was derived from drop size measurements we conducted at six stations with six laser optical disdrometers and two micro rain radars (MRR) within the CAOS Project (DFG Research Group: From Catchments as Organized Systems to Models based on Functional Units (FOR 1598). The time series of rain rates and radar reflectivity factors (reflectivities) were calculated (derived) via the 3.5th and 6th statistical moments of the drop size distributions using the particular raw data of drop sizes and fall velocities. The primary reason for the measurements was to improve radar based quantitative precipitation estimation in general and the conversion of the reflectivity Z (measured by operational weather radar) to a rain rate R at the ground via the so-called Z-R relation within a mesoscale catchment. GENERAL CONVENTIONS:• Time extent: 1.10.2012 00:00 – 30.09.2016 23:00 (35064 values)• Time reference: UTC • Time stamp: end• Time resolution: 1h• Time series are equidistant and gapless• Missing values: NaN• Delimiter: ; (semicolon)• decimal separator: . (point) STATION LOCATIONS:Name; Abbreviation; Latitude (WGS-84); Longitude(WGS-84); height a.s.l; InstrumentationOberpallen;OPA; 49.73201°; 5.84712°;287 m; disdrometer Useldange;USL; 49.76738°; 5.96756°; 280 m; disdrometer and MRR Ell;ELL; 49.76558°; 5.84401°; 290 m; disdrometer Post;POS; 49.75394°; 5.75481°; 345 m; disdrometer Petit-Nobressart;PIN; 49.77938°; 5.80526°; 374 m; disdrometer and MRR Hostert-Folschette;HOF; 49.81267°; 5.87008°; 435 m; disdrometer HEADER – VARIABLES DESCRIPTION:Name - description:Date-UTC – Date as yyyy-mm-dd HH:MM (4 digit year-2 digit month – 2 digit day 2 digit hour: 2 digit minute)Time Zone: UTC. Decade – tenner day of the year (that is 1st to 10th of January = 1 ; 11th to 20th of January = 2 ; 21th to 30th of January = 3 ; … 21st to 31st of December = 36.Month – Month of the year (1: January, 2: February, 3:March,…, 12: December).dBZ0_DIS_ELL – reflectivity at ground level (in dBZ) at the station Ell derived from disdrometer measurements.dBZ0_DIS_HOF – reflectivity at ground level (in dBZ) at the station Hostert-Folschette derived from disdrometer measurements.dBZ0_DIS_OPA – reflectivity at ground level (in dBZ) at the station Oberpallen derived from disdrometer measurements.dBZ0_DIS_PIN – reflectivity at ground level (in dBZ) at the station Petit-Nobressart derived from disdrometer measurements.dBZ0_DIS_POS – reflectivity at ground level (in dBZ) at the station Post derived from disdrometer measurements.dBZ0_DIS_USL – reflectivity at ground level (in dBZ) at the station Useldange derived from disdrometer measurements.dBZ100_MRR_PIN – reflectivity 100 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ100_MRR_USL – reflectivity 100 m above ground (in dBZ) at the station Useldange derived from MRR measurements.dBZ1500_MRR_PIN – reflectivity 1500 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ1500_MRR_USL – reflectivity 1500 m above ground (in dBZ) at the station Useldange derived from MRR measurements.RR0_DIS_ELL – rain rate at ground level (in mm/h) at the station Ell derived from disdrometer measurements.RR0_DIS_HOF – rain rate at ground level (in mm/h) at the station Hostert-Folschette derived from disdrometer measurements.RR0_DIS_OPA – rain rate at ground level (in mm/h) at the station Oberpallen derived from disdrometer measurements.RR0_DIS_PIN– rain rate at ground level (in mm/h) at the station Petit-Nobressart derived from disdrometer measurements.RR0_DIS_POS – rain rate at ground level (in mm/h) at the station Post derived from disdrometer measurements.RR0_DIS_USL – rain rate at ground level (in mm/h) at the station Useldange derived from disdrometer measurements.RR100_MRR_PIN – rain rate 100 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR100_MRR_USL – rain rate 100 m above ground (in mm/h) at the station Useldange derived from MRR measurements.RR1500_MRR_PIN – rain rate 1500 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR1500_MRR_USL – rain rate 1500 m above ground (in mm/h) at the station Useldange derived from MRR measurements. The instruments were maintained and cleaned monthly. The data was quality checked. Cases with solid precipitation were excluded using the output form the Pasivel² present weather sensor software, which especially was needed since disdrometer data was contaminated by cobwebs. But since the present weather analyzer classified these (due to their slow movement within the wind) as snow, these then could easily be eliminated.
DISDROMETER:We deployed six second generation OTT Particle Size and Velocity (PARSIVEL², see Löffler-Mang and Joss, 2000 - https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2) optical disdrometers in the study area to measure drop size distributions at ground level in 1-minute resolution. Two were located at the same sites as the MRRs (Useldange and Petit-Nobressart). The others were placed such as to both capture the hydroclimatic variations in the study area and to cover it as uniformly as possible. We applied a quality control to the raw data as described by Friedrich et al. (2013 - https://doi.org/10.1175/JTECH-D-12-00254.1 and https://doi.org/10.1175/MWR-D-12-00116.1 ), converted the filtered data to drop-size concentrations per unit air volume to make them comparable to weather radar and MRR data, then converted them to reflectivity and rain rate using the 3.5th and 6th statistical moments of the drop size distributions and finally took 1-hour averages and sums thereof. MRR: From two vertical pointing K-band METEK micro rain radars (MRR) (Löffler-Mang et al. 1999 - https://doi.org/10.1175/1520-0426(1999)016<0379:OTPOAL>2.0.CO;2 and Peters et al. 2002 - Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Env. Res. 7: 353–362, 2002) measurements located at the sites Useldange and Petit-Nobressart drop size spectra were retrieved at 1500 meter and 100 meter above ground . We operated the MRR's at 100 vertical meters and 10 seconds temporal resolution, but for reasons of storage and processing efficiency did all further processing on 1-minute aggregations thereof. The raw Doppler spectra were transformed to drop size distributions via the drop size – fall velocity relation given in Atlas et al (1973 - https://doi.org/10.1029/RG011i001p00001). From the drop size distributions the rain rate and the reflectivity were calculated using the 3.5th and the 6th statistical moments of the drop size distributions. In doing so we assumed the vertical velocity of the air to be negligible.
# 6
Deng, Bin • Schönebeck, Jan • Warsitzka, Michael • Rosenau, Matthias
Abstract: This dataset provides friction data from ring-shear tests (RST) on natural and artificial granular materials used for analogue modelling in the experimental laboratory of the Chengdu University of Technology (CDUT, China). Six samples, four types of quartz sands and two types of glass beads, have been characterized by means of friction coefficients µ and cohesions C. The material samples have been analysed at the Helmholtz Laboratory for Tectonic Modelling (HelTec) at the GFZ German Research Centre for Geosciences in Potsdam in the framework of the EPOS (European Plate Observing System) Transnational Access (TNA) call of the Thematic Core Service (TCS) Multi-scale Laboratories (MSL) in 2017 as a remote service for the CDUT. According to our analysis the materials show a Mohr-Coulomb behaviour characterized by a linear failure envelope. Peak friction coefficients µP of the quartz sand samples range between 0.62 and 0.79 and µP of the glass beads between 0.61 and 0.64. Except for one quartz sand sample, peak cohesions CP of all materials are smaller than or around zero meaning that these materials are cohesionsless. All materials show a minor rate-weakening of 1-2 % per ten-fold change in shear velocity v.
# 7
Pilz, Marco • Woith, Heiko • Festa, Gaetano
Abstract: This data set contains continuous recordings of seismic noise, which have been made on the surface of a shallow volcanic crater in the Phlegrean Fields volcanic complex near Naples, Italy, where a significant level of volcanic-hydrothermal activity is presently concentrated (MED-SUV = Mediterranean Supersite Volcanoes). As part of the Phlegrean Fields, the Solfatara crater is a 0.4 × 0.5 km sub-rectangular structure whose geometry is mainly due to the control exerted by N40–50W and N50E trending normal fault systems, along which geothermal fluids can ascend. These systems crosscut the study area and have been active several times in the past.
# 8
Dobslaw, Henryk • Dill, Robert • Dahle, Christoph
Abstract: Spherical harmonic coefficients that represent the sum of the ATM (or GAA) and OCN (or GAB) coefficients during the specified timespan. These coefficients represent anomalous contributions of the non-tidal dynamic ocean to ocean bottom pressure, the non-tidal atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure, and the upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.
# 9
Dobslaw, Henryk • Dill, Robert • Dahle, Christoph
Abstract: Spherical harmonic coefficients that are zero over the continents, and provide the anomalous simulated ocean bottom pressure that includes non-tidal air and water contributions elsewhere during the specified timespan. These coefficients differ from GLO (or GAC) coefficients over the ocean domain by disregarding upper air density anomalies. The anomalous signals are relative to the mean field from 2003-2014.
# 10
Dobslaw, Henryk • Dill, Robert • Dahle, Christoph
Abstract: Spherical harmonic coefficients that represent anomalous contributions of the non-tidal atmosphere to the Earth's mean gravity field during the specified timespan. This includes the contribution of atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure elsewhere, and the contribution of upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.
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