Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. This dataset is an extension to Caravan, a global community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 20231). The extension includes a subset of those hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy (Attribution 4.0 International; CC BY 4.0). In total, the dataset covers stations from 5357 catchments and 25 countries worldwide with a time series record from 1950 – 2022. GRDC is an international data centre operating under the auspices of the World Meteorological Organization (WMO) at the German Federal Institute of Hydrology (BfG). Established in 1988, it holds the most substantive collection of quality assured river discharge data worldwide. Primary providers of river discharge data and associated metadata are the National Hydrological and Hydro-Meteorological Services of WMO Member States. 1Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). https://doi.org/10.1038/s41597-023-01975-w
The data contains vulnerability estimates (climate niche factor analysis) for 49 native fish species in the upper Danube River basin. The upper Danube River basin is mainly located in Germany and Austria. The time frame covered is 300 years from 1800 to 2100 including two Representative Concentration Pathways, RCP 4.5 and RCP 8.5. Vulnerability estimates are calculated for three time frames (1800-1830; 1900-1930and 2070-2100 (including two RCPs)) with the time frame 1970-2000 as the baseline.
Time series of daily and/or monthly river discharge data of more than 9200 stations from 160 countries, comprising around 390,000 station-years with an average time series length of about 42 years. The earliest daily data are from the year 1806, the most recent from the year 2016.
The GRDC Freshwater Fluxes into the World’s Oceans is a web application provided by the Global Runoff Data Centre (GRDC) to present the Freshwater Fluxes into the World’s Oceans data product. Continental freshwater input into the oceans is computed by the GRDC at irregular intervals, most recently in December 2020 referencing the time period 1901–2016. Previous data sets prepared in 2004, 2009 and 2014 are integrated into the service.
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). https://www.gfz-potsdam.de/sektion/geomorphologie/projekte/pressure/
The Long-Term Mean Monthly Discharges and Annual Characteristics offer basic statistics of all stations being represented in the Global Runoff Database: - annual means, lowest, and highest monthly discharges for individual years, - long-term mean, lowest, and highest monthly discharges of time series with at least five years of observation each with at least ten monthly values, - long-term inner-annual mean, lowest and highest monthly discharges of time series with at least five values of a specific month, and their standard deviations. The monthly primary values are provided station by station as ASCII-text files, named with pvm-prefix and station numbers. For download, all files are grouped by WMO Regions. Please note, that statistics are only provided for timeseries of daily discharge values from years with less than ten months and months with less than ten days missing.
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145).We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146).The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145).The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b).The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo.The data are provided in one netcdf file for each variable and experiment described by Schaphoff et al. (2017b). Crop yields and sowing dates are not provided for the LPJmL4-GSI-GlobFIRE-PNV experiment as this represents natural vegetation only. An overview of all variables and the number of bands are given in the file inventory.
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 spectraand 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
Version history:We recommend to use the revised version of this data publication (http://doi.org/10.5880/fidgeo.2018.011) which contains further data collected (2010-2018), 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 2010-2016 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 are available in .csv format folllowing the CUAHSI Community Observations Data Model (ODM) as .zip files via this DOI Landing Page and directly from the CUASI HIS Database via http://hydroportal.cuahsi.org/isabena/cuahsi_1_1.asmx?WSDL.The data are provided 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 spectraFor 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 (ODMGuide.xlsx in folder ODM_Guide_2017.zip).
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