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22 results for “GRDC”
GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre
<p>Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. This dataset is an extension to <em>Caravan</em>, a global community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 2023).</p> <p>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 5356 catchments and 25 countries worldwide with a time series record from 1950 – 2023.</p> <p>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.</p> <p>Reference:</p> <p>Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). <a href="https://doi.org/10.1038/s41597-023-01975-w">https://doi.org/10.1038/s41597-023-01975-w</a></p> <p><strong>Update:</strong></p> <p>With version 0.2 a bug has been fixed that affected the time series of four bands of all GRDC gauges in the GRDC extension. The affected bands were total_precipitation, surface_net_solar_radiation, surface_net_thermal_radiation and potential_evaporation, i.e. all features that are accumulated over the day, as per definition of ERA5-Land.<br>For details look at https://github.com/kratzert/Caravan/issues/26.</p> <p>Version 0.3: Data description file added.<br><br>Version 0.4: Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) in the meteorological forcing data and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").<br><br>Version 0.5: License overview of the respective countries has been added.<br>Dataset description has been modified and improved.<br><br>Version 0.6: The attribute tables are sorted alphabetically. Minor inconsistencies in the data description file have been corrected.</p> <p> </p> <p><strong>Dataset structure:</strong></p> <p>The dataset is provided in the following two file formats:<br>1. caravan-grdc-extension-csv.zip: provides the time series data as comma-separated text files (CSV) (downloadable as 8.8 GB zip archive)<br>2. caravan-grdc-extension-nc.zip: provides the time series data in the Network Common Data Form (NetCDF) (downloadable as 7.6 GB zip archive)</p> <p><strong>The data in the versions 0.1-0.3 are identical. Version 0.4 added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND.</strong></p> <p>Further details of the structure of the dataset are described in the data description file.</p>
Peak flow identified at selected GRDC stations and the corresponding hydrological and hydrometeorological state variables
Data set contains a list of peak flows at selected GRDC stations as well as the start, peak, and end dates of each selected event. Hydrometeorlogical variables and hydrological state variables simulated by a hydrological model E-HYPE corresponding to each selected event are also listed in the dataset. Further description and content of each data file is available in the included metadata.
Flood changes in Europe: Flood peak magnitude and frequency from GRDC stations
The dataset includes results of FLOOD change experiment performed in Virtual Water Science Lab. It consists of three parts: (1) Flood_change_results.txt presents a list of 629 analysed catchments, their characteristics and results of trend analysis. It is a tab delimited text file with 69 columns. (2) Catchments.zip contains a shape file (flood_changes_GRDC.shp) of catchment boundaries of 629 GRDC stations. (3) FloodCHange.R is an R-script used to analyse flood peak changes.
The use of GRDC gauging stations for calibrating large-scale hydrological models
<p>The Global Runoff Data Centre provides time series of observed discharges that are very valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology which, though investigated several times, has not been satisfactorily solved. Grid-based hydrological models need to fit the reported station location to the river network depending on the resolution, to compare simulated discharge with observed discharge. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the wrong basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcseconds) river network, and we provide source codes and high- and low-resolution watershed boundaries.</p> <p>Same as release on Github: https://github.com/iiasa/CWATM_grdc_calibration_stations/releases/tag/V1.0</p>
Catalogue of identified flood peaks from GRDC dataset (FLOOD TYPE experiment)
This dataset provides flood peaks identified from GRDC dataset for gaugung stations in Europe.
SWOT-compliant GRDC river flow dataset for the estimation of Flow-Duration Curves
<p>This repository hosts the data used in the manuscript "Potential legacy of SWOT mission for the estimation of Flow-Duration Curves” by Alessio Domeneghetti, Serena Ceola, Alessio Pugliese, Simone Persiano, Irene Palazzoli, Attilio Castellarin, Alberto Marinelli, Armando Brath</p> <p><span>The study investigates the potential of the Surface Water and Ocean Topography (SWOT) mission for the estimation of Flow-Duration Curves (FDCs) globally. SWOT-like river flow data is derived from the Global Runoff Data Centre (GRDC) dataset by selecting river gauging stations with a cross section wider than 100 m and with more than 10-year long daily river flow time series. Overall, 1200 river gauging stations are considered, from which 24 alternative SWOT-like river flow datasets can be derived by assuming different satellite revisiting times, biases and random errors, as detailed in the manuscript.</span></p> <p><span>The dataset includes detailed features on the selected 1200 river gauging stations. GRDC river flow daily time series for each station is provided as .csv. A .shp file provides the geographical location of each GRDC gauging station, including GRDC number, river, gauging station municipality, country, latitude and longitude, contributing area, altitude and Köppen-Geiger climate classification.</span></p>
ISLSCP II UNH/GRDC Composite Monthly Runoff
The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region 2024-06
<p>A collection of connectivity performance metrics, including data usage, location mapping, uptime/downtime, wan quality, latency, captured on a device-basis at each of 3 farms participation in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of June 2024</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-06
<p>A collection of connectivity performance metrics, including data usage, location mapping, uptime/downtime, wan quality, latency, captured on a device-basis at each of 3 farms participation in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of June 2024</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-06
<p>A collection of connectivity performance metrics, including data usage, location mapping, uptime/downtime, wan quality, latency, captured on a device-basis at each of 3 farms participation in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of June 2024</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024.</p>
Connectivity performance metrics Grain Automate GRDC Western region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality, captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of August 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality captured on a device-basis at each of 3 farms participatiing in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of August 2024.</p>
Connectivity performance metrics Grain Automate GRDC Southern region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality, captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer to peer learning through the development of autonomous working farms, for the month of August 2024</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-09
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the September 2024.</p>
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