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
CO-MICC is a data portal for freshwater-related climate change risk assessment at multiple spatial scales. It is named after the research project during which it was developed, i.e. the CO-MICC (CO-development of Methods to utilize uncertain multi-model-based Information on freshwater-related hazards of Climate Change) project (2017-2021). The aim of CO-MICC is to support decision making in the public and private spheres dealing with future availability of freshwater resources. This climate service is operated and maintained by the International Centre for Water Resources and Global Change (ICWRGC), and more broadly by the German Federal Institute of Hydrology. The portal comprises data of over 80 indicators of freshwater-related hazards of climate change, which can be visualized in the form of global maps or interactive graphs. The indicators are dynamically calculated based on modelled annual and monthly gridded (0.5°) data sets of climate and hydrological variables. These data sets were computed by a multi-model ensemble comprising four Representative Concentration Pathways (RCPs), four General Circulation Models (GCMs), three Global Hydrological Models (GHMs) and two variants per hydrological model, which amounts to 96 ensemble members in total. They were provided by three European research modelling teams that are part of the ISIMIP consortium. The indicator data correspond to absolute or relative changes averaged over future 30-year periods, as compared to the reference period 1981-2010.
The dataset consists of a GIS vector layer of the contours of 24,148 elementary catchments in Europe and the associated representation of the streamflow regime in terms of empirical flow–duration curves (FDCs). FDCs are estimated by means of the geostatistical procedure termed total negative deviation top-kriging (TNDTK), starting from the empirical FDCs available for 2484 discharge measurement stations across Europe. Together with the estimated FDCs' percentiles, for each catchment, indicators of the accuracy and reliability of the performed large-scale geostatistical prediction are provided. The file is stored using the ESRI Shapefile format in the ETRS89 (European Terrestrial Reference System 1989) – LAEA (Lambert Azimuthal Equal Area) datum and geographic coordinate system.
RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over páramos and montane catchments.
The RAIN4PE data are available for the terrestrial land surface between 19°S-2°N and 82-67°W, at 0.1° spatial and daily temporal resolution from 1981 to 2015. The precipitation dataset is provided in netCDF format.
For a detailed description of the RAIN4PE development and evaluation of RAIN4PE applicability for hydrological modeling of Peruvian and Ecuadorian watersheds, readers are advised to read Fernandez-Palomino et al. (2021).