BALTEX (the Baltic Sea Experiment) was launched in 1992 as a Continental-scale Experiment (CSE) of the Global Energy and Water Exchanges Project (GEWEX) within the World Climate Research Program (WCRP). The research focus of BALTEX was primarily on the hydrological cycle and the exchange of energy between the atmosphere and the surface of the Earth. The study region of BALTEX is the Baltic Sea and its huge catchment region. In 2015, the BALTEX Hydrological Dataset moved from Swedish Meteorological and Hydrological Institute (SMHI) to the GRDC in order to ensure sustainable operation and regular updates as an integral part of the Global Runoff Database. By release and on behalf of the National Hydrological Services, the former BALTEX stations and flow data are integrated in the Global Runoff Database.

The Global Runoff Data Centre is an International data centre operating under the auspices of the World Meteorological Organization (WMO). Its primary objective consists in supporting the water and climate related programmes and projects of the United Nations, its specialised agencies and the scientific research community by collecting and disseminating hydrological data across national borders in a long-term perspective.

The Arctic-HYCOS forms the Arctic region component of the the World Hydrological Cycle Observing System (WHYCOS) implemented to support existing international initiatives. The objective of the Arctic-HYCOS Project is to allow the collection and sharing of hydrological data to evaulate freshwater fluxes to the Arctic Ocean and Seas, monitor the changes and enhance understanding of the hydrological regime of the Arctic region. GRDC hosts this dataset on behalf of the Arctic-HYCOS member countries.

The GEMStat Dashboard visualizes the water quality of global water bodies on different spatial and temporal scales, based on data from the GEMStat Database . Available data includes the parameters dissolved oxygen, nitrogen, phosphorus and pH, all core parameters of the UN SDG 6.3.2 water quality indicator. The data are voluntarily provided by countries and organizations worldwide within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP) .

The GEMStat Data Portal provides access to freshwater quality data and statistical vizualisations at different spatial scales. The data are voluntarily provided by countries and organizations worldwide within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP) .

National Hydrological Services have identified “climate sensitive” stations on request of the WMO Commission for Hydrology in order to assess the variability and trends in hydrological data associated with climate variability and change. GRDC has offered to integrate this dataset into the Global Runoff Database.

The European Water Archive (EWA) collected long-term daily flow data and catchment information from more than 4000 river gauging stations in 30 countries. In October 2014 the EURO-FRIEND-Water meeting in Montpellier concluded that the EWA will no longer be updated and will be "frozen at its current state". By release and on behalf of the data providers, the former EWA stations and flow data will be integrated in the Global Runoff Database. The archived data remain unchanged and will be provided by GRDC on special request by writing to grdc@bafg.de. Please use the GRDC station catalogue on the GRDC Portal for up-to-date data of stations in Europe.

The Southern Africa Flow Database, established between 1992 and 1997 to support rainfall-runoff modelling, contains flow time series data from about 815 stations across Southern Africa (SA). Initially hosted at the University of Dar es Salaam during the SA FRIEND Phase I and maintained by CEH in Wallingford in Phase II, the SA Flow Database is operated by GRDC since November 2010.

The Annual Characteristics and Long-Term Statistics offer basic hydrological statistics of timeseries data of the gauging stations being represented in the Global Runoff Database. Annual characteristics are derived from monthly discharge data, either through aggregated daily data or originally provided monthly data. Long-term statistics and long-term variability are derived from annual characteristics too.