eWaterCycle is an open source hyperresolution (10 km × 10 km) global hydrological forecasting framework that runs an ensemble of hydrological models. Forced with a weather forecast ensemble, it predicts river discharge and river discharge uncertainty nine days ahead. Daily satellite soil moisture observations are assimilated into the state of the model ensemble using an Ensemble Kalman Filter. We demonstrate that it is feasible to build such a system using pre-exisiting, open source, components that communicate through standard interfaces. The PCRGLOBWB2.0 (van Beek et al., 2011; Sutanudjaja et al., 2014) model is used to model hydrology globally, forced with GFS (Kanamitsu, 1989; Kanamitsu et al., 1991; Moorthi et al., 2001) weather forecast. The operational soil moisture product from the HSAF (Drusch et al., 2009; De Rosnay et al., 2011) service is assimilated into the model ensemble using OpenDA (Velzen et al., 2016), a data assimilation framework. Output of the model ensemble is presented in a Cesium (Analytical Graphics, 2011) based visualization. All communication between framework components is through standard file types (NetCDF)(Rew and Davis, 1990) and services (Web Map Service) (de La Beaujardiere, 2006). Communication between model and data assimilation framework is through the Basic Model Interface (BMI) (Peckham et al., 2013). The forecasts is available at forecast.ewatercycle.org . By using standard open interfaces, the different components of the model can be replaced with relative ease, facilitating future model comparison studies without the need of extensive Computer Science support. This makes eWaterCycle, in addition to an operational forecasting model, a testbed environment where the impact of different model structures, input sources and/or data assimilation schemes can easily be studied. Setup instructions to run the eWaterCycle project on local hardware are provided, allowing the hydrological community to build on this open source framework.