Water is central to many global challenges and opportunities, including food production, drinking water supplies, energy production, and transportation. Researchers often use global hydrologic models to study these large-scale water questions because these models are designed to simulate both natural land surface water resources and a wide range of human interactions with them. To better protect water supplies, food and energy production, and infrastructure, it is in the national interest to provide local, state, regional, and national stakeholders with reliable information needed to make informed decisions. The provision of such information requires global hydrologic models that run quickly on cutting edge computational platforms, make use of emerging large datasets, and are fully transparent in how results are obtained. The water resource research community currently uses Global Hydrologic Models (GHMs) by downloading large datasets, running model simulations on a local computer system, then uploading results to public repositories. This project aims to accelerate innovation in water resources research by removing the bottlenecks caused by downloading and uploading large amounts of data and by increasing training and collaboration opportunities so scientists can more rapidly test and develop hydrologic models that can be used and trusted by other research teams. These goals are achieved by building the OpenGHM platform, a computational system that leverages existing NSF-sup