Hydrologic modeling can be used to aid in decision-making at the local scale. Developed countries usually have their own hydrologic models; however, developing countries often have limited hydrologic modeling capabilities due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. However, large-scale forecasting systems come with their own challenges. These New hydroinformatic challenges can prevent these models from reaching their full potential of becoming useful in the decision making process. This article discusses these challenges along with the background leading to the development of a large-scale streamflow prediction system. In addition, we present a large-scale streamflow prediction system developed using the GloFAS-RAPID model. The developed model covers Africa, North America, South America, and South Asia. The results from this model are made available using a Hydrologic Modeling as a Service approach (HMaaS) as an answer to some of the discussed challenges. In contrast to the traditional modeling approach, which makes results available only to those with the resources necessary to run hydrologic models, the HMaaS approach makes results available using web services that can be accessed by anyone with an internet connection. Web applications and services for providing improved data accessibility, and addressing the discussed hydroinformamtic challenges are also presented. The HydroViewer app, a custom application to display model results and facilitate data consumption and integration at the local level is presented. We also conducted validation tests to ensure that model results are acceptable. Some of the countries where the presented services and applications have been tested include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. Overall, a HMaaS approach to operationalize a GSPS and provide meaningful and easily accessible results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.