The GRIP-GL (Great Lakes Runoff Intercomparison Project for the entire Great Lakes watershed) is a large-scale modeling initiative aimed at assessing hydrologic model performance and uncertainty across the full 1 million km² Great Lakes basin. Building on earlier projects focused on individual lakes, GRIP-GL brings together 13 diverse hydrologic models—including machine learning, lumped, semi-distributed, and gridded models—within a standardized experimental framework. All models use the same input datasets, forcings, routing, and evaluation locations, and simulate not just streamflow (Q), but also actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). This comprehensive setup allows for consistent comparison of model behavior and uncertainty across space, time, and output types, making GRIP-GL a valuable resource for improving large-scale hydrologic modeling and understanding the limits of model transferability and predictability.
IDEAS-Watersheds is a U.S. Department of Energy initiative aimed at advancing predictive, systems-level understanding of watershed function in response to environmental change. As part of the Environmental System Science (ESS) program, the project supports integration of observations, experiments, and modeling to improve how biogeochemical and hydrological processes are represented in watershed models—especially in headwaters and river corridors. IDEAS-Watersheds builds on the foundational IDEAS-Classic effort and focuses on enhancing existing ESS Science Focus Area (SFA) projects, developing shared software tools and workflows, and training a new generation of interdisciplinary computational scientists. Ultimately, it aims to accelerate progress toward robust, interoperable modeling systems that improve scientific understanding and management of watershed-scale hydrobiogeochemical processes.
Models facilitate anticipatory governance. But models are not neutral - they conceal value-laden choices, such as in how model approaches are selected or model applications are framed. This implies that certain perspectives are favoured by the model. Since decisions based on such value-laden models have real-world consequences, it is essential to understand how these models obtained the legitimacy to serve as policy advisors. The model code itself reveals only the tip of the iceberg, because it does not show the negotiations before the model was established. Therefore, the LEGIT project aims to uncover the factors and processes through which especially water-management models acquire legitimacy to support decisions and to develop theory on establishing legitimacy. To achieve this goal, my team and I scrutinize three case studies and explore the social-political-institutional, the socio-technical, and the simulation perspective through respectively policy document analysis and interviews with decision-makers; model documentation analysis, interviews with modellers and ethnographic observations of modellers; and extensive uncertaintyand sensitivity analysis of model applications. This rich collection of data allows to formulate a preliminary theory of legitimacy of water-management models as decision support tools. LEGIT aims to explain how a model got authority, despite its concealed values. This paves the way to more transparency in the process of using models as decision support tools, thereby contributing to a stronger democracy.