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Guided learning journeys that build knowledge step by step.
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This graduate-level learning path equips students with the knowledge and skills to design and conduct rigorous research in environmental modeling. It covers the full spectrum from foundational modeling concepts and statistical methods to advanced model development, experiment design, data analysis, and research ethics. The path emphasizes practical application through a structured research project, ensuring learners can integrate theory with practice.
This advanced graduate-level path equips learners with the skills to integrate satellite-based Earth observation (EO) data into environmental models. It covers the fundamentals of remote sensing, environmental modeling, data assimilation, validation, and synergy between EO and models, culminating in a capstone project.
This advanced graduate-level learning path equips students in economics and environmental science with the knowledge to model socio-economic dimensions of environmental issues. It covers economic modeling foundations, integrated assessment models (IAMs), scenario analysis, and policy design, emphasizing the interplay between human systems and the environment.
This advanced graduate-level path equips meteorology and hydrology students with the knowledge and skills to develop real-time environmental forecasts. It covers data streams, data assimilation, operational modeling, and forecasting, integrating both modeling and programming competencies.
This graduate-level learning path equips sustainability students with the knowledge and skills to model coupled human-environment systems. It covers foundational systems thinking, key social science theories, environmental modeling techniques, and their integration for decision-making in land and resource use.
This learning path guides graduate students in modeling and computer science through the foundational concepts, enabling technologies, and applications of digital twins for environmental systems. Starting with core modeling and data science principles, it progresses through simulation, IoT, and data integration, culminating in a comprehensive understanding of environmental digital twins and their practical implementation.
This learning path equips environmental model developers and users with the skills to create comprehensive documentation and reports for their models. It covers documentation standards, metadata, and reproducibility practices, ensuring models are transparent, credible, and usable by others.
This learning path equips risk assessors with the knowledge and skills to apply environmental models in risk assessment. It covers the core concepts of risk assessment, exposure and effect modeling, uncertainty analysis, and decision-making, providing a structured approach to integrating modeling into professional practice.
This advanced professional learning path equips watershed managers with the knowledge and skills to model surface water and watershed systems using the Soil and Water Assessment Tool (SWAT) and the Hydrological Simulation Program – FORTRAN (HSPF). Starting with fundamental hydrological processes and data requirements, the path progresses through model setup, calibration, validation, and application to real-world watershed management scenarios, emphasizing practical, career-oriented skills.
This learning path equips hydrogeologists with the knowledge and skills to develop, calibrate, and apply groundwater models using MODFLOW and related tools. It covers essential hydrogeology concepts, numerical methods, model design, calibration, and practical applications.