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Guided learning journeys that build knowledge step by step.
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This learning path equips graduate students in environmental science and geography with the knowledge and skills to model land-use and land-cover change (LUCC). It covers foundational concepts, modeling paradigms such as cellular automata and agent-based models, and practical applications in GIS. Learners will understand drivers, simulate future scenarios, and evaluate model outputs.
This graduate-level learning path equips climate science students with the skills to model climate change impacts and vulnerability. It covers climate model outputs, downscaling, sectoral impact models, and vulnerability index construction, with a focus on uncertainty and adaptation planning.
This learning path equips graduate students in water management with the knowledge and skills to model water resources systems, focusing on reservoir operations, water allocation, and conjunctive use. It covers essential hydrological modeling concepts, systems analysis techniques, and practical modeling tools, culminating in the ability to construct and evaluate integrated water resources models.
This graduate-level path equips environmental science students with the knowledge and skills to develop integrated models that couple natural and human subsystems. It covers systems thinking, core environmental modeling, programming, and multi-model coupling frameworks, culminating in a project-based application.
This learning path equips computational science graduate students with the knowledge and skills to apply high-performance computing (HPC) techniques to environmental modeling. It covers parallel computing fundamentals, HPC architectures (clusters and cloud), optimization strategies, and the integration of these with environmental models, culminating in a capstone project.
This advanced graduate-level path equips students with the knowledge to understand, configure, and apply chemical transport models (CTMs) such as CMAQ and CAMx. It covers the underlying atmospheric chemistry, transport processes, emissions, and deposition, culminating in hands-on model application and evaluation.
This path equips graduate students in geochemistry with the knowledge and skills to apply geochemical models to environmental systems. It covers essential thermodynamics and kinetics, speciation and reaction path modeling, transport processes, and hands-on use of PHREEQC and reactive transport codes.
This advanced graduate learning path systematically develops the fluid dynamics knowledge required to understand and model environmental flows. It starts with the governing equations, progresses through turbulence and computational methods, and culminates in applications to atmospheric and oceanic circulation.
This graduate-level learning path equips students with the skills to quantify and communicate uncertainty in environmental model predictions. It covers probability and statistics foundations, error propagation, Bayesian methods, and ensemble techniques, with practical applications in environmental modeling.
This path guides environmental modeling students through the essential knowledge and skills needed to effectively visualize and communicate modeling results. It covers data handling, visualization design, mapping, interactive dashboards, and communication strategies, with a focus on practical application in environmental contexts.