This course explores the science of design and Design Thinking, including vetted ways of approaching and defining design problems, assessing stakeholder needs, ideation and concept generation, and prototyping and experimental design. Gives students experience conducting work in teams of engineering designers to solve complex, real-world engineering problems. Introduces methods to assess problem-solving skills and assumptions, and employ computational thinking. Reinforces core mathematics and science knowledge, while emphasizing advanced professional and communication skills in an engineering design team setting.

The course explores modern toolkits for analyzing variability and uncertainty in the hydrologic cycle. We cover probability models, parameter estimation, ensemble forecasting and verification, time-series analysis, multivariate distributions, and introductory machine learning. The course frames each method through hydrologic applications in rainfall, streamflow, soil moisture, reactive transport, and land–atmosphere interactions from catchment to global scales.

The course develops a rigorous, mechanics-based framework to understand, model, and predict how contaminants move and transform in groundwater systems. We begin with the classical advection–dispersion equation (ADE) and build upward to include sorption/retardation, first-order reactions, and mixing controls on reactive processes. The course addresses when and why the ADE fails and equips you with modern models to interpret heavy-tailed breakthrough curves and late-time tailing commonly observed in aquifers. Applications emphasize parameter estimation from field/laboratory data, steady-state and transient plume behavior, and implications for monitoring and remediation design.