BEE 4110/6110 Hydrologic Engineering in a Changing Climate

This course introduces methods in hydrologic engineering to assess and cope with climate variability and change. The course covers both statistical and physical approaches to analyzing and modeling hydrologic systems. Students learn the core concepts of traditional statistical analyses in hydrology, and also learn the limitations of these approaches in a changing climate. Students become familiar with physical modeling approaches to understand hydrologic response under future climate projections and their limitations. They learn to recognize the rapidly changing nature of the field of hydrologic engineering as it tries to adapt to the impacts of climate change. Course topics : extreme event frequency analysis; trend detection; water balance modeling; hydrologic simulations under projected climate change.

BEE 4310/6310 Multivariate Statistics for Environmental Applications

This class provides an introduction to relatively simple but powerful multivariate statistical techniques needed to analyze and model complex datasets frequently encountered in the environmental sciences. Emphasis is given to developing the mathematical foundation of these methods to foster a deeper understanding of the benefits and limitations of different approaches. The goal is to provide students in the applied environmental sciences with a toolbox of methods not taught in more introductory statistical courses, but also to ensure that students can use these methods in their own work without viewing them as a “black box”. Applications are presented from the geophysical, ecological, and other environmental sciences. Course topics : multivariate linear regression and regularization; multivariate normal distribution; principal component analysis; discrimination and classification; clustering.

BEE 4510 Sustainable Water Resources System Design

This course provides a capstone experience in the design of multi-objective water resources systems. The course is designed to teach the fundamentals of planning and management as practiced in water resources engineering, and to simulate interdisciplinary project teams common in industry and government. The course takes a “systems” focus, including the identification and quantification of objectives that reflect the interests of multiple stakeholder groups; the development of alternative designs for water system management; and the utilization of a computational modeling framework to evaluate how those alternative designs define tradeoffs between objectives. We adopt a focus on sustainable water systems, taking into account both ecological impacts of water management and the potential threats of climate change on system performance. Students will explore these aspects of system design in the context of Lake Ontario, one of the largest regulated lakes in the world that is currently undergoing a major revision to the design of its management plan.

These courses are supported by DataCamp, an intuitive learning platform for data science. The DataCamp platform helps students learn R, Python, and SQL through a combination of short expert videos and hands-on-the-keyboard exercises. These courses focus on R, and make use a a handful of DataCamp exercises. However, there are over 100+ courses on topics such as importing data, data visualization or machine learning that past students have found very useful.