Stochastic Hydrology

Catalog description

Hydrological processes like precipitation, streamflow, and groundwater flow are highly variable over time and across locations. Quantifying the uncertainty in hydrological models and simulating future conditions is critical for informing the development and management of civil infrastructure systems. This course introduces students to statistical methods used in hydrology for data analysis, risk and uncertainty analysis, and simulation. Topics include: flood and drought frequency, time series analysis, rainfall-runoff modeling, and lake water quality. Methods include: applied probability theory, extreme value theory, parameter estimation, regression, time series analysis, transfer functions, Bayesian methods. Prerequisites: CEE 266A or equivalent and a class in probability and/or statistics.