A survey of mountain and cold climate hydrology, with an emphasis on catchment scale processes, datalogger programming, instrumentation and analytical techniques.
GEOG 461 covers two of the cornerstone approaches to hydrology: modelling and empirical observations. This course is structured around two linked projects: (1) collection and analysis of hydrological data from a 3-day field trip to a mountain catchment in the Southern Alps, and (2) a subsequent modelling exercise based on the same catchment.
|Paper title||Mountain Hydrology|
|Teaching period||Second Semester|
|Domestic Tuition Fees (NZD)||$1,282.09|
|International Tuition Fees (NZD)||$5,357.07|
- No prior experience in undergraduate hydrology is required for this paper.
- More information link
- View further information about GEOG 461
- Teaching staff
- Course Coordinators: Dr Sarah Mager and Dr Daniel Kingston
- Paper Structure
- There are two modules in this paper, the first focusses on natural variations in water quality variables in mountain catchments, of which the core component is a report based on a three-day field trip. The second module is based on hydrological modelling, and is centred around the development and use of a hydrological model to assess the impacts of climate change on river flow in a mountain catchment.
- Teaching Arrangements
- One 2-hour session each week, but most learning will be self-directed each week; and one field trip.
- No textbook is required, but wide reading of resources is expected and guided by a reading list.
- Graduate Attributes Emphasised
- Critical thinking, Information literacy, Research, Teamwork.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students will be introduced to the core concepts of hydrological modelling and parametisation of models, as well as appropriate techniques for collecting, storing, and analysing water samples. Such activities will require effective time management and team work to complete the field components, as well as navigating a wide range of academic resources and critically interrogating the issues of uncertainty in scientific data