About this course

Sampling and analysis (physical, chemical and biological) are essential for understanding freshwater ecosystems and for managing freshwater resources in a knowledge-based manner. The course includes lectures, field and laboratory exercises, where processes in different lake types are coupled. Students will carry out field work, conduct laboratory analyses and report data. In the final report, the results will be placed in a limnological context, and a critical assessment of the data material will be made.

Learning outcome

After completing the course, students should have the following learning outcomes:

Knowledge:

• Have an overview of relevant sample sites and important field methods in lake surveys.

• Be able to account for important processes in lakes based on limnological data and information about the catchment area.

Skills:

• Be able to carry out important physical, chemical and biological analyses of freshwater. • Be able to carry out representative sampling and assess measurement uncertainty in limnological field surveys. General competence:

• Understand that freshwater is a vulnerable resource, and that knowledge-based management is necessary to maintain good water quality and biodiversity in freshwater systems.

  • Learning activities

    Lectures on basic field and laboratory methodology in freshwater. Fieldwork, where students gain experience with various sampling methods, in situ measurements used in limnological surveys, water flow measurements, as well as knowledge of uncertainty related to water sampling. Field work in different types of lakes with different land use in the catchment. Laboratory analyses of collected sample material (water and sediment samples). Final reporting where field observations and laboratory measurements are presented and related to limnological processes.

  • Teaching support

    The course is supported by skilled persons within limnology and hydrology, and laboratory technicians during the whole period.

  • Syllabus

    Handed out in lectures.

  • Prerequisites

    VANN210

  • Recommended prerequisites

    VANN200, VANN220, KJM240.

  • Assessment method

    The students are assessed during the course through field and laboratory work. Furthermore, the students are assessed in a group report where they summarise their results.

    Samlet vurdering: The evaluation of the course is based on the report (50 %) and the work in the field and laboratory (50 %).

    The student has to pass both examination parts.

    Students who have previously passed one or more components of the overall assessment in the course do not need to complete these components again when retaking the course.

    Grading system: Pass / Fail.



    Portfolio Karakterregel: Passed / Not Passed

  • About use of AI

    K3 - Full use of AI.

    The use of AI is permitted, but it must comply with the Guidelines for Use of Artificial Intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    External examiner evaluates the students' performance in the course (final student report).

  • Mandatory activity

    Both participation in field and laboratory activities and writing of reports must be passed for the students to pass the course.

  • Notes

    Students who want to take the course must apply for admission in Studentweb no later than April 1st. After that, the places in the course will be distributed.

    If there are few students, the course may either be postponed by one year or offered with an alternative teaching arrangement.

  • Teaching hours

    Three weeks duration. Lectures: 12 hours. Field work: 3-4 days. Laboratory work: 5-6 days. Report writing: 5-7 days.

  • Preferential right

    B-MILJØ og M-MILJØ

  • Admission requirements

    VANN210 eller tilsvarende