Exploring Eddy Covariance Method
Eddy Covariance
The most defensible method to measure the exchange of important measurements such as heat, water, CO2, methane and other trace gasses across time
Involves high resolution measurement with complex data processing
Course Objective:
Primary goal: To bridge the gap for students between raw data and insights that can be achieved from eddy covariance data
Translated to these specific goals:
Explore and observe patterns from raw eddy covariance data and implications towards net ecosystem exchange (NEE)
Visualize and understand differences between seasons (summer vs winter) and time of day (night vs day)
Discover important meteorological and phenological properties that contribute towards overall ecosystem NEE
Use eddy covariance data to estimate an annual carbon budget
Lab: Exploring Eddy Covariance Method
BIO16: Ecology
Fall 2020
Project Overview
Flux measurement allows ecologists to explore the exchange of important measurements such as heat, water, CO2, methane and other trace gasses across time. The Eddy Covariance (EC) method is one of the most direct and defensible ways of measuring such fluxes, and is an important tool towards understanding properties of ecosystems. EC often involves a network of flux towers that uses a variety of sensors to make measurements. The processes associated with EC (both tower infrastructure and analysis of signals) are complicated.
Figure 1. Schematic of flux tower measurements as part of the eddy covariance method (Source: https://www.neonscience.org/data-collection/flux-tower-measurements)
The purpose of this lab therefore is to introduce students to the basics of EC, explore raw measurement data to observe visible patterns across seasons and time of day, as well as being able to discover meaningful relationships between variables important to the ecosystem.
Learning Objectives
By completing this lab, you will:
Explore and observe patterns from raw eddy covariance data and implications towards net ecosystem exchange (NEE)
Visualize and understand differences between seasons (summer vs winter) and time of day (night vs day)
Discover important meteorological and phenological properties that contribute towards overall ecosystem NEE
Use eddy covariance data to estimate an annual carbon budget
Instructions
We will be using an interactive data exploration tool developed by the DIFUSE team at Dartmouth ( https://difuse-dartmouth.shinyapps.io/DIFUSEEddyCovariance//). For each section of the problem set below, explore the corresponding graph on the website, answer questions and take screenshots to illustrate your responses where relevant.
What is Eddy Covariance
Read the section on flux tower measurements on the NEON (National Ecological Observatory Network) website and answer the following questions
Review Ecosystem Carbon Balance
What is raw EC data and what can it tell you?
In this section, we have raw eddy covariance data from a flux tower at the Silas Little Experimental Forest. We have data sets from both summer and winter, with 10Hz measurements of air temperature, CO2, water vapor and 3D-wind speed. Each data set available on the website will be one day (24 hours) of measurements, averaged within each second. Use the tool to explore different aspects of the raw data and answer the following questions:
What do you expect will happen to the CO2 when wind vertical wind speed is positive at night? At day? Summer vs Winter?
Ecological properties associated with net ecosystem exchange (NEE) inferred from EC data
In this section, we explore eddy covariance data from the same source. This data has passed through the data pipeline, resulting in estimated flux for 30-minute intervals. This data is for the entire year of 2018 and contains more variables than the raw data above. Use the tool to explore the processed data and answer the following questions.
What do expect for these relationships and why? (NEE v T, NEE v PPFD, NEE v humidity)
Draw them out.
Then create them in the shiny app: https://difuse-dartmouth.shinyapps.io/DIFUSEEddyCovariance
For more information email: difuse-pi-group@dartmouth.edu