# Calculating forest carbon stocks This is a practical class I run with

## Transcription

Calculating forest carbon stocks This is a practical class I run with

Calculating forest carbon stocks This is a practical class I run with my final year ‘Climate Change and Carbon Cycling’ class to reinforce that week’s lecture on global carbon stocks in the terrestrial biosphere. I’ve provided some annotations in red to give you a worked example and hopefully guide you through the process. You may wish to simplify some of the steps and/or modify to suit to your own needs. This is only one example of how simple measurements can be related to the bigger carbon picture but hopefully this will give some ideas that you can use in your teaching. I would be interested in hearing back about how you have used the ideas and if it worked well in class! Gareth Clay, August 2016 [email protected] Twitter: @garethdclay Aims of the exercise 1) To become familiar with the concept of allometry 2) To calculate the carbon stocks of a tropical forest based on a real dataset of measured trees Measuring carbon stocks There are different ways in which we can estimate the amount of carbon stored in forests. We can take a ‘top-down’ approach where we look at a landscape scale often through remote sensing tools e.g. satellite data. We can also use ‘bottom-up’ or inventory methods where we might look at individual plots or land uses. Here the individual trees or stands of trees are directly measured to estimate their size and biomass. Often satellite data is compared to these field measurements in order to calibrate their estimates. In this practical we are going to look at some of the steps in calculating the carbon stocks for a tropical rainforest using an inventory approach. What is (tree) allometry? Allometry is the study of the relationship between size and shape of an individual e.g. animals, organs. Imagine a cube of 1cm length (L) sides, the surface area (SA) is 6cm 2. At L=2, SA = 24; L=3, SA = 54 etc. We can say that SA ∝ L2. Equally we can do the same with volume and length (Volume ∝ L3) In the context of land use carbon stocks, tree allometry establishes quantitative relationships between easy to measure parameters and difficult to measure parameters e.g. between tree diameter (easy to measure; Figure 1) and total biomass (harder to measure) Figure 1. Calliper measuring tree diameter (Elsner, G. 2006. Wikimedia Commons, CC BY-SA) Allometric Coefficients Above we used the symbol “∝” to mean “is proportional to”. The trouble is this relationship between the two variables is not an equation we can solve – we need to know something known as the allometric coefficient. To obtain the allometric coefficient, we could express the first variable as a function of the second, using the general power equation: α (Equation 1) y = βx In this equation, α (alpha) is the allometric coefficient relating the x-variable to the y-variable. The coefficient β (beta) is a constant. In the case of the cubes earlier on, SA = 6L2 (hint: try working this out yourself to confirm this!). Figure 2 shows some hypothetical relationships between surface area and volume of some fictitious animals. The equations describe the best-fit lines in the form of Equation 1. In the case of A, β = 0.9842 and α = 0.6680; likewise in B, β = 1.0354 and α = 1.4945 Figure 2. Hypothetical curves between Surface Area and Volume (after Goldman et al., 1990) Task Calculate the carbon stock of the Kakamega rainforest, Kenya, using allometric relationships and literature values Calculate allometric relationships 1) Open the Excel file “Tropical forest.xls” 2) Open the Worksheet titled “Data for allometry”. Here you find data for diameter at breast height (dbh) and above-ground biomass for nearly 170 trees from moist tropical forests around the world (Brown, 1997) 3) Using this data plot above-ground biomass against dbh. 4) Fit a power law trendline and r2 a. What is the equation? And what is the r2 value? 50000 30000 y = 0.1178x2.5324 R² = 0.9682 20000 Series1 10000 Power (Series1) Biomass 40000 0 0 50 100 150 200 DBH 5) You now have an allometric relationship between biomass and dbh for moist tropical rainforests in the form of y = βxα . i.e. y=0.1178x2.5324 Calculate biomass for an unknown dataset. The worked answers are given in the associated spreadsheet. 6) Switch to “Kakamega data” worksheet. This sheet lists tree girth data from 36 trees collected by Dr Gareth Clay and colleagues during fieldwork in January 2013 in a tropical rainforest in western Kenya – Kakamega. (For more information on the Kakamega forest see Glenday (2006)) 7) Firstly, you need to convert tree girth (circumference) into dbh values. Assume that the tree trunk is circular. Circumference =π* diameter Divide circumference by pi. 8) Calculate the biomass in kg for each of the trees based on the general relationship you found above. [Hint: In Excel, the symbol ^raises a number to a power] 9) The data comes from 3 different regions in the forest: A, B and C. Calculate the average tree biomass in each of the 3 regions. Calculate the carbon stock of the Kakamega forest 10) Calculate amount of carbon in each of the trees. Assume that 50% of the biomass is carbon 11) If we assume there is an average of 50 trees per hectare (ha) in the forest, calculate the average carbon density (kgC/ha) for each of the 3 regions 12) Convert this to tonnesC/ha 13) There is approximately 3,000ha in region A, 14,000ha in region B and 600 ha in region C. Calculate the total carbon stock in tonnesC for each of these sections and also calculate the total carbon stock of the above-ground biomass in the Kakamega forest. Region Average Tree Biomass (kg) A 8581 B 9163 C 1814.4 Average C content per tree (kgC) Carbon density (tonnesC per ha) Total Carbon (tonnesC) 4290.5 214.5 643,575.9 4581.5 229.1 3,207,034.2 907.2 45.4 27,215.5 Total 3,877,825.6 Questions 1. Which of the regions has the greatest amount of carbon? B 2. What can we say about the data quality? [Think about sample size for each region and how standard deviations are affected]. Small sample size for section A (only two data point) lead to high StDev 3. Comment on the assumptions used throughout the calculations Assumed the general allometric relationship for moist tropical forests holds true for this forest in Kenya. Assume 50% C is approx OK, but does vary from 40-55% Trees per ha is probably low and is based on an estimate from Nigeria Assumed trees are circular 4. Figures in Glenday (2006) suggest Region A should be ~280±77 tonnes C/ha; Region B ~330±63 tonnes C/ha and Region C 250±78 tonnes C/ha. a. Are your numbers within the reported ranges? b. How do your values differ from these values? c. If the numbers are substantially different, what do you think could be causing the difference? Within range on A, but out of range on B and C (below) Assumptions used are likely leading to differences. In scientific writing it is best practice to state your assumptions. Useful information Power law equation: y = βxα Carbon concentration = 50% Average trees per hectare = 50 Circumference = π*dbh Area section A = 3,000ha Area section B = 14,000 ha Area section C = 600ha Further reading GlobAllomeTree is an international web platform to share data for assessing volume, biomass and carbon stock of trees and forests (http://www.globallometree.org/). Here you can sign-up and download a range of allometric relationships for different tree species and regions. I’ve not had a chance to explore this database yet but looks really valuable – let me know if you find it useful! References Brown, S. 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer (FAO Forestry Paper – 134) FAO, Rome. Available at: http://www.fao.org/docrep/W4095E/W4095E00.htm Glenday, J., 2006. Carbon storage and emissions offset potential in an East African tropical rainforest. Forest Ecology and Management, 235(1–3): 72-83. Goldman, C. A., Snell, R.R., Thomason, J.J. and Brown, D.B. 1990. Principles of Allometry. Pages 4372, in Tested studies for laboratory teaching. Volume 11. (C. A. Goldman, Editor). Proceedings of the Eleventh Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 195 pages. Available at: http://www.ableweb.org/volumes/vol-11/4-goldman.pdf