IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v431y2020ics0304380020302350.html
   My bibliography  Save this article

The Canadian model for peatlands (CaMP): A peatland carbon model for national greenhouse gas reporting

Author

Listed:
  • Bona, Kelly Ann
  • Shaw, Cindy
  • Thompson, Dan K.
  • Hararuk, Oleksandra
  • Webster, Kara
  • Zhang, Gary
  • Voicu, Mihai
  • Kurz, Werner A.

Abstract

A model framework for national greenhouse gas emission and removal estimation for Canadian peatlands (CaMP v2.0) was developed and tested. It provides a module that can work alongside the upland forest Generic Carbon Budget Model (GCBM) developed to eventually replace the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) as the core model in Canada's National Forest Carbon Monitoring, Accounting and Reporting System. The CaMP (v2.0) provides a simple model foundation that can be applied nationally for 11 different peatland categories. It tracks the growth, turnover and decay in annual time steps of different vegetation components (foliage, branches, stems, and roots of trees, shrubs, sedges and mosses). It uses a Q10 relationship to model peat C pool decomposition as a function of mean annual temperature, and models methane flux response to deviations in annual water table depth. The CaMP takes a simple approach to modeling hydrology for large spatial scales by using the nationally-available Canadian Fire Weather Index Drought Code to predict long-term and annual water table depth. The CaMP (v2.0) provides the framework needed to model disturbances but only includes wildfire in this version. Model behavior and sensitivity were assessed, and evaluated against observed flux data. Results suggest that the CaMP (v2.0) provides an appropriate structure for large spatial- and temporal-scale estimation of emissions, owing to the model behaving as expected relative to shifts in environmental variables, and to reasonably small mean observed to modeled residuals. Methane was overestimated by the model on average by 6 g C ha−1y − 1 (n = 53 years of data across 11 peatland sites), and by 8 g C ha−1y − 1 when weighted by site location (n = 12 sites, ≥ 3 years of data per site). The model overestimated net ecosystem exchange (NEE) by 20 g C ha−1y − 1 (n = 36 years of data across 12 peatland sites), and by 2 g C ha−1y − 1 when weighted by site location (n = 11 sites, ≥ 3 years of data per site), and results demonstrate that inter-site variation is greater than temporal variation across NEE measures. Several aspects were identified as requiring further work to increase explained variation in finer-scale emission estimates. Recommendations include further expanding the existing peatland databases to re-calibrate peat decomposition rates and better parameterize NPP rates by region for certain vegetation layers and peatland types, as well as developing a national annual-scale soil temperature model that could serve to replace the air temperature (Q10) decay relationship currently used in the CaMP (v2.0). Data gaps that were identified include the need for annualized methane flux datasets with appropriate annual-scale meta-data. Future work is required to include permafrost dynamics, as well as additional natural, and anthropogenic disturbances.

Suggested Citation

  • Bona, Kelly Ann & Shaw, Cindy & Thompson, Dan K. & Hararuk, Oleksandra & Webster, Kara & Zhang, Gary & Voicu, Mihai & Kurz, Werner A., 2020. "The Canadian model for peatlands (CaMP): A peatland carbon model for national greenhouse gas reporting," Ecological Modelling, Elsevier, vol. 431(C).
  • Handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302350
    DOI: 10.1016/j.ecolmodel.2020.109164
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020302350
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109164?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hararuk, Oleksandra & Shaw, Cindy & Kurz, Werner A., 2017. "Constraining the organic matter decay parameters in the CBM-CFS3 using Canadian National Forest Inventory data and a Bayesian inversion technique," Ecological Modelling, Elsevier, vol. 364(C), pages 1-12.
    2. Kurz, W.A. & Dymond, C.C. & White, T.M. & Stinson, G. & Shaw, C.H. & Rampley, G.J. & Smyth, C. & Simpson, B.N. & Neilson, E.T. & Trofymow, J.A. & Metsaranta, J. & Apps, M.J., 2009. "CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards," Ecological Modelling, Elsevier, vol. 220(4), pages 480-504.
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joan P. Casas-Ruiz & Pascal Bodmer & Kelly Ann Bona & David Butman & Mathilde Couturier & Erik J. S. Emilson & Kerri Finlay & Hélène Genet & Daniel Hayes & Jan Karlsson & David Paré & Changhui Peng & , 2023. "Integrating terrestrial and aquatic ecosystems to constrain estimates of land-atmosphere carbon exchange," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. JANSSENS, Jochen & DE CORTE, Annelies & SÖRENSEN, Kenneth, 2016. "Water distribution network design optimisation with respect to reliability," Working Papers 2016007, University of Antwerp, Faculty of Business and Economics.
    2. Raymond Hernandez & Elizabeth A. Pyatak & Cheryl L. P. Vigen & Haomiao Jin & Stefan Schneider & Donna Spruijt-Metz & Shawn C. Roll, 2021. "Understanding Worker Well-Being Relative to High-Workload and Recovery Activities across a Whole Day: Pilot Testing an Ecological Momentary Assessment Technique," IJERPH, MDPI, vol. 18(19), pages 1-17, October.
    3. Christopher Hassall & Michael Nisbet & Evan Norcliffe & He Wang, 2024. "The Potential Health Benefits of Urban Tree Planting Suggested through Immersive Environments," Land, MDPI, vol. 13(3), pages 1-12, February.
    4. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. F J Heather & D Z Childs & A M Darnaude & J L Blanchard, 2018. "Using an integral projection model to assess the effect of temperature on the growth of gilthead seabream Sparus aurata," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    6. Valentina Krenz & Arjen Alink & Tobias Sommer & Benno Roozendaal & Lars Schwabe, 2023. "Time-dependent memory transformation in hippocampus and neocortex is semantic in nature," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Morán-Ordóñez, Alejandra & Ameztegui, Aitor & De Cáceres, Miquel & de-Miguel, Sergio & Lefèvre, François & Brotons, Lluís & Coll, Lluís, 2020. "Future trade-offs and synergies among ecosystem services in Mediterranean forests under global change scenarios," Ecosystem Services, Elsevier, vol. 45(C).
    8. Jack McDonnell & Thomas McKenna & Kathryn A. Yurkonis & Deirdre Hennessy & Rafael Andrade Moral & Caroline Brophy, 2023. "A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 1-19, March.
    9. Ana Pinto & Tong Yin & Marion Reichenbach & Raghavendra Bhatta & Pradeep Kumar Malik & Eva Schlecht & Sven König, 2020. "Enteric Methane Emissions of Dairy Cattle Considering Breed Composition, Pasture Management, Housing Conditions and Feeding Characteristics along a Rural-Urban Gradient in a Rising Megacity," Agriculture, MDPI, vol. 10(12), pages 1-18, December.
    10. Damian M. Herz & Manuel Bange & Gabriel Gonzalez-Escamilla & Miriam Auer & Keyoumars Ashkan & Petra Fischer & Huiling Tan & Rafal Bogacz & Muthuraman Muthuraman & Sergiu Groppa & Peter Brown, 2022. "Dynamic control of decision and movement speed in the human basal ganglia," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Kathrin Stenchly & Marc Victor Hansen & Katharina Stein & Andreas Buerkert & Wilhelm Loewenstein, 2018. "Income Vulnerability of West African Farming Households to Losses in Pollination Services: A Case Study from Ouagadougou, Burkina Faso," Sustainability, MDPI, vol. 10(11), pages 1-12, November.
    12. Dongyan Liu & Chongran Zhou & John K. Keesing & Oscar Serrano & Axel Werner & Yin Fang & Yingjun Chen & Pere Masque & Janine Kinloch & Aleksey Sadekov & Yan Du, 2022. "Wildfires enhance phytoplankton production in tropical oceans," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    13. Zhaogeng Yang & Yanhui Li & Peijin Hu & Jun Ma & Yi Song, 2020. "Prevalence of Anemia and its Associated Factors among Chinese 9-, 12-, and 14-Year-Old Children: Results from 2014 Chinese National Survey on Students Constitution and Health," IJERPH, MDPI, vol. 17(5), pages 1-10, February.
    14. Marco Lopez-Cruz & Fernando M. Aguate & Jacob D. Washburn & Natalia Leon & Shawn M. Kaeppler & Dayane Cristina Lima & Ruijuan Tan & Addie Thompson & Laurence Willard Bretonne & Gustavo los Campos, 2023. "Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Baumann, Elias & Kern, Jana & Lessmann, Stefan, 2019. "Usage Continuance in Software-as-a-Service," IRTG 1792 Discussion Papers 2019-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Metsaranta, J.M. & Kurz, W.A., 2012. "Inter-annual variability of ecosystem production in boreal jack pine forests (1975–2004) estimated from tree-ring data using CBM-CFS3," Ecological Modelling, Elsevier, vol. 224(1), pages 111-123.
    17. Alexandra M. Cheney & Stephanann M. Costello & Nicholas V. Pinkham & Annie Waldum & Susan C. Broadaway & Maria Cotrina-Vidal & Marc Mergy & Brian Tripet & Douglas J. Kominsky & Heather M. Grifka-Walk , 2023. "Gut microbiome dysbiosis drives metabolic dysfunction in Familial dysautonomia," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    18. repec:cup:judgdm:v:16:y:2021:i:1:p:201-237 is not listed on IDEAS
    19. C. Gabriel Hidalgo Pizango & Eurídice N. Honorio Coronado & Jhon del Águila-Pasquel & Gerardo Flores Llampazo & Johan de Jong & César J. Córdova Oroche & José M. Reyna Huaymacari & Steve J. Carver & D, 2022. "Sustainable palm fruit harvesting as a pathway to conserve Amazon peatland forests," Nature Sustainability, Nature, vol. 5(6), pages 479-487, June.
    20. Myrto Pantazi & Olivier Klein & Mikhail Kissine, 2020. "Is justice blind or myopic? An examination of the effects of meta-cognitive myopia and truth bias on mock jurors and judges," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(2), pages 214-229, March.
    21. Loreto A Correa & Cecilia León & Juan Ramírez-Estrada & Álvaro Ly-Prieto & Sebastián Abades & Loren D Hayes & Mauricio Soto-Gamboa & Luis A Ebensperger, 2021. "One for all and all for one: phenotype assortment and reproductive success in masculinized females," Behavioral Ecology, International Society for Behavioral Ecology, vol. 32(6), pages 1266-1275.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.