Why models underestimate West African tropical forest primary productivity

Huanyuan Zhang-Zheng (), Xiongjie Deng, Jesús Aguirre-Gutiérrez, Benjamin D. Stocker, Eleanor Thomson, Ruijie Ding, Stephen Adu-Bredu, Akwasi Duah-Gyamfi, Agne Gvozdevaite, Sam Moore, Imma Oliveras Menor, I. Colin Prentice () and Yadvinder Malhi ()
Additional contact information
Huanyuan Zhang-Zheng: University of Oxford
Xiongjie Deng: University of Oxford
Jesús Aguirre-Gutiérrez: University of Oxford
Benjamin D. Stocker: University of Bern
Eleanor Thomson: University of Oxford
Ruijie Ding: Silwood Park Campus
Stephen Adu-Bredu: Council for Scientific and Industrial Research
Akwasi Duah-Gyamfi: Council for Scientific and Industrial Research
Agne Gvozdevaite: University of Oxford
Sam Moore: University of Oxford
Imma Oliveras Menor: University of Oxford
I. Colin Prentice: Silwood Park Campus
Yadvinder Malhi: University of Oxford

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Tropical forests dominate terrestrial photosynthesis, yet there are major contradictions in our understanding due to a lack of field studies, especially outside the tropical Americas. A recent field study indicated that West African forests have among the highest forests gross primary productivity (GPP) yet observed, contradicting models that rank them lower than Amazonian forests. Here, we show possible reasons for this data-model mismatch. We found that biometric GPP measurements are on average 56.3% higher than multiple global GPP products at the study sites. The underestimation of GPP largely disappears when a standard photosynthesis model is informed by local field-measured values of (a) fractional absorbed photosynthetic radiation (fAPAR), and (b) photosynthetic traits. Remote sensing products systematically underestimate fAPAR (33.9% on average at study sites) due to cloud contamination issues. The study highlights the potential widespread underestimation of tropical forests GPP and carbon cycling and hints at the ways forward for model and input data improvement.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-53949-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53949-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-53949-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().