Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability

Jinyan Yang; Belinda E. Medlyn; Craig V.M. Barton; Amber C. Churchill; Martin G. De Kauwe; Mingkai Jiang; Arjunan Krishnananthaselvan; David T. Tissue; Elise Pendall; Sally A. Power

doi:10.1016/j.agrformet.2022.109252

Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability

Jinyan Yang^*
, Belinda E. Medlyn
, Craig V.M. Barton
, Amber C. Churchill
, Martin G. De Kauwe
, Mingkai Jiang
, Arjunan Krishnananthaselvan
, David T. Tissue
, Elise Pendall
, Sally A. Power

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

13 Citations (Scopus)

24 Downloads (Pure)

Abstract

Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.

Original language	English
Article number	109252
Number of pages	13
Journal	Agricultural and Forest Meteorology
Volume	328
Early online date	23 Nov 2022
DOIs	https://doi.org/10.1016/j.agrformet.2022.109252
Publication status	Published - 15 Jan 2023

Bibliographical note

Funding Information:
We thank the reviewers and editors whose suggestions advanced the paper. We would like to send our special gratitude to Rodolfo Nobrega for his insightful and comprehensive review. We also thank Burhan Amiji, Uffe Nielsen and Jeff Chieppa for help in the setup and maintenance of the cameras. This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government, NCRIS-enabled project. The PACE facility was established with funding from Meat and Livestock Australia's Donor Company (P.PSH. 0793), Dairy Australia (C100002357) and Western Sydney University.

Funding Information:
J.Y. and B.M. conceived and designed the research; J.Y. performed model fitting and statistical analysis with inputs from B.M. and M. J.; C.B, A.C. A.K. collected the pictures from the cameras; J.Y., C.B. A.C. extracted greenness from pictures; J.Y. wrote the paper with input from all authors. This research is supported by funding from B.M., D.T., E.P., S.P.

Funding Information:
This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government

Publisher Copyright:
© 2022 Elsevier B.V.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Climate change
Foliage phenology model
Grassland
Soil moisture
Traits

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10.1016/j.agrformet.2022.109252

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Yang, J., Medlyn, B. E., Barton, C. V. M., Churchill, A. C., De Kauwe, M. G., Jiang, M., Krishnananthaselvan, A., Tissue, D. T., Pendall, E., & Power, S. A. (2023). Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability. Agricultural and Forest Meteorology, 328, Article 109252. https://doi.org/10.1016/j.agrformet.2022.109252

@article{658e9545dbdd46208ec3a6d898a30911,

title = "Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability",

abstract = "Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7\%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species{\textquoteright} responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.",

keywords = "Climate change, Foliage phenology model, Grassland, Soil moisture, Traits",

author = "Jinyan Yang and Medlyn, \{Belinda E.\} and Barton, \{Craig V.M.\} and Churchill, \{Amber C.\} and \{De Kauwe\}, \{Martin G.\} and Mingkai Jiang and Arjunan Krishnananthaselvan and Tissue, \{David T.\} and Elise Pendall and Power, \{Sally A.\}",

note = "Funding Information: We thank the reviewers and editors whose suggestions advanced the paper. We would like to send our special gratitude to Rodolfo Nobrega for his insightful and comprehensive review. We also thank Burhan Amiji, Uffe Nielsen and Jeff Chieppa for help in the setup and maintenance of the cameras. This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government, NCRIS-enabled project. The PACE facility was established with funding from Meat and Livestock Australia's Donor Company (P.PSH. 0793), Dairy Australia (C100002357) and Western Sydney University. Funding Information: J.Y. and B.M. conceived and designed the research; J.Y. performed model fitting and statistical analysis with inputs from B.M. and M. J.; C.B, A.C. A.K. collected the pictures from the cameras; J.Y., C.B. A.C. extracted greenness from pictures; J.Y. wrote the paper with input from all authors. This research is supported by funding from B.M., D.T., E.P., S.P. Funding Information: This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2023",

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doi = "10.1016/j.agrformet.2022.109252",

language = "English",

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journal = "Agricultural and Forest Meteorology",

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T2 - Modelling grassland foliage phenology responses to soil moisture availability

AU - Yang, Jinyan

AU - Medlyn, Belinda E.

AU - Barton, Craig V.M.

AU - Churchill, Amber C.

AU - De Kauwe, Martin G.

AU - Jiang, Mingkai

AU - Krishnananthaselvan, Arjunan

AU - Tissue, David T.

AU - Pendall, Elise

AU - Power, Sally A.

N1 - Funding Information: We thank the reviewers and editors whose suggestions advanced the paper. We would like to send our special gratitude to Rodolfo Nobrega for his insightful and comprehensive review. We also thank Burhan Amiji, Uffe Nielsen and Jeff Chieppa for help in the setup and maintenance of the cameras. This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government, NCRIS-enabled project. The PACE facility was established with funding from Meat and Livestock Australia's Donor Company (P.PSH. 0793), Dairy Australia (C100002357) and Western Sydney University. Funding Information: J.Y. and B.M. conceived and designed the research; J.Y. performed model fitting and statistical analysis with inputs from B.M. and M. J.; C.B, A.C. A.K. collected the pictures from the cameras; J.Y., C.B. A.C. extracted greenness from pictures; J.Y. wrote the paper with input from all authors. This research is supported by funding from B.M., D.T., E.P., S.P. Funding Information: This research was supported by Australian Research Council Discovery Project DP180102203 and used research infrastructure provided to OzFlux and Ecosystem Processes by Terrestrial Ecosystem Research Network (TERN), an Australian Government Publisher Copyright: © 2022 Elsevier B.V.

PY - 2023/1/15

Y1 - 2023/1/15

N2 - Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.

AB - Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.

KW - Climate change

KW - Foliage phenology model

KW - Grassland

KW - Soil moisture

KW - Traits

UR - https://www.scopus.com/pages/publications/85149840841

U2 - 10.1016/j.agrformet.2022.109252

DO - 10.1016/j.agrformet.2022.109252

M3 - Article (Academic Journal)

AN - SCOPUS:85149840841

SN - 0168-1923

VL - 328

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

M1 - 109252

ER -

Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability

Abstract

Bibliographical note

UN SDGs

Keywords

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