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Organized Oral Session

Session: Understanding drivers of grassland phenology under a changing climate

OOS 18-3 - Green-up and brown-down: Modelling grassland foliage phenology responses to soil water availability in eastern Australia

Tuesday, August 16, 2022

2:00 PM – 2:15 PM EDT

Location: 520D

Presenting Author(s)

JY

Jinyan Yang

Hawkesbury Institute for the Environment, Western Sydney University
Beaumont Hills, New South Wales, Australia

Slides (PDF)

Co-author(s)

BM

Belinda Medlyn

Prof
Western Sydney University
CB

Craig Barton

Western Sydney University
Amber C. Churchill

Postdoctoral Research Associate
Cedar Creek Ecosystem Science Reserve, Department of Ecology, Evolution and Behavior, University of Minnesota
SAINT PAUL, Minnesota, United States
MD

Martin De Kauwe

University of bristol
Mj

Mingkai jiang

Zhejiang University
AK

Arjunan Krishnananthaselvan

University of Vavuniya
DT

David Tissue

Western Sydney University
EP

Elise Pendall

Western Sydney University
SP

Sally Power

Western Sydney University

Background/Question/Methods
Climate change is expected to bring greater variability in the amount and frequency of rainfall in future. Grassland productivity is strongly affected by rainfall variability, mainly via the timing of foliage green-up (i.e., when new growth exceeds senescence) and brown-down (i.e.,when senescence exceeds new growth). New ecosystem monitoring techniques provide opportunities to examine the processes governing grassland phenology, thereby facilitating the development of predictive ecosystem models. Here, we used fixed cameras (phenocams) to monitor grassland “greenness”, as a proxy for foliage phenology. Greenness, along with meteorological data, was measured at the plot scale in rainfall manipulation field experiments (n=52) and pasture monitoring sites (n=1) in western Sydney, Australia. These sites include both monospecies and multispecies plots. The data were used to evaluate an empirical model linking phenology to soil water content (SWC) and temperature. Within a model data assimilation framework, we quantified the sensitivities of leaf growth and senescence to SWC.

Results/Conclusions
Previous models have assumed a linear sensitivity of growth to SWC. In contrast, here we found that the sensitivity of growth to SWC varied by species, with the majority of species showing a non-linear, concave-down response. Similarly, models commonly assume a constant senescence rate, whereas we found a strong nonlinear increase in senescence rate with declining SWC. Incorporating the sensitivities of growth and senescence to SWC helped predict the thresholds of greenness and SWC of the onset of brown-down and was important to reproduce the observations. Model evaluation against data under drought treatments indicated that these sensitivities to SWC enabled the prediction of drought impacts on greenness, and helped explain differences among species’ responses to variable rainfall. Our results suggested the modelling framework as a useful way to improve the representation of soil moisture impacts on grassland phenology in land surface models.