Green-up and brown-down: Modelling grassland foliage phenology responses
to soil moisture availability
Abstract
Grassland responses to drought are strongly mediated 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 in vegetation models. Incorporating nonlinear responses in
the empirical model reduced the error in cover predictions by 12%.
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.