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

Abstract:

Climate change is expected to bring greater variability in the amount and frequency of future rainfall. Grassland productivity is strongly affected by this rainfall variability, mainly via the timing of foliage green-up and brown-down. New developments in ecosystem monitoring provide opportunities to examine the processes governing grassland phenology, facilitating the development of predictive ecosystem models. Here, we used fixed cameras (phenocams) to monitor grassland “greenness”, a proxy for foliage phenology. Greenness, along with soil moisture and meteorological data, was measured at the plot scale in rainfall manipulation field experiments and pasture monitoring sites across a rainfall gradient of 400 mm yr^-1 to 800 mm yr^-1 in New South Wales, southern Australia. These data were used to drive and assess an empirical model linking phenology to soil water content, temperature and solar irradiance. Within the framework of the empirical model, we tested alternative formulations of two key factors: the degree of plant water stress as a function of soil water content and the sensitivity of leaf phenology to water stress.

The data demonstrated that foliage green-up happened rapidly after rainfall events (>1mm). In contrast, the timing and rate of brown-down were determined by existing greenness, water stress, solar irradiance and temperature. Incorporating alternative representations of the two key factors improved model fidelity to observations. Our findings can be linked to the ecophysiological mechanisms underpinning these responses (e.g., soil and plant hydraulics, sensitivity of leaf gas exchange to drought, and optimal temperature for photosynthesis) and thus provided guidance for ways to improve representations of leaf phenology in land surface models.