Abstract
Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.
Highlights
Vegetation phenology refers to the response of vegetation to inter- and intra-annual variations in climate, irradiance, temperature and water (Myneni et al, 1997; White et al, 1997; Zhang et al, 2003)
We evaluated the mean and variability of the peak and minimum magnitude across the 14-year time series to investigate the inter-annual variations in vegetation phenology
We provide a characterization of annual and non-annual phenological cycles of vegetation greening and browning for Australia based on MODerate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data
Summary
Vegetation phenology refers to the response of vegetation to inter- and intra-annual variations in climate, irradiance, temperature and water (Myneni et al, 1997; White et al, 1997; Zhang et al, 2003). Vegetation phenology is a useful indicator in the study of the response of ecosystems to climate variability (Zhang et al, 2012; Richardson et al, 2013), and an important parameter for land surface, climate and biogeochemical models that quantify the exchange of water, energy and gases between vegetation and the atmosphere (Pitman, 2003; Eklundh and Jönsson, 2010). Existing algorithms aiming to characterize phenological cycles from remotely sensed spectral vegetation “greenness” indices perform well for ecosystems in temperature-driven mid and high latitudes (Eklundh and Jönsson, 2010; Ganguly et al, 2010). In ecosystems where rainfall is limited and highly variable, such as semi-arid and arid systems (i.e., drylands; United Nations, 2011), phenological cycles may be irregular in their length, timing, amplitude and reoccurrence interval, occur at any time of the year, or not occur at all in a given year (Brown and de Beurs, 2008; Ma et al, 2013; Walker et al, 2014; Bradley and Mustard, 2007)
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