Terrestrial hydrological controls on land surface phenology of African savannas and woodlands

Abstract

This paper presents a continental-scale phenological analysis of African savannas and woodlands.We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green-up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem-water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an "optimal rainy season length," after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change.

Characterizing Land Cover Impacts on the Responses of Land Surface Phenology to the Rainy Season in the Congo Basin

“…Daily rainfall was generated by summing up the three-hourly rainfall based on the measured rainfall rate. The onset and end of the rainy season (hereafter referred to as ORS and ERS, respectively) in Africa have been previously determined using a suite of algorithms [8,11,12,19,31]. For example, ORS and ERS can be determined using a pre-defined threshold of the cumulative rainfall over a fixed time period [8,11,12,31].…”

“…2017, 9, 461 5 of 17 and end of the rainy season (hereafter referred to as ORS and ERS, respectively) in Africa have been previously determined using a suite of algorithms [8,11,12,19,31]. For example, ORS and ERS can be determined using a pre-defined threshold of the cumulative rainfall over a fixed time period [8,11,12,31]. Alternatively, yearly ORS and ERS can be determined based on climatological anomalous accumulation (CAA), which refers to the accumulation of the difference between the long-term daily average rainfall and the mean value across the daily average rainfall time series [19].…”

“…Land surface phenology (LSP) characterizes seasonal greenness variations over vegetated land surfaces [10]. The relationship between rainfall seasonality and LSP in the Congo Basin has been examined in numerous studies [8,11,12]. However, few attempts have been made to understand the effects of rainfall seasonality on LSP in evergreen rainforests across the entire equatorial Congo Basin.…”

“…Rainfall seasonality in the Congo Basin is driven by the seasonal migration of the Intertropical Convergence Zone [11,19,20], which results in unimodal rainfall regimes in the NCB and the SCB, and a bimodal rainfall regime in ECB. CGC in the Congo Basin is driven by rainfall seasonality.…”

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

“…In contrast, the University of Montana (UMT) global Land Parameter Data Record version 1 (LPDR v1) was developed to exploit AMSR-E multifrequency T b observations for global daily mapping of multiple synergistic land parameters related to the status and storage of water in the atmosphere, vegetation, and soil . The LPDR v1 database has been applied for a variety of environmental studies, including quantifying surface water inundation impacts on tundra methane emissions (Watts et al, 2014), boreal wildfire disturbance and recovery assessments (Jones et al, 2013), evaluating hydroclimatic controls on vegetation phenology (Alemu and Henebry, 2013;Guan et al, 2014), biodiversity modeling and prediction (Waltari et al, 2014), and vector-borne disease risk assessments (Chuang et al, 2012). The LPDR v1 has also served as a baseline for evaluating other AMSR-E algorithm retrievals (Mladenova et al, 2014) and refinements (Jang et al, 2014;Du et al, 2014).…”