SPECIAL—Savanna Patterns of Energy and Carbon Integrated across the Landscape

Beringer, Jason; Hacker, Jörg; Hutley, Lindsay B.; Leuning, R.; Arndt, Stefan K.; Amiri, Reza; Bannehr, Lutz; Cernusak, Lucas A.; Grover, Samantha; Hensley, Carol; Hocking, Darren R.; Isaac, Peter; Jamali, Hizbullah; Kanniah, Kasturi Devi; Livesley, Stephen J.; Neininger, B.; U, Kyaw Tha Paw; Sea, William B.; Straten, Dennis; Tapper, Nigel; Weinmann, Richard; Wood, Stephen A.; Zegelin, Steve

doi:10.1175/2011bams2948.1

Cited by 55 publications

(46 citation statements)

References 58 publications

(42 reference statements)

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“…This is typical of most grassland, where gross and net photosynthesis are strongly correlated to green biomass (leaf area index) Li et al, 2005;Otieno et al, 2009;Beringer et al, 2011). Given that shoot N also peaked in the wet season, the combined increase in green biomass (photosynthetic surface area), shoot N and light utilization efficiency facilitated rapid CO 2 uptake resulting into the high GPP and NEE.…”

Section: Ecosystem Productivity and Carbon Fluxesmentioning

confidence: 83%

“…However, despite the renewed interest in the carbon budget of African savannah in the second half of the last decade, the continent's savannah is extensive and both spatially and temporally variable (Ciais et al, 2009;Williams et al, 2007). Such great variability in the continent's largest ecosystem is greatly impacting its carbon balance since exchanges of CO 2 between land surface and atmosphere in savannah are highly modulated by ecosystem characteristics such as structure, species and physiological functions (Beringer et al, 2011). The variability of the African carbon balance has been noted and is estimated to account for about 50% of inter-annual variability in global atmospheric CO 2 .…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Dynamics in Carbon Dioxide Flux Es of the Herbaceous Layer of a Moist Kenyan Savannah

K'Otuto¹

2014

Appl Ecol Env Res

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Section: Ecosystem Productivity and Carbon Fluxesmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Seasonal Dynamics in Carbon Dioxide Flux Es of the Herbaceous Layer of a Moist Kenyan Savannah

K'Otuto¹

2014

Appl Ecol Env Res

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“…In Australia, rainfall variability is crucial to the structure and productivity of the landscape, particularly across the vast extent of the savanna biome which accounts for 25 % of the continent (Beringer et al, , 2011b; Eamus et al, 2013;Haverd et al, 2016;Kanniah et al, 2011). Not only do savanna landscapes cover a large portion of Australia, they also account for 15 % of the land surface of the planet (Beringer et al, 2011b).…”

Section: D Wilks Rogers and J Beringer: Describing Rainfall In Nmentioning

confidence: 99%

“…Not only do savanna landscapes cover a large portion of Australia, they also account for 15 % of the land surface of the planet (Beringer et al, 2011b). This area has the potential to increase in size due to climate change (Franchito et al, 2011), making the improved knowledge of these landscapes important in understanding future rainfall trends in Australia and around the world.…”

Section: D Wilks Rogers and J Beringer: Describing Rainfall In Nmentioning

confidence: 99%

“…Human populations and ecosystems are highly susceptible to changes in both intensity and timing of precipitation events through their impact on global energy, water and carbon cycling (Beringer et al, 2011b). Knowledge of atmospheric processes and circulation patterns is important for assessing rainfall patterns under both natural variability and anthropogenic climate change.…”

Section: Introductionmentioning

confidence: 99%

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Describing rainfall in northern Australia using multiple climate indices

Rogers

Beringer

2017

Biogeosciences

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Abstract. Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmospherebiosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the IndianAustralian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño-Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

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Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach

Merlin

Stefan

Amazirh

et al. 2016

Water Resources Research

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International audienceA meta-analysis data-driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02-0.56, a sand fraction range of 0.05-0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance ($r_{ss}$) formulation based on surface soil moisture ($\theta$) and two resistance parameters $r_{ss,ref}$ and $\theta_{efolding}$. The data-driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut-off soil moisture value $\theta_{1/2}$ at which SEE=0.5, and first derivative of SEE at $\theta_{1/2}$, named $\Delta\theta_{1/2}^{-1}$. An analytical relationship between $(r_{ss,ref};\theta_{efolding})$ and $(\theta_{1/2};\Delta\theta_{1/2}^{-1})$ is first built by running a soil energy balance model for two extreme conditions with $r_{ss} = 0$ and $r_{ss}\sim\infty$ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair $(\theta_{1/2} ; \Delta\theta_{1/2}^{-1})$ either from the time series of SEE and $\theta$ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear $\text{SEE}(\theta)$ relationships and potentially large random deviations of observed SEE from the mean observed $\text{SEE}(\theta)$. The second method parameterizes $\theta_{1/2}$ as a multi-linear regression of clay and sand percentages, and sets $\Delta\theta_{1/2}^{-1}$ to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol-Biosph\`{e}re-Atmosph\`{e}re), H-TESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land-surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data

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SPECIAL—Savanna Patterns of Energy and Carbon Integrated across the Landscape

Cited by 55 publications

References 58 publications

Seasonal Dynamics in Carbon Dioxide Flux Es of the Herbaceous Layer of a Moist Kenyan Savannah

Seasonal Dynamics in Carbon Dioxide Flux Es of the Herbaceous Layer of a Moist Kenyan Savannah

Describing rainfall in northern Australia using multiple climate indices

Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach

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