scholarly journals Challenges to Reproduce Vegetation Structure and Dynamics in Amazonia Using a Coupled Climate–Biosphere Model

2009 ◽  
Vol 13 (11) ◽  
pp. 1-28 ◽  
Author(s):  
Mônica Carneiro Alves Senna ◽  
Marcos Heil Costa ◽  
Lucía Iracema Chipponelli Pinto ◽  
Hewlley Maria Acioli Imbuzeiro ◽  
Luciana Mara Freitas Diniz ◽  
...  
Keyword(s):  
Vegetation Structure ◽  
Carbon Allocation ◽  
Net Primary Production ◽  
Ecosystem Structure ◽  
Intergovernmental Panel ◽  
Area Index ◽  
Carbon Cycle Model ◽  
Accurate Representation ◽  
Structure And Dynamics ◽  
Biosphere Model

Abstract The Amazon rain forest constitutes one of the major global stocks of carbon. Recent studies, including the last Intergovernmental Panel on Climate Change report and the Coupled Climate Carbon Cycle Model Intercomparison Project, have suggested that it may reduce in size and lose biomass during the twenty-first century through a savannization process. A better understanding of how this ecosystem structure, dynamics, and carbon balance may respond to future climate changes is needed. This article investigates how well a fully coupled atmosphere–biosphere model can reproduce vegetation structure and dynamics in Amazonia to the extent permitted by available data. The accurate representation of the coupled climate–biosphere dynamics requires the accurate representation of climate, net primary production (NPP), and its partition among the several carbon pool components. The simulated climate is validated against precipitation (within 5% of four datasets) and incident solar radiation (within 7% of observations). The authors also validate (i) simulated land cover, which reproduces well the observed patterns; (ii) NPP, within 5% of observations; and (iii) respiration rates, within 15% of observations. The performance of simulated variables that depend on carbon allocation, like NPP partitioning, leaf area index, and aboveground live biomass, although good on a regional mean, is significantly low when spatial patterns are considered. These errors may be attributed to fixed carbon allocation and residence time parameters assumed by the model. Carbon allocation apparently varies spatially, and to simulate this spatial variability is quite a challenge.

scholarly journals Influence of climate variability, fire and phosphorus limitation on the vegetation structure and dynamics in the Amazon-Cerrado border

2016 ◽  
Author(s):  
Emily Ane Dionizio da Silva ◽  
Marcos Heil Costa ◽  
Andrea Almeida Castanho ◽  
Gabrielle Ferreira Pires ◽  
Beatriz Schwantes Marimon ◽  
...  
Keyword(s):  
Climate Variability ◽  
Vegetation Structure ◽  
Carbon Allocation ◽  
Net Primary Production ◽  
Field Measurements ◽  
Phosphorus Limitation ◽  
Area Index ◽  
Soil P ◽  
Structure And Dynamics ◽  
Nutritional Limitation

Abstract. Climate, fire and soil nutritional limitation are important elements that affect the vegetation dynamics in areas of forest-savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of climate variability, fire and phosphorus limitation on the Amazon-Cerrado transitional vegetation structure and dynamics. We assess how each element affects the net primary production, leaf area index and biomass and compare the simulations of aboveground biomass to observed biomass map. We used two climate datasets – the 1960–1990 average seasonal climate and the 1948 to 2008 interannual climate variability, two regional datasets of total soil P content in soil, based on regional (field measurements) and global data and the INLAND fire module. Our results show that climate interannual variability, phosphorus limitation and fire occurrence gradually improve simulated vegetation types and these effects are not homogeneous along the latitudinal/longitudinal gradient showing a synergistic effect among them. In terms of magnitude, the effect of fire is stronger, and is the main driver of vegetation changes along the transition. The nutritional limitation, in turn, is stronger than the effect of climate variability acting on the transitional ecosystems dynamics. Overall, INLAND typically simulates more than 80 % of the biomass variability in the transition zone. However, in many places, the biomass is clearly not well simulated indicating that important soil and physiological factors in the Amazon-Cerrado border, such as lithology and water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

scholarly journals Influence of climate variability, fire and phosphorus limitation on the vegetation structure and dynamics in the Amazon-Cerrado border

2017 ◽  
Author(s):  
Emily Ane Dionizio da Silva ◽  
Marcos Heil Costa ◽  
Andrea Almeida Castanho ◽  
Gabrielle Ferreira Pires ◽  
Beatriz Schwantes Marimon ◽  
...  
Keyword(s):  
Climate Variability ◽  
Vegetation Structure ◽  
Carbon Allocation ◽  
Net Primary Production ◽  
Field Measurements ◽  
Area Index ◽  
Soil P ◽  
Structure And Dynamics ◽  
Nutritional Limitation ◽  
P Limitation

Abstract. Climate, fire and soil nutritional limitation are important elements that affect the vegetation dynamics in areas of forest-savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of inter-annual climate variability, fire and phosphorus (P) limitation on the Amazon-Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects the net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations of observed AGB map. We used two climate datasets – the 1960–1990 average seasonal climate and the 1948 to 2008 inter-annual climate variability, two regional datasets of total soil P content in soil, based on regional (field measurements) and global data and the INLAND fire module. Our results show that inter-annual climate variability, P limitation and fire occurrence gradually improve simulated vegetation types and these effects are not homogeneous along the latitudinal/longitudinal gradient showing a synergistic effect among them. In terms of magnitude, the effect of fire is stronger, and is the main driver of vegetation changes along the transition. The nutritional limitation, in turn, is stronger than the effect of inter-annual climate variability acting on the transitional ecosystems dynamics. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon-Cerrado border, such as lithology and water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

scholarly journals Coupling carbon allocation with leaf and root phenology predicts tree–grass partitioning along a savanna rainfall gradient

2016 ◽  
Vol 13 (3) ◽  
pp. 761-779 ◽  
Author(s):  
V. Haverd ◽  
B. Smith ◽  
M. Raupach ◽  
P. Briggs ◽  
L. Nieradzik ◽  
...  
Keyword(s):  
Vegetation Dynamics ◽  
Carbon Allocation ◽  
Net Primary Production ◽  
Regional Scale ◽  
Basal Area ◽  
Ecosystem Dynamics ◽  
Tree Cover ◽  
Rainfall Gradient ◽  
Area Index ◽  
Temporal Shift

Abstract. The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree–grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximize long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model.We demonstrate the approach by encoding it in a new simple carbon–water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at five tower sites along the North Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area, and foliage projective cover along the NATT. The model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, mediated by shifting above- versus below-ground resources, and not from imposed hypotheses about the controls on tree–grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.

scholarly journals Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient

2015 ◽  
Vol 12 (19) ◽  
pp. 16313-16357 ◽  
Author(s):  
V. Haverd ◽  
B. Smith ◽  
M. Raupach ◽  
P. Briggs ◽  
L. Nieradzik ◽  
...  
Keyword(s):  
Vegetation Dynamics ◽  
Carbon Allocation ◽  
Net Primary Production ◽  
Regional Scale ◽  
Ecosystem Dynamics ◽  
Tree Cover ◽  
Biogeochemical Model ◽  
Rainfall Gradient ◽  
Area Index ◽  
Temporal Shift

Abstract. The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree/grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximise long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model. We demonstrate the approach by encoding it in a new simple carbon/water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely-sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at 5 tower sites along the Northern Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area and foliage projective cover along the NATT. The model behaviour emerges from complex feed-backs between the plant physiology and vegetation dynamics, mediated by shifting above- vs. below-ground resources, and not from imposed hypotheses about the controls on tree/grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.

scholarly journals Influence of climate variability, fire and phosphorus limitation on vegetation structure and dynamics of the Amazon–Cerrado border

2018 ◽  
Vol 15 (3) ◽  
pp. 919-936 ◽  
Author(s):  
Emily Ane Dionizio ◽  
Marcos Heil Costa ◽  
Andrea D. de Almeida Castanho ◽  
Gabrielle Ferreira Pires ◽  
Beatriz Schwantes Marimon ◽  
...  
Keyword(s):  
Climate Variability ◽  
Vegetation Structure ◽  
Phosphorus Limitation ◽  
Ecosystem Dynamics ◽  
Border Region ◽  
Data Sets ◽  
Area Index ◽  
Structure And Dynamics ◽  
P Limitation ◽  
Interannual Climate Variability

Abstract. Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest–savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon–Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961–1990 and interannual climate variability for 1948–2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon–Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

scholarly journals The Yale Interactive terrestrial Biosphere model version 1.0: description, evaluation and implementation into NASA GISS ModelE2

2015 ◽  
Vol 8 (8) ◽  
pp. 2399-2417 ◽  
Author(s):  
X. Yue ◽  
N. Unger
Keyword(s):  
Carbon Cycle ◽  
Atmospheric Chemistry ◽  
Climate Model ◽  
Carbon Allocation ◽  
Carbon Fluxes ◽  
Terrestrial Biosphere ◽  
Area Index ◽  
Carbon Cycle Model ◽  
Model Version ◽  
Vegetation Damage

Abstract. The land biosphere, atmospheric chemistry and climate are intricately interconnected, yet the modeling of carbon–climate and chemistry–climate interactions have evolved as entirely separate research communities. We describe the Yale Interactive terrestrial Biosphere (YIBs) model version 1.0, a land carbon cycle model that has been developed for coupling to the NASA Goddard Institute for Space Studies (GISS) ModelE2 global chemistry–climate model. The YIBs model adapts routines from the mature TRIFFID (Top-down Representation of Interactive Foliage and Flora Including Dynamics) and CASA (Carnegie–Ames–Stanford Approach) models to simulate interactive carbon assimilation, allocation, and autotrophic and heterotrophic respiration. Dynamic daily leaf area index is simulated based on carbon allocation and temperature- and drought-dependent prognostic phenology. YIBs incorporates a semi-mechanistic ozone vegetation damage scheme. Here, we validate the present-day YIBs land carbon fluxes for three increasingly complex configurations: (i) offline local site level, (ii) offline global forced with WFDEI (WATCH Forcing Data methodology applied to ERA-Interim data) meteorology, and (iii) online coupled to the NASA ModelE2 (NASA ModelE2-YIBs). Offline YIBs has hourly and online YIBs has half-hourly temporal resolution. The large observational database used for validation includes carbon fluxes from 145 flux tower sites and multiple satellite products. At the site level, YIBs simulates reasonable seasonality (correlation coefficient R > 0.8) of gross primary productivity (GPP) at 121 out of 145 sites with biases in magnitude ranging from −19 to 7 % depending on plant functional type. On the global scale, the offline model simulates an annual GPP of 125 ± 3 Pg C and net ecosystem exchange (NEE) of −2.5 ± 0.7 Pg C for 1982–2011, with seasonality and spatial distribution consistent with the satellite observations. We assess present-day global ozone vegetation damage using the offline YIBs configuration. Ozone damage reduces global GPP by 2–5 % annually with regional extremes of 4–10 % in east Asia. The online model simulates annual GPP of 123 ± 1 Pg C and NEE of −2.7 ± 0.7 Pg C. NASA ModelE2-YIBs is a useful new tool to investigate coupled interactions between the land carbon cycle, atmospheric chemistry, and climate change.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

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