The spatial patterns of litter turnover time in Chinese terrestrial ecosystems

2020 ◽  
Author(s):  
Andong Cai ◽  
Naijie Chang ◽  
Wenju Zhang ◽  
Guopeng Liang ◽  
Xubo Zhang ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Litter Turnover

Inferring non-steady-state terrestrial vegetation carbon turnover times from multi-decadal space-borne observations on global scale

2020 ◽  
Author(s):  
Naixin Fan ◽  
Simon Besnard ◽  
Maurizio Santoro ◽  
Oliver Cartus ◽  
Nuno Carvalhais
Keyword(s):  
Climate Change ◽  
Steady State ◽  
Carbon Fixation ◽  
Carbon Sink ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Turnover Time ◽  
Terrestrial Vegetation ◽  
Time Step

<p>The global biomass is determined by the vegetation turnover times (τ) and carbon fixation through photosynthesis. Vegetation turnover time is a central parameter that not only partially determines the terrestrial carbon sink but also the response of terrestrial vegetation to the future changes in climate. However, the change of magnitude, spatial patterns and uncertainties in τ as well as the sensitivity of these processes to climate change is not well understood due to lack of observations on global scale. In this study, we explore a new dataset of annual above-ground biomass (AGB) change from 1993 to 2018 from spaceborne scatterometer observations. Using the long-term, spatial-explicit global dynamic dataset, we investigated how τ change over almost three decades including the uncertainties. Previous estimations of τ under steady-state assumption can now be challenged acknowledging that terrestrial ecosystems are, for the most of cases, not in balance. In this study, we explore this new dataset to derive global maps of τ in non-steady-state for different periods of time. We used a non-steady-state carbon model in which the change of AGB is a function of Gross Primary Production (GPP) and τ (ΔAGB = α*GPP-AGB/ τ). The parameter α represents the percentage of incorporation of carbon from GPP to biomass. By exploring the AGB change in 5 to 10 years of time step, we were able to infer τ and α from the observations of AGB and GPP change by solving the linear equation. We show how τ changes after potential disturbances in the early 2000s in comparison to the previous decade. We also show the spatial distributions of α from the change of AGB. By accessing the change in biomass, τ and α as well as their associated uncertainties, we provide a comprehensive diagnostic on the vegetation dynamics and the potential response of biomass to disturbance and to climate change.   </p><p></p><p></p><p></p><p></p><p></p><p></p>

scholarly journals Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – Results from UAS based high resolution remote sensing

2021 ◽  
Author(s):  
Marc Wehrhan ◽  
Daniel Puppe ◽  
Danuta Kaczorek ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Biomass ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Promising Tool ◽  
Technical Features

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most of these studies were performed at relatively small plots with an intended low heterogeneity in soils and plant canopy composition, and results were extrapolated to larger spatial units up to global scale implicitly assuming similar environmental conditions. However, the emergence of new technical features and increasing knowledge on details in Si cycling leads to a more complex picture at landscape or catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation, but its biomass distribution and related Si stocks. Maximum Likelihood (ML) classification was applied to multispectral imagery captured by an Unmanned Aerial System (UAS) aiming the identification of land cover classes (LCC). Subsequently, the Normalized Difference Vegetation Index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e., comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

scholarly journals Effects of carbon turnover time on terrestrial ecosystem carbon storage

2017 ◽  
Vol 14 (23) ◽  
pp. 5441-5454 ◽  
Author(s):  
Yaner Yan ◽  
Xuhui Zhou ◽  
Lifeng Jiang ◽  
Yiqi Luo
Keyword(s):  
Primary Production ◽  
Spatial Variation ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Soil C ◽  
C Storage ◽  
Key Factor

Abstract. Carbon (C) turnover time is a key factor in determining C storage capacity in various plant and soil pools as well as terrestrial C sink in a changing climate. However, the effects of C turnover time on ecosystem C storage have not been well explored. In this study, we compared mean C turnover times (MTTs) of ecosystem and soil, examined their variability to climate, and then quantified the spatial variation in ecosystem C storage over time from changes in C turnover time and/or net primary production (NPP). Our results showed that mean ecosystem MTT based on gross primary production (GPP; MTTEC_GPP =  Cpool/GPP, 25.0 ± 2.7 years) was shorter than soil MTT (MTTsoil =  Csoil/NPP, 35.5 ± 1.2 years) and NPP-based ecosystem MTT (MTTEC_NPP =  Cpool/NPP, 50.8 ± 3 years; Cpool and Csoil referred to ecosystem or soil C storage, respectively). On the biome scale, temperature is the best predictor for MTTEC (R2 =  0.77, p < 0.001) and MTTsoil (R2 =  0.68, p < 0.001), while the inclusion of precipitation in the model did not improve the performance of MTTEC (R2 =  0.76, p < 0.001). Ecosystem MTT decreased by approximately 4 years from 1901 to 2011 when only temperature was considered, resulting in a large C release from terrestrial ecosystems. The resultant terrestrial C release caused by the decrease in MTT only accounted for about 13.5 % of that due to the change in NPP uptake (159.3 ± 1.45 vs. 1215.4 ± 11.0 Pg C). However, the larger uncertainties in the spatial variation of MTT than temporal changes could lead to a greater impact on ecosystem C storage, which deserves further study in the future.

scholarly journals Biomass turnover time in terrestrial ecosystems halved by land use

2016 ◽  
Vol 9 (9) ◽  
pp. 674-678 ◽  
Author(s):  
Karl-Heinz Erb ◽  
Tamara Fetzel ◽  
Christoph Plutzar ◽  
Thomas Kastner ◽  
Christian Lauk ◽  
...  
Keyword(s):  
Land Use ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Biomass Turnover

scholarly journals Complex Networks Unveiling Spatial Patterns in Turbulence

2016 ◽  
Vol 26 (13) ◽  
pp. 1650223 ◽  
Author(s):  
Stefania Scarsoglio ◽  
Giovanni Iacobello ◽  
Luca Ridolfi
Keyword(s):  
Complex Networks ◽  
Turbulent Flows ◽  
Spatial Patterns ◽  
Physical Distance ◽  
Turnover Time ◽  
Complex Network Theory ◽  
Pattern Size ◽  
Large Eddy ◽  
Point Correlation ◽  
Systematic Identification

Numerical and experimental turbulence simulations are nowadays reaching the size of the so-called big data, thus requiring refined investigative tools for appropriate statistical analyses and data mining. We present a new approach based on the complex network theory, offering a powerful framework to explore complex systems with a huge number of interacting elements. Although interest in complex networks has been increasing in the past years, few recent studies have been applied to turbulence. We propose an investigation starting from a two-point correlation for the kinetic energy of a forced isotropic field numerically solved. Among all the metrics analyzed, the degree centrality is the most significant, suggesting the formation of spatial patterns which coherently move with similar vorticity over the large eddy turnover time scale. Pattern size can be quantified through a newly-introduced parameter (i.e. average physical distance) and varies from small to intermediate scales. The network analysis allows a systematic identification of different spatial regions, providing new insights into the spatial characterization of turbulent flows. Based on present findings, the application to highly inhomogeneous flows seems promising and deserves additional future investigation.

scholarly journals Apparent ecosystem carbon turnover time: uncertainties and robust features

2020 ◽  
Author(s):  
Naixin Fan ◽  
Sujan Koirala ◽  
Markus Reichstein ◽  
Martin Thurner ◽  
Valerio Avitabile ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Spatial Patterns ◽  
Soil Depth ◽  
Global Scale ◽  
Turnover Time ◽  
Latitudinal Gradients ◽  
Climate Feedback ◽  
Carbon Turnover ◽  
Terrestrial Carbon ◽  
Total Uncertainty

Abstract. The turnover time of terrestrial carbon (τ) controls the global carbon cycle – climate feedback and, yet, is poorly simulated by the current Earth System Models (ESMs). In this study, by assessing apparent carbon turnover time as the ratio between carbon stocks and fluxes, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble, we estimated the global average τ to be 42−5+9 years when the full soil depth is considered, longer than the previous estimates of 23−4+7 years. Only considering the top 1 m (assuming maximum active layer depth is up to 1 meter) of soil carbon in circumpolar regions yields a global τ of 35−4+9 years. Csoil in circumpolar regions account for two thirds of the total uncertainty in global τ estimates, whereas Csoil in non-circumpolar contributes merely 9.38 %. GPP (2.25 %) and Cveg (0.05 %) contribute even less to the total uncertainty. Therefore, the high uncertainty in Csoil is the main factor behind the uncertainty in global τ, as reflected in the larger range of full-depth Csoil (3152–4372 PgC). The uncertainty is especially high in circumpolar regions with a uncertainty of 50 % and the spatial correlations among different datasets are also low compared to other regions. Overall, we argue that current global datasets do not support robust estimates of τ globally, for which we need clarification on variations of Csoil with soil depth and stronger estimates of Csoil in circumpolar regions. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ that emerge regardless of soil depth and differences in data sources of Csoil, Cveg and GPP. Our findings show that the latitudinal gradients of τ are consistent across different datasets and soil depth. Furthermore, there is a strong consensus on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30º N–60º N) than in subtropical and tropical zones (30º S–30º N). The identified robust patterns can be used to infer the response of τ to climate and for constraining contemporaneous behaviour of ESMs which could contribute to uncertainty reductions in future projections of the carbon cycle – climate feedback. The dataset of the terrestrial turnover time ensemble (DOI: 10.17871/bgitau.201911) is openly available from the data portal: https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).

scholarly journals Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – results from UAS-based high-resolution remote sensing

2021 ◽  
Vol 18 (18) ◽  
pp. 5163-5183
Author(s):  
Marc Wehrhan ◽  
Daniel Puppe ◽  
Danuta Kaczorek ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Biomass ◽  
Terrestrial Ecosystems ◽  
Promising Tool ◽  
Landscape Scales ◽  
Technical Features

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

scholarly journals Convergent modeling of past soil organic carbon stocks but divergent projections

2015 ◽  
Vol 12 (5) ◽  
pp. 4245-4272 ◽  
Author(s):  
Z. Luo ◽  
E. Wang ◽  
H. Zheng ◽  
J. A. Baldock ◽  
O. J. Sun ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Field Experiments ◽  
Environmental Changes ◽  
Agricultural Soils ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Carbon Stocks ◽  
Biogeochemical Model ◽  
Carbon Modeling ◽  
Carbon Composition

Abstract. Soil carbon models are important tool to understand soil carbon balance and project carbon stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil carbon models can vary among studies even when the same model and dataset are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model to 90 individual field experiments (ranging from 5 to 82 years of experimental duration) across the Australian cereal-growing regions, we demonstrated that well-designed calibration procedures enabled the model to accurately simulate changes in measured carbon stocks, but did not guarantee convergent forward projections (100 years). Major causes of the projection uncertainty were due to insufficient understanding of how microbial processes and soil carbon composition change to modulate carbon turnover. For a given site, the uncertainty significantly increased with the magnitude of future carbon input and years of the projection. Across sites, the uncertainty correlated positively with temperature, but negatively with rainfall. On average, a 331% uncertainty in projected carbon sequestration ability can be inferred in Australian agricultural soils. This uncertainty would increase further if projections were made for future warming and drying conditions. Future improvement in soil carbon modeling should focus on how microbial community and its carbon use efficiency change in response to environmental changes, better quantification of composition of soil carbon and its change, and how the soil carbon composition will affect its turnover time.

Spatial patterns and climate controls of seasonal variations in carbon fluxes in China's terrestrial ecosystems

2020 ◽  
Vol 189 ◽  
pp. 103175
Author(s):  
Lang Han ◽  
Qiu-Feng Wang ◽  
Zhi Chen ◽  
Gui-Rui Yu ◽  
Guang-Sheng Zhou ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Seasonal Variations ◽  
Terrestrial Ecosystems ◽  
Carbon Fluxes ◽  
Climate Controls
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