Process-based analysis of land carbon flux predictability

2021 ◽  
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Interannual Variability ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Environmental Drivers ◽  
Terrestrial Carbon ◽  
State Dependency

<p>The land-atmosphere CO<sub>2</sub> exchange exhibits a very high interannual variability which dominates variability in atmospheric CO<sub>2</sub> concentration. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is even predictable, and which processes explain the predictability. In this study, the perfect model approach is used to assess the potential predictability of net primary production (NPP) and heterotrophic respiration (Rh) by using initialized ensemble experiments simulated with the Max Planck Institute Earth System Model. In order to determine which processes are causing the derived predictability patterns, carbon flux predictability was decomposed into individual drivers. Regression analysis was used to determine the contribution of the predictability of different environmental drivers to the predictability of NPP and Rh (Soil moisture, temperature and radiation for NPP and soil organic carbon, temperature and precipitation for Rh). The main drivers of NPP predictability are soil moisture and temperature, while the predictability signal from radiation is lost after the first month of simulation. Rh predictability is predominantly driven by soil organic carbon, temperature and locally by precipitation. This decomposition of predictability shows that the relatively high Rh predictability is due to the generally high predictability of soil organic carbon. The assessed seasonality in predictability patterns can be explained by the change in limiting factors of NPP and Rh over the wet and dry months. This leads to the adjustment of carbon flux predictability to the predictability of the currently limiting environmental factor. Differences in the predictability between initializations can be attributed to the interannual variability in soil moisture and temperature predictability. This variability is caused by the state dependency of nonlinear ecosystem processes. These results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.</p>

scholarly journals Process-based analysis of terrestrial carbon flux predictability

2021 ◽  
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Pierre Friedlingstein ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Air Temperature ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Terrestrial Carbon ◽  
Ensemble Simulations

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is predictable, and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max-Planck-Institute Earth System Model. In order to asses the role of local carbon flux predictability (CFpred) on the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature and radiation for NPP and soil organic carbon, air temperature and precipitation for Rh). NPPpred is driven to 62 and 30 % by the predictability of soil moisture and temperature, respectively. Rhpred is driven to 52 and 27 % by the predictability of soil organic carbon temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

scholarly journals Process-based analysis of terrestrial carbon flux predictability

2021 ◽  
Vol 12 (4) ◽  
pp. 1413-1426
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Pierre Friedlingstein ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Air Temperature ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Terrestrial Carbon ◽  
Ensemble Simulations

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

scholarly journals Soil greenhouse gas fluxes from different tree species on Taihang Mountain, North China

2014 ◽  
Vol 11 (6) ◽  
pp. 1649-1666 ◽  
Author(s):  
X. P. Liu ◽  
W. J. Zhang ◽  
C. S. Hu ◽  
X. G. Tang
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Ph ◽  
Tree Species ◽  
Soil Bulk Density ◽  
Total N ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes

Abstract. The objectives of this study were to investigate seasonal variation of greenhouse gas fluxes from soils on sites dominated by plantation (Robinia pseudoacacia, Punica granatum, and Ziziphus jujube) and natural regenerated forests (Vitex negundo var. heterophylla, Leptodermis oblonga, and Bothriochloa ischcemum), and to identify how tree species, litter exclusion, and soil properties (soil temperature, soil moisture, soil organic carbon, total N, soil bulk density, and soil pH) explained the temporal and spatial variation in soil greenhouse gas fluxes. Fluxes of greenhouse gases were measured using static chamber and gas chromatography techniques. Six static chambers were randomly installed in each tree species. Three chambers were randomly designated to measure the impacts of surface litter exclusion, and the remaining three were used as a control. Field measurements were conducted biweekly from May 2010 to April 2012. Soil CO2 emissions from all tree species were significantly affected by soil temperature, soil moisture, and their interaction. Driven by the seasonality of temperature and precipitation, soil CO2 emissions demonstrated a clear seasonal pattern, with fluxes significantly higher during the rainy season than during the dry season. Soil CH4 and N2O fluxes were not significantly correlated with soil temperature, soil moisture, or their interaction, and no significant seasonal differences were detected. Soil organic carbon and total N were significantly positively correlated with CO2 and N2O fluxes. Soil bulk density was significantly negatively correlated with CO2 and N2O fluxes. Soil pH was not correlated with CO2 and N2O emissions. Soil CH4 fluxes did not display pronounced dependency on soil organic carbon, total N, soil bulk density, and soil pH. Removal of surface litter significantly decreased in CO2 emissions and CH4 uptakes. Soils in six tree species acted as sinks for atmospheric CH4. With the exception of Ziziphus jujube, soils in all tree species acted as sinks for atmospheric N2O. Tree species had a significant effect on CO2 and N2O releases but not on CH4 uptake. The lower net global warming potential in natural regenerated vegetation suggested that natural regenerated vegetation were more desirable plant species in reducing global warming.

scholarly journals Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Alice Mufur Magha ◽  
Primus Azinwi Tamfuh ◽  
Lionelle Estelle Mamdem ◽  
Marie Christy Shey Yefon ◽  
Bertrand Kenzong ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Water ◽  
Total Variance ◽  
Organic Carbon Content ◽  
Available Water ◽  
High Soil ◽  
Local Soil ◽  
Very High

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

scholarly journals The ballast effect of lithogenic matter and its influences on the carbon fluxes in the Indian Ocean

2019 ◽  
Vol 16 (2) ◽  
pp. 485-503 ◽  
Author(s):  
Tim Rixen ◽  
Birgit Gaye ◽  
Kay-Christian Emeis ◽  
Venkitasubramani Ramaswamy
Keyword(s):  
Indian Ocean ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Primary Production ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Matter Content ◽  
Organic Carbon Flux ◽  
The Indian Ocean ◽  
Organic Carbon Fluxes

Abstract. Data obtained from long-term sediment trap experiments in the Indian Ocean in conjunction with satellite observations illustrate the influence of primary production and the ballast effect on organic carbon flux into the deep sea. They suggest that primary production is the main control on the spatial variability of organic carbon fluxes at most of our study sites in the Indian Ocean, except at sites influenced by river discharges. At these sites the spatial variability of organic carbon flux is influenced by lithogenic matter content. To quantify the impact of lithogenic matter on the organic carbon flux, the densities of the main ballast minerals, their flux rates and seawater properties were used to calculate sinking speeds of material intercepted by sediment traps. Sinking speeds in combination with satellite-derived export production rates allowed us to compute organic carbon fluxes. Flux calculations imply that lithogenic matter ballast increases organic carbon fluxes at all sampling sites in the Indian Ocean by enhancing sinking speeds and reducing the time of organic matter respiration in the water column. We calculated that lithogenic matter content in aggregates and pellets enhances organic carbon flux rates on average by 45 % and by up to 62 % at trap locations in the river-influenced regions of the Indian Ocean. Such a strong lithogenic matter ballast effect explains the fact that organic carbon fluxes are higher in the low-productive southern Java Sea compared to the high-productive western Arabian Sea. It also implies that land use changes and the associated enhanced transport of lithogenic matter from land into the ocean may significantly affect the CO2 uptake of the organic carbon pump in the receiving ocean areas.

Influence of Plastic Mulching on Soil Moisture, Nutrient and Microbial Biomass in Pineapple Cultivation on Undulating Hilly Areas of Nagaland

Agropedology ◽  
2019 ◽  
Vol 29 (1) ◽  
Author(s):  
Christy Sangma ◽  
◽  
A. Thirugnanavel ◽  
Ph. Romen Sharma ◽  
G. Rajesha ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Microbial Biomass Carbon ◽  
Fertility Level ◽  
Biomass Carbon ◽  
Available N ◽  
Plastic Mulching

The pineapple var. Kew was planted on black polythene film mulching with double hedgerow planting to find out the influence of mulches on soil and plant. The soil samples were collected twice (kharif and rabi) at two different depths (0-15 and 15-30 cm), and the pH, soil organic carbon (SOC), nitrogen, phosphorus, potassium, basal respiration and soil microbial biomass carbon were analysed. The data revealed that soil organic carbon and available N, P, and K content were slightly higher in the bottom hill than the top hill. The mulched field had higher nutrients than the non-mulched field. The fertility level varied slightly between the seasons. The biological parameters (microbial biomass carbon) were observed to be significantly higher (P≤0.05) in the bottom hill in both the seasons than the non-mulched field. The soil moisture content ranged from 5.9 % in March to 24.24 % August in the bottom hill (15-30 cm depth). The moisture content in the non-mulched field was lower than the mulched field.

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

<p>Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.</p><p>In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization. </p><p>This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (<30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.</p>

Lateral carbon export from polygonal tundra catchments on Samoylov Island, Lena River Delta

2020 ◽  
Author(s):  
Lutz Beckebanze ◽  
Josefine Walz ◽  
Benjamin R.K. Runkle ◽  
David Holl ◽  
Irina V. Fedorova Fedorova ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Time Scales ◽  
Carbon Flux ◽  
Carbon Balance ◽  
Dissolved Inorganic Carbon ◽  
Growing Season ◽  
Carbon Fluxes ◽  
Carbon Export ◽  
Lena River ◽  
Annual Time

<p>Permafrost-affected soils contain a large quantity of soil organic carbon (SOC). Two processes control the amount of carbon stored in soils. The photosynthetic activity of plants produces biomass that may accumulate in the soil, while microorganism’s respiration leads to a depletion of the soil carbon stocks through decomposition. The carbon balance defines whether a soil acts as a source or sink of carbon. In recent decades, many researchers observed and analyzed the carbon balance of permafrost soils. In most cases, the focus lays on observations of the vertical carbon flux (CO<sub>2</sub> and CH<sub>4</sub>) to estimate the carbon balance. However, there is lack of information regarding the lateral losses of carbon via dissolved organic carbon (DOC) or dissolved inorganic carbon (DIC) in ground- or rainwater.</p><p>In this study, we estimate the lateral carbon fluxes from a permafrost-affected site in north-eastern Siberia, Russia. Long-term measurements of vertical carbon fluxes have been conducted at this study site. By considering both, the vertical and the lateral carbon fluxes, we estimate the complete carbon balance for one growing season in 2014 and discuss the contribution of the lateral carbon flux to the overall carbon balance.</p><p>The results show that the vertical CO<sub>2</sub> fluxes dominate the carbon balance during the growing season from June 8<sup>th</sup> – September 8<sup>th</sup> (-19 ± 1.2 kg-C m<sup>-2</sup>). The lateral fluxes of DOC and DIC reached values of +0.1 ± 0.01 and +1.4 ± 0.09 kg-C m<sup>-2</sup>, respectively, whereas the vertical fluxes of CH<sub>4</sub> had values of +0.7 ± 0.02 kg-C m<sup>-2 </sup>integrated over this time. By considering the lateral carbon export, the net ecosystem carbon balance of the study area was reduced by 8%. On shorter time scales of days, the relationship between lateral and vertical flux changes within the growing season. Early in the growing season, the lateral carbon flux outpaces the weak vertical CO<sub>2</sub> uptake for a few days and converts the estimated carbon balance from a sink to a source.</p><p>We conclude that lateral carbon fluxes have a larger influence on the carbon balance of our study site on time scales of days (early and late growing season) and that this influence decreases with annual time scales. Therefore, the vertical carbon flux can be seen as a good approximation for the carbon balance of this study site on annual time scales.</p>

scholarly journals Global soil organic carbon removal by water erosion under climate change and land use change during 1850–2005 AD

2018 ◽  
Author(s):  
Victoria Naipal ◽  
Philippe Ciais ◽  
Yilong Wang ◽  
Ronny Lauerwald ◽  
Bertrand Guenet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Organic Carbon ◽  
Land Use Change ◽  
Soil Organic Carbon ◽  
Climate Variability ◽  
Carbon Cycle ◽  
Atmospheric Co2 ◽  
Terrestrial Carbon

Abstract. The onset and expansion of agriculture has accelerated soil erosion by rainfall and runoff substantially, mobilizing vast quantities of soil organic carbon (SOC) globally. Studies show that at timescales of decennia to millennia this mobilized SOC can significantly alter previously estimated carbon emissions from land use change (LUC). However, a full understanding of the impact of erosion on land-atmosphere carbon exchange is still missing. The aim of our study is to better constrain the terrestrial carbon fluxes by developing methods compatible with Earth System Models (ESMs) in order to explicitly represent the links between soil erosion by rainfall and runoff and carbon dynamics. For this we use an emulator that represents the carbon cycle of a land surface model, in combination with the Revised Universal Soil Loss Equation model. We applied this modeling framework at the global scale to evaluate the effects of potential soil erosion (soil removal only) in the presence of other perturbations of the carbon cycle: elevated atmospheric CO2, climate variability, and LUC. We found that over the period 1850–2005 AD acceleration of soil erosion leads to a total potential SOC removal flux of 100 Pg C of which 80 % occurs on agricultural, pasture and natural grass lands. Including soil erosion in the SOC-dynamics scheme results in a doubling of the cumulative loss of SOC over 1850–2005 due to the combined effects of climate variability, increasing atmospheric CO2 and LUC. This additional erosional loss decreases the cumulative global carbon sink on land by 5 Pg for this specific period, with the largest effects found for the tropics, where deforestation and agricultural expansion increased soil erosion rates significantly. We also show that the potential effects of soil erosion on the global SOC stocks cannot be ignored when compared to the effects of climate change or land use change on the carbon cycle. We conclude that it is necessary to include soil erosion in assessments of LUC and evaluations of the terrestrial carbon cycle.

Sign in / Sign up

Export Citation Format

Share Document