scholarly journals Quantifying the effects of harvesting on carbon fluxes and stocks in northern temperate forests

2014 ◽  
Vol 11 (23) ◽  
pp. 6667-6682 ◽  
Author(s):  
W. Wang ◽  
J. Xiao ◽  
S. V. Ollinger ◽  
A. R. Desai ◽  
J. Chen ◽  
...  
Keyword(s):  
Temperate Forests ◽  
Ecosystem Respiration ◽  
Carbon Fluxes ◽  
Stand Age ◽  
Model Parameters ◽  
Area Index ◽  
C Dynamics ◽  
C Stocks ◽  
Assimilation Capacity ◽  
Northern Temperate Forests

Abstract. Harvest disturbance has substantial impacts on forest carbon (C) fluxes and stocks. The quantification of these effects is essential for the better understanding of forest C dynamics and informing forest management in the context of global change. We used a process-based forest ecosystem model, PnET-CN, to evaluate how, and by what mechanisms, clear-cuts alter ecosystem C fluxes, aboveground C stocks (AGC), and leaf area index (LAI) in northern temperate forests. We compared C fluxes and stocks predicted by the model and observed at two chronosequences of eddy covariance flux sites for deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) in the Upper Midwest region of northern Wisconsin and Michigan, USA. The average normalized root mean square error (NRMSE) and the Willmott index of agreement (d) for carbon fluxes, LAI, and AGC in the two chronosequences were 20% and 0.90, respectively. Simulated gross primary productivity (GPP) increased with stand age, reaching a maximum (1200–1500 g C m−2 yr−1) at 11–30 years of age, and leveled off thereafter (900–1000 g C m−2 yr−1). Simulated ecosystem respiration (ER) for both plant functional types (PFTs) was initially as high as 700–1000 g C m−2 yr−1 in the first or second year after harvesting, decreased with age (400–800 g C m−2 yr−1) before canopy closure at 10–25 years of age, and increased to 800–900 g C m−2 yr−1 with stand development after canopy recovery. Simulated net ecosystem productivity (NEP) for both PFTs was initially negative, with net C losses of 400–700 g C m−2 yr−1 for 6–17 years after clear-cuts, reaching peak values of 400–600 g C m−2 yr−1 at 14–29 years of age, and eventually stabilizing in mature forests (> 60 years old), with a weak C sink (100–200 g C m−2 yr−1). The decline of NEP with age was caused by the relative flattening of GPP and gradual increase of ER. ENF recovered more slowly from a net C source to a net sink, and lost more C than DBF. This suggests that in general ENF may be slower to recover to full C assimilation capacity after stand-replacing harvests, arising from the slower development of photosynthesis with stand age. Our model results indicated that increased harvesting intensity would delay the recovery of NEP after clear-cuts, but this had little effect on C dynamics during late succession. Future modeling studies of disturbance effects will benefit from the incorporation of forest population dynamics (e.g., regeneration and mortality) and relationships between age-related model parameters and state variables (e.g., LAI) into the model.

scholarly journals Quantifying legacies of clearcut on carbon fluxes and biomass carbon stock in northern temperate forests

2014 ◽  
Vol 11 (6) ◽  
pp. 8789-8828
Author(s):  
W. Wang ◽  
J. Xiao ◽  
S. V. Ollinger ◽  
A. R. Desai ◽  
J. Chen ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Ecosystem Respiration ◽  
Ecosystem Model ◽  
Stand Age ◽  
Forest Types ◽  
Area Index ◽  
C Dynamics ◽  
C Stocks ◽  
Harvesting Intensity ◽  
Assimilation Capacity

Abstract. Stand-replacing disturbances including harvests have substantial impacts on forest carbon (C) fluxes and stocks. The quantification and simulation of these effects is essential for better understanding forest C dynamics and informing forest management in the context of global change. We evaluated the process-based forest ecosystem model, PnET-CN, for how well and by what mechanisms changes of ecosystem C fluxes, aboveground C stocks (AGC), and leaf area index (LAI) arise after clearcuts. We compared the effects of stand-replacing harvesting on C fluxes and stocks using two chronosequences of eddy covariance flux sites for deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) in the Upper Midwest region of northern Wisconsin and Michigan, USA. The average values of normalized root mean square error (NRMSE) and the Willmott index of agreement (d) between simulated and inferred from observation variables including gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), LAI, and AGC in the two chronosequences were 20% and 0.90, respectively. Simulated GPP increased with stand age, reaching a maximum (∼1200–1500 g C m−2 yr−1) at 11–30 years of age, and leveled off thereafter (∼900–1000 g C m−2 yr−1). Simulated ER for both forest types was initially as high as ∼700–1000 g C m−2 yr−1 in the first or second year after clearcuts, decreased with age (∼400–800 g C m−2 yr−1) before canopy closure at 10–25 years of age, and increased to ∼800–900 g C m−2 yr−1 with stand development after canopy recovery. Simulated NEP for both forest types was initially negative with the net C losses of ∼400–700 g C m−2 yr−1 for 6–17 years after harvesting, reached the peak values of ∼400–600 g C m−2 yr−1 at 14–29 years of age, and became stable and a weak C sink (∼100–200 g C m−2 yr−1) in mature forests (>60 years old). The decline of NEP with age was caused by the relative flatting of GPP and gradual increasing of ER. ENF recovered slower from net C source to net sink and lost more C than DBF, suggesting ENF are likely slower to recover C assimilation capacity after stand-replacing harvests due to slower development of photosynthesis with stand age. Model results indicated that increasing harvesting intensity would delay recovery of NEP after clearing, but had little effect on C dynamics during late succession. Further improvements in numerical process-based forest population dynamic models for predicting the effects of climate change and forest harvests are considered.

scholarly journals Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiye Zeng ◽  
Tsuneo Matsunaga ◽  
Zheng-Hong Tan ◽  
Nobuko Saigusa ◽  
Tomoko Shirai ◽  
...  
Keyword(s):  
Random Forest ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Carbon Fluxes ◽  
Mitigation Policy ◽  
Terrestrial Biosphere ◽  
Area Index ◽  
The World ◽  
Global Land

Abstract The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the world. This leads to using various methods to upscale site measurements to the whole terrestrial biosphere. This article reports a product obtained by using a Random Forest to upscale terrestrial net ecosystem exchange, gross primary production, and ecosystem respiration from FLUXNET 2015. Our product covers land from −60°S to 80°N with a spatial resolution of 0.1° × 0.1° every 10 days during the period 1999–2019. It was compared with four existing products. A distinguishable feature of our method is using three derived variables of leaf area index to represent plant functional type (PFT) so that measurements from different PFTs can be mixed better by the model. This product can be valuable for the carbon-cycle community to validate terrestrial biosphere models and cross check datasets.

The spectrum of ecosystem functional properties

2020 ◽  
Author(s):  
Mirco Migliavacca ◽  
Talie Musavi ◽  
Miguel D. Mahecha ◽  
Jacob A. Nelson ◽  
Juergen Knauer ◽  
...  
Keyword(s):  
Functional Properties ◽  
Ecosystem Respiration ◽  
Investment Strategy ◽  
Two Dimensions ◽  
Stand Age ◽  
Area Index ◽  
Steady State Condition ◽  
The Third ◽  
Use Efficiency ◽  
Third Dimension

<p>Understanding the coordination of ecosystem functions across biomes and climate is still a major challenge that hampers our ability to properly predict biosphere response to climate change. Theories such as the leaf economics spectrum and the least cost investment strategy postulate that plants optimize the rate of investment in transpiration, photosynthetic capacity, and nitrogen (N) allocation dependent on the ratio of their costs to gain given their resources and environment.</p><p>In this contribution we test whether theories about functional traits coordination at leaf and organs level are emerging at ecosystem scale. We further investigate the existence of a global spectrum of ecosystem functional properties, and analyze how state of the art terrestrial biosphere models reproduce the spectrum.</p><p>To do so we used data of CO<sub>2</sub>, water and energy exchange for 164 sites (1237 site years) from the FLUXNET LaThuile and FLUXNET 2015 datasets with at least 3 years of data. For 61 sites, we were able to compile site information on canopy-scale measurements of foliar N concentration, maximum leaf area index , and stand age, from the literature.</p><p>We find evidence that a global spectrum of ecosystem functional properties exist, and that most of the variability (66.2%) is captured by three dimensions. The first dimension represents ecosystem productivity; the second the water availability gradient, and climate limitations to productivity; the third dimension reflects ecosystem respiration potential and carbon-use efficiency and is related to aridity and stand age and disturbance regimes. The first two dimensions of the spectrum are well captured by ecosystem models, while the third dimension is poorly reproduced. This might be related to the spin up of the models (steady-state condition) or to an incomplete representation of processes related to age that might limit the ability of models to accurately predict the dynamic carbon, water and nutrient cycling in ecosystems in disturbed areas.</p><p>Finally, we show across ecosystems globally that leaf level theories can be in some cases translated to the ecosystem scale. As a main example we found an inverse relationship between photosynthetic N and water use efficiency as postulated by the least cost investment theory across FLUXNET sites. However, this is possible only when the contribution of vegetation is properly accounted for, and evaporation from soil and wet surfaces is removed from the analysis. This highlights that emerging biological patterns at ecosystem scale might be masked by other factors related to physical rather than biological responses.</p>

scholarly journals Constraining a global ecosystem model with multi-site eddy-covariance data

2012 ◽  
Vol 9 (10) ◽  
pp. 3757-3776 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Chevallier ◽  
C. Bacour ◽  
F. Maignan ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Parameter Analysis ◽  
Single Site ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Area Index

Abstract. Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI).

scholarly journals Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

2010 ◽  
Vol 7 (2) ◽  
pp. 1705-1744 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
C. Rüdiger ◽  
A. L. Barbu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Atmospheric Variables ◽  
Assimilation System

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15 m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

scholarly journals Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

2010 ◽  
Vol 14 (6) ◽  
pp. 1109-1124 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
C. Rüdiger ◽  
A. L. Barbu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Atmospheric Variables ◽  
Assimilation System

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

scholarly journals The carbon budget of the managed grasslands of Great Britain constrained by earth observations

2021 ◽  
Author(s):  
Vasileios Myrgiotis ◽  
Thomas Luke Smallman ◽  
Mathew Williams
Keyword(s):  
Great Britain ◽  
Fold Increase ◽  
Number Fields ◽  
Livestock Grazing ◽  
Model Parameters ◽  
Summer Drought ◽  
Area Index ◽  
C Dynamics ◽  
C Balance ◽  
Sentinel 2

Abstract. Grasslands cover around two thirds of the land area of Great Britain (GB) and are important reservoirs of terrestrial biological carbon (C). Outside a few well-monitored sites the quantification of C dynamics in managed grasslands is made complex by the spatio-temporal variability of weather conditions combined with grazing and cutting patterns. Earth observation (EO) missions produce high-resolution frequently-retrieved proxy data on the state of grassland canopies but synergies between EO data and biogeochemical modelling to estimate grassland C dynamics are under-explored. Here, we show the potential of model-data fusion (MDF) to provide robust near-real time analyses of managed grasslands of GB (England, Wales andScotland). We combine EO data and process-based modelling to estimate grassland C balance and to examine the role of management. We implement a MDF algorithm to (1) infer grassland management from vegetation reduction data (Proba-V), (2) optimise model parameters by assimilating leaf area index (LAI) data (Sentinel-2) and (3) simulate livestock grazing, grass cutting, and C allocation and loss to the atmosphere. The MDF algorithm was applied for 2017 and 2018 at 1855 fields sampled from across GB. The algorithm was able to effectively assimilate the Sentinel-2 based LAI time series (overlap = 80 %, RMSE = 1 gCm−2, bias = 0.35 gCm−2) and predict livestock densities per area that correspond with independent census-based data (r = 0.68). The mean total removed biomass across all simulated fields was 6 (±1.8) tDM ha−1 y−1. The simulated grassland ecosystems were on average C sinks in 2017 and 2018; the GB-average net ecosystem exchange (NEE) and net biome exchange (NBE) for 2017 was −232 ± 94 and for 2018 was −120 ± 103 gCm−2 y−1. The 2018 summer drought reduced C sinks, with a 9-fold increase in the number fields that were C sources (NBE > 0) in 2018 compared to 2017. We conclude that management in the form of sward condition and the timing, intensity and type of defoliation are key determinants of the C balance of managed grasslands. Nevertheless, extreme weather, such as prolonged droughts, can convert grassland C sinks to sources.

scholarly journals Constraining a global ecosystem model with multi-site eddy-covariance data

2012 ◽  
Vol 9 (3) ◽  
pp. 3317-3380 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Chevallier ◽  
C. Bacour ◽  
F. Maignan ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Parameter Analysis ◽  
Single Site ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Area Index

Abstract. Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from twelve temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root mean square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross carbon flux (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS set of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based measurements of normalized difference vegetation index (NDVI).

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