scholarly journals Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model

2016 ◽  
Author(s):  
Nitin Chaudhary ◽  
Paul A. Miller ◽  
Benjamin Smith
Keyword(s):  
Vegetation Dynamics ◽  
Carbon Fluxes ◽  
Published Data ◽  
Vegetation Model ◽  
Climate Conditions ◽  
Dynamic Vegetation Model ◽  
Fertilization Effect ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Dynamic global vegetation models (DGVMs) are designed for the study of past, present and future vegetation patterns together with associated biogeochemical cycles and climate feedbacks. However, current DGVMs lack functionality for the representation of peatlands, an important store of carbon at high latitudes. We demonstrate a new implementation of peatland dynamics in a customised "Arctic" version of the dynamic vegetation model LPJ-GUESS, simulating the long-term evolution of selected northern peatland ecosystems and assessing the effect of changing climate on peatland carbon balance. Our approach employs a dynamic multi-layer soil with representation of freeze-thaw processes and litter inputs from a dynamically-varying mixture of the main peatland plant functional types; mosses, dwarf shrubs and graminoids. The model was calibrated and tested for a sub-arctic mire in Stordalen, Sweden, and validated at a temperate bog site in Mer Bleue, Canada. A regional evaluation of simulated carbon fluxes, hydrology and vegetation dynamics encompassed additional locations spread across Scandinavia. Simulated peat accumulation was found to be generally consistent with published data and the model was able to capture reported long-term vegetation dynamics, water table position and carbon fluxes. A series of sensitivity experiments were carried out to investigate the vulnerability of high latitude peatlands to climate change. We found that the Stordalen mire may be expected to sequester more carbon in the first half of the 21st century due to milder and wetter climate conditions, a longer growing season, and CO2 fertilization effect, turning into a carbon source after mid-century because of higher decomposition rates in response to warming soils.

scholarly journals Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model

2017 ◽  
Vol 14 (10) ◽  
pp. 2571-2596 ◽  
Author(s):  
Nitin Chaudhary ◽  
Paul A. Miller ◽  
Benjamin Smith
Keyword(s):  
Vegetation Dynamics ◽  
Carbon Fluxes ◽  
Published Data ◽  
Climate Feedbacks ◽  
Climate Conditions ◽  
Fertilization Effect ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Dynamic global vegetation models (DGVMs) are designed for the study of past, present and future vegetation patterns together with associated biogeochemical cycles and climate feedbacks. However, most DGVMs do not yet have detailed representations of permafrost and non-permafrost peatlands, which are an important store of carbon, particularly at high latitudes. We demonstrate a new implementation of peatland dynamics in a customized Arctic version of the LPJ-GUESS DGVM, simulating the long-term evolution of selected northern peatland ecosystems and assessing the effect of changing climate on peatland carbon balance. Our approach employs a dynamic multi-layer soil with representation of freeze–thaw processes and litter inputs from a dynamically varying mixture of the main peatland plant functional types: mosses, shrubs and graminoids. The model was calibrated and tested for a sub-Arctic mire in Stordalen, Sweden, and validated at a temperate bog site in Mer Bleue, Canada. A regional evaluation of simulated carbon fluxes, hydrology and vegetation dynamics encompassed additional locations spread across Scandinavia. Simulated peat accumulation was found to be generally consistent with published data and the model was able to capture reported long-term vegetation dynamics, water table position and carbon fluxes. A series of sensitivity experiments were carried out to investigate the vulnerability of high-latitude peatlands to climate change. We found that the Stordalen mire may be expected to sequester more carbon in the first half of the 21st century due to milder and wetter climate conditions, a longer growing season, and the CO2 fertilization effect, turning into a carbon source after mid-century because of higher decomposition rates in response to warming soils.

scholarly journals Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170315 ◽  
Author(s):  
Cleiton B. Eller ◽  
Lucy Rowland ◽  
Rafael S. Oliveira ◽  
Paulo R. L. Bittencourt ◽  
Fernanda V. Barros ◽  
...  
Keyword(s):  
Tropical Forest ◽  
El Niño ◽  
Optimization Model ◽  
El Nino ◽  
Soil Drought ◽  
Forest Sites ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration ( E ) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

scholarly journals ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model

2018 ◽  
Vol 11 (6) ◽  
pp. 2249-2272 ◽  
Author(s):  
Wei Li ◽  
Chao Yue ◽  
Philippe Ciais ◽  
Jinfeng Chang ◽  
Daniel Goll ◽  
...  
Keyword(s):  
Lignocellulosic Biomass ◽  
Management Practices ◽  
Carbon Allocation ◽  
Field Measurements ◽  
Bioenergy Crops ◽  
Climate Mitigation ◽  
Bioenergy Crop ◽  
Vegetation Model ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Bioenergy crop cultivation for lignocellulosic biomass is increasingly important for future climate mitigation, and it is assumed on large scales in integrated assessment models (IAMs) that develop future land use change scenarios consistent with the dual constraint of sufficient food production and deep decarbonization for low climate-warming targets. In most global vegetation models, there is no specific representation of crops producing lignocellulosic biomass, resulting in simulation biases of biomass yields and other carbon outputs, and in turn of future bioenergy production. Here, we introduced four new plant functional types (PFTs) to represent four major lignocellulosic bioenergy crops, eucalypt, poplar and willow, Miscanthus, and switchgrass, in the global process-based vegetation model ORCHIDEE. New parameterizations of photosynthesis, carbon allocation, and phenology are proposed based on a compilation of field measurements. A specific harvest module is further added to the model to simulate the rotation of bioenergy tree PFTs based on their age dynamics. The resulting ORCHIDEE-MICT-BIOENERGY model is applied at 296 locations where field measurements of harvested biomass are available for different bioenergy crops. The new model can generally reproduce the global bioenergy crop yield observations. Biases in the model results related to grid-based simulations versus the point-scale measurements and the lack of fertilization and fertilization management practices in the model are discussed. This study sheds light on the importance of properly representing bioenergy crops for simulating their yields. The parameterizations of bioenergy crops presented here are generic enough to be applicable in other global vegetation models.

scholarly journals Towards a better understanding of Siberian wildfires: linking paleoenvironmental fire reconstructions with an individual-based spatially explicit fire-vegetation model

2021 ◽  
Author(s):  
Ramesh Glückler ◽  
Elisabeth Dietze ◽  
Josias Gloy ◽  
Ulrike Herzschuh ◽  
Stefan Kruse
Keyword(s):  
Forest Fires ◽  
Fire Regimes ◽  
Ecosystem Functions ◽  
Spatially Explicit ◽  
Eastern Siberia ◽  
Vegetation Model ◽  
Individual Tree ◽  
Vegetation Models ◽  
The Individual

<p>Wildfires are an essential ecological process, located at the interface between atmosphere, biosphere, and geosphere. Climate-related changes in their appearance and frequency will shape the boreal forest of tomorrow, the largest terrestrial biome responsible for numerous important ecosystem functions. Changing fire regimes could also increase pressure on fire management and become a threat for humans living in Siberia. However, a lack of long-term fire reconstructions complicates the understanding of the main drivers in the larch-dominated forests of eastern Siberia. At the same time, this lack of long-term understanding also aggravates the validation of fire-vegetation models, and thus predictions of future changes of fire regimes in this vital region.</p><p>Here, we present a new fire module being built for the individual-based, spatially explicit vegetation model LAVESI (<em>Larix</em> Vegetation Simulator). LAVESI is able to simulate fine-scale interactions in individual tree’s life stages and detailed population dynamics, now expanded by the ability of wildfires igniting and damaging biomass. Fire-vegetation simulations were computed around the catchment of Lake Khamra (SW Yakutia), which experienced forest fires in the years 2007 and 2014 according to remote sensing imagery. From the lake, we previously contributed a new, sedimentary charcoal-based fire reconstruction of the late Holocene. Testing the fire module at a current study site, where modern and historic data has already been collected, allows us to improve it, and look into ways in which the fire reconstruction might help inform the model, before eventually scaling it up to cover larger regions. This represents a first step towards a reliable fire-vegetation model, able to predict future impacts of fires on both the forests of eastern Siberia, as well as the humans living there.</p>

Forcing climate variability has large impacts on terrestrial carbon storage in a dynamic global vegetation model

2021 ◽  
Author(s):  
Andreas Krause ◽  
Katharina Küpfer ◽  
Anja Rammig
Keyword(s):  
Climate Variability ◽  
Carbon Storage ◽  
Precipitation Variability ◽  
Terrestrial Carbon ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Climate Conditions ◽  
Temperature Radiation ◽  
High Precipitation ◽  
Global Vegetation

<p>Terrestrial carbon storage is largely driven by prevailing climate conditions. However, ecosystems are not only affected by mean climate conditions but also by day-to-day climate variability, which is projected to increase in the future. Here we explore the effects of low vs. high climate variability on global terrestrial carbon storage in the dynamic global vegetation model LPJ-GUESS. Low variability corresponds to linear interpolation between monthly means while high variability corresponds to daily means. We conduct three factorial simulations: one driven by low variability for temperature, radiation, and precipitation; one with low temperature and radiation variability but high precipitation variability; and one with high variability for all climatic drivers. All three options are commonly used in existing LPJ-GUESS studies but have so far not been compared to each other in terms of carbon cycle impacts. Surprisingly, the low variability simulation results in the smallest terrestrial carbon stocks globally (1963 Gt C), while low temperature/radiation variability but high precipitation variability simulates the largest carbon storage (2171 Gt C). Differences are most pronounced in high latitudes and deviations from the global trend also occur in some regions. Exploring the underlying processes, we find that differences in carbon stocks are largely driven by differences in ecosystem productivity. In LPJ-GUESS, high precipitation variability increases nitrogen availability via enhanced nitrogen mineralisation and reduced leaching, thereby promoting plant growth. In contrast, high temperature variability decreases productivity as the optimum temperature range for photosynthesis is often exceeded in temperate and boreal regions. Differences in fire mortality and soil water availability across simulations seem to be less important. Our results suggest that future changes in climate variability could impact ecosystem carbon storage via subtle effects on photosynthesis and coupled carbon-nutrient cycling. They also imply that ecosystem modellers need to be aware that changing the temporal resolution of the input climate (e.g. from monthly to daily means) may substantially affect their simulation results.</p>

scholarly journals Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA‐TV)

2020 ◽  
Vol 26 (8) ◽  
pp. 4478-4494 ◽  
Author(s):  
Isabel Martínez Cano ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
S. Joseph Wright ◽  
Matteo Detto ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Size Structure ◽  
Carbon Fluxes ◽  
Vegetation Model ◽  
Dynamic Vegetation Model

scholarly journals Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9333): Implementation and evaluation of simulations for Europe

2021 ◽  
Author(s):  
Mats Lindeskog ◽  
Fredrik Lagergren ◽  
Benjamin Smith ◽  
Anja Rammig
Keyword(s):  
Forest Management ◽  
Carbon Sink ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Species Structure ◽  
Vegetation Model ◽  
Dynamic Vegetation Model ◽  
Growing Stock ◽  
Vegetation Models ◽  
Wood Harvest

Abstract. Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to making projections of the development of forest carbon sink capacity in a future climate. Forest management capabilities in dynamic vegetation models are important to include the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the introduction of a forest management module in the dynamic vegetation model LPJ-GUESS. Different age- and species-structure setup strategies and harvest alternatives are introduced. The model is used to represent current European forests and an automated harvest strategy is applied. Modelled carbon stocks and fluxes are evaluated against observed data at the continent and country levels. Including wood harvest in simulations increases the total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha−1 y−1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kgC m−2 y−1) is 63 % of reported values. The fit of modelled values and observations for individual European countries vary, but NAI is generally closer to observations when including wood harvest in simulations.

scholarly journals ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model

2018 ◽  
Author(s):  
Wei Li ◽  
Chao Yue ◽  
Philippe Ciais ◽  
Jinfeng Chang ◽  
Daniel Goll ◽  
...  
Keyword(s):  
Lignocellulosic Biomass ◽  
Management Practices ◽  
Carbon Allocation ◽  
Field Measurements ◽  
Bioenergy Crops ◽  
Climate Mitigation ◽  
Bioenergy Crop ◽  
Vegetation Model ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Bioenergy crop cultivation for lignocellulosic biomass is increasingly important for future climate mitigation, and it is assumed on large scales in Integrated Assessment Models (IAMs) that develop future land use change scenarios consistent with the dual constraint of sufficient food production and deep de-carbonization for low climate warming targets. In most global vegetation models, there is no specific representation of crops producing lignocellulosic biomass, resulting in simulation biases of biomass yields and other carbon outputs, and in turn of future bioenergy production. Here, we introduced four new plant functional types (PFTs) to represent four major lignocellulosic bioenergy crops, eucalypt, poplar and willow, Miscanthus, and switchgrass, in the global process-based vegetation model, ORCHIDEE. New parameterizations of photosynthesis, carbon allocation and phenology are proposed based on a compilation of field measurements. A specific harvest module is further added to the model to simulate the rotation of bioenergy tree PFTs based on their age dynamics. The resulting ORCHIDEE-MICT-BIOENERGY model is applied at 296 locations where field measurements of harvested biomass are available for different bioenergy crops. The new model can generally reproduce the global bioenergy crop yield observations. Biases of the model results related to grid-based simulations versus the point-scale measurements and the lack of fertilization and fertilization management practices in the model are discussed. This study sheds light on the importance of properly representing bioenergy crops for simulating their yields. The parameterizations of bioenergy crops presented here are generic enough to be applicable in other global vegetation models.

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