scholarly journals Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models

2019 ◽  
Vol 16 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Matthias Forkel ◽  
Niels Andela ◽  
Sandy P. Harrison ◽  
Gitta Lasslop ◽  
Margreet van Marle ◽  
...  
Keyword(s):  
Population Density ◽  
Satellite Observations ◽  
Burned Area ◽  
Fire Occurrence ◽  
Climate Variables ◽  
Evaluation Approach ◽  
Previous Season ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled dynamic global vegetation models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the “Fire Modeling Intercomparison Project” (FireMIP) that were run using a common protocol and forcing data sets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in previous-season leaf area index and plant productivity in most fire-prone ecosystems, which was largely underestimated by most DGVMs. Hence, our pattern-oriented model evaluation approach allowed us to diagnose that vegetation effects on fire are a main deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change.

scholarly journals Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

2018 ◽  
Author(s):  
Matthias Forkel ◽  
Niels Andela ◽  
Sandy P. Harrison ◽  
Gitta Lasslop ◽  
Margreet van Marle ◽  
...  
Keyword(s):  
Population Density ◽  
Leaf Area ◽  
Model Evaluation ◽  
Satellite Observations ◽  
Burned Area ◽  
Fire Occurrence ◽  
Climate Variables ◽  
Evaluation Approach ◽  
Previous Season ◽  
Vegetation Models

Abstract. Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005–2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.

scholarly journals SPITFIRE-2: an improved fire module for Dynamic Global Vegetation Models

2012 ◽  
Vol 5 (3) ◽  
pp. 2347-2443 ◽  
Author(s):  
M. Pfeiffer ◽  
J. O. Kaplan
Keyword(s):  
Terrestrial Ecosystems ◽  
Explicit Calculation ◽  
Burned Area ◽  
Modern World ◽  
Natural Fire ◽  
Disturbance Factor ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Way

Abstract. Fire is the primary disturbance factor in many terrestrial ecosystems. Wildfire alters vegetation structure and composition, affects carbon storage and biogeochemical cycling, and results in the release of climatically relevant trace gases, including CO2, CO, CH4, NOx, and aerosols. Assessing the impacts of global wildfire on centennial to multi-millennial timescales requires the linkage of process-based fire modeling with vegetation modeling using Dynamic Global Vegetation Models (DGVMs). Here we present a new fire module, SPITFIRE-2, and an update to the LPJ-DGVM that includes major improvements to the way in which fire occurrence, behavior, and the effect of fire on vegetation is simulated. The new fire module includes explicit calculation of natural ignitions, the representation of multi-day burning and coalescence of fires and the calculation of rates of spread in different vegetation types, as well as a simple scheme to model crown fires. We describe a new representation of anthropogenic biomass burning under preindustrial conditions that distinguishes the way in which the relationship between humans and fire are different between hunter-gatherers, obligate pastoralists, and farmers. Where and when available, we evaluate our model simulations against remote-sensing based estimates of burned area. While wildfire in much of the modern world is largely influenced by anthropogenic suppression and ignitions, in those parts of the world where natural fire is still the dominant process, e.g. in remote areas of the boreal forest, our results demonstrate a significant improvement in simulated burned area over previous models. With its unique properties of being able to simulate preindustrial fire, the new module we present here is particularly well suited for the investigation of climate-human-fire relationships on multi-millennial timescales.

Advancing representation of anthropogenic fire in dynamic global vegetation models

2021 ◽  
Author(s):  
James Millington ◽  
Oliver Perkins ◽  
Matthew Kasoar ◽  
Apostolos Voulgarakis
Keyword(s):  
Land Use ◽  
Fire Regimes ◽  
Economic Indicators ◽  
Burned Area ◽  
Agent Based ◽  
Anthropogenic Fire ◽  
Fire Use ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

<div> <p>It is now commonly-understood that improved understanding of global fire regimes demands better representation of anthropogenic fire in dynamic global vegetation models (DGVMs). However, currently there is no clear agreement on how human activity should be incorporated into fire-enabled DGVMs and existing models exhibit large differences in the sensitivities of socio-economic variables. Furthermore, existing approaches are limited to empirical statistical relations between fire regime variables and globally available socio-economic indicators such as population density or GDP. Although there has been some limited representation in global models of the contrasting ways in which different classes of actors use or manage fires, we argue that fruitful progress in advancing representation of anthropogenic fire in DGVMs will come by building on agent-based modelling approaches. Here, we report on our progress developing a global agent-based representation of anthropogenic fire and its coupling with the JULES-INFERNO fire-enabled DGVM.  </p> </div><div> <p>Our modelling of anthropogenic fire adopts an approach that classifies ‘agent functional types’ (AFTs) to represent human fire activity based on land use/cover and Stephen Pyne’s fire development stages. For example, the ‘swidden’ AFT represents shifting cultivation farmers managing cropland and secondary vegetation in a pre-industrial development setting. This approach is based on the assumption that anthropogenic fire use and management is primarily a function of land use but influenced by socio-economic context, leading different AFTs to produce qualitatively distinct fire regimes. The literature empirically supports this assumption, however data on human fire interactions are fragmented across many academic fields (including anthropology, geography, land economics). Therefore, we developed a Database of Anthropogenic Fire Impacts (DAFI) containing 1798 case studies of fire use/management from 519 publications, covering more than 100 countries and all major biomes (except Arctic/Antarctic). We discuss DAFI development, patterns in the resulting data, and possible applications. Specifically, DAFI is used with ancillary data (e.g. biophysical, socio-economic indicators), classification and regression methods to test and refine our initial AFT classification, characterise AFT fire variables, and distribute AFTs spatially. Our model will then simulate AFT distributions for alternative scenarios of change (e.g. specified by the Shared Socioeconomic Pathways). </p> </div><div> <p>Coupling distinct models can be achieved in a variety of ways, but broadly we can distinguish between ‘loose’ coupling in which information flow is uni-directional, and ‘tight’ coupling in which information flows are integrated with feedbacks and dynamic updating. Our intention is to tightly couple our AFT model with JULES-INFERNO, such that fire use and suppression behaviours from the former influence simulated fire ignitions and burned area in the latter. Reciprocally, total burned area simulated by JULES-INFERNO will feedback to influence spatial distribution of AFTs in the next time step, modifying anthropogenic fire patterns for the next step of DGVM simulation. We discuss the potential for this tight model coupling to capture socio-ecological feedbacks in fire regimes, as well as  possible pitfalls and steps needed to test and verify model outputs. These are early steps in an important journey to improve representation of anthropogenic fire in DGVMs.</p> </div>

scholarly journals Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models

2016 ◽  
Vol 76 (2) ◽  
pp. 341-351
Author(s):  
L. F. C. Rezende ◽  
B. C. Arenque-Musa ◽  
M. S. B. Moura ◽  
S. T. Aidar ◽  
C. Von Randow ◽  
...  
Keyword(s):  
Data Mining ◽  
Native Species ◽  
Classification And Regression Tree ◽  
Northeastern Brazil ◽  
Semiarid Region ◽  
Global Changes ◽  
Data Mining Techniques ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

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 Identifying required model structures to predict global fire activity from satellite and climate data

2016 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Satellite Observations ◽  
Burned Area ◽  
Human Society ◽  
Fire Emissions ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation ◽  
Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Spread ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Fractal Properties ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

Where is the carbon going? - A multi-model comparison

2021 ◽  
Author(s):  
Michael O'Sullivan ◽  
Pierre Friedlingstein ◽  
Stephen Sitch
Keyword(s):  
Model Comparison ◽  
Net Primary Productivity ◽  
Relative Increase ◽  
Historical Period ◽  
Residence Times ◽  
Vegetation Carbon ◽  
Dynamic Global Vegetation Models ◽  
Spatio Temporal ◽  
Vegetation Models ◽  
Global Vegetation

<p>Net terrestrial carbon uptake is primarily driven by increases in net primary productivity (NPP) and/or the residence time of carbon in vegetation and soil. As such, it is of critical importance to accurately quantify spatio-temporal variation in both terms and determine their drivers. Both NPP and residence times are modulated by changing environmental conditions, including climate change and variability, atmospheric CO<sub>2</sub>, and Land Use and Land Cover Changes (LULCC). For the historical period, 1901-2019, outputs from a suite of Dynamic Global Vegetation Models (DGVMs) from the TRENDY consortium, driven with observed changes in climate, CO<sub>2</sub>, and LULCC are analysed. Changes in global and regional carbon fluxes, stocks, and residence times are quantified, as well as an attribution to the underlying drivers. We find that over the historical period the majority of models simulate an increase in NPP, predominantly driven by enhanced atmospheric CO<sub>2</sub> concentrations. This generally leads to increased carbon storage in both vegetation and soils, however there is no agreement across models on the partitioning between vegetation and soils. This increased storage also acts to reduce soil carbon residence times due to a relative increase in carbon allocated in the faster decomposing soil pools. LULCC over this period has acted to reduce carbon inputs to the system and reduce vegetation carbon residence times due to conversion of forests to shorter vegetation. We find there is a large variation in simulated global and regional fluxes, stocks, and residence times in resonse to changes in climate, implying there are considerable uncertainties in current DGVMs. We therefore use long-term global observations of productivity and biomass change to constrain model estimates and provide insight into a process attribution for biospheric change as well as highlighting areas for future model improvement.</p>

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