Determinants and predictability of global wildfire emissions

Abstract. Biomass burning is one of the largest sources of atmospheric trace gases and aerosols globally. These emissions have a major impact on the radiative balance of the atmosphere and on air quality, and are thus of significant scientific and societal interest. Several datasets have been developed that quantify those emissions on a global grid and offered to the atmospheric modelling community. However, no study has yet attempted to systematically quantify the dependence of the inferred pyrogenic emissions on underlying assumptions and input data. Such a sensitivity study is needed for understanding how well we can currently model those emissions and what the factors are that contribute to uncertainties in those emission estimates. Here, we combine various satellite-derived burned area products, a terrestrial ecosystem model to simulate fuel loads and the effect of fire on ecosystem dynamics, a model of fuel combustion, and various emission models that relate combusted biomass to the emission of various trace gases and aerosols. We carry out simulations with varying parameters for combustion completeness and fuel decomposition rates within published estimates, four different emissions models and three different global burned-area products. We find that variations in combustion completeness and simulated fuel loads have the largest impact on simulated global emissions for most species, except for some with highly uncertain emission factors. Variation in burned-area estimates also contribute considerably to emission uncertainties. We conclude that global models urgently need more field-based data for better parameterisation of combustion completeness and validation of simulated fuel loads, and that further validation and improvement of burned area information is necessary for accurately modelling global wildfire emissions. The results are important for chemical transport modelling studies, and for simulations of biomass burning impacts on the atmosphere under future climate change scenarios.

Download Full-text

Determinants and predictability of global wildfire emissions

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-4243-2012 ◽

2012 ◽

Vol 12 (2) ◽

pp. 4243-4278 ◽

Cited By ~ 1

Author(s):

W. Knorr ◽

V. Lehsten ◽

A. Arneth

Keyword(s):

Biomass Burning ◽

Trace Gases ◽

Combustion Process ◽

Fuel Combustion ◽

Ecosystem Dynamics ◽

Future Climate Change ◽

Burned Area ◽

Climate Change Scenarios ◽

Data Set ◽

Terrestrial Ecosystem Model

Abstract. Biomass burning is one of the largest sources of atmospheric trace gases and aerosols globally. These emissions have a major impact on the radiative balance of the atmosphere and on air quality, and are thus of significant scientific and societal interest. Several datasets have been developed that quantify those emissions on a global grid and offered to the atmospheric modelling community. However, no study has yet attempted to systematically quantify the dependence of the inferred pyrogenic emissions on underlying assumptions and input data. Such a sensitivity study is needed for understanding how well we can currently model those emissions and what the factors are that contribute to uncertainties in those emissions estimates. Here, we combine various satellite-derived burned area products, a terrestrial ecosystem model to simulate fuel loads and the effect of fire on ecosystem dynamics, a model of fuel combustion, and various emission models that relate combusted biomass to the emission of various trace gases and aerosols. We vary one key parameter of a simple fuel combustion model, the emission model, and the burned area product and assess its impact on the computed emissions fields and their uncertainties. We find that choice of burned area data set has by far the largest impact on interannual variability of simulated emissions. For total global emissions, burned area and combustion completeness have the largest impact on emissions for most species. We conclude that reliable information on burned area is key for accurately modelling spatial and interannual variations of wildfire emissions, but uncertainties about the combustion process have a similar impact on the magnitude of global emission estimates. The results are important for chemical transport modelling studies, and for simulations of biomass burning impacts on the atmosphere under future climate change scenarios.

Download Full-text

Improving prediction and assessment of global fires using multilayer neural networks

Scientific Reports ◽

10.1038/s41598-021-81233-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jaideep Joshi ◽

Raman Sukumar

Keyword(s):

Global Climate ◽

Grid Cell ◽

Temporal Correlation ◽

Environmental Drivers ◽

Future Climate Change ◽

Burned Area ◽

Climate Change Scenarios ◽

Data Driven Approach ◽

Climate Interactions ◽

Fire Climate Interactions

AbstractFires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans have historically used and managed fires, the current role of anthropogenic drivers of fires remains less quantified. Whereas patterns in fire–climate interactions are consistent across the globe, fire–human–vegetation relationships vary strongly by region. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability as compared to many state-of-the-art fire models, with global spatial correlation of 0.92, monthly temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.60, between predicted and observed burned area. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of burned area to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.

Download Full-text

Climate, CO<sub>2</sub>, and demographic impacts on global wildfire emissions

Biogeosciences Discussions ◽

10.5194/bgd-12-15011-2015 ◽

2015 ◽

Vol 12 (17) ◽

pp. 15011-15050 ◽

Cited By ~ 4

Author(s):

W. Knorr ◽

L. Jiang ◽

A. Arneth

Keyword(s):

Climate Change ◽

Population Density ◽

Population Growth ◽

Biomass Burning ◽

Human Population ◽

Population Change ◽

Future Climate Change ◽

Burned Area ◽

Human Population Density ◽

Semi Empirical

Abstract. Wildfires are by far the largest contributor to global biomass burning and constitute a large global source of atmospheric traces gases and aerosols. Such emissions have a considerable impact on air quality and constitute a major health hazard. Biomass burning also influences the radiative balance of the atmosphere and is thus not only of societal, but also of significant scientific interest. There is a common perception that climate change will lead to an increase in emissions as hot and dry weather events that promote wildfire will become more common. However, even though a few studies have found that the inclusion of CO2 fertilization of photosynthesis and changes in human population patterns will tend to somewhat lower predictions of future wildfire emissions, no such study has included full ensemble ranges of both climate predictions and population projections, including the effect of different degrees of urbanisation. Here, we present a series of 124 simulations with the LPJ-GUESS-SIMFIRE global dynamic vegetation – wildfire model, including a semi-empirical formulation for the prediction of burned area based on fire weather, fuel continuity and human population density. The simulations comprise Climate Model Intercomparison Project 5 (CMIP5) climate predictions from eight Earth system models using two Representative Concentration Pathways (RCPs) and five scenarios of future human population density based on the series of Shared Socioeconomic Pathways (SSPs), sensitivity tests for the effect of climate and CO2, as well as a sensitivity analysis using two alternative parameterisations of the semi-empirical burned-area model. Contrary to previous work, we find no clear future trend of global wildfire emissions for the moderate emissions and climate change scenario based on the RCP 4.5. Only historical population change introduces a decline by around 15 % since 1900. Future emissions could either increase for low population growth and fast urbanisation, or continue to decline for high population growth and slow urbanisation. Only for high future climate change (RCP8.5), wildfire emissions start to rise again after ca. 2020 but are unlikely to reach the levels of 1900 by the end of the 21st century. We find that climate warming will generally increase the risk of fire, but that this is only one of several equally important factors driving future levels of wildfire emissions, which include population change, CO2 fertilisation causing woody thickening, increased productivity and fuel load, and faster litter turnover in a warmer climate.

Download Full-text

The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests

10.5194/essd-2019-220 ◽

2019 ◽

Cited By ~ 2

Author(s):

Christopher P. O. Reyer ◽

Ramiro Silveyra Gonzalez ◽

Klara Dolos ◽

Florian Hartig ◽

Ylva Hauf ◽

...

Keyword(s):

Climate Change ◽

Nitrogen Deposition ◽

Forest Stand ◽

Climate Impacts ◽

Ecosystem Dynamics ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Flat File ◽

Wide Range ◽

Vegetation Models

Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation.

Download Full-text

The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Earth System Science Data ◽

10.5194/essd-12-1295-2020 ◽

2020 ◽

Vol 12 (2) ◽

pp. 1295-1320 ◽

Cited By ~ 6

Author(s):

Christopher P. O. Reyer ◽

Ramiro Silveyra Gonzalez ◽

Klara Dolos ◽

Florian Hartig ◽

Ylva Hauf ◽

...

Keyword(s):

Climate Change ◽

Nitrogen Deposition ◽

Forest Stand ◽

Climate Impacts ◽

Ecosystem Dynamics ◽

Future Climate Change ◽

Climate Change Scenarios ◽

European Forests ◽

Wide Range ◽

Vegetation Models

Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.

Download Full-text

Improving prediction and assessment of global wildfires using neural networks

10.1101/2020.08.07.241638 ◽

2020 ◽

Author(s):

Jaideep Joshi ◽

Raman Sukumar

Keyword(s):

Global Climate ◽

Grid Cell ◽

Temporal Correlation ◽

Environmental Drivers ◽

Future Climate Change ◽

Burned Area ◽

Climate Change Scenarios ◽

Data Driven Approach ◽

Fire Climate Interactions ◽

In Fire

ABSTRACTFires determine vegetation patterns, impact human societies, and provide complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Especially, the role of anthropogenic drivers remains less understood. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability than previously reported, with global spatial correlation of 0.92, temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.6, between predicted and observed burned area. Our analysis reveals universal global patterns in fire-climate interactions, coupled with strong regional differences in fire-human relationships. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of fire extent to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands, show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.

Download Full-text

Immersive landscapes: modelling ecosystem reference conditions in virtual reality

Landscape Ecology ◽

10.1007/s10980-021-01313-8 ◽

2021 ◽

Author(s):

Tom Chandler ◽

Anna E. Richards ◽

Bernhard Jenny ◽

Fiona Dickson ◽

Jiawei Huang ◽

...

Keyword(s):

Virtual Reality ◽

Reference Conditions ◽

Three Dimensional ◽

3D Models ◽

Ecosystem Dynamics ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Resource Managers ◽

Virtual Landscapes ◽

Natural Resource Managers

Abstract Context Understanding the variability and dynamics of ecosystems, as well as their responses to climate or land use change, is challenging for policy makers and natural resource managers. Virtual reality (VR) can be used to render virtual landscapes as immersive, visceral experiences and communicate ecosystem dynamics to users in an effective and engaging way. Objectives To illustrate the potential and believability of VR, a team of landscape ecologists and immersive visualisation researchers modelled a reference Australian Box Gum Grassy Woodland landscape, an endangered eucalypt woodland ecosystem that is difficult to observe in its pre-European colonisation form. Methods We document considerations for designing the immersive virtual landscape, including the creation of animated three-dimensional (3D) plants that alternate between the seasons, and soundscapes that change through the course of a simulated day. We used a heuristic evaluation with experts to assess the potential of immersive VR landscape modeling. Results This cross disciplinary collaboration resulted in a VR experience that was evaluated in a series of meetings by 27 ecologists and managers in biodiversity conservation, many of whom were familiar with Box Gum Grassy Woodlands. 88% of participants stated that the simulation was believable and participants thought that virtual landscapes held great potential for education, public engagement and land management. Conclusions Possible future directions include open-source libraries of ecological 3D models, and the visual simulation of historic landscapes and future climate change scenarios.

Download Full-text

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Biogeosciences ◽

10.5194/bg-13-267-2016 ◽

2016 ◽

Vol 13 (1) ◽

pp. 267-282 ◽

Cited By ~ 50

Author(s):

W. Knorr ◽

L. Jiang ◽

A. Arneth

Keyword(s):

Climate Change ◽

Population Density ◽

Population Growth ◽

Biomass Burning ◽

Human Population ◽

Population Change ◽

Future Climate Change ◽

Burned Area ◽

Human Population Density ◽

Semi Empirical

Abstract. Wildfires are by far the largest contributor to global biomass burning and constitute a large global source of atmospheric traces gases and aerosols. Such emissions have a considerable impact on air quality and constitute a major health hazard. Biomass burning also influences the radiative balance of the atmosphere and is thus not only of societal, but also of significant scientific interest. There is a common perception that climate change will lead to an increase in emissions as hot and dry weather events that promote wildfire will become more common. However, even though a few studies have found that the inclusion of CO2 fertilisation of photosynthesis and changes in human population patterns will tend to somewhat lower predictions of future wildfire emissions, no such study has included full ensemble ranges of both climate predictions and population projections, including the effect of different degrees of urbanisation.Here, we present a series of 124 simulations with the LPJ–GUESS–SIMFIRE global dynamic vegetation–wildfire model, including a semi-empirical formulation for the prediction of burned area based on fire weather, fuel continuity and human population density. The simulations use Climate Model Intercomparison Project 5 (CMIP5) climate predictions from eight Earth system models. These were combined with two Representative Concentration Pathways (RCPs) and five scenarios of future human population density based on the series of Shared Socioeconomic Pathways (SSPs) to assess the sensitivity of emissions to the effect of climate, CO2 and humans. In addition, two alternative parameterisations of the semi-empirical burned-area model were applied. Contrary to previous work, we find no clear future trend of global wildfire emissions for the moderate emissions and climate change scenario based on the RCP 4.5. Only historical population change introduces a decline by around 15 % since 1900. Future emissions could either increase for low population growth and fast urbanisation, or continue to decline for high population growth and slow urbanisation. Only for high future climate change (RCP8.5), wildfire emissions start to rise again after ca. 2020 but are unlikely to reach the levels of 1900 by the end of the 21st century. We find that climate warming will generally increase the risk of fire, but that this is only one of several equally important factors driving future levels of wildfire emissions, which include population change, CO2 fertilisation causing woody thickening, increased productivity and fuel load and faster litter turnover in a warmer climate.

Download Full-text

Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

Ecological Processes ◽

10.1186/s13717-020-00259-0 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Nabaz R. Khwarahm

Keyword(s):

Climate Change ◽

Habitat Suitability ◽

Environmental Variables ◽

Climate Changes ◽

Future Climate ◽

Annual Precipitation ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Kurdistan Region ◽

Oak Tree

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

Download Full-text