Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area burnt satellite data

Hoelzemann, Judith J.

doi:10.1029/2003jd003666

Cited by 281 publications

(245 citation statements)

References 55 publications

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“…Average fuel consumption on the other hand, was higher in southern hemisphere Africa largely because relatively more fires were detected in woodlands, whereas almost all the fires in northern hemisphere Africa occurred in grasslands with lower percentages of woody vegetation. Other studies have reported lower emissions Barbosa et al, 1999;Hoelzemann et al, 2004), but relatively higher IAV (Barbosa et al, 1999). Some of this difference can probably be attributed to higher fuel loads in our study as our burned area estimates are comparable or even lower than those reported in previous studies.…”

Section: Emissionsmentioning

confidence: 95%

“…As burned area estimates improve from higher resolution satellite data and refined algorithms, uncertainties in fuel loads may become the limiting factor in estimating emissions Hoelzemann et al, 2004). Although using satellite data has improved estimates of spatial and temporal variability in fuel loads, approaches for calibrating these estimates using measured values are still in their infancy.…”

Section: Fuel Loadsmentioning

confidence: 99%

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Interannual variability in global biomass burning emissions from 1997 to 2004

et al. 2006

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Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001-2004 was derived using newly available active fire and 500 m. burned area datasets from MODIS following the approach described by Giglio et al. (2006). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including organic soil layer and peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (R h ), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fA-PAR). For the 1997-2004 period, we found that on average approximately 58 Pg C year −1 was fixed by plants as NPP, and approximately 95% of this was returned back to the atmosphere via R h . Another 4%, or 2.5 Pg C year −1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year −1 , Correspondence to: G. R. van der Werf (guido.van.der.werf@falw.vu.nl) with a maximum in 1998 (3.2 Pg C year −1 ) and a minimum in 2000 (2.0 Pg C year −1 ).

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Section: Emissionsmentioning

confidence: 95%

Section: Fuel Loadsmentioning

confidence: 99%

Interannual variability in global biomass burning emissions from 1997 to 2004

et al. 2006

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“…In Europe corresponding emissions are estimated to be about 1% (65 Kton) of the total inventoried emissions It mainly takes place between 1 and 5 km altitude and mostly in southern Europe (Meijer et al, 2001;Friedrich et al, 2008). The available wild fires estimates range between 20-50 Kt (Friedrich et al, 2009;Hoelzemann et al, 2004) and also occur mostly in countries around the Mediterranean. Given the short life time of NO x and the rather short transport distance of nitrate we assume the impact of lightning and wild land fires can be negligible (<1%) in the Netherlands.…”

Section: Natural Fractionmentioning

confidence: 99%

Anthropogenic and natural constituents in particulate matter in the Netherlands

Weijers

Schaap²,

Nguyen

et al. 2011

Atmos. Chem. Phys.

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Abstract. To develop mitigation strategies for reducing concentrations of both PM 2.5 and PM 10 , the origin of particulate matter (PM) needs to be established. An intensive, one-year measurement campaign from August 2007 to August 2008 was carried out to determine the composition of PM 10 and PM 2.5 at five locations in the Netherlands, aiming at reducing the uncertainties on the origin of PM. Generally, a considerable conformity in the chemical composition of PM 2.5 (and PM 10 ) is observed. From all constituents present in PM 2.5 , the secondary inorganic aerosol is the most dominant (42-48%), followed by the total carbonaceous matter (22-37%). Contributions from sea salt (maximum 8%), mineral dust and metals (maximum 5%) are relatively low. For the first time, a detailed overview of the composition of the coarse fraction can be presented. Compared to the fine fraction, contributions of sea salt, mineral dust and metals are larger resulting in a more balanced distribution between the various constituents. Through mass closure a considerable part of the PM mass could be defined (PM 2.5 : 80-94%). The chemical distribution on days with high PM levels shows a distinct increase in nitrate as well as in the unaccounted mass. Contributions of the other constituents remain equal or are lower (sea salt) when expressed in percentages. A correspondence between nitrate and the unaccounted mass is observed hinting at the presence of water on the filters. The contribution from natural sources in the Netherlands (at a rural station) was estimated to be 19 to 24% for PM 10 and 13 to 17% for PM 2.5 .

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“…FINNv1 uses MODIS satellite observations for active fires, land cover and vegetation density. The emission factors are from Akagi et al (2011), the estimated fuel loading are assigned using model results from Hoelzemann et al (2004), and the fraction of biomass burned is assigned as a function of tree cover (Wiedinmyer et al, 2006). The Global Fire Assimilation System (GFAS, Kaiser et al, 2012) calculates biomass burning emissions by assimilating Fire Radiative Power (FRP) observations from MODIS at a daily frequency and 0.5 • resolution and is available for the time period 2000-2013.…”

Section: Biomass Burningmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

934

733

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Abstract. The global methane (CH 4 ) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH 4 over the past decade. Emissions and concentrations of CH 4 are continuing to increase, making CH 4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH 4 sources that overlap geographically, and from the destruction of CH 4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). . Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH 4 yr −1 , range 51-72, −14 %) and higher emissions in Africa (86 Tg CH 4 yr −1 , range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models.The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions.

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Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area burnt satellite data

Cited by 281 publications

References 55 publications

Interannual variability in global biomass burning emissions from 1997 to 2004

Interannual variability in global biomass burning emissions from 1997 to 2004

Anthropogenic and natural constituents in particulate matter in the Netherlands

The global methane budget 2000–2012

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