Analysis fire patterns and drivers with a global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations

Abstract

Abstract. Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics. We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices. We further suggest possible development directions to enable such models to better project future fire activity.

Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation-fire model SSiB4/TRIFFID-Fire v1.0

“…4c). Some studies have reported that humans have a substantial impact on SHAF fire, which limits the effect of climate-induced IAV (Archibald et al, 2010;Venevsky et al, 2019). In addition, the simulated IAV of burned areas is lower than observations in BONA and BOAS (Figs.…”

“…The simulated burned area is underestimated in BONA and BOAS (Fig. 4b) where fire has a lower incidence but a longer duration compared to the global average (Ward et al, 2018;Venevsky et al, 2019). As we assume all fires persist for one day, the burned area in boreal regions is therefore underestimated.…”

“…Nevertheless, the burned area there is 350 relatively small compared to that in major fire regions such as SHAF and NHAF. This shortcoming has been reported in a number of fire models (Pfeiffer and Kaplan, 2012;Lasslop et al, 2014;Yue et al, 2014;Venevsky et al, 2019). The reasons include an underestimate of anthropogenic suppression, inaccurate description of fuel pattern/grassland fraction, and landscape fragmentation from roads and other anthropogenic features.…”

“…Since the early 2000s, fire models have been developed within Dynamic Global Vegetation Models (DGVMs) to explicitly describe the burned area, fire emissions, and fire disturbance on terrestrial ecosystems (Thonicke et al, 2001;Venevsky et al, 2002;Arora and Boer, 2005;Thonicke et al, 2010;Li et al, 2012;Pfeiffer et al, 2013;Lasslop et al, 2014;Yue et al, 2014;55 Rabin et al, 2018;Burton et al, 2019;Venevsky et al, 2019). These fire models have various levels of complexity, from simple statistical models (SIMFIRE; Knorr et al, 2016) to complicated https://doi.org/10.5194/gmd-2020-122 Preprint.…”

Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0

“…Based on the latest satellite estimates, global fires emit 1.5-4.2 Pg C yr −1 carbon, 7-8.2 Pg C yr −1 CO 2 , and 1.9-6.0 Tg C yr −1 black carbon to the atmosphere (Chuvieco et al, 2016;Li et al, 2019). Fire emissions contribute to increases in greenhouse gases and cloud condensation nuclei through geochemistry processes (Scholes et al, 1996), affecting radiative forcing, the hydrology cycle (Ward et al, 2012;Jiang H. Huang et al: Modeling long-term fire impact on ecosystem characteristics et al, Hamilton et al, 2018), and air quality Johnston et al, 2012).…”