Estimation of CO2 Emissions from Wildfires Using OCO-2 Data

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian 'black summer'

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian 'Black Summer' (November 2019-February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO₂) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO₂ and fire radiative power. The fire emissions have led to an approximately 50-80 folds increase in total CO₂ emission in Australia compared with the similar seasons of 2014-2019. The excess CO₂ from wildfires has offset almost half of the global anthropogenic CO₂ emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO₂.

Managing Wildfire Risk in Mosaic Landscapes: A Case Study of the Upper Gata River Catchment in Sierra de Gata, Spain

Fire prevention and suppression approaches that exclusively rely on silvicultural measures and containment infrastructure have become increasingly ineffective in stopping the spread of wildfires. As agroforestry landscape mosaics consisting of a mix of different land cover and use types are considered less prone to fire than forests, approaches that support the involvement of rural people in agriculture and forestry activities have been proposed. However, it is unknown whether, in the current socio-economic context, these land-use interventions will nudge fire-prone landscapes towards more fire-resistant ones. We report on a case study of the Gata river catchment in Sierra de Gata, Spain, which is a fire-prone area that has been a pilot site for Mosaico-Extremadura, an innovative participatory fire-risk-mitigation strategy. Our purpose is to assess the efficacy of project interventions as “productive fuel breaks” and their potential for protecting high-risk areas. Interventions were effective in reducing the flame length and the rate of spread, and almost 40% of the intervention area was in sub-catchments with high risk. Therefore, they can function as productive fuel breaks and, if located strategically, contribute to mitigating wildfire risk. For these reasons, and in view of other economic and social benefits, collaborative approaches for land management are highly recommended.

An Assessment of Forest Fires and CO2 Gross Primary Production from 1991 to 2019 in Mação (Portugal)

Forest-fire rates have increased in Southern European landscapes. These fires damage forest ecosystems and alter their development. During the last few decades, an increase in fast-growing and highly fuel-bearing plant species such as bush, Eucalyptus globulus Labill., and Pinus pinaster Ait. has been observable in the interior of Portugal. This study aims to verify this assumption by the quantification of the biomass carbon sink in the forests of the Mação municipality. Maps of fire severity and forest biomass evolution after a wildfire event were produced for the period of 1991 to 2019. To quantify carbon retention in this region, this evolution was correlated with gross primary production (GPP) on the basis of satellite imagery from Landsat 5, Landsat 8, and MODIS MYD17A2H. Results show that wildfires in Mação increased in area and severity with each passing decade due to the large accumulation of biomass promoted by the abandonment of rural areas. Before the large fires of 2003, 2017, and 2019, carbon rates reached a daily maximum of 5.4, 5.3, and 4.7 gC/m2/day, respectively, showing a trend of forest-biomass accumulation in the Mação municipality.

Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO₂ monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO₂ monitoring layout for Iran, the eighth-ranked country emitting CO₂ worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.

Special Issue Atmospheric Composition and Cloud Cover Observations

A Special Issue of Atmosphere, “Atmospheric Composition and Cloud Cover Observations”, is focused on presenting some of the latest results of observations of clouds and atmospheric composition, mainly by referring to new equipment or experimental set-ups [...]

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.