Planning for compound hazards during the COVID-19 pandemic: The role of climate information systems

Roundtable on Compound Hazards and COVID-19 What: An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. When: 30 June 2021 Where: Online, convened by the World Meteorological Organization and hosted by the American Geophysical UnionPeer Reviewed"Article signat per 12 autors/es: Benjamin F. Zaitchik, Judy Omumbo, Rachel Lowe, Maarten van Aalst, Liana O. Anderson, Erich Fischer, Charlotte Norman, Joanne Robbins, Rosa Barciela, Juli Trtanj, Rosa von Borries, and Jürg Luterbacher"Postprint (published version

Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90 and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings

Mapping and characterizing social-ecological land systems of South America

Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world

Detection of forest degradation caused by fires in Amazonia from time series of MODIS fraction images

A new method is presented to detect and assess the extent of burned forests in a tropical ecosystem. Our study area is located in Mato Grosso state southern flank of the Brazilian Amazon region. MODIS images are used over the dry season of year 2010. The proposed method is based on (i) linear spectral mixing model applied to MODIS imagery to derive soil and shade fraction images and (ii) image segmentation and classification applied to a multi-temporal dataset of MODIS-derived images. In a first step, deforested areas are identified and mapped from the soil fraction images while burned areas are identified and mapped from the shade fraction images. Then, burned forest areas are mapped by combining a forest/non forest mask with the resulting burned area map. Our results show that 14,220 km2 of forests were degraded by fire in Mato Grosso during year 2010. Our approach can be potentially used operationally for detecting forest degradation due to fires. The proposed method can also be applied to time series of medium and high spatial resolution images for regional and local analysis.JRC.H.3-Forest Resources and Climat

Fire Responses to the 2010 and 2015/2016 Amazonian Droughts

Extreme droughts in Amazonia cause anomalous increase in fire occurrence, disrupting the stability of environmental, social, and economic systems. Thus, understanding how droughts affect fire patterns in this region is essential for anticipating and planning actions for remediation of possible impacts. Focused on the Brazilian Amazon biome, we investigated fire responses to the 2010 and 2015/2016 Amazonian droughts using remote sensing data. Our results revealed that the 2015/2016 drought surpassed the 2010 drought in intensity and extent. During the 2010 drought, we found a maximum area of 846,800 km2 (24% of the Brazilian Amazon biome) with significant (p ≤ 0.05) rainfall decrease in the first trimester, while during the 2015/2016 the maximum area reached 1,702,800 km2 (47% of the Brazilian Amazon biome) in the last trimester of 2015. On the other hand, the 2010 drought had a maximum area of 840,400 km2 (23% of the Brazilian Amazon biome) with significant (p ≤ 0.05) land surface temperature increase in the first trimester, while during the 2015/2016 drought the maximum area was 2,188,800 km2 (61% of the Brazilian Amazon biome) in the last trimester of 2015. Unlike the 2010 drought, during the 2015/2016 drought, significant positive anomalies of active fire and CO2 emissions occurred mainly during the wet season, between October 2015 and March 2016. During the 2010 drought, positive active fire anomalies resulted from the simultaneous increase of burned forest, non-forest vegetation and productive lands. During the 2015/2016 drought, however, this increase was dominated by burned forests. The two analyzed droughts emitted together 0.47 Pg CO2, with 0.23 Pg CO2 in 2010, 0.15 Pg CO2 in 2015 and 0.09 Pg CO2 in 2016, which represented, respectively, 209%, 136%, 82% of annual Brazil’s national target for reducing carbon emissions from deforestation by 2017 (approximately 0.11 Pg CO2 year-1 from 2006 to 2017). Finally, we anticipate that the increase of fires during the droughts showed here may intensify and can become more frequent in Amazonia due to changes in climatic variability if no regulations on fire use are implemented

Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha−1 yr−1) compared to those in the east (1.3 ± 0.3 Mg C ha−1 yr−1). Disturbances reduce regrowth rates by 8–55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr−1 until 2030, contributing ~5.5% to Brazil’s 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution

Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management

FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept

As fire seasons in Brazil lengthen and intensify, the need to enhance fire simulations and comprehend fire drivers becomes crucial. Yet determining what drivers burning in different Brazilian biomes is a major challenge, with the highly uncertain relationship between drivers and fire. Finding ways to acknowledge and quantify that uncertainty is critical in ascertaining the causes of Brazil’s changing fire regimes. We propose FLAME (Fire Landscape Analysis using Maximum Entropy), a new fire model that integrates Bayesian inference with the Maximum Entropy (MaxEnt) concept, enabling probabilistic reasoning and uncertainty quantification. FLAME utilizes bioclimatic, land cover and human driving variables to model fires. We apply FLAME to Brazilian biomes, evaluating its performance against observed data for three categories of fires: all fires (ALL), fires reaching natural vegetation (NAT), and fires in non-natural vegetation (NON). We assessed burned area responses to variable groups. The model showed adequate performance for all biomes and fire categories. Maximum temperature and precipitation together are important factors influencing burned area in all biomes. The number of roads and amount of forest boundaries (edge densities), and forest, pasture and soil carbon showed higher uncertainties among the responses. The potential response of these variables displayed similar spatial likelihood of the observations given the model, between the ALL, NAT and NON categories. Overall, the uncertainties were larger for the NON-category, particularly for Pampas and Pantanal. Customizing variable selection and fire categories based on biome characteristics could contribute to a more biome-focused and contextually relevant analysis. Moreover, prioritizing regional-scale analysis is essential for decision-makers and fire management strategies. FLAME is easily adaptable to be used in various locations and periods, serving as a valuable tool for more informed and effective fire prevention measures

Fire weakens land carbon sinks before 1.5 °C

To avoid the worst impacts of climate change, the Paris Agreement committed countries to pursue efforts to limit global warming to 1.5 °C by urgently reducing greenhouse gas emissions. However, the Paris temperature ambitions and remaining carbon budgets mostly use models that lack feedback among fire, vegetation and carbon, which are essential for understanding the future resilience of ecosystems. Here we use a coupled fire–vegetation model to explore regional impacts and feedbacks across global warming levels. We address whether the 1.5 °C goal is consistent with avoiding significant ecosystem changes when considering shifts in fire regimes. We find that the global warming level at which fire began to impact global carbon storage significantly was 1.07 °C (0.8–1.34 °C) above pre-industrial levels and conclude that fire is already playing a major role in decreasing the effectiveness of land carbon sinks. We estimate that considering fire reduces the remaining carbon budget by 25 Gt CO2 (~5%) for limiting temperature rise to 1.5 °C and 64 GtCO2 (~5%) for 2.0 °C compared to previous estimates. Whereas limiting warming to 1.5 °C is still essential for avoiding the worst impacts of climate change, in many cases, we are already reaching the point of significant change in ecosystems rich in carbon and biodiversity