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Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

To the Editor -Nations will reaffirm their commitment to reducing greenhouse gas (GHG) emissions during the 26th United Nations Climate Change Conference (COP26; www.ukcop26.org), in Glasgow, Scotland, in November 2021. Revision of the national commitments will play a key role in defining the future of Earth's climate. In past conferences, the main target of Amazonian nations was to reduce emissions resulting from land-use change

Abstract:Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse spatial distributions of traditional rain gauge stations. However, the accuracy of these satellite rainfall products over different spatial and temporal scales is unknown. In this study, we examined the potential of the TRMM 3B42 and Hydroe rainfall products to provide reliable rainfall estimates for a mountainous watershed in a humid subtropical climate region of Brazil. The purpose was to develop useful guidelines for future hydrologic studies on the potential and uncertainties of the rainfall products at different spatial and temporal resolutions. We compared the satellite products to reference rainfall data collected at 11 rain gauge stations irregularly distributed in the area. The results showed different levels of accuracy for each temporal scale evaluated. TRMM 3B42 performed better at the daily, monthly, and seasonal scales than Hydroe, while Hydroe presented a better correlation at the annual scale. In general, TRMM 3B42 overestimated the rainfall over the watershed at all evaluated temporal scales, whereas Hydroe underestimated it except for June-August at the seasonal scale. An evaluation based on contingency tables indicated that TRM 3B42 was better able to represent the local rainfall than Hydroe. The findings of this study indicate that satellite rainfall products are better suited for applications at the monthly and annual scales rather than the daily scale.

Timely spatially explicit warning of areas with high fire occurrence probability is an important component of strategic plans to prevent and monitor fires within South American (SA) Protected Areas (PAs). In this study, we present a five-level alert system, which combines both climatological and anthropogenic factors, the two main drivers of fires in SA. The alert levels are: High Alert, Alert, Attention, Observation and Low Probability. The trend in the number of active fires over the past three years and the accumulated number of active fires over the same period were used as indicators of intensification of human use of fire in that region, possibly associated with ongoing land use/land cover change (LULCC). An ensemble of temperature and precipitation gridded output from the GloSea5 SeasonalThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Fire is one of the main anthropogenic drivers that threatens the Amazon. Despite the clear link between rainfall and fire, the spatial and temporal relationship between these variables is still poorly understood in the Amazon. Here, we stratified the Amazon basin according to the dry season onset/end and investigated its relationship with the spatio-temporal variation of fire. We used monthly time series of active fires from 2003 to 2019 to characterize the fire dynamics throughout the year and to identify the fire peak months. More than 50% (32 246) of the annual mean active fires occurred in the peak month. In 52% of the cells, the peaks occurred between August–September and in 48% between October–March, showing well-defined seasonal patterns related to spatio-temporal variation of the dry season. Fire peaks occurred in the last two months of the dry season in 67% of the cells and in 20% in the first month of the rainy season. The shorter the dry season, the more concentrated was the occurrence of active fires in the peak month, with a predominance above 70% in cells with a dry season between one and three months. We defined a Critical Fire Period by identifying the consecutive months that concentrated at least 80% of active fires in the year. This period included two to three months between January and March in the northwest, and in the far north it lasted up to seven months, ending in March–April. In the south, it varied between two and three months, starting in August. In the northeast, it was three to four months, between August and December. By quantifying the role of the dry season in driving fire seasonality across the Amazon basin, we provide recommendations to monitor fire dynamics that can support decision makers in management policies and measures to avoid environmentally or socially harmful fires.

Satellite rainfall estimates (SRFE) are a promising alternative for the lack of reliable, densely distributed, precipitation data common in developing countries and remote locations. SRFE may be significantly improved when corrected based on rain gauge data. In the present study the first complete validation of the Tropical Rainfall Measuring Mission (TRMM) 3B42-based MERGE product is performed by means of ground truthing and hydrological modeling-based applications. Four distinct, highly anthropogenic watersheds were selected in the Upper Paraíba do Sul River Basin (UPSRB)—Brazil. The results show that when compared to TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 at the watershed scale, MERGE has a higher correlation with observed data. Likewise, root mean square errors and bias are significantly lower for MERGE products. When hydrologically validated, MERGE-based streamflow simulations have shown the capacity of reproducing the overall hydrological regime with “good” to “very good” results for the downstream lowland sections. Limitations were observed in the hydrological modeling of the upstream, highly anthropogenic, dammed watersheds. However, such limitations may not be attributed to MERGE precipitation since they were also obtained for the individually calibrated rain gauge-based simulations. The results indicate that the used MERGE dataset as a hydrological model input is better suited for application in the UPSRB than the TMPA 3B42V7.

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