• Energy Research

Global CHIRPS MCWD Dataset The MCWD (Maximum Cumulative Water Deficit) is a measure of drought severity, which corresponds to the maximum value of the monthly accumulated water deficit reached for each pixel within the year. The MCWD is a useful indicator of meteorologically induced water stress without taking into account local soil conditions and plant adaptations, which are poorly understood in Amazonia. The full method of MCWD is described in Aragão et al. (2007; https://doi.org/10.1029/2006GL028946). Detail about CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) can be found in Funk et al. (2015; https://doi.org/10.1038/sdata.2015.66). Coverage: Spanning 50°S-50°N (and all longitudes/land areas) Period: 1981 to 2020 Spatial resolution: 0.05-degree Temporal resolution: Annual Coordinate reference system: Geographic Coordinate System (Datum WGS84) File format: The zip file containing 40 files (one per year) in compressed TIFF format. Code: https://zenodo.org/record/5034650 Dataset usage: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration. For the use of this dataset, please cite: Silva Junior, C.H.L. et al. Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset. Zenodo (2021). DOI: 10.5281/zenodo.4903340. https://doi.org/10.5281/zenodo.4903340 Silva Junior, C.H.L. et al. Fire Responses to the 2010 and 2015/2016 Amazonian Droughts. Front. Earth Sci. (2019). DOI: 10.3389/feart.2019.00097. https://doi.org/10.3389/feart.2019.00097 Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data (2015). DOI: 10.1038/sdata.2015.66. https://doi.org/10.1038/sdata.2015.66

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 and land management by committing to decrease deforestation rates, a well-known and efficient strategy1,2. However, human-induced forest degradation caused by fires, selective logging, and edge effects can also result in large carbon dioxide (CO2) emissions1,2,3,4,5, which are not yet explicitly reported by Amazonian countries. Despite its considerable impact, forest degradation has been largely overlooked in previous policy discussions5. It is vital that forest degradation is considered in the upcoming COP26 discussions and incorporated into future commitments to reduce GHG emissions.

AbstractTropical 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.

Maximum Cumulative Water Deficit - MCWD: a R language script DOI: 10.5281/zenodo.2625772 (https://doi.org/10.5281/zenodo.2652630) Script for the calculation of MCWD (Maximum Cumulative Water Deficit) in rainfall raster data (e.g. CHIRPS and TRMM). Calculation based in Aragão et al. (2007, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2006GL028946). Please cite as: "Campanharo, W. A., and Silva Junior, C. H. L. (2019). Maximun Cumulative Water Deficit - MCWD: a R language script. doi:10.5281/zenodo.2652630." and "Silva Junior, et al. (2019). Fire responses to the 2010 and 2015/2016 Amazonian droughts. Frontiers in Earth Science. doi:10.3389/feart.2019.00097." Created by: Wesley A. Campanharo (https://www.researchgate.net/profile/Wesley_Campanharo) and Celso H. L. Silva Junior (https://www.researchgate.net/profile/Celso_Silva_Junior).

AbstractFragmented tropical forest landscapes preserve much of the remaining biodiversity and carbon stocks. Climate change is expected to intensify droughts and increase fire hazard and fire intensities, thereby causing habitat deterioration, and losses of biodiversity and carbon stock losses. Understanding the trajectories that these landscapes may follow under increased climate pressure is imperative for establishing strategies for conservation of biodiversity and ecosystem services. Here, we used a quantitative predictive modelling approach to project the spatial distribution of the aboveground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest (AF) domain. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report. Our AGB models had a satisfactory performance (area under the curve > 0.75 and p value < .05). The models projected a significant increase of 8.5% in the total carbon stock. Overall, the projections indicated that 76.9% of the AF domain would have suitable climatic conditions for increasing biomass by 2100 considering the RCP 4.5 scenario, in the absence of deforestation. Of the existing forest fragments, 34.7% are projected to increase their AGB, while 2.6% are projected to have their AGB reduced by 2100. The regions likely to lose most AGB—up to 40% compared to the baseline—are found between latitudes 13° and 20° south. Overall, although climate change effects on AGB vary latitudinally for the 2071–2100 period under the RCP 4.5 scenario, our model indicates that AGB stocks can potentially increase across a large fraction of the AF. The patterns found here are recommended to be taken into consideration during the planning of restoration efforts, as part of climate change mitigation strategies in the AF and elsewhere in Brazil.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84