Recent extreme drought events in the Amazon rainforest: assessment of different precipitation and evapotranspiration datasets and drought indicators

Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (meanCombining double low line2.7) ×106km2 (37%-51% of the Amazon basin, meanCombining double low line45%), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly <-0.5). In 2010, the affected area was about 16% larger, ranging from 3.0 up to 4.4 (meanCombining double low line3.6) ×106km2 (51%-74%, meanCombining double low line61%). In 2016, the mean area affected by drought stress was between 2005 and 2010 (meanCombining double low line3.2×106km2; 55% of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106km2 (40%-69%). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60%), followed by the choice of the precipitation dataset (20%) and the evapotranspiration dataset (20%). Using a fixed, constant evapotranspiration rate instead of variable evapotranspiration can lead to an overestimation of drought stress in the parts of Amazon basin that have a more pronounced dry season (for example in 2010). We highlight that even for well-known drought events the spatial extent and intensity can strongly depend upon the drought indicator and the data sources it is calculated with. Using only one data source and drought indicator has the potential danger of under- or overestimating drought stress in regions with high measurement uncertainty, such as the Amazon basin.

Fil: Papastefanou, Phillip. Universitat Technical Zu Munich; Alemania

Fil: Zang, Christian S.. Weihenstephan-Triesdorf University of Applied Sciences; Alemania

Fil: Angelov, Zlatan. Universitat Technical Zu Munich; Alemania

Fil: De Castro, Aline Anderson. National Institute for Spatial Research; Brasil

Fil: Jimenez, Juan Carlos. Universidad de Valencia; España

Fil: Campos De Rezende, Luiz Felipe. National Institute for Spatial Research; Brasil

Fil: Ruscica, Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina

Fil: Sakschewski, Boris. No especifíca;

Fil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina

Fil: Thonicke, Kirsten. No especifíca;

Fil: Vera, Carolina Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina

Fil: Viovy, Nicolas. Universite Paris-Saclay;

Fil: Von Randow, Celso. National Institute for Spatial Research; Brasil

Fil: Rammig, Anja. Universitat Technical Zu Munich; Alemania