Quantifying the spatial extent and intensity of recent extreme drought events in the Amazon rainforest and their impacts on the carbon cycle

Abstract. Over the last decades, the Amazon rainforest was hit by multiple severe drought events. Here we assess the severity and spatial extent of the extreme drought years 2005, 2010, and 2015/2016 in the Amazon region and their impacts on the carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (ΔMCWD). Evaluating an ensemble of ten state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.8 to 4.2 (mean = 3.2) million km² (46–71 % of the Amazon basin, mean = 53 %) where ΔMCWD indicates at least moderate drought conditions (ΔMCWD anomaly < 25 mm). In 2010, the affected area was about 16 % larger, ranging from 3.1 up to 4.6 (mean = 3.7) million km² (52–78 %, mean = 63 %). In 2016, the mean area affected by drought stress was similar to 2005 (mean = 3.2 million km²; 55 % of the Amazon basin), but the general disagreement between data sets was larger, ranging from 2.4 up to 4.1 million km² (40–70 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a rainfall anomaly index (RAI). We find that scPDSI shows a much stronger, and RAI a much weaker drought impact in terms of extent and severity for 2016 compared to ΔMCWD. Using an empirical ΔMCWD-mortality relationship, we calculate biomass losses of the three drought events. We show that eight of ten datasets agree on biomass losses of about 1.8 PgC for the drought years 2005 and 2010, indicating that the more intense drought in 2005 equals a larger total area of the 2010 drought regarding biomass loss. For the 2015/2016 drought event, datasets show a large variability of biomass loss induced by drought stress ranging from 1.3 to 2.7 PgC with a mean loss of 1.8 PgC. Disagreement across datasets increased, (1) when comparing the total area of more severe and extreme drought signals and (2) when comparing spatial drought location across datasets. Generally, only half of the datasets agreed on the location of a drought event. We conclude that for deriving impacts of droughts to the Amazon Basin based on precipitation, an ensemble of datasets should be considered. This is especially relevant when assessing the impact of drought on the Amazon rainforest and its carbon cycle.