Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

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

Urheber*innen

Papastefanou,  P.
External Organizations;

Zang,  C.S.
External Organizations;

Angelov,  Z.
External Organizations;

Anderson de Castro,  A.
External Organizations;

Jimenez,  J.C.
External Organizations;

Campos De Rezende,  L.F.
External Organizations;

Ruscica,  R.
External Organizations;

/persons/resource/Boris.Sakschewski

Sakschewski,  Boris
Potsdam Institute for Climate Impact Research;

Sörensson,  A.
External Organizations;

/persons/resource/Kirsten.Thonicke

Thonicke,  Kirsten
Potsdam Institute for Climate Impact Research;

Vera,  C.
External Organizations;

Viovy,  N.
External Organizations;

von Randow,  C.
External Organizations;

Rammig,  A.
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)

27185oa.pdf
(Verlagsversion), 5MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Papastefanou, P., Zang, C., Angelov, Z., Anderson de Castro, A., Jimenez, J., Campos De Rezende, L., Ruscica, R., Sakschewski, B., Sörensson, A., Thonicke, K., Vera, C., Viovy, N., von Randow, C., Rammig, A. (2022): Recent extreme drought events in the Amazon rainforest: Assessment of different precipitation and evapotranspiration datasets, and drought indicators. - Biogeosciences, 19, 16, 3843-3861.
https://doi.org/10.5194/bg-19-3843-2022


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_27185
Zusammenfassung
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 (mean =2.7) ×106 km2 (37 %–51 % of the Amazon basin, mean =45 %), 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 (mean =3.6) ×106 km2 (51 %–74 %, mean =61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean = 3.2 x 106 km2; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106 km2 (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.