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Abstract/Summary
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<jats:p>Abstract. The 2015–2016 Amazon drought was characterized by below-average regional precipitation for an entire year, which distinguishes it from the dry-season-only droughts in 2005 and 2010. Studies of vegetation indices (VIs) derived from optical remote sensing over the Amazonian forests indicated three stages in canopy response during the 2015–2016 drought, with below-average greenness during the onset and end of the drought, and above-average greenness during the intervening months. To date, a satisfactory explanation for this broad temporal pattern has not been found. A better understanding of rainforest behaviors during this unusually long drought should help predict their response to future droughts. We hypothesized that negative VI anomalies could be caused by water and heat stress exceeding the tolerance ranges of the rainforest. To test our hypothesis, based on monthly observations of terrestrial water storage (TWS), land surface temperature (LST), and vapor pressure deficit (VPD) for January 2003 to December 2016, we proposed an approach to categorize regions into two groups: (1) those exceeding normal hydrological and thermal ranges and (2) those within normal ranges. Accordingly, regions exceeding normal ranges during different stages of the 2015–2016 event were delineated. The results showed a gradual southward shift in these regions: from the northeastern Amazon during August to October 2015 to the north–central part during November 2015 to February 2016 and finally to the southern Amazon in July 2016. Over these regions exceeding normal ranges during droughts, negative VI anomalies were expected, irrespective of radiation anomalies. Over the regions within normal ranges, VI anomalies were assumed to respond positively to radiation anomalies, as is expected under normal conditions. We found that our proposed approach can explain more than 70 % of the observed spatiotemporal patterns in VI anomalies during the 2015–2016 drought. These results suggest that our “exceeding normal ranges”-based approach combining (i) water storage, (ii) temperature, and (iii) atmospheric moisture demand drivers can reasonably identify the most likely drought-affected regions at monthly to seasonal timescales. Using observation-based hydrological and thermal condition thresholds can help with interpreting the response of the Amazon rainforest to future drought events.
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Hinweis
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DeepGreen-ID: cfeb38b10eff4ae8ade1bf7ac161f844 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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