Drought Prone Pronunciation

[Eleanor Heidecker]

English pronunciations of drought from the Cambridge Advanced Learner's Dictionary & Thesaurus and from the Cambridge Academic Content Dictionary, both sources Cambridge University Press)

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The mean state of a system is not usually informative of changes in resilience; either one can change whilst the other remains constant16,17. Thus, higher-order statistical characteristics that respond more sensitively to destabilization than the mean need to be considered to quantify resilience. To measure the changing resilience of the Amazon rainforest, we use a stability indicator used to predict the approach of a dynamical system towards a bifurcation-induced critical transition. The predictability arises from the phenomenon of critical slowing down22,23 (CSD): as the currently occupied equilibrium state of a system becomes less stable, it responds more sluggishly to short-term perturbations (for example, weather variability for the Amazon). This loss of resilience, which is itself typically defined24 as the return rate from perturbations, reflects a weakening of negative feedbacks that maintain stability. The behaviour can be detected by an increase in lag-1 autocorrelation (AR(1)) in time series capturing the system dynamics25,26. It may also manifest as an increase in variance over time but variance can also be easily influenced by changing variability of the perturbations driving the system27. Increasing AR(1) has been used to detect CSD before bifurcation-induced state transitions in a number of systems, including but not limited to climate25,28 and ecology29. In particular, CSD has recently been detected in reconstructions of western Greenland ice sheet height changes30 as well as of the variability of the Atlantic Meridional Overturning Circulation31. A caveat, highlighted by analysis of model projections before Amazon dieback27, is that a system should be forced slower than its intrinsic response time scale for CSD to occur (Methods). Hence, the absence of CSD may not rule out the possibility of a forthcoming critical transition. Conversely, increasing AR(1) can sometimes occur for other physical reasons. A space-for-time substitution has previously revealed that tropical forest resilience as measured by mean AR(1) (on a grid cell basis) is lower for less annual rainfall sums32 but changes of Amazon resilience over time have not been investigated so far.

We reiterate that changes in the mean state of a system do not directly relate to changes in resilience. Model studies show that large parts of the Amazon rainforest can be committed to dieback16 before showing a strong change in mean state. Indeed, from our CSD indicators we infer a marked loss of Amazon rainforest resilience since the early 2000s, in vast areas where the BL fraction has not strongly decreased (compare Figs. 1b and 2a or Supplementary Fig. 8b).

Given that lower baseline MAP (Fig. 4) and greater proximity to human interference (Fig. 5) are both statistically associated with greater loss of resilience, we hypothesize that low MAP and increasing human interference could both be contributing to the large-scale loss of resilience (Fig. 2). What remains to be explained is why these two factors might play such an important role and why the large-scale resilience loss started in the early 2000s.

Increasing human land use also appears to be contributing to the observed Amazon resilience loss, with human land-use areas increasing in both reach and intensity (Fig. 6c and Supplementary Fig. 14). Notably, the expansion of human land use accelerates after 2010, in an interval that also shows accelerated resilience loss (Fig. 6b) but less striking northern tropical Atlantic SST anomalies (Fig. 6a). Greater proximity to human land use can increase disturbance factors such as direct removal of trees, construction of roads and fires, conceivably reducing absolute resilience (Fig. 5) and making the forest more prone to resilience loss.

Other factors, including rising atmospheric temperatures in response to anthropogenic greenhouse gas emissions, may additionally have negative effects on Amazon resilience (and are contributing to the warming of northern tropical Atlantic SSTs; Fig. 6a). Furthermore, the rapid change in climate is triggering ecological changes but ecosystems are having difficulties in keeping pace. In particular, the replacement of drought-sensitive tree species by drought-resistant ones is happening slower than changes in (hydro)meteorological conditions50, potentially reducing forest resilience further.

In summary, we have revealed empirical evidence that the Amazon rainforest has been losing resilience since the early 2000s, risking dieback with profound implications for biodiversity, carbon storage and climate change at a global scale. We further provided empirical evidence suggesting that overall drier conditions, culminating in three severe drought events, combined with pronounced increases in human land-use activity in the Amazon, probably played a crucial role in the observed resilience loss. The amplified loss of Amazon resilience in areas closer to human land use suggests that reducing deforestation will not just protect the parts of the forest that are directly threatened but also benefit Amazon rainforest resilience over much larger spatial scales.

We measure the minimum distance between forested Amazon basin grid cells and human land-use grid cells in 2016 (believing this to be the most cautious and least biased way to measure distance) using the latitude and longitude of each grid point and computing the great-circle distance. We use human land-use grid cells over a larger area than the basin, so that we can determine the closest distance to human land use, regardless of whether this human land use lies within the basin. We also measure the minimum distance from human land use or roads in Brazil, where we have reliable data on state and federal roads ( -road-network-federal-and-state-highways). As in the main text, we reiterate that these minimum distances can be viewed as the maximum distance from human land use as our data will not include roads for the full Amazon basin, or non-federal or non-state roads in Brazil that will have human activity associated with them.

The VOD dataset is available from The NDVI dataset is available from :C01558/html. The MODIS Land Cover dataset is available from The CHIRPS precipitation dataset is available from -2.0/global_2-monthly/tifs. The Amazon basin shapefile is available from :amapoly_ivb. Brazilian road data are available from -road-network-federal-and-state-highways. SST data from HadISST are available from

N.B. and C.A.B. conceived and designed the study with input from T.M.L. C.A.B. performed the numerical analysis with contributions from N.B. All authors discussed and interpreted results, drew conclusions and wrote the paper.

A natural disaster is the very harmful impact on a society or community after a natural hazard event. Examples of natural hazard events are avalanches, blizzards, droughts, dust storms, earthquakes, floods, hails, heat waves, impact events, landslides, sinkholes, tornadoes, tropical cyclones, tsunamis, volcanic activity and wildfires.[1] A natural disaster can cause loss of life or damage property. It typically causes economic damage. How bad the damage is depends on how well people are prepared for disasters and how strong the buildings, roads, and other structures are.[2] Scholars have been saying that the term natural disaster is unsuitable and should be abandoned.[3] Instead, the simpler term disaster could be used. At the same time the type of hazard would be specificed.[4][5][6] A disaster happens when a natural or human-made hazard impacts a vulnerable community. It results from the combination of the hazard and the exposure of a vulnerable society.

Nowadays it is hard to distinguish between natural and human-made disasters.[3][7][8] The term natural disaster was already challenged in 1976.[6] Human choices in architecture,[9] fire risk,[10][11] and resource management[12] can cause or worsen natural disasters. Climate change also affects how often disasters due to extreme weather hazards happen. These "climate hazards" are floods, heat waves, wildfires, tropical cyclones, and the like.[13]

Some things can make natural disasters worse. Examples are inadequate building norms, marginalization of people and poor choices on land use planning.[3] Many developing countries do not have proper disaster risk reduction systems.[14] This makes them more vulnerable to natural disasters than high income countries. An adverse event only becomes a disaster if it occurs in an area with a vulnerable population.[15][16] .mw-parser-output .toclimit-2 .toclevel-1 ul,.mw-parser-output .toclimit-3 .toclevel-2 ul,.mw-parser-output .toclimit-4 .toclevel-3 ul,.mw-parser-output .toclimit-5 .toclevel-4 ul,.mw-parser-output .toclimit-6 .toclevel-5 ul,.mw-parser-output .toclimit-7 .toclevel-6 uldisplay:none

A natural disaster is the highly harmful impact on a society or community following a natural hazard event. The term "disaster" itself is defined as follows: "Disasters are serious disruptions to the functioning of a community that exceed its capacity to cope using its own resources. Disasters can be caused by natural, man-made and technological hazards, as well as various factors that influence the exposure and vulnerability of a community."[17]

The US Federal Emergency Management Agency (FEMA) explains the relationship between natural disasters and natural hazards as follows: "Natural hazards and natural disasters are related but are not the same. A natural hazard is the threat of an event that will likely have a negative impact. A natural disaster is the negative impact following an actual occurrence of natural hazard in the event that it significantly harms a community.[1] An example of the distinction between a natural hazard and a disaster is that an earthquake is the hazard which caused the 1906 San Francisco earthquake disaster.

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