Spatio-temporal variability of NDVI–precipitation over southernmost South America:possible linkages between climate signals and epidemics

Abstract

Climate–environment variability affects the rates of incidence of vector-borne and zoonoticdiseases and is possibly associated with epidemics outbreaks. Over southernmostSouth America the joint spatio-temporal evolution of climate–environment isanalyzed for the 1982–2004 period. Detailed mapping of normalized differencevegetation index (NDVI) and rainfall variability are then compared to zones withpreliminary epidemiological reports. A significant quasi-biennial signal (2.2- to 2.4-yearperiods, or QB) for joint NDVI–rainfall variability is revealed. From rotated EOFs,dominant NDVI patterns are partitioned according to their lead frequencies: (1)the ‘QB group’ (2.1-to 3-year periods) includes six modes over southern Brazil,Uruguay, northern-central Argentina (two modes), the southern Paraguay–northernArgentina border, and the Santa Cruz Province; (2) the QB1 (2.4- to 3-year periods)+ quasi-quadrennial (QQ) mode over the Misiones Province; and (3) the QB2 (2.1- to 2.5-year periods)+ QQ + inter-annual (IA) (3- to 7-year periods) two modes over south-eastern Argentina. Modeswithin the ‘QB group’ are positively correlated with global climate signals and SST.The Uruguayan mode is correlated with global ENSO (8-month lag) whilst thesouthern Entre-Rios/northern Buenos Aires provinces are correlated with centralequatorial Pacific SSTs (3-month lag). The Santa Cruz (Patagonia) Province ismost correlated with the Pacific South America (PSA) index and SST patterns(3-month lag) along the Antarctica circumpolar current. The spatial distributionof lead NDVI modes includes the Formosa, Misiones, Chaco and Buenos Airesprovinces among others, known for being prone to vector-borne epidemics such asdengue fever, malaria, leishmaniasis (American cutaneous leishmaniasis or ACL),hantivirus, chagas and Argentine hemorrhagic fever (AHF). Some provinces alsocorrespond to regions where lead NDVI PCs’ modes are associated with high-frequencyclimate signals such as the quasi-biennial oscillation in northwest Argentina. Thejoint preliminary results (climate–environment–public health reports) presentedhere for the first time are meant: (1) to contribute to a better understanding ofclimate–environment–epidemics process-based and modeling studies and (2) tofacilitate, in the long run, the implementation of local and regional health earlywarning systems (HEWS) over southernmost South America. The latter is becomingcrucial with ever-increasing migration, urban sprawl (re-emergence of denguefever epidemics since the late 1990s), all embedded in a climate change context.