Influencia de la temperatura superficial del mar y la precipitación sobre la dinámica del NDVI en el bosque seco del norte del Perú

We quantified the influence of sea surface temperature (SST) in the Niño 1+2 region and precipitation on the NDVI dynamics of the dry forest along Peru’s northern coast during 2003–2023. We used monthly series from MODIS (NDVI), CHIRPS (precipitation), and NOAA SST, seasonally standardized (z-score)...

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Detalles Bibliográficos
Autores: Mendoza, Marili, Rojas , Joel, Choccña Rejas, Michel Aristóteles, Guerrero Salinas, Jhon Brayan, Rojas, Eduardo, Mendoza Nolorbe, Juan Neil, Moreno Moreno , Nick Jhonatan, Recavarren Estares, Rodrigo Omar, Badaracco Meza, Rolando Renee, Rojas, Joel, Choccña, Michel, Guerrero, Jhon, Mendoza, Juan, Moreno, Nick, Recavarren, Rodrigo, Badaracco, Rolando
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:Perú
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Idioma:español
OAI Identifier:oai:revistasinvestigacion.unmsm.edu.pe:article/31136
Acceso en línea:https://revistasinvestigacion.unmsm.edu.pe/index.php/fisica/article/view/31136
Access Level:acceso abierto
Palabra clave:ARX
NDVI
TSM
precipitación
ENSO
bosque seco
sea surface temperature (SST)
precipitation
dry forest
Descripción
Sumario:We quantified the influence of sea surface temperature (SST) in the Niño 1+2 region and precipitation on the NDVI dynamics of the dry forest along Peru’s northern coast during 2003–2023. We used monthly series from MODIS (NDVI), CHIRPS (precipitation), and NOAA SST, seasonally standardized (z-score). We applied a 36-month rolling correlation, time–frequency analysis (XWT/WTC), and autoregressive models with exogenous regressors (ARX). Cross-correlation showed that NDVI responds with a positive one-month lag to both SST and precipitation, with correlation coefficients of 0.764 and 0.613, respectively. Among four AR(2) models evaluated, the ARX with lagged SST provided the best fit (AIC = 295.35), slightly outperforming the combined SST+precipitation model (AIC = 295.46). Akaike weights favored the former on grounds of parsimony. The results indicate a positive sensitivity of NDVI to SST, which serves as a more robust early predictor than local precipitation. XWT and WTC analyses revealed annual and 2–4-year coherence, with strengthened coupling during the 2017 and 2023 events. We conclude that thermal variability in the eastern equatorial Pacific exerts a more stable control on dry-forest phenology, offering an early-warning tool for monitoring and planning protected areas in the face of ENSO and droughts.

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