Global nonlinear and nonstationary climate change effects on regional precipitation and forest phenology in Panama, Central America

Abstract

The inherent effects of global sea surface temperature (SST) anomalies on hydrological cycle and vegetation cover complicate the structure of tropical climate at the regional scale. Assessing hydrological processes related to climate forcing is important in Central America because it is surrounded by both the Pacific and Atlantic oceans and two continental landmasses. In this study, the use of high‐resolution remote sensing imagery in wavelet analysis helps identify nonstationary characteristics of hydrological and ecological responses. The wavelet‐based empirical orthogonal function (WEOF) further reflects the nonlinear relationship between the Atlantic and Pacific SST and the greenness of a pristine forested site in Panama, La Amistad International Park. Integrated WEOF and descriptive statistics for data analysis reveal a higher temporal variability in terrestrial precipitation relative to in situ land surface temperature and its probable effects on the presence of dry periods. Such teleconnection signals of SST were identified as a driving force of decline in tropical forest greenness during dry periods. The results of our remote sensing‐based wavelet analysis showed intra‐annual high‐frequency and biennial to triennial low‐frequency signals between enhanced vegetation index/precipitation datasets and SST indices in both Atlantic and Pacific oceans. A spatiotemporal priority search further confirmed the importance of the effects of the El Niño–Southern Oscillation (ENSO) over terrestrial responses in the selected study site. Coincidence of the effect of ENSO teleconnection patterns on precipitation and vegetation suggests possible impacts of El Niño‐associated droughts in Central America, accompanied by reduced rainfall, especially during the first months of rainy season (June, July, and August), and decline in vegetation cover during the dry season (March and April). Copyright © 2014 John Wiley & Sons, Ltd.

Seasonal variation in abundance and diversity of eavesdropping frog‐biting midges (Diptera, Corethrellidae) in a neotropical rainforest

“…This information is particularly important now, considering that precipitation patterns are changing, rainfall events are becoming more extreme, and the contrast between seasons is decreasing as a result of warming sea surface temperatures linked to anthropogenic climate change (Aguilar et al, 2005;Chang et al, 2015).…”

Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems

“…The identification of variables that enhance fire susceptibility is complicated, as it can vary significantly through sub-continents (Parisien & Moritz 2009;Oliveira et al 2012), and in the Central American tropical countries, where the orographic effect on climate and the effect of sea surface temperature (SST) variations on vegetation greenness and climate are large (Waylen & Quesada 2002;Brenes et al 2003;Chang et al 2015), these variables can even vary at the local level. This variation could be observed during the 1998 El Niño event in Central America and Mexico where wildfire events increased greatly (Dom ınguez & Rodriguez Trejo 2008).…”

“…The Central American environment is highly diverse in terms of climate, vegetation, and topographic characteristics, which leads to varying levels of wildfire susceptibility (Dom ınguez & Rodriguez Trejo 2008). In addition, Central America because of its unique location between two continental land masses and between the Pacific and Atlantic Oceans, has a demonstrated decline in precipitation rates and vegetation greenness during the El Niño-Southern Oscillation (ENSO) years (Chang et al 2015), making the forests more prone to wildfires. Therefore, it is relevant to identify the geographic regions where wildfires are likely to occur and their major drivers.…”

Hydrological Drought Forecasting Incorporating Climatic and Human-Induced Indices

“…In recent years, the continuous changes in the climatic system and human activities have called the stationarity hypothesis in hydrological processes into question (Hejazi and Markus 2009;Zhang et al 2012;Chang et al 2015;Wang et al 2013). Among drought forecasting research, only a few studies have considered the nonstationary behavior of drought in the model structure to improve drought forecasting.…”