Modes of Variability of Annual and Seasonal Rainfall in Mexico

Abstract

The aim of this study is to identify temporal and spatial variability patterns of annual and seasonal rainfall in Mexico. A set of 769 weather stations located in Mexico was examined. The country was divided into 12 homogeneous rainfall regions via principal component analysis. A Pettitt test was conducted to perform a homogeneity analysis for detecting abrupt changes in mean rainfall levels, and a Mann‐Kendall test was conducted to examine the presence of monotonically increasing/decreasing patterns in the data. In total, 14.4% of the annual series was deemed nonstationary. Fourteen percent of the samples were nonstationary in the winter and summer, and 9% were nonstationary in the spring and autumn. According to the results, the nonstationarity of some seasonal rainfall series may be associated with the presence of atmospheric phenomena (e.g., El Niño/Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation). A rainfall frequency analysis was performed for the nonstationary annual series, and significant differences in the return levels xfalse^T can be expected for the scenarios analyzed. The identification of areas that are more susceptible to changes in rainfall levels will improve water resource management plans in the country, and it is expected that these plans will take into account nonstationary theory.

Correlations Between Extreme Atmospheric Hazards and Global Teleconnections: Implications for Multihazard Resilience

“…They suggest that correlations between December–February Niño 3.4 index and rainfall anomalies are most robust in the year prior, and typically peak in July–August ( r =− 0.6 to −0.5, 95% level). However, a station‐based study by Alvarez‐Olguin and Escalante‐Sandoval () finds the opposite relationship. They find that the warm phase of the ENSO increases rainfall levels, whereas the cold phase decreases rainfall and suggest that the relationship with ENSO is most robust in winter ( r = 0.34 to 0.6, 95% level).…”

“…Although authors (e.g., Alvarez‐Olguin & Escalante‐Sandoval , Méndez & Magaña, , and references therein) discuss the impacts of low‐frequency climate modulators on rainfall, the NAO and associated NPO are the only other interannual modulators to be associated with Mexico rainfall (e.g., Linkin & Nigam, ; Santillán et al, ). The station‐based study of Alvarez‐Olguin and Escalante‐Sandoval () also investigates the effects of the NAO, but although the NAO shows some isolated significant correlations from December to May ( r = 0.27 to 0.28, 95% level), most regions do not show any significant correlation at any time of year. In association with U.S. rainfall anomalies, Linkin and Nigam () show the NPO to have a positive influence on rainfall over western Mexico, but do not specify a correlation.…”

Climatic trends and regional climate models intercomparison over the CORDEX‐CAM (Central America, Caribbean, and Mexico) domain

“…13; Jones et al ., ). In Mexico for example, only few studies have focused on temperature (e.g., Peralta‐Hernández et al ., ; Gutiérrez‐Ruacho et al ., ; García‐Cueto et al ., ; ; López‐Díaz et al ., ; Cruz‐Rico et al ., ; Alvarez‐Olguin and Escalante‐Sandoval, ) and precipitation (Cavazos et al ., ; Arriaga‐Ramirez and Cavazos, ; Martinez‐Lopez et al ., ) trends using station data in some regions of the country. Increases of extreme summer precipitation in the North American monsoon (NAM) region have been associated with the intensification of tropical cyclones (Cavazos et al ., ; Arriaga‐Ramirez and Cavazos, ) and the negative phase of the AMO (Curtis, ).…”

Evaluating the Performance and Applicability of Satellite Precipitation Products over the Rio Grande–San Juan Basin in Northeast Mexico

“…Otherwise, studies have analyzed trends over time and space in recent decades using the national rain gauge stations and meteorological station networks [27][28][29][30][31]. Studies from Alvarez-Olguin [32] and Campos-Aranda [33] have found that trends, apart from increasing or decreasing, are mostly non-significant, which could indicate the presence of variability in the study period. Also, this means that precipitation has not been constant enough to determine if they have been significant in recent years.…”