Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO

• Main Authors: Tue M. Vu, Ashok K. Mishra, Goutam Konapala
• Format: Article
• Language: English
• Published: MDPI AG 2018-01-01
• Series: Entropy
• Subjects: information entropy; mutual information; kernel density estimation; ENSO; nonlinear relation
• Online Access: http://www.mdpi.com/1099-4300/20/1/38

Understanding the teleconnections between hydro-meteorological data and the El Niño–Southern Oscillation cycle (ENSO) is an important step towards developing flood early warning systems. In this study, the concept of mutual information (MI) was applied using marginal and joint information entropy to quantify the linear and non-linear relationship between annual streamflow, extreme precipitation indices over Mekong river basin, and ENSO. We primarily used Pearson correlation as a linear association metric for comparison with mutual information. The analysis was performed at four hydro-meteorological stations located on the mainstream Mekong river basin. It was observed that the nonlinear correlation information is comparatively higher between the large-scale climate index and local hydro-meteorology data in comparison to the traditional linear correlation information. The spatial analysis was carried out using all the grid points in the river basin, which suggests a spatial dependence structure between precipitation extremes and ENSO. Overall, this study suggests that mutual information approach can further detect more meaningful connections between large-scale climate indices and hydro-meteorological variables at different spatio-temporal scales. Application of nonlinear mutual information metric can be an efficient tool to better understand hydro-climatic variables dynamics resulting in improved climate-informed adaptation strategies.