A Spatio-Temporal Approach To Evolution Of Spatial Homogeneity Of Monsoon Extremes Over India

Abstract:

There is a growing literature on climate change w.r.t. severe changes in extreme events related to the Monsoon rainfall over India. Evidence of increasing spatial variability of extreme rainfall triggers several aspects such as: how are the spatially homogeneous regions or clusters changing w.r.t. extreme rainfall over time? Is there detectable evidences of changes in cluster behavior? Can we arrive at a forecast? Purpose of this study is twofold: firstly, we introduce a novel discrete-time finite state-space hidden Markov models with non-constant transition matrix depending on a set of exogenous covariates including cluster level temporal information. We present a space-time varying dynamic with conditionally independent Generalized Extreme value (GEV) distribution as modeling extremes. Secondly, we introduce a model based spatio-temporal clustering algorithm based on the latter model. We illustrate this technique based on Monsoon rainfall data and show that the cluster characteristics are significantly changing along with the number of clusters. We also obtain one step ahead future patterns of the clusters.