Exploring Spatial Coherence in Inter-annual Changes and Annual Extremes of Rainfall over India

TL;DR

This study analyzes spatial coherence in inter-annual rainfall changes and extremes over India using data mining, revealing significant spatial patterns and correlations at sub-national scales relevant for improved monsoon forecasting.

Contribution

It introduces a data-driven approach to identify spatially homogeneous rainfall clusters and characterizes coherence in rainfall phases and extremes at sub-national levels.

Findings

Grid-level rainfall phase is spatially coherent and correlates with all-India mean rainfall phase.

Extreme rainfall years at the grid level are not strongly linked to national extremes.

Local extremes and phases often co-occur in spatially contiguous clusters.

Abstract

Forecasts of monsoon rainfall for India are made at national scale. But there is spatial coherence and heterogeneity that is relevant to forecasting. This paper considers year-to-year rainfall change and annual extremes at sub-national scales. We use Data Mining techniques to gridded rain-gauge data for 1901-2011 to characterize coherence and heterogeneity and identify spatially homogeneous clusters. We study the direction of change in rainfall between years (Phase), and extreme annual rainfall at both grid level and national level. Grid-level Phase is found to be spatially coherent, and significantly correlated with all-India mean rainfall (AIMR) phase. Grid-level extreme-rainfall years are not strongly associated with corresponding extremes in AIMR, although in extreme AIMR years local extremes of the same type occur with higher spatial coherence. Years of extremes in AIMR entail widespread phase of the corresponding sign. Furthermore, local extremes and phase are found to frequently co-occur in spatially contiguous clusters.