Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps

Abstract

Regionalization of rainfall or delineation of a region into areas having similar rainfall characteristics is useful for many hydrologic applications and water resources management. In this study, we aim to regionalize India into regions having similar rainfall and climatic characteristics using self-organizing maps (SOM), an artificial neural network algorithm derived clustering technique. The SOM algorithm is applied to observed gauge-based gridded Indian Meteorological Department (IMD) rainfall data (0.25° × 0.25°) for 34 years duration (1980–2013). Information about climatic variables like air temperature, specific humidity, geo-potential height, surface pressure, etc., which directly influence the rainfall characteristics is derived from the Modern Era-Retrospective Analysis for Research and Applications reanalysis data-set. Four cluster validity indices are used to identify the optimal number of clusters. While, 10 homogeneous regions are identified over India when accounting rainfall characteristics only, incorporation of climatic variables added more heterogeneity dividing India into 15 homogeneous rainfall regions. These 15 homogeneous rainfall zones effectively capture the spatial variability of rainfall and its spell-characteristics over India. Moreover, none of the 15 delineated regions are found to be heterogeneous when subjected to regional homogeneity test. The present study will aid in regional frequency analysis, forecasting and downscaling of rainfall, land-use management and agriculture planning.