Abstracto

Spatial and Statistical Clustering Based Regionalization of Precipitation and Trend Identification in Pranhita Catchment, India

Rajashree Bothale, Yashwant Katpatal

Clustering based regionalisation and the trends in precipitation in the Pranhita catchment, India using the annual precipitation data pertaining to 131 rainfall stations during 1970 - 2011 is reported here. The rainfall data was subjected to various quality checks, homogeneity tests and discordancy measures which identify nonclimatic factors responsible for breaks in the data and the outliers in the dataset. The area was subdivided into homogeneous zones using statistical and spatial clustering. The heterogeneity measure based on coefficient of variations showed that the identified clusters are homogeneous in nature. The change in precipitation has been calculated by trend analysis on the Precipitation Index (PI). Though trend line of the entire study area did not show any variation, a positive trend was observed in Region #1.This clearly indicated that within large homogeneous areas, minor variations in climate are possible and mappable. Thus there is a need to divide large catchments into smaller regions to understand the subtle variations in the climate for further detailed analysis.