No abstract

Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18% of the total time series data was detected as homogeneous, and 95.35% of time series data were found to have insigni cant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, MK and SR tests to 41 time series, and mMK test to two time series. The MK and SR tests detected 14.63% of the time series as a signi cant trend, whereas the ITA method could detect 93.02% of the total time series data. The MK and SR tests captured signi cant trends for the winter season in two districts, but the two tests detected a signi cant trend only in one district for the summer season. None could be captured for monsoon season. A signi cant trend was captured for the post-monsoon season in two districts; however, the tests could detect a signi cant trend in one district for annual rainfall. The mMK test revealed a positive trend for the post-monsoon season in a district. The ITA method could capture a signi cant trend for all the seasons in most districts.

Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years (1901–2002) for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18% of the total time series data was detected as homogeneous, and 95.35% of time series data were found to have insignificant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, MK and SR tests to 41 time series, and mMK test to two time series. The MK and SR tests detected 14.63% of the time series as a significant trend, whereas the ITA method could detect 93.02% of the total time series data. The MK and SR tests captured significant trends for the winter season in two districts, but the two tests detected a significant trend only in one district for the summer season. None could be captured for monsoon season. A significant trend was captured for the post-monsoon season in two districts; however, the tests could detect a significant trend in one district for annual rainfall. The mMK test revealed a positive trend for the post-monsoon season in a district. The ITA method could capture a significant trend for all the seasons in most districts.