Spatial and temporal trend analysis of rainfall in Nagaland (India) using machine learning techniques

Abstract

Rainfall plays a vital role in the field of agriculture as it affects agricultural production and associated economy. However, the changing trend of rainfall has become a global concern. So the study of changes in the trend of rainfall is necessary. In the present study, an innovative trend analysis method was adopted to assess the changing trend in the state of Nagaland. Data of 40 years was taken for performing the trend analysis using ITA. The entire process of trend change analysis was automated using Python programming. The analysis indicated that out of the 11 stations considered, three stations indicated a rising trend, eight indicated falling trends (annual), four rising and seven falling (monsoon), 0 rising and 11 falling (winter). The extent of trend change varied from –34.5 to 1.1. The spatial distribution of the trend change was also performed. It was observed that the southeast part of Nagaland’s rising trend was more pronounced compared to the southwest. The change was more prominent during the winter season followed by pre-monsoon and monsoon. The trend analysis is important for making appropriate water management decisions, such as water conservation in areas with falling trends and soil conservation in areas affected by rising trends.