An Assessment of Trend Analysis and Variability of Rainfall at Upper Seonath River Basin
DOI:
https://doi.org/10.30732/CSVTURJ.20200902007Keywords:
Rainfall; , Trend analysis; , Mann-Kendall; GIS; Sen’s slopeAbstract
The quantitative analysis of the rainfall events is one of the significant techniques for effective management of hydrological system. Evaluating the spatial and temporal complexities of meteorological parameters in the perspective of climate transition, especially in countries where rainfed farming is predominant, is essential for assessing climate-induced changes and suggesting suitable management approaches. The variability of the annual rainfall has great implications for the planning of irrigation related projects and thus such studies are critical for the planning of agriculture in India. Agriculture is one of the important activities that are highly influenced by the variation of rainfall and temperature. This is an attempt to analyze one of the most significant climate variables i.e. precipitation, to analyze the trend in rainfall in the area. Rainfall variability can be calculated in many ways, both in terms of time and location. Evaluation of rainfall events can be helpful to identify the climatic condition of the particular region. The magnitude or variations of factors differs depending on wherever the area is located[1]. This study is an attempt to investigate about the spatio-temporal variability of rainfall at Upper Seonath basin. Mann-Kendall test has been used to analyze the trend in rainfall data at different gauging stations and the Sen’s slope method has been used to determine the magnitude of the change[2]. Trend analysis is one of the active area of interest to investigate the rainfall variability over the years. Trend analysis for the observed rainfall series was undertaken using Mann-Kendall test. Innovative trend analysis was considered to be an effective method for detecting the overall trend of rainfall events[3]. Long term annual average rainfall and coefficient of variability at each station have been computed and Geographic Information System (GIS) has been used to represent the spatio-temporal variation of rainfall. Daily rainfall data of 34 years from 1980 to 2013 has been analysed in the study to find out the variability of rainfall. Trend analysis of rainfall events on yearly basis represents declining trend across the study region[4]
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