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Long term spatial and temporal rainfall trend analysis using GIS and statistical methods in Lower Bhavani basin, Tamil Nadu, India
Indian Journal of Geo-Marine Sciences ( IF 0.5 ) Pub Date : 2020-05-26
B. Anand, D. Karunanidhi

The present study aims to identify the long term spatial and temporal distribution of rainfall, using the methods from GIS and statistical tools, in Lower Bhavani basin. For this attempt, the spatial distribution of rainfall pattern was studied, by using 33 years of rainfall data (1983 – 2015), from 22 rain gauge stations. The rainfall pattern was interpolated by Inverse Distance Weighted (IDW) method using Arc GIS 10.2.1 on the monthly, seasonal and annual basis. The mean annual rainfall of the region was 666.84 mm. The increasing monthly rainfall was reported in the month of October (164 mm) and decreasing rainfall was observed in the month of January (4.99 mm). The seasonal rainfall changes were prominent during the north east Monsoon season. From 1983 to 2015, 2010 witnessed the highest rainfall pattern in the area. The statistical analysis of Mann Kendall (MK) Test was used, to detect the monotonic increasing and decreasing trend over time, on a monthly basis. A significant level (α = 0.05) was used to detect the positive and negative trend that existed over the basin. To be precise, the positive trend existed during the October over 10 rain gauge stations and it was located on the upper northern part of the basin. The negative trend was observed, over six rain gauge stations in southern part of the basin, in the month of January. The result indicated that spatial distribution of rainfall and trend pattern ensured influences in the topography, agriculture productivity and groundwater management across the basin.

中文翻译:

使用GIS和统计方法对印度泰米尔纳德邦下巴瓦尼盆地进行长期时空降雨趋势分析

本研究旨在利用下巴瓦尼盆地的GIS和统计工具确定降雨的长期时空分布。为此,我们使用了22个雨量站的33年降雨数据(1983年至2015年),研究了降雨模式的空间分布。在每月,季节性和每年的基础上,使用Arc GIS 10.2.1通过反距离加权(IDW)方法对降雨模式进行插值。该地区的年平均降雨量为666.84毫米。据报告,十月(164毫米)月降雨量增加,一月(4.99毫米)月降雨量减少。在东北季风季节,季节性降雨变化显着。从1983年到2015年,2010年是该地区降雨量最高的时期。使用Mann Kendall(MK)检验的统计分析,每月检测一次随时间变化的单调上升和下降趋势。使用显着水平(α= 0.05)来检测流域上存在的正趋势和负趋势。确切地说,在10月的10个雨量计站中存在积极趋势,它位于流域的北部北部。一月份,在流域南部的六个雨量计站观测到了负面趋势。结果表明,降雨的空间分布和趋势模式确保了流域地形,农业生产力和地下水管理的影响。05)被用来检测盆地上存在的正趋势和负趋势。确切地说,在10月的10个雨量计站上存在着积极的趋势,它位于流域的北部北部。一月份,在流域南部的六个雨量计站观测到了负面趋势。结果表明,降雨的空间分布和趋势模式确保了流域地形,农业生产力和地下水管理的影响。05)被用来检测盆地上存在的正趋势和负趋势。确切地说,在10月的10个雨量计站中存在积极趋势,它位于流域的北部北部。一月份,在流域南部的六个雨量计站观测到了负面趋势。结果表明,降雨的空间分布和趋势模式确保了流域地形,农业生产力和地下水管理的影响。在一月份。结果表明,降雨的空间分布和趋势模式确保了流域地形,农业生产力和地下水管理的影响。在一月份。结果表明,降雨的空间分布和趋势模式确保了整个盆地对地形,农业生产力和地下水管理的影响。
更新日期:2020-07-28
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