利用基于地理信息系统的空间分析,通过分析印度 kuzhithuraiyar 子流域的降雨模式调查气候变化情况

Belfin Raj Selvaraj , Srinivasan Krishnasamy , Jessy Mol Irudhaya Dhason
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引用次数: 0

摘要

全球气候变化可能对世界上许多不同国家的若干环境因素产生重大影响。降水模式的变化会对水资源管理产生直接影响。为了改进水资源管理技术,研究降雨模式在时间和地理上的差异至关重要。本研究考察了坎亚库马里地区科达亚尔盆地 Kuzhithuraiyar 子盆地的降雨模式。利用四个不同雨量计位置的月平均数据,我们对 30 年(1991-2020 年)的降雨量统计数据进行了全面整理和分析。为了精确地进行时间分类,我们将这些数据细致地分为四个季节分类:季风前、季风后、西南季风(SW)和东北季风(NE)。利用 ArcGIS 10.8 对降雨模式进行了分析,并采用反距离加权法(IDW)绘制了上述四季的空间分布图。该地区每年平均降水量为 1456.78 毫米,季风后、季风前、西南季风和东北季风的降水量分别为 32.87 毫米、335.28 毫米、538.67 毫米和 549.97 毫米。结果表明,该模型在泰米尔纳德邦 Kuzhithuraiyar 亚盆地地区的平均降雨量和降雨变率之间建立了关联,在极端降雨量的数量及其空间分布图方面具有一定的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of climate change by analysing the rainfall pattern in kuzhithuraiyar sub-basin of India using GIS-based spatial analysis

Global climate change may have a significant effect on several environmental components in many different nations worldwide. Variations in the pattern of precipitation have an immediate impact on the management of water resources. To improve water resource management techniques, it is critical to investigate differences in the temporal and geographical rainfall patterns. The current study looks at rainfall patterns in the Kuzhithuraiyar sub-basin of the Kodayar basin, Kanyakumari District. Utilizing the mean monthly data from four distinct rain gauge locations, we have comprehensively compiled and analysed rainfall statistics over an extensive timespan of 30 years (1991–2020). For precision in temporal categorization, this data was meticulously divided into four seasonal classifications: pre-monsoon, post-monsoon, southwest monsoon (SW), and northeast monsoon (NE). Using ArcGIS 10.8, the rainfall pattern was analysed, and spatial distribution maps for the four seasons listed above were created using the inverse distance weighting (IDW) approach. The region receives an average of 1456.78 mm of precipitation each year, with the post-monsoon, pre-monsoon, SW monsoon, and NE monsoon providing 32.87 mm, 335.28 mm, 538.67 mm, and 549.97 mm, respectively. The results reveal that the model correlates mean rainfall and rainfall variability over areas of Tamil Nadu's Kuzhithuraiyar sub-basin with some accuracy in terms of the number of rainfall extremes and their spatial distribution maps.

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