Change point detection and trend analysis are the adopted techniques of time series analysis. We have applied non-parametric methods on the temporal and spatial-scale data of 115 years between 1901 and 2015 from ten different federal states in the north and the northwestern India to examine the change points as well as to estimate the future scenarios by examining the past trends. The change points were examined by Pettitt’s test, Standard Normal Homogeneity (SNH) test, and Buishand’s test, whereas the trend analyses of monthly, annual, and seasonal rainfall data were carried out using Sen’s slope estimator after assessing their statistical significance by Mann–Kendall (M-K) test. The trend analyses showed non-zero slope values and a few among them were of statistical significance. The results of our statistical experiment concluded that the trends of reduction in winter, pre-monsoon, and post-monsoon rainfalls would have notable effects on the rain-fed agricultural production in the near future, particularly in the areas without proper irrigation facility (e.g., parts of Uttar Pradesh). More extreme events of higher rainfall in some states (e.g., Goa, Maharashtra, and Jammu and Kashmir), however might cause disasters like landslide and flooding.

变更点检测和趋势分析是时间序列分析采用的技术。我们对印度北部和印度西北部十个不同联邦州在1901年至2015年之间115年的时空数据应用了非参数方法，以研究变化点并通过检查过去的趋势。通过Pettitt检验，标准正常均质性（SNH）检验和Buishand检验检验了变化点，而在通过Mann-Kendall评估了统计显着性之后，使用Sen斜率估算器对月，年和季节性降雨数据进行了趋势分析。 （MK）测试。趋势分析显示非零斜率值，其中一些具有统计意义。我们的统计实验结果得出结论，冬季，季风前和季风后降雨减少的趋势将在不久的将来对雨育农业生产产生显着影响，特别是在没有适当灌溉设施的地区（例如，北方邦的部分地区）。在某些州（例如，果阿，马哈拉施特拉邦，查mu和克什米尔），更多的极端极端降雨事件，但是可能导致诸如滑坡和洪水等灾害。