Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901–2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann–Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations.

中文翻译：

---

印度科西河流域降水和极端事件的长期趋势分析

分析局部尺度上长期降水和极端降水分布的时空变化，对于水灾的防灾减灾具有重要意义。在本研究中，我们分析了印度洪水泛滥的河流之一科西河流域（KRB）116 年（1901-2016 年）的长期降水趋势和极端降水指数的分布，使用从印度气象局 (IMD) 获得的 0.25° × 0.25° 分辨率网格降水数据集。非参数 Mann-Kendall 趋势检验与 Sen 斜率估计器一起用于确定降水时间序列的趋势和趋势的大小。年度和季风季节显示出下降趋势，Sen 的斜率值为 -1.88 和 -0.408，分别。对于极端指数即。R10 和 R20 天，从流域东北部到西南部有减少的趋势，而在单日降水量最高的情况下（RX1 天），没有发现明显的趋势。通过这项研究提供的信息对决策者有用，并且可能在洪水管理、径流和与流域水文过程相关的理解方面发挥重要作用。这将有助于更好地了解改变降雨模式的潜在风险，尤其是气候变化导致的极端降雨事件。通过这项研究提供的信息对决策者有用，并且可能在洪水管理、径流和与流域水文过程相关的理解方面发挥重要作用。这将有助于更好地了解改变降雨模式的潜在风险，尤其是气候变化导致的极端降雨事件。通过这项研究提供的信息对决策者有用，并且可能在洪水管理、径流和与流域水文过程相关的理解方面发挥重要作用。这将有助于更好地了解改变降雨模式的潜在风险，尤其是气候变化导致的极端降雨事件。