The identification of trends in hydrological data is crucial for sustainable planning and management of water resources under the climate-change scenario. This research, identify the long-term temporal trend and magnitude (m³/s/time scale) in monthly, seasonal, and annual streamflow by employing three non-parametric approaches conventional Mann-Kendall (MK), Innovative-Şen trend (IŞT), and Sen-slope (SS) on 5% level of significance. The monthly streamflow data of 32-years (1976–2007) were recorded at Naula and Kedar stations positioned in the upper Ramganga River catchment (RRC), Uttarakhand State (India). Results of scrutiny reveal a significant negative trend in 17 time-series was detected by conventional MK test, and significant positive/negative trend in 1/30 time-series was inspected by the IŞT method with changing magnitude over monthly, seasonal, and annual scales at both stations, respectively. Furthermore, a comparison among results of the MK and IŞT showed that the IŞT method examined the unseen trend that cannot be detected by the MK technique at the Naula watershed. The pattern of trend detected on annual, seasonal, and monthly time-scales by three non-parametric approaches can help the water resources management authorities and hydrologists to comprehend the hazard and vulnerability under climate-change scenario over the study catchment area.

在气候变化情景下，确定水文数据趋势对于水资源的可持续规划和管理至关重要。这项研究确定了长期的时间趋势和幅度（m ³/ s /时间尺度），通过采用三种非参数方法，即传统的Mann-Kendall（MK），Innovative-enen趋势（IŞT）和Sen-slope（SS），采用5％的显着性水平。在位于北阿坎德邦邦（印度）Ramganga河上游流域（RRC）的瑙拉站和基达尔站记录了32年（1976-2007年）的月流量数据。仔细检查的结果显示，通过常规MK检验可以检测到17个时间序列的显着负趋势，并且通过IŞT方法检查了1/30时间序列的显着正/负趋势，其幅度在月度，季节和年度范围内变化在两个车站分别。此外，MK和IŞT结果的比较表明，IŞT方法检查了Naula流域无法通过MK技术检测到的看不见的趋势。通过三种非参数方法在年度，季节和月度时间尺度上检测到的趋势模式可以帮助水资源管理机构和水文学家了解研究集水区气候变化情景下的危害和脆弱性。