The power of the river does not always work to the benefit of living things; rather, it has the ability to destroy human society and has negative effects on the environment. River Teesta has a prodigious significance on the riverine dweller and its changing precarious behaviour has repercussions on the lives of millions. Utilizing satellite images and 41 years of temporal analysis, the mapping of channel extraction and channel shifting has been demonstrated. Delineating the exact channel boundary is a laborious operation, but MNDWI technique is really valuable in this situation to produce valuable results. Descriptive statistics are used to analyze the Teesta River's active flow line migration trend and channel migration behaviour. A particular river's oscillating character over time has both beneficial and detrimental power, destroying important resources for environmental sustainability and human society while also providing fertile mother earth as a source of wealth. As a result, another goal of the current study was to determine how vulnerable riverine residents were. A livelihood vulnerability index (LVI) based on the riverine inhabitants' exposure, sensitivity, and capacity for adaptation was created. According to the findings, the river's erratic behaviour is putting the people who live beside it in danger. Therefore, it is vitally necessary to implement a capable management strategy to stop bank failure at the Teesta-Dharala confluence in order to save the riverine residents.

Understanding the mechanics of future climate and land use requires an understanding of hydrological consequences, which include changes in soil usage. The study's analysis of rainfall data from 13 monitoring stations spread over the Jaldhaka River Basin on monthly, seasonal and annual time periods revealed the substantial temporal pattern. Using data from 1901 to 2022 (122 years) from thirteen IMD gridded stations spread over the Jaldhaka River Basin in India, the temporal variation and trends of the annual, seasonal and monthly rainfall distribution at the Himalayan Foreland were investigated. The nonparametric Mann-Kendal (MK) & modified Mann-Kendal (MMK) along with Sen’s non-parametric estimator of slope and Buishand U test were applied to detect trends and step change analysis, respectively. Three distinct time series' autocorrelation coefficients (ACCs) were computed at a one-time lag and assessed at the 95% significance level in order to apply the MMK test. Significant positive and negative trends have been noticed in the monthly, seasonal, and yearly rainfall time series in all 13 IMD gridded stations in and around the Indian Jaldhaka River Basin (JRB), according to the analysis's findings. In the ArcGIS 10.8 environment, the spatial analyst tool was used to interpolate the spatial variation of the monthly, seasonal, and yearly rainfall time series data. In the Jaldhaka River Basin, rainfall patterns and variability study, together with accompanying maps, would be helpful to local stakeholders in organising the effective use of water resources.

Understanding the mechanics of future climate and land use requires an understanding of hydrological consequences, which include changes in soil usage. The study's analysis of rainfall data from 13 monitoring stations spread over the Jaldhaka River Basin on monthly, seasonal and annual time periods revealed the substantial temporal pattern. Using data from 1901 to 2022 (122 years) from thirteen IMD gridded stations spread over the Jaldhaka River Basin in India, the temporal variation and trends of the annual, seasonal and monthly rainfall distribution at the Himalayan Foreland were investigated. The nonparametric Mann-Kendal (MK) & modified Mann-Kendal (MMK) along with Sen’s non-parametric estimator of slope and Buishand U test were applied to detect trends and step change analysis, respectively. Three distinct time series' autocorrelation coefficients (ACCs) were computed at a one-time lag and assessed at the 95% significance level in order to apply the MMK test. Significant positive and negative trends have been noticed in the monthly, seasonal, and yearly rainfall time series in all 13 IMD gridded stations in and around the Indian Jaldhaka River Basin (JRB), according to the analysis's findings. In the ArcGIS 10.8 environment, the Thiessen polygon (TP) method was used to interpolate the spatial variation of the monthly, seasonal, and yearly rainfall time series data. In the Jaldhaka River Basin, rainfall patterns and variability study, together with accompanying maps, would be helpful to local stakeholders in organising the effective use of water resources.