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The changing pattern of climate variables has caused extreme weather events and severe disasters especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine trends and forecast of meteorological variables using scientific modeling approach at micro level. This study makes an attempt to examine trend in temperature and rainfall using Modified Mann–Kendall test and Sen’s slope estimator during 1980–2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trend for the next 20 years (2022–2041) to understand the temporal dynamics in Shimla district of Indian Himalayan state. Correlation coefficient (R), mean squared error (MSE), mean absolute error (MAE), and root mean squared error mean (RMSE) performance were determined to assess effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased, especially during the monsoon season (June–September) during 1980–2021. Annual maximum, minimum, and mean temperatures have exhibited significant variability while annual rainfall has shown a decreasing trend. The forecast analysis revealed significant trend for rainfall during monsoon season and increasing trend in the maximum temperature has been observed during summer and winter seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat the effect of climate change in the hilly regions. The methodology adopted in the study may help in future progression of the research in different geographical regions of Western Himalayas.

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The process of erosion as an inevitable and disastrous mechanism has caused migration of bank lines of rivers globally. In its middle reaches, the Brahmaputra River has eroded many pockets of land, eventually leading to drastic bank line shifting. This study aims to analyze the bank line migration of the Brahmaputra river in the Middle Brahmaputra floodplains of Assam, India, over a period of 30 years (1990-2020) and forecast their future positioning. The study was carried out using digital shoreline analysis system (DSAS). End point rate (EPR) was used to estimate bank line migration over three decades (1990-2000, 2000-2010 and 2010-2020). Both end point rate (EPR) and linear regression rate (LRR) were used for calculating long-term migration from 1990 to 2020. The findings revealed that bank line migration was more prominent along the river’s south bank and the river channel was observed to be migrating in a southward direction. The average shift of the right bank of the river was around -8.15 m/y, 11.83 m/y and -4.5 m/y during 1990-2000, 2000-2010 and 2010-2020 respectively. The left bank of the river showed an erosive trend with an average positional shift of -57.02 m/y, -53.65 m/y and -38.66 m/y during 1990-2000, 2000-2010 and 2010-2020 respectively. The forecasting of the bank lines for 2030 and 2040 showed that the river would likely continue to erode its banks leading to channel widening. The study demonstrated the severity of riverbank erosion and bank line migration processes in the Middle Brahmaputra floodplains. This work might help policymakers find solutions to protect the invaluable lands and lessen the vulnerability of the affected population.

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