Abstract
The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact.
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Sharma, Y., Sajjad, H., Saha, T.K. et al. Analyzing and forecasting climate variability in Nainital district, India using non-parametric methods and ensemble machine learning algorithms. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04920-y
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DOI: https://doi.org/10.1007/s00704-024-04920-y