Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Acta Geophysica

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Analyzing trend and forecast of rainfall and temperature in Valmiki Tiger Reserve, India, using non parametric test and random forest machine learning algorithm

Roshani, Sajjad Haroon, Saha Tamal Kanti, Rahaman Md Hibjur, Masroor Md, Sharma Yatendra, Pal Swades

Acta Geophysica

2023

Vol. 71, no. 1

531--552

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen’s slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations.

Assessment of evidence based climate variability in Bhagirathi sub basin of India: a geostatistical analysis

Rehman Sufa, Sajjad Haroon, Masroor Md, Rahaman Md Hibjur, Roshani, Ahmed Raihan, Sahana Mehebub

Acta Geophysica

2022

Vol. 70, no 1

445--463

Climate variability analysis is essential for predicting the behavior of various extreme weather events and making communities resilient. Notwithstanding the profound concerns, climate variability assessment faces numerous challenges due to inadequate and sometimes unavailability of data at spatiotemporal scales. This study makes an attempt to analyse climate variability in the Bhagirathi Sub-basin of India. Six meteorological variables were analysed from fourteen weather stations located in the Sub-basin during 1968–2017. Modified Mann–Kendall test was employed to ascertain the trends in meteorological variables. One-way ANOVA was used to assess the relationship between and within the variables. A total of 432 households were selected for reaffirming climate variability and impact on landscape. Significant trends were detected in highest maximum, mean maximum (Mmax) and mean minimum (Mmin) temperatures, relative humidity (Rh), rainfall and vapour pressure (Vp) at annual and seasonal scales. Stations located in eastern and deltaic Sub-basins registered varying trends in these meteorological variables due to anthropogenic activities-induced land use changes. ANOVA revealed a robust relation among rainfall, Vp, Mmin and Mmax. Perceptions of the sampled households revealed that climate variability has considerably affected food intensity, vegetation, soil, water resources and agricultural pattern. We find modified Mann– Kendall method effective in analysing climate variability in the Sub-basin. Thus, this method can be utilized for effective analysis of climate variability at spatial scales in geographical regions.