Evaluation of Microphysics and Cumulus Schemes of WRF for Forecasting of Heavy Monsoon Rainfall over the Southeastern Hilly Region of Bangladesh

Uttarakhand, a Himalayan state of India, may experience an increase in temperature of 1.4°C to 5.8°C by 2100 due to global warming. The rise in temperature may melt the glaciers of the state and may have some significant impact on the rainfall. In this study, we have quantified the changes in the rainfall of the state. Also, an attempt has been made to evaluate the impact of climate change on rainfall. The future rainfall can be estimated by using a global circulation model (GCM). However, due to the very coarse spatial resolution of the different GCM, we cannot use them directly. For matching this spatial inequality between the GCM output and historical precipitation data, we used the statistical downscaling technique. In the present study, we have examined the suitability of the artificial neural network with principal component analysis for downscaling the rainfall for different hilly districts of the state. We used the GCM model developed by Canadian Earth System Model, and the Indian metrological department gridded rainfall data. We performed the analysis for the different scenarios to visualize the impact of climate change on rainfall trends for all nine hilly districts of Uttarakhand. Results show that there was a clear indication of climate change in upper Himalayan Districts like Pithoragarh, Rudraprayag, and Chamoli, which was observed from the peak of monthly rainfall. The percentage change of monsoon rainfall in the future may go up to 200 % in the case of RCP8.5, and the change maybe around 180% for RCP4. Also, the volume of rainfall may increase in the case of RCP8.5 from July to September as compared to the historical data, i.e., there may be a shifting of monsoon rainfall in the future.

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Forecasting of Summer Monsoon Rainfall over Gangetic West Bengal, India Utilising Intrinsic Mode Functions, Linear and Neural Regression

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.1.60 ◽

2020 ◽

Vol 12 (1) ◽

pp. 60-69 ◽

Cited By ~ 1

Author(s):

Pijush Basak

Keyword(s):

Neural Network ◽

West Bengal ◽

Monsoon Rainfall ◽

Linear Part ◽

Intrinsic Mode Functions ◽

Quasi Biennial Oscillation ◽

Nonlinear Part ◽

South West ◽

South West Monsoon ◽

West Monsoon

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real data. The statistical forecasts of SWM rainfall for GWB for the years 2012 and 2013 are108.71 cm and 126.21 cm respectively, where as corresponding to the actual rainfall of 93.19 cm 115.20 cm respectively which are within one standard deviation of mean rainfall.

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Climate resilience through natural regeneration in degraded natural forests of south-eastern hilly region of Bangladesh

Bangladesh Journal of Botany ◽

10.3329/bjb.v48i3.47720 ◽

2019 ◽

Vol 48 (3) ◽

pp. 417-425

Author(s):

Md Khayrul Alam Bhuiyan ◽

Md Akhter Hossain ◽

Abdul Kadir Ibne Kamal ◽

Mohammed Kamal Hossain ◽

Mohammed Jashimuddin ◽

...

Keyword(s):

Tree Species ◽

Natural Regeneration ◽

Diversity Indices ◽

Acacia Auriculiformis ◽

Quantitative Structure ◽

Natural Forests ◽

Climate Resilience ◽

Hilly Region ◽

Major Cluster ◽

Ecological Dominance

A study was conducted by using 5m × 5m sized 179 quadrates following multistage random sampling method for comparative regenerating tree species, quantitative structure, diversity, similarity and climate resilience in the degraded natural forests and plantations of Cox's Bazar North and South Forest Divisions. A total of 70 regenerating tree species were recorded representing maximum (47 species) from degraded natural forests followed by 43 species from 0.5 year 39 species from 1.5 year and 29 species from 2.5 year old plantations. Quantitative structure relating to ecological dominance indicated dominance of Acacia auriculiformis, Grewia nervosa and Lithocarpus elegans seedlings in the plantations whereas seedlings of Aporosa wallichii, Suregada multiflora and Grewia nervosa in degraded natural forests. The degraded natural forests possess higher natural regeneration potential as showed by different diversity indices. The dominance-based cluster analysis showed 2 major cluster of species under one of which multiple sub-clusters of species exists. Poor plant diversity and presence of regenerating exotic species in the plantations indicated poor climate resilience of forest ecosystem in terms of natural regeneration.

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