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Predictors and their domain for statistical downscaling of climate in Bangladesh
Mahiuddin Alamgir1, Sahar Hadi Pour2, Morteza Mohsenipour3, Tarmizi Ismail4, M. Mehedi Hasan5.
Reliable projection of future rainfall in Bangladesh is very important for the assessment
of possible impacts of climate change and implementation of necessary adaptation
and mitigation measures. Statistical downscaling methods are widely used for
downscaling coarse resolution general circulation model (GCM) output at local scale.
Selection of predictors and their spatial domain is very important to facilitate
downscaling future climate projected by GCMs. The present paper reports the finding
of the study conducted to identify the GCM predictors and demarcate their climatic
domain for statistical downscaling in Bangladesh at local or regional scale. Twenty-six
large scale atmospheric variables which are widely simulated GCM predictors from 45
grid points around the country were analysed using various statistical methods for this
purpose. The study reveals that large-scale atmospheric variables at the grid points
located in the central-west part of Bangladesh have the highest influence on rainfall. It
is expected that the finding of the study will help different meteorological and
agricultural organizations of Bangladesh to project rainfall and temperature at local
scale in order to provide various agricultural or hydrological services.
Affiliation:
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- University of Rajshahi, Bangladesh
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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