Spatial and temporal variability of future extreme precipitation in Bangladesh using CMIP6 models

ABSTRACT This research utilized a multimodel ensemble (MME) of 13 bias-corrected Coupled Model Intercomparison Project Phase 6 general circulation models to project variability in eight precipitation indices for the near future (2021–2060) and the far future (2061–2100), using two shared socioeconomic pathways (SSPs), namely, SSP2-4.5 and SSP5-8.5. This study intends to assess future spatiotemporal changes in the MME mean of extreme precipitation indices over Bangladesh, considering each index's reference period (1985–2014) for the corresponding future periods. The results showed that 72 and 83% of sites showed an increasing trend in total precipitation (PRTOT) and precipitation intensity (simple daily intensity index) compared to the reference period. The maximum 1-day and maximum consecutive 5 days of precipitation and 20-percentile precipitation will rise at a higher rate in southeast and northeast areas in the near future compared to the far future and reference period. The northeast region experienced a higher day with precipitation above 95 percentiles compared to the west and northwest parts. Extreme precipitation indices have shifted to the left, which shows high regional heterogeneity and a significant rate of change for all timeframes that make water resources more spatially imbalanced. This research underlines the need to protect the welfare and future viability of the monsoon-dominated country in response to a shifting climate.

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Publication Details

Journal
Journal of Water and Climate Change
Published
2025-04-01
DOI
https://doi.org/10.2166/wcc.2025.415
Citations
5
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
5.53
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article

Spatial and temporal variability of future extreme precipitation in Bangladesh using CMIP6 models

Mohammad Kamruzzaman, Javed Mallick, Abu Reza Md. Towfiqul Islam, Mst Yeasmin Akter et al.
5 citations
Journal of Water and Climate Change
Climate variability and models
5.53
article

Spatial and temporal variability of future extreme precipitation in Bangladesh using CMIP6 models

Mohammad Kamruzzaman, Javed Mallick, Abu Reza Md. Towfiqul Islam, Mst Yeasmin Akter, Mohammed Abdus Salam, Subodh Chandra Pal, Md. Abdul Fattah, Rifat Ara Mishu
article en
5 citations

Abstract

ABSTRACT This research utilized a multimodel ensemble (MME) of 13 bias-corrected Coupled Model Intercomparison Project Phase 6 general circulation models to project variability in eight precipitation indices for the near future (2021–2060) and the far future (2061–2100), using two shared socioeconomic pathways (SSPs), namely, SSP2-4.5 and SSP5-8.5. This study intends to assess future spatiotemporal changes in the MME mean of extreme precipitation indices over Bangladesh, considering each index's reference period (1985–2014) for the corresponding future periods. The results showed that 72 and 83% of sites showed an increasing trend in total precipitation (PRTOT) and precipitation intensity (simple daily intensity index) compared to the reference period. The maximum 1-day and maximum consecutive 5 days of precipitation and 20-percentile precipitation will rise at a higher rate in southeast and northeast areas in the near future compared to the far future and reference period. The northeast region experienced a higher day with precipitation above 95 percentiles compared to the west and northwest parts. Extreme precipitation indices have shifted to the left, which shows high regional heterogeneity and a significant rate of change for all timeframes that make water resources more spatially imbalanced. This research underlines the need to protect the welfare and future viability of the monsoon-dominated country in response to a shifting climate.

Journal of Water and Climate Change
Khulna University of Engineering and Technology (BD), University of Burdwan (IN), Begum Rokeya University (BD), Noakhali Science and Technology University (BD), Bangladesh Rice Research Institute (BD), King Khalid University (SA)
Openalex Percentile: Top 4%
Climate variability and models
5.53
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