Assessment of characteristic changes of regional estimation of extreme rainfall under climate change: A case study in a tropical monsoon region with the climate projections from CMIP6 model

Das, Samiran; Kamruzzaman, Mohammad; Islam, Abu Reza Md. Towfiqul

doi:10.1016/j.jhydrol.2022.128002

Cited by 35 publications

(22 citation statements)

References 71 publications

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“…However, a large increase in temperature might lead to an increase in rainfall through more evaporation. A number of studies also reported increasing rainfall in these regions (Fahad et al, 2018;Pour et al, 2018;Kamruzzaman et al, 2019b;Karim et al, 2020;Das et al, 2022b;Islam et al, 2022). Extreme rainfall events may increase with the increase in mean rainfall.…”

Section: Spatiotemporal Temperature (Tmax and Tmin) Variability And C...mentioning

confidence: 93%

“…However, they still cannot fully describe many regional climate dynamics because of their coarser resolution (Ali et al, 2021;Wang et al, 2021). Downscaling strategies, which can retrieve higher resolution data from coarse resolution datasets, are crucial for addressing this difficulty for regional or local scale climate modeling (Kamruzzaman et al, 2019b;Kamruzzaman et al, 2021a;Kamruzzaman et al, 2021b;Das et al, 2022a;Das et al, 2022b). Statistical Downscaling (SD) and Dynamic Downscaling (DD) are the two main approaches used for climate downscaling.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Islam

Kamruzzaman

Shahid

et al. 2023

Front. Environ. Sci.

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Temperature rise is a concern for future agriculture in different regions of the globe. This study aimed to reveal the future changes and variabilities in minimum temperature (Tmin) and maximum temperature (Tmax) in the monthly, seasonal, and annual scale over Bangladesh using 40 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) for two radiative concentration pathways (RCPs, RCP4.5 and RCP8.5). The statistical downscaling climate model (SimCLIM) was used for downscaling and to ensemble temperature projections (Tmax and Tmin) for the near (2021–2060) and far (2071–2100) periods compared to the base period (1986–2005). Multi-model ensemble (MME) exhibited increasing Tmax and Tmin for all the timescales for all future periods and RCPs. Sen’s slope (SS) analysis showed the highest increase in Tmax and Tmin in February and relatively less increase in July and August. The mean annual Tmax over Bangladesh would increase by 0.61°C and 1.75°C in the near future and 0.91°C and 3.85°C in the far future, while the mean annual Tmin would rise by 0.65°C and 1.85°C in the near future and 0.96°C and 4.07°C in the far future, for RCP4.5 and RCP8.5, respectively. The northern and northwestern parts of the country would experience the highest rise in Tmax and Tmin, which have traditionally been exposed to temperature extremes. In contrast, the southeastern coastal region would experience the least rise in temperature. A higher increase in Tmin than Tmax was detected for all timescales, signifying a future decrease in the diurnal temperature range (DTR). The highest increase in Tmax and Tmin will be in winter compared to other seasons for both the periods and RCPs. The spatial variability of Tmax and Tmin changes can be useful for the long-term planning of the country.

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Section: Spatiotemporal Temperature (Tmax and Tmin) Variability And C...mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Islam

Kamruzzaman

Shahid

et al. 2023

Front. Environ. Sci.

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“…Some studies around the world evaluated the performance of PDFs with more than 3 parameters in modeling hydrological series of extreme values such as AMDR and Annual Maximum Streamflow (AMS), and found similar results (Cassalho et al 2019;Peleg et al 2018;Ye et al 2018;Brunner et al 2019;Heo et al 2020;Das, 2022;Ibrahim, 2022). In Brazil, beyond the results presented in this study for the entire country, similar results were reported in few other researches for some Brazilian states, such as Blain and Meschiatti (2014) It should be mentioned that GUM is undoubtedly the most used PDF for modeling AMDR in Brazil and is usually recommended by public engineering councils and by decision-makers in projects developed by the private sector.…”

Section: Resultsmentioning

confidence: 84%

“…In Brazil, the PDFs most commonly used for modeling extreme rainfall are the simpler ones, such as Gumbel, Gama, Exponential, and the 2 and 3 parameters Log-Normal (Caldeira et al 2015;Souza et al 2017;Back et al 2020). However, studies carried out in several countries (Beskow et al 2015;Alemaw and Chaoka 2016;Mamoon and Rahman 2017;Ye et al 2018;Coronado-Hernandez et al 2020;Ogarekpe et al 2020;Heo et al 2020;Lima et al 2021;Moccia et al 2021;Das 2022;Ibrahim 2022;Rodrigues et al 2023b) have shown that PDFs with more than three parameters (p.e Wakeby -WAK and Kappa -KAP) outperform the others in the probabilistic modeling of extreme rainfall and, consequently, produce more accurate and reliable estimates.…”

Section: Introductionmentioning

confidence: 99%

Frequency analysis of annual maximum daily rainfall in Brazil with multiparameter probability density functions

Rodrigues,

Beskow,

Siqueira

et al. 2024

Preprint

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For water resources engineering and management, understand the extreme rainfall events it’s essential. Using rainfall frequency analysis, one can fit many Probability Density Functions (PDFs) to the rainfall series and identify the best fit through the goodness-of-fit tests, allowing the estimate of Annual Maximum Daily Rainfall (AMDR) quantiles for different Return Periods (RP). Recommendations regarding the best PDFs for this have been made for some countries, however, in the opposite direction, Brazil has no guidelines or recommendations such as the above mentioned, and Gumbel distribution still is the most used PDF for modeling AMDR, frequently without testing others. That said, we focus in modeling thousands of AMDR series in Brazil, evaluating ten PDF candidates to find the best fit and defining the most indicated to describe AMDR in the country. The methodology consisted of: acquisition, structuration and screening process by temporal and statistical criteria; fit of the 2-, 3- and multiparameter PDFs to the AMDR series based on the L-moments method; quantile estimation; and PDFs performance assessment by Filliben test and the relative absolute error. From the almost 4 thousand AMDR series investigated, we concluded that: Gumbel and Exponential provided the poorest performance (32.1–60.2% of non-satisfactory fits); multiparametric PDFs (Wakeby and Kappa) are the most indicated for modeling AMDR in Brazil; Gumbel had the highest error values for quantile estimate, especially for high RP; novelties and advances on probabilistic modeling of AMDR in Brazil were provided, helping decision makers with accurate and essential technical information for many purposes.

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“…Global climate models (GCMs) are frequently utilized to explore different scenarios of CC. Scholars have reported notable improvements in GCMs in the most recent edition of the Coupled Model Intercomparison Project (CMIP), known as CMIP6 14,15 . The improvements include re ning the model's architecture, increasing the geographic resolution, reducing uncertainty, enhancing the simulation of clouds, and improving the ability to replicate synoptic progressions 16 .…”

Section: Introductionmentioning

confidence: 99%

Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles

Kamruzzaman,

Islam,

Mainuddin

et al. 2024

Preprint

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The effects of climatic alteration caused by global warming on people, the environment, and ecosystems can be better understood by examining thermal bioclimatic indicators (TBIs) changes. Evaluating such alterations is of utmost significance for the Ganges Delta (GD) coastal region, which offers the world's most extensive biological variety. This study utilizes a multi-model ensemble (MME) of 16 CMIP6 Global Climate Models (GCMs) to assess prospective alterations in thermal bioclimatic indicators (TBIs) across the coastal region of the Ganges Delta (GD) for two Shared Socioeconomic Pathways (SSPs): SSP245 (moderate) and SSP585 (severe). We employ ensemble median, 5th, and 95th percentiles to analyze temporal shifts and associated uncertainty in TBIs during the near (2020–2059) and far (2060–2100) futures. Our projections reveal a significant escalation in annual temperatures throughout the GD, with MME median average in-crease anticipated to range from 0.77–2.80°C (SSP2-4.5) to 1.03–4.65°C (SSP5-8.5) by 2059. Moreover, notable transformations in thermal patterns are expected, with a projected decrease in both diurnal temperature range (DTR) by 0.02–0.87°C and isothermality by 3.30-12.09%. Additionally, the average temperature during the driest months is anticipated to rise higher than in the wettest months. These findings underscore climate change's existential threat to the GD and its rich biodiversity. They provide vital information for formulating crucial mitigation strategies to curb greenhouse gas emissions and robust adaptation measures to bolster the resilience of communities and eco-systems. Urgent action is paramount to safeguard the future of this invaluable ecological treasure.

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Assessment of characteristic changes of regional estimation of extreme rainfall under climate change: A case study in a tropical monsoon region with the climate projections from CMIP6 model

Cited by 35 publications

References 71 publications

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Frequency analysis of annual maximum daily rainfall in Brazil with multiparameter probability density functions

Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles

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