Developing a high-resolution gridded rainfall product for Bangladesh during 1901–2018

Dewan, Ashraf; Shahid, Shamsuddin; Bhuian, Md. Hanif; Hossain, Shaikh M. Jobayed; Nashwan, Mohamed Salem; Chung, Eun‐Sung; Hassan, Quazi K.; Asaduzzaman, Md

doi:10.1038/s41597-022-01568-z

Cited by 13 publications

(11 citation statements)

References 55 publications

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“…During the monsoon season, the country experiences heavy rainfall that amounts to ~80% of the annual total; the post-monsoon season is marked by rainfall associated with episodic tropical cyclones (Hoque, 2010;Shamsudduha et al, 2020). Mean annual precipitation is ~2,300 mm but noticeable variations in rainfall depths occur; eastern and northeastern areas of Bangladesh receive over 5,000 mm of rainfall whereas rainfall in western areas is 1,200 mm (Shahid, 2010;Dewan et al, 2022).…”

Section: Study Area: the Bengal Basinmentioning

confidence: 99%

What drives changes in surface water salinity in coastal Bangladesh?

Haq,

Shamsudduha,

Zahid

et al. 2024

Front. Water

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The salinisation of freshwater resources is a priority concern in Asian mega-deltas including the Bengal Basin of Bangladesh where the livelihoods, ecosystem services and health of coastal populations are increasingly under threat. Understanding trends and variations in surface water salinity is required to inform sustainable use of freshwater supplies in the face of hydrological regimes intensified by global environmental change. We interrogate 86 time series of fortnightly records of in situ surface water salinity between 1990 and 2019. Statistical and geospatial techniques were employed to characterise spatiotemporal distributions and trends in surface water salinity and identify key drivers of change in the coastal region of Bangladesh. The observed spatiotemporal structure in surface water salinity is primarily controlled by the magnitude and seasonality of river discharge entering the delta. In the moribund delta north of the Sundarbans, the contour between fresh and brackish water migrates seasonally between ~20 and ~40 km northward during the dry season. Abrupt increases in surface water salinity are observed throughout the coastal region in response to variations in upstream river discharge; the prominence of these events highlights their importance in driving salinity increases. Areas which are better connected to more voluminous seasonal river discharge (active delta), show greater recovery to these episodic inundations by cyclones than those less-well flushed by river discharge (moribund delta). Our reported observations provide a baseline for understanding the consequences of global environmental change and informing more climate-resilient adaptation strategies in coastal environments of Bangladesh and other Asian mega-deltas.

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Section: Study Area: the Bengal Basinmentioning

confidence: 99%

What drives changes in surface water salinity in coastal Bangladesh?

Haq,

Shamsudduha,

Zahid

et al. 2024

Front. Water

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“…Reasonably, many soft computing methods generally consider a sole dataset for validating for testing, which may mislead or give partly strong conclusions because the performance capabilities are solely described to an exact distribution of the main dataset and may not be identical [16,30]. The definition of the data combination included in each data combination is important for evaluating the method's performance.…”

Section: Resultsmentioning

confidence: 99%

“…The northwest area has a dry winter and a humid monsoon season with an unpredictable rainfall pattern. In total, 82% of precipitation occurred between May and October (monsoon season), whereas the remaining 18% occurred between November and April (dry season) [30]. Rajshahi had the lowest average annual rainfall (1428 mm), while Rangpur had the most (2262 mm).…”

Section: Case Study Areamentioning

confidence: 99%

Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction

et al. 2022

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Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide.

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“…The cooler and drier winter starts at the end of November and prevails until the beginning of February. The annual average temperature is 29 • C, and annual rainfall ranges from 2540 to 3810 mm [27], whereas the annual average rainfall ranges from 1000 to 2500 mm in the flatter part of Bangladesh [29]. Sangu is not a Himalayan river, so it depends solely on rainfall; fortunately, the watershed lies in a territory with significant annual rainfall.…”

Section: Study Areamentioning

confidence: 99%

An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5

Hasan,

Rasmy,

Koike

et al. 2024

Water

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The Sangu River basin significantly contributes to national economy significantly; however, exposures to water-related hazards are frequent. As it is expected that water-related disasters will increase manifold in the future due to global warming, the Government of Bangladesh has formulated the Bangladesh Delta Plan 2100 (BDP-2100) to enhanced climate resilience. Accordingly, this study assessed the hydro-meteorological characteristics of the Sangu River basin under the changing climate. This study scientifically selected five General Circulation Models (GCMs) to include the model climate sensitivity and statistically bias-corrected their outputs. The Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model was used to simulate the hydrological responses of the basin. The analysis of five GCMs under the Representative Concentration Pathway (RCP8.5) revealed that all selected GCMs estimate a 2–13% increase in annual rainfall and a 3–12% increase in annual discharge in the near-future (2025–2050), whereas four GCMs project an 11–52% increase in annual rainfall and a 7–59% increase in annual discharge in the far-future (2075–2100). The projected more frequent and intense increased extreme rainfall and flood occurrences in the future indicate an increase in flood disaster risk, whereas increased meteorological and hydrological drought in the future reflects a scarcity of water during dry periods. The number of projected affected people shows an increasing trend due to the increased inundation in the future. However, an increasing trend of transpiration indicates agricultural productivity will increase in the future. Policymakers can utilize this evidence-based information to implement BDP-2100 and to reduce the disaster risks in the basin.

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Developing a high-resolution gridded rainfall product for Bangladesh during 1901–2018

Cited by 13 publications

References 55 publications

What drives changes in surface water salinity in coastal Bangladesh?

What drives changes in surface water salinity in coastal Bangladesh?

Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction

An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5

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