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This paper presents a review of water research, development, and management in Bangladesh, with examples drawn from the past and present. A bibliometric analysis is adopted here to analyze the water-related publication data of Bangladesh. Water-quality-related research is the dominating research field in Bangladesh as compared to water-quantity (floods and droughts)-related ones. The most productive author was found to be Ahmed KM for water-related publication in Bangladesh. The arsenic contamination in Bangladesh has received the highest attention (13 out of the top 15 highly cited papers are related to arsenic contamination). Climate-change-related topics have been showing an increasing trend in research publications over the last 5 years. Bangladesh Delta Plan 2100, prepared recently, is a visionary master plan that is expected to shape water management in Bangladesh in the coming decades to adapt to climate change. A set of recommendations is made here to achieve sustainable water management in Bangladesh. [ABSTRACT FROM AUTHOR]

This paper investigates the extent to which in-utero exposure to droughts influences the health outcomes of Bangladeshi children in early childhood. Exploiting the plausibly exogenous deviations of rainfall from the location-specific norms, we find that deficient rainfall during the prenatal period is harmful to child health. Specifically, in-utero exposure to droughts decreases the height-for-age, weight-for-height, and weight-for-age z-scores by 0.10, 0.11, and 0.11 standard deviations among children under five years old, respectively. Our heterogeneity analyses reveal that the adverse health setbacks fall disproportionately on children of disadvantaged backgrounds. Exploring the differential effects by trimesters of exposure, we further show that experiencing droughts during the second and the third trimesters leaves injurious effects on early childhood health., Competing Interests: The authors have declared that no competing interests exist.

The lack of stringent policies to avert climate change has increased the importance of effective and timely adaptation. Adequate adaptation is particularly important for agricultural communities in developing countries, which may most suffer the consequences of climate change. Evidence is still scarce on how people in the most vulnerable areas form climate change beliefs and whether such beliefs exhibit cognitive biases. Using survey data from rural households in Bangladesh together with a meteorological measure of excess dryness relative to historical averages, I study the effect of long-term average drought exposure and short-term deviations on beliefs about drought frequency and the interpretation of drought events. To explore how individuals interpret past droughts, I use an instrumental variable approach and investigate whether individual beliefs lead to asymmetric distortion of objective information. The results show that individuals recollect and overweight evidence tilted towards their prior beliefs, providing evidence of confirmation bias as a directional motivated reasoning mechanism. The findings highlight the need for models that account for behavioral factors and cognitive biases in the study of climate change beliefs for effective communication and adaptation policies. [ABSTRACT FROM AUTHOR]

Climate change's impact on crop production is a global concern. A better understanding of farmers' perceptions of climate change and adaptation strategies will benefit farmers and policymakers in outlining an effective adaptation mechanism to climate change. Therefore, this study assessed wheat farmers' perceptions of climate change, identified major adaptation strategies, factors influencing adaptations, and barriers to effective adaptation by surveying 160 wheat farmers in northwest Bangladesh. The results revealed that farmers experienced more frequent droughts due to higher temperatures, decreased and irregular precipitation, reduced ground and surface water availability, and shorter winter seasons over the last two decades. Key adaptation strategies identified were more irrigation, switching to other crops, and changing fertilizer and insecticide usage. Multinomial logit model results indicate that farming experience, access to climate information and extension services, access to subsidies, farm size, family size, and electricity for irrigation were the significant factors influencing farmers' adaptation decisions. Limited access to climate information, inadequate knowledge of appropriate adaptation measures, and low price of wheat represented major adaptation barriers. The study recommends strengthening agricultural research and extension services to farmers, including education and training, to develop effective adaptation strategies to climate change. [ABSTRACT FROM AUTHOR]

A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901–2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product's ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of −2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R2) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901–2018 was in the range of −4.5 to 4.3%, NRMSE between 9 and 19% and R2 in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh. Measurement(s) water-based rainfall Technology Type(s) weather station [ABSTRACT FROM AUTHOR]

Urban groundwater resources (GWRs) have declined substantially in recent decades, due to rapid urbanization, population growth, groundwater exploitation, land use/land cover changes, and climate change. However, the knowledge about the role of the underlying variables on groundwater level (GWL) fluctuation on a local scale in the drought-prone urban areas of Bangladesh is still not explored. To gain better insight into the relative contributions of underlying factors on GWL fluctuation, this study proposed a novel hybrid ensemble modeling framework based on locally weighted linear regression (LWLR) and four Gaussian process regressions (GPRs), e.g., poly kernel, Pearson universal kernel (PUK), radian basis function (RBF), and normalized poly kernel. The proposed framework has been employed to predict GWL at six wells in the drought-prone local areas of the north-western urban region of Bangladesh, where GWL is declining rapidly. The rainfall, temperature (Tave), soil moisture (SM), normalized difference vegetation index (NDVI), Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), Nina3.4, and population growth rate for the period 1993–2017 were utilized as inputs to develop GWL models. The best input combination was explored using the best subset regression model and sensitivity analysis, and the optimal input combination was applied in LWLR and GPRs to estimate the monthly GWL fluctuation. On average, the hybrid LWLR-GPR-PUK model, improves the prediction accuracy by 10 to 50% during the training stage and 20 to 70% during the testing stage compared to other models. The proposed modeling tool could be a good alternative to physical law-based models when there is insufficient groundwater data to make them. This is true for drought-prone areas in urban areas where groundwater data is scarce. [ABSTRACT FROM AUTHOR]

Understanding household disaster risk perception is crucial to formulate and apply disaster risk reduction strategies. Using survey data from 300 households from three highly disaster-prone areas of the lower Teesta River basin in Bangladesh, this study explores households' risk perception of drought, earthquake, and flood at the local level. The ordered probit regression model was applied to identify the factors influencing household disaster risk perception. Most of the respondents perceived the likelihood of occurring drought, earthquake, and flood hazards on a large scale in the selected areas which cause negative impacts on their quality of life and financial losses. They have lack knowledge on mitigation actions which makes them unable to control the devastating impacts of disasters. Econometric results show that households' age, gender, education, and income-generating sources had significantly influenced the respondent's drought, earthquake, and flood risk perception. Female participants have less knowledge on mitigations actions and are less capable of controlling the hazards than their counterparts making them more vulnerable to the impacts of hazards. Urgent action is required to improve their socio-economic conditions, and to reduce the knowledge gap between males and females as well as to improve the household's understanding of mitigation and preparedness for disaster risk. [ABSTRACT FROM AUTHOR]

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods. [ABSTRACT FROM AUTHOR]

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Intense droughts in recent years are a global concern. The duration and timing of drought for forests have not been researched as much as crops. This study aims to utilize big earth data to analyse the consequences of drought on forest based on drought and vegetation indices derived from remote sensing. The study area is Sal Forest (Shorea robusta) in Bangladesh. Vegetation indices such as NDVI and EVI are correlated with drought indices for the years 2000–2020. The data is collected via Google Earth Engine and the R statistical software environment is used for data analysis. Seasonal adjustment was conducted to remove the biases due to seasonality. Results showed that the timing for the loss of forest vitality is dependent on drought severity. Lagged correlation was used to identify the lags between drought severity and vitality via drought and vegetation indices. From 2000 to 2020, the intensity of drought was moderate (0.95). There is a lag between drought and a decrease in forest vitality. This lag decreases as the severity and duration of drought increase. Severe droughts in Sal Forest create an almost immediate impact with worsening conditions but show a lag of 3–4 months and 2–3 months for low and moderate drought severity, respectively. The NDVI anomaly shows that drought has adverse effects during growth periods. The methodology of this study can be replicated across various forest types, and further improvements would require in situ data coupled with phenological observations. [ABSTRACT FROM AUTHOR]

Because of evident climatic variations and significant contribution to national food production, Bangladesh is a climate extreme hotspot region of examination for climatic consequences for rice (Oryza Sativa) crop production. This study intends to explore the variability of climatic variables (e.g., variations in mean temperature, rainfall, relative humidity, and sunshine duration) with rice yields (e.g., Aus, Aman, and Boro rice varieties) in northwest Bangladesh. The modified Mann–Kendall test, Theil–Sen slope estimator, and multiple linear regression (MLR) modeling were used to estimate the association among these factors. Heteroskedasticity and autocorrelation constant standard error (HAC) and feasible generalized least square (FGLS) technique were adopted to measure the climate-rice crop nexus using the regional level dataset for 1976–2015. Furthermore, the spatiotemporal variation of rice yield trends with climatic variables was mapped and assessed by the coefficient of variation. The results show that observed temperature and humidity trends were beneficial for Aus and Aman yields but not Boro yields. In contrast, observed rainfall and sunshine trends were negative for all three rice seasons. The outcomes of the MLR model explained 67%, 92%, and 83% of the variability in Aus, Aman, and Boro rice yields in the study region. The model outcomes showed that humidity and rainfall have negatively affected Aus and Aman rice crops, while temperature and rainfall positively influence Boro rice yield. Regarding the climate change issues and safeguarding food safety at the regional level, the concerned authorities should provide substantial attention to improving heat and drought-tolerance high-yielding varieties against climate effects on Aus and Aman rice varieties. [ABSTRACT FROM AUTHOR]

An alteration of rainfall variability and changes in rainfall driven extremes have been noticed across the globe with rising earth temperature. Such changes will undoubtedly be more devastating for agriculture-based developing countries. This study evaluated possible changes in rainfall and droughts in Bangladesh, a high climate change susceptible country, due to 1.5 and 2 °C temperature rise scenarios. Projections of global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) for two shared socioeconomic pathway (SSP) scenarios, SSP-119 and SSP-126, were used for this purpose. The results showed an increase in annual rainfall over Bangladesh for both scenarios. However, the changes in rainfall variability would cause a drastic change in the drought pattern. Overall, drought frequency may decrease in the drought-prone western region up to -50% and increase in the east up to 50 to 70%, making droughts more homogeneously distributed over the country. However, a higher increase in the east than a decrease in the west for SSP119 indicates a possible shift in the country's drought-prone region. The drought scenarios for SSP119 and SSP126 revealed that a 0.5 °C further rise in temperature might cause an increase in extreme drought frequency by 30% in the central-eastern region. Bangladesh should take effective drought mitigation measures to sustain its agricultural development. [ABSTRACT FROM AUTHOR]

Background: Drought is the most familiar abiotic stress limiting chickpea production. Genotypes may vary in their capacity to tolerate drought stress. Therefore, the study was undertaken to find out suitable genotypes in the drought prone ecosystems. Methods: The experiments were carried out at the Bangladesh Agricultural Research Institute, Gazipur, Bangladesh during 2017 and 2018 with thirty chickpea genotypes and four moisture stresses. Through cluster analysis, genotypes were distinguished in five clusters based on similarity in characters. Result: Among the thirty chickpea genotypes BD-6048 and BD-6045 were found the most droughts tolerant based on field performance. Dendrogram was prepared based on similarity by Euclidean distance among genotypes. Dendrogram showed that members falling in clusters III and IV were more diverse. Data certified that significant genetic variation exists in chickpea genotypes in performance of various attributes in this study and these genotypes can potentially be used for chickpea breeding program. [ABSTRACT FROM AUTHOR]

Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling. [ABSTRACT FROM AUTHOR]