• Energy Research

Résumé Comprendre la variabilité spatio-temporelle des précipitations et de la température et leurs projections futures est essentiel pour évaluer les dangers environnementaux et planifier l'atténuation et l'adaptation à long terme. Dans cette étude, 18 modèles climatiques mondiaux (MCG) de la phase 6 du plus récent projet d'intercomparaison de modèles couplés (CMIP6) ont été utilisés pour projeter les précipitations annuelles, saisonnières et mensuelles moyennes, la température maximale de l'air (Tmax) et la température minimale de l'air (Tmin) au Bangladesh. Les projections GCM ont été corrigées en biais à l'aide de la technique de cartographie quantique simple (SQM). En utilisant la moyenne de l'ensemble de modèles multiples (MME) de l'ensemble de données corrigées des biais, les changements attendus pour les quatre voies socio-économiques partagées (SSP1-2.6, SSP2-4.5, SSP3-7.0 et SSP5-8.5) ont été évalués pour les futurs proches (2015-2044), moyens (2045-2074) et lointains (2075-2100) par rapport à la période historique (1985–2014). Dans un avenir lointain, les précipitations annuelles moyennes prévues ont augmenté de 9,48 %, 13,63 %, 21,07 % et 30,90 %, tandis que le Tmax moyen (Tmin) a augmenté de 1,09 (1,17), 1,60 (1,91), 2,12 (2,80) et 2,99 (3,69) °C pour SSP1-2,6, SSP2-4,5, SSP3-7,0 et SSP5-8,5, respectivement. Selon les prévisions pour le scénario SSP5-8.5 dans un avenir lointain, il devrait y avoir une augmentation substantielle des précipitations (41,98 %) pendant la saison post-mousson. En revanche, on prévoyait que les précipitations hivernales diminueraient le plus (11,12 %) au milieu du futur pour SSP3-7,0, tandis qu'elles augmenteraient le plus (15,62 %) dans le futur lointain pour SSP1-2,6. On prévoyait que le Tmax (Tmin) augmenterait le plus en hiver et le moins pendant la mousson pour toutes les périodes et tous les scénarios. Le Tmin a augmenté plus rapidement que le Tmax dans toutes les saisons pour tous les SSP. Les changements prévus pourraient entraîner des inondations plus fréquentes et plus graves, des glissements de terrain et des impacts négatifs sur la santé humaine, l'agriculture et les écosystèmes. L'étude souligne la nécessité de stratégies d'adaptation localisées et spécifiques au contexte, car les différentes régions du Bangladesh seront affectées différemment par ces changements. Resumen Comprender la variabilidad espaciotemporal en la precipitación y la temperatura y sus proyecciones futuras es fundamental para evaluar los peligros ambientales y planificar la mitigación y adaptación a largo plazo. En este estudio, se emplearon 18 Modelos Climáticos Globales (GCM) del más reciente Proyecto de Intercomparación de Modelos Acoplados fase 6 (CMIP6) para proyectar la precipitación media anual, estacional y mensual, la temperatura máxima del aire (Tmax) y la temperatura mínima del aire (Tmin) en Bangladesh. Las proyecciones de GCM se corrigieron con sesgo utilizando la técnica de mapeo cuantitativo simple (SQM). Utilizando la media del conjunto de modelos múltiples (MME) del conjunto de datos corregidos por sesgo, se evaluaron los cambios esperados para las cuatro vías socioeconómicas compartidas (SSP1-2.6, SSP2-4.5, SSP3-7.0 y SSP5-8.5) para los futuros near (2015–2044), mid (2045-2074) y Far (2075-2100) en comparación con el período histórico (1985–2014). En un futuro lejano, la precipitación media anual prevista aumentó en un 9,48%, 13,63%, 21,07% y 30,90%, mientras que la Tmáx (Tmín) media aumentó en 1,09 (1,17), 1,60 (1,91), 2,12 (2,80) y 2,99 (3,69) °C para SSP1-2,6, SSP2-4,5, SSP3-7,0 y SSP5-8,5, respectivamente. De acuerdo con las predicciones para el escenario SSP5-8.5 en un futuro lejano, se espera que haya un aumento sustancial en las precipitaciones (41.98%) durante la temporada posterior al monzón. Por el contrario, se predijo que la precipitación invernal disminuiría más (11.12%) en el futuro medio para SSP3-7.0, mientras que aumentaría más (15.62%) en el futuro lejano para SSP1-2.6. Se predijo que el Tmáx (Tmín) aumentaría más en el invierno y menos en el monzón para todos los períodos y escenarios. La Tmín aumentó más rápidamente que la Tmáx en todas las estaciones para todos los SSP. Los cambios proyectados podrían conducir a inundaciones, deslizamientos de tierra e impactos negativos más frecuentes y severos en la salud humana, la agricultura y los ecosistemas. El estudio destaca la necesidad de estrategias de adaptación localizadas y específicas del contexto, ya que las diferentes regiones de Bangladesh se verán afectadas de manera diferente por estos cambios. Abstract Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015–2044), mid (2045–2074), and far (2075–2100) futures in comparison to the historical period (1985–2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes. الملخص يعد فهم التباين المكاني والزماني في هطول الأمطار ودرجة الحرارة وتوقعاتها المستقبلية أمرًا بالغ الأهمية لتقييم المخاطر البيئية والتخطيط للتخفيف والتكيف على المدى الطويل. في هذه الدراسة، تم استخدام 18 نموذجًا للمناخ العالمي (GCMs) من أحدث مرحلة من مشروع المقارنة بين النماذج المقترنة (CMIP6) لإسقاط متوسط هطول الأمطار السنوي والموسمي والشهري، والحد الأقصى لدرجة حرارة الهواء (Tmax)، والحد الأدنى لدرجة حرارة الهواء (Tmin) في بنغلاديش. تم تصحيح توقعات GCM باستخدام تقنية رسم الخرائط الكمية البسيطة (SQM). باستخدام متوسط مجموعة النماذج المتعددة (MME) لمجموعة البيانات المصححة بالتحيز، تم تقييم التغييرات المتوقعة للمسارات الاجتماعية والاقتصادية المشتركة الأربعة (SSP1-2.6 و SSP2-4.5 و SSP3-7.0 و SSP5-8.5) للعقود الآجلة NEAR (2015-2044) و MID (2045-2074) و FAR (2075-2100) مقارنة بالفترة التاريخية (1985–2014). في المستقبل البعيد، ارتفع متوسط هطول الأمطار السنوي المتوقع بنسبة 9.48 ٪ و 13.63 ٪ و 21.07 ٪ و 30.90 ٪، في حين ارتفع متوسط Tmax (Tmin) بمقدار 1.09 (1.17) و 1.60 (1.91) و 2.12 (2.80) و 2.99 (3.69) درجة مئوية لـ SSP1-2.6 و SSP2-4.5 و SSP3-7.0 و SSP5-8.5 على التوالي. وفقًا لتوقعات سيناريو SSP5-8.5 في المستقبل البعيد، من المتوقع أن يكون هناك ارتفاع كبير في هطول الأمطار (41.98 ٪) خلال موسم ما بعد الرياح الموسمية. في المقابل، كان من المتوقع أن ينخفض هطول الأمطار في فصل الشتاء أكثر (11.12 ٪) في منتصف المستقبل لـ SSP3-7.0، بينما يزداد أكثر (15.62 ٪) في المستقبل البعيد لـ SSP1-2.6. كان من المتوقع أن يرتفع Tmax (Tmin) أكثر في فصل الشتاء وأقل في الرياح الموسمية لجميع الفترات والسيناريوهات. زادت Tmin بسرعة أكبر من Tmax في جميع المواسم لجميع مزودي خدمات SSP. يمكن أن تؤدي التغييرات المتوقعة إلى فيضانات وانهيارات أرضية أكثر تواتراً وشدة وتأثيرات سلبية على صحة الإنسان والزراعة والنظم الإيكولوجية. تسلط الدراسة الضوء على الحاجة إلى استراتيجيات تكيف محلية ومحددة السياق حيث ستتأثر مناطق مختلفة من بنغلاديش بشكل مختلف بهذه التغييرات.

Rationale: Greenhouse gas (GHG) emissions from crop agriculture are of great concern in the context of changing climatic conditions; however, in most cases, data based on lifecycle assessments are not available for grain yield variations or the carbon footprint of maize. The current study aimed to determine net carbon emissions and sequestration for maize grown in Bangladesh. Methods: The static closed-chamber technique was used to determine total GHG emissions using data on GHG emissions from maize fields and secondary sources for inputs. A secondary source for regional yield data was used in the current study. GHG emission intensity is defined as the ratio of total emissions to grain yield. The net GHG emission/carbon sequestration was determined by subtracting total GHG emissions (CO2 eq.) from net primary production (NPP). Results: Grain yields varied from 1590 to 9300 kg ha−1 in the wet season and from 680 to 11,820 kg ha−1 in the dry season. GHG emission intensities were 0.53–2.21 and 0.37–1.70 kg CO2 eq. kg−1 grain in the wet and dry seasons, respectively. In Bangladesh, the total estimated GHG emissions were 1.66–4.09 million tonnes (MT) CO2 eq. from 2015 to 2020, whereas the net total CO2 sequestration was 1.51–3.91 MT. The net CO2 sequestration rates were 984.3–5757.4 kg ha−1 in the wet season and 1188.62–5757.39 kg ha−1 in the dry season. This study observed spatial variations in carbon emissions and sequestration depending on growing seasons. In the rice–maize pattern, maize sequestered about 1.23 MT CO2 eq. per year−1, but rice emitted about 0.16 MT CO2 eq. per year−1. This study showed potential spatiotemporal variations in carbon footprints. Recommendation: Special care is needed to improve maize grain yields in the wet season. Fertiliser and water use efficiencies need to be improved to minimise GHG emissions under changing climatic conditions. Efforts to increase the area under cultivation with rice–maize or other non-rice crop-based cropping systems are needed to augment CO2 sequestration. The generation of a regional data bank on carbon footprints would be beneficial for combating the impact of climate change.

Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide.

Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km2) of the area is very low, 3.83% (63.18 km2) of the area is low, 56.2% (927.05 km2) of the area is medium, 39.25% (647.46 km2) of the area is high, and the rest 0.72% (11.82 km2) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management.

Abstract The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.

Abstract The global mean surface temperature is expected to continue to rise as a result of climate change. However, the effects of this transformation are not uniformly distributed across the globe, making regional analysis essential. As a monsoon region with tropical and low-lying terrain, Bangladesh is especially susceptible to the effects of climate change. Despite this, no effort has been made to evaluate the potential changes in thermal bioclimatic indicators (TBIs) in practical applications, which is crucial. Using a multi-model ensemble (MME) of 18 CMIP6 GCMs, this study projected the variations in 11 TBIs across Bangladesh for the near (2015–2044), mid (2045–2074), and far (2075–2100) futures under three SSPs: low (SSP126), medium (SSP245), and high (SSP585). The study revealed that in the future, the average annual temperature in Bangladesh will increase by 0.62 to 1.85°C for SSP126, by 0.51 to 2.81°C for SSP245, and by 0.54 to 4.88°C for SSP585, indicating a rise in temperature that is consistent with the global average. In addition, the study predicted that the diurnal temperature range (DTR) could decrease by -0.17 to -2.50°C, and that isothermality could decrease by as much as -0.30% at many stations. The projected temperature rise would be highly variable, ranging from 0.14 to 0.39°C in the northeast and southeast to 0.17 to 2.66°C in the northwestern, central, and southwestern regions. In addition, the study revealed a considerable increase in average temperature between the coldest and warmest quarters. The average temperature would increase significantly more in the drier quarter than in the wettest quarter. These findings are crucial for establishing mitigation goals and adapting to climate change in Bangladesh, underscoring the urgency of taking the necessary steps to combat the negative effects of global warming.

The impacts of climate change on precipitation and drought characteristics over Bangladesh were examined by using the daily precipitation outputs from 29 bias-corrected general circulation models (GCMs) under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. A precipitation-based drought estimator, namely, the Effective Drought Index (EDI), was applied to quantify the characteristics of drought events in terms of the severity and duration. The changes in drought characteristics were assessed for the beginning (2010&ndash;2039), middle (2040&ndash;2069), and end of this century (2070&ndash;2099) relative to the 1976&ndash;2005 baseline. The GCMs were limited in regard to forecasting the occurrence of future extreme droughts. Overall, the findings showed that the annual precipitation will increase in the 21st century over Bangladesh; the increasing rate was comparatively higher under the RCP8.5 scenario. The highest increase of rainfall is expected to happen over the drought-prone northern region. The general trends of drought frequency, duration, and intensity are likely to decrease in the 21st century over Bangladesh under both RCP scenarios, except for the maximum drought intensity during the beginning of the century, which is projected to increase over the country. The extreme and medium-term drought events did not show any significant changes in the future under both scenarios except for the medium-term droughts, which decreased by 55% compared to the base period during the 2070s under RCP8.5. However, extreme drought days will likely increase in most of the cropping seasons for the different future periods under both scenarios. The spatial distribution of changes in drought characteristics indicates that the drought-vulnerable areas are expected to shift from the northwestern region to the central and the southern region in the future under both scenarios due to the effects of climate change.