Month: August 2024

Temperature extremes Projections over Bangladesh from CMIP6 Multi-model Ensemble

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Abstract

Bangladesh, a sub-tropical monsoon climate with low-lying areas, is very susceptible to the impacts of climate change. However, there has been a shortage of studies about the periodicity and projected changes in extreme temperature in this area, which is a crucial part of adapting to climate change. A study employed a multimodal ensemble (MME) mean of 13 bias-corrected CMIP6 GCMs to fill this knowledge gap. The purpose of this study was to project changes in 8 extreme temperature indices (ETIs) across Bangladesh for the near future (2021–2060) and far future (2061–2100) under two different Shared Socioeconomic Pathways (SSPs): medium (SSP2-4.5) and high (SSP5-8.5) scenarios. The research analyzed the average spatiotemporal changes by considering the reference period from 1995 to 2014 for each indicator in future periods. The results indicate that Bangladesh is projected to see a rise in average annual temperature in the 21st century, aligning with the global average. Warm days (TX90p) and nights (TN90p) were projected to increase, while cold days (TX10p) and nights (TN10p) were expected to decrease across the country for both the near (2021–2060) and far future (2061–2100). The projected highest increase in TX90p and TN90p was 6.90 days/decade in the northeast, and the highest decrease in TX10p and TN10p was 6.22 days/decade in the southwest. The study revealed a higher rise in TN90p than TX90p, indicating a faster decline in cold extremes than a rise in hot extremes. The rising temperature would cause an increase in the spell duration index (WSDI) and growing degree day (GDD) by 5–6 and 6–7 days/decade, respectively. Therefore, immediate measures must be taken to mitigate the detrimental effects of extreme temperatures, leading to heat stress. To reduce the effects on agriculture, ecosystems, human health, and biodiversity, policymakers and stakeholders must understand these anticipated changes and adopt appropriate actions.

An AI-Based Method for Estimating the Potential Runout Distance of Post-Seismic Debris Flows

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Abstract

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.

Harnessing Collective Intentionality for Climate Action: An Institutional Perspective on Sustainability

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Abstract

This paper explores the epistemic and moral responsibility individuals and institutions bear for climate change and sustainability. Highlighting challenges individuals face in understanding climate information, it emphasises the pivotal role of governments and intergovernmental institutions in exercising collective intentionality regarding climate change mitigation and sustainability education. Despite the commendable efforts of other collective entities, such as NGOs and climate movements, this responsibility belongs solely to national governments and intergovernmental institutions because they have a unique ability to create social rules. However, such action remains a desideratum. Current data on ecological crises show that there is a pressing need for heightened awareness and decisive, concrete action.