Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.
Purpose – The purpose of this study is to provide recommendations for improving the social performance of warnings using mobile services in flash flood prone communities. A warning cannot be considered effective until it is received, understood and responded to by those at risk. This is defined as the social performance of warning communication techniques. Mobile services offer opportunities for improving this, particularly in Bangladesh, but have been underutilised. In this research, characteristics of the warning, mobile services and community are found to influence the social performance. Design/methodology/approach – A framework on the factors affecting the social performance was developed and applied using data collected through interviews at the national and regional level along with focus-group discussions (FGDs) and key informant interviews at the local level in the Sunamganj District, Bangladesh. Findings – The study demonstrated that mobile services are the preferred means of warning communication. Communities strongly preferred voice short messaging service (SMS) and interactive voice response (IVR) because of easier accessibility and understanding of the message. Text-based services [SMS and cell broadcasting service (CBS)] were still found to be acceptable. These should be simple, use symbols and refer to additional sources of information. Further recommendations include mixing push (e.g. SMS and CBS) and pull-based (e.g. IVR) mobile services, utilising local social networks, decentralising the dissemination process and raising awareness. Research limitations/implications – A limited sample of interviews and FGDs were used. Practical implications – Concrete recommendations are made for overcoming obstacles related to the effective use of mobiles services. Social implications – The suggestions made can contribute to improving the social performance of flood early warning communication. Originality/value – The conceptualisation of mobile services’ contribution to social performance of flood warning and field-level application.
Abstract. In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents for the first time an attribution of this precipitation-induced flooding from a combined meteorological and hydrological perspective. Experiments were conducted with three observational data sets and two climate models to estimate changes in extreme 10-day precipitation event frequency over the Brahmaputra basin. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation data sets the climate change trends for extreme precipitation similar to observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model shows a significant positive influence of anthropogenic climate change, whereas the other simulates a cancellation between the increase due to greenhouse gases and a decrease due to sulphate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than for precipitation, but the 95 % confidence interval still encompasses no change in risk. For the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation, and about a factor 1.5 more likely for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than one and of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available, or as an additional measure to confirm qualitative conclusions. Besides, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.
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