Search

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 to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 ∘C warming since pre-industrial times. 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 datasets the climate change trends for extreme precipitation similar to that 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 ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate 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 in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to 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 being more likely by a factor of about 1.5 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 1 and of a 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 this, 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.

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.

In 2017, Bangladesh experienced the worst floods in recent decades. Based on a forecast and pre-defined trigger level, a Red Cross Red Crescent project distributed an unconditional cash grant of BDT 5000 (USD 60 equivalent) to 1039 poor households in highly vulnerable, flood-prone communities in the Brahmaputra river basin before an early flood peak. Systems that can deliver forecast-based cash grants are a potential adaptation strategy to deal with changes in extreme events linked to climate change. This paper presents the results of a mixed-methods, quasi-experimental study, based on a post-disaster household survey. The research assesses the effectiveness of the forecast-based cash distribution in helping beneficiaries to take preparatory early actions and reduce the negative impacts of the flood on their health, well-being, assets and livelihoods. The assessment shows that the cash grants contributed to improving households' access to food, a reduction in high-interest debt accrual of vulnerable households, and reduced psychosocial stress during and after the flood period, compared to a control group of similarly vulnerable and flood-affected communities that did not receive the forecast-based cash assistance. The intervention may also have prevented households from being forced to make destitution sales of valuable assets, as indicated by qualitative data collected in July, but we do not see these benefits sustained after a second flood peak in August 2017. There is a need for further research to assess the longer-term effects of forecast-based cash on the socio-economic development and well-being of the most vulnerable.

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 MIKE21Two-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.

Disaster Risk Reduction (DRR) is one of the prime concerns of development policies throughout the world. Bangladesh, being a disaster prone country affected by severe natural hazards like flood, cyclone, drought, has been undertaking disaster risk reduction initiatives from past few decades. A large amount of investment was made with assistance from development partners. The general assumption that informs these activities is that investment in DRR will result in less damage and loss when a disaster strikes and thus a less costly and timely response and recovery. This paper focuses on the results of a study conducted in Bangladesh based on the available data on disaster events, damage, DRR and response activities to test the hypothesis that disaster risk reduction reduces response costs. Using simple data analysis, taking absolute values of costs on both risk reduction and response measures of the limited data available, the results of the study provide a very clear indication in favour of the hypothesis. The findings of the study indicate that there has been a dramatic downward shift in the cost of responding to disasters since 1984 due to a steady increase in DRR investment.