Towards impact-based flood forecasting and warning in Bangladesh: a case study at the local level in Sirajganj district

Abstract

Abstract. Impact-based forecasting and warning services aim to bridge the gap between producers and users of warning information by connecting and increasing synergies between the components of effective early warning systems. We tested qualitatively whether a warning message based on colour codes is understandable and useful to trigger risk mitigation 15 actions at the local level in the flood-exposed communities of Rajapur and Ghorjan unions in Sirajganj district, Bangladesh.With a community-based approach for different groups of users (i.e. sectors), flood-impact scenarios were determined from past events and related to colour codes. These were developed into impact-based forecasting and warnings that can connect water levels, through the colour code, to localised guidance information tailored to sectors" needs on how to respond to the expected flood. This approach was tested through a limited number of focus group discussions and interviews at the 20 community level. Overall, the colour coded impact-based warnings were found to be an easy and understandable way to link water level forecasts to the necessary risk mitigation actions, however, further investigation is needed to validate these findings under real-time conditions. IBFW has huge potential in Bangladesh but its integration requires significant institutional changes, such as an inter-facing agency (long term) or team (short term), adjusted policy frameworks (standing orders on disasters), and new resource allocations for skills development and technological innovation from national to local 25 levels. Overall, this paper aims to offer a first insight into impact-based forecasting and warning services in Bangladesh to trigger further research and project developments.

Recent Changes in Hydroclimatic Patterns over Medium Niger River Basins at the Origin of the 2020 Flood in Niamey (Niger)

“…The flood warning system adopted in Niamey is based on the four-colour classification of risk scenarios associated with critical hydraulic levels and potential impacts. The fourcolour coded classes currently used respond to the international standards (International Organisation for Standardisation-ISO 22324:2015) [37]. Thresholds are based on the river water level (H) at the hydrometric station as follows: Yellow: H = 530 cm; Orange: H = 580 cm; Red: H = 620 cm.…”

Community and Impact Based Early Warning System for Flood Risk Preparedness: The Experience of the Sirba River in Niger

“…The results of the study are discussed herein according to the indicators identified by Sai et al [19] for steering each of the four components of effective EWS (See Table A1 in Appendix A).…”

“…They are associated with warning content (the core of the message is the color), with the colored hydrometric staff gauges (qualitative gauging staffs) installed along the river, as well as information panels in the villages indicating priority actions to be taken. We adopted the four color-coded classes currently used by different countries (i.e., United Kingdom-the Met Office; EU-Meteoalarm, Philippine-Pegasa, Italy-Protezione Civile, India Meteorological Department) [19] and responding to the international standards (International Organization for Standardization -ISO 22324:2015). The classes are related to discharge, return periods and impacts on the main riverine settlements according to the classification, as described by Massazza et al [27]-essentially, green stands for the normal condition, meaning a no-impact scenario, yellow (Stationary Return Period 10 years) stands for minor impacts, orange (Stationary Return Period 30 years) stands for significant impacts and red (Stationary Return Period 100 years) stands for severe impacts.…”

“…Appendix A Table A1. Indicators of effective EWS identified by Sai et al [19] and the approach adopted to address them within SLAPIS.…”

Flash Flood warning in context: combining local knowledge and large-scale hydro-meteorological patterns

“…A recent survey of the development of operational forecasting systems for floods (Perera et al, 2020) shows that in many countries and river basins good progress has been made, though such progress is often limited in least developed countries, hampered by a lack of monitoring networks as well as human and technical capacities. These also often focus on large space and time scale riverine floods, which have attracted most attention of the flood forecasting, warning and response research community (Alfieri, et al, 2018, Kauffeldt, et al, 2015, Sai, et al, 2018. Flash floods, in contrast, occur at smaller spatial and temporal scales, resulting in severe damage to infrastructure and the environment, and are more deadly than riverine floods (Jonkman, 2005).…”