Climate smart disaster risk reduction: Indigenous knowledge practiced for early warning in Coastal Bangladesh

Background: The coastal region of Bangladesh faces severe vulnerability to climate change and climate-induced natural disasters annually. Inhabitants in these areas have developed a wealth of indigenous knowledge over generations, comprising techniques to mitigate the impact of these disasters. This study, conducted in Dashmina Upazila within the Patuakhali District of the southern coastal region of Bangladesh, aimed to identify, evaluate, and scale up existing indigenous knowledge practices for early warning in Climate Smart Disaster Risk Reduction. Methods: Information was gathered through various methods such as direct observations, key informant interviews, group discussions, case studies, and a comprehensive literature review. There are 14 Focus Group Discussions (FGD) and communal meetings; 16 Key Informant Interviews (KII) and 5 case histories were conducted. SWOT analysis employed to assess the strengths, weaknesses, opportunities, and threats of these practices. Data were analyzed using Microsoft Excel 2010 for descriptive qualitative analysis, with a SWOT analysis employed to assess the strengths, weaknesses, opportunities, and threats of these practices. Findings: The study revealed several indigenous indicators for early warning, including the observation that ants climbing into houses with stored food signify imminent heavy rainfall, excessive frog barking indicating forthcoming heavy rain, the belief that crows soar high into the sky to summon rain, a combination of heavy clouds with strong wind suggesting little or no rainfall, muddy river waters signaling potential large floods or storm surges, peculiar crowing sounds at night predicting cyclones, and the occurrence of hot winds from specific directions along with black clouds hinting at imminent norwesters. Conclusion: The study strongly recommends integrating this indigenous knowledge into decision-making processes for developing disaster risk reduction strategies and incorporating them into future development projects within the region. The study faces recall bias, the study has a single-district scope, and it lacks quantitative validation. Novelty/Originality of this article: This research is novel because it systematically documents the indigenous early-warning knowledge of coastal communities in Dashmina Upazila, Bangladesh knowledge that has rarely recorded or scientifically analyzed and invisible to outsiders. It uniquely integrates these traditional cues with modern disaster-risk-reduction perspectives, offering new insights for community-based early warning systems.