How do rural Lao communities adapt to water insecurity? Qualitative perspectives on climate-related floods, droughts, and community health

Background Rural communities in the Lao People’s Democratic Republic (Lao PDR) face increasing water insecurity as floods and droughts intensify under climate and environmental change. These shocks threaten livelihoods, water quality, and health. Evidence on how communities perceive risks and adapt in practice remains limited. Methods We conducted a qualitative study in Savannakhet and Champassak provinces and Vientiane Capital, using purposive sampling to capture multiple governance levels and community groups. Data comprised 92 key-informant interviews (ministries, provincial/district officials, mass organizations, UN partners) and 28 focus group discussions with 168 villagers (men, women, elders, village leaders). Audio-recorded interviews were transcribed (Lao), translated (English), and thematically analyzed by two researchers. Findings reflect participants’ perceptions and lived experiences; no meteorological or epidemiological time-series were analyzed. Results Participants perceived floods and droughts as more unpredictable and damaging, with profound impacts on agriculture, income, and health (e.g., diarrheal and vector-borne disease risk during floods; constrained hygiene and nutrition during droughts). Adaptation relied heavily on informal self-help and social solidarity. Preparedness measures existed (e.g., Disaster Response Committees) but were described as under-resourced, fragmented, and inconsistently implemented. Key gaps included early warning systems (EWS), across-sectors coordination, and resilient water infrastructure. Comparative insights between provinces pointed to contextual differences in exposure and institutional capacity. Conclusion Findings underscore the need for locally tailored EWS, integrated water governance, and community-driven adaptation supported by multi-level coordination and predictable resources. Given the study’s qualitative, perception-based design, recommendations are exploratory; future work should triangulate with meteorological, health, and agricultural datasets to strengthen inference and track change over time.