Water security and risks – Early findings from a ‘water diary’ study in Bangladesh, Ethiopia and Kenya

Authors: Sonia Hoque*, University of Oxford, Robert Hope, University of Oxford
Topics: Water Resources and Hydrology, Sustainability Science
Keywords: drinking water services, sustainable development goals, water use behavior, wealth inequalities
Session Type: Paper
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Balcony A, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Water is intrinsically linked to people’s day to day lives; examining the experience of water (in)security require intensive research methods that ‘capture life as it is lived’ and record events and processes as they unfold over time. Current approaches, often based on infrequent cross-sectional surveys and interviews, are ineffective in unpacking the complexity of water-society challenges, particularly in contexts characterized by unreliable supplied, erratic weather patterns or fluctuating income flows. To address this methodological challenge, we designed the ‘water diary’ method – an intensive longitudinal tool that captures daily records of household water sources, amount, uses and costs, along with expenditure data for food, health, education, transport and other categories. Here, we present unprecedented data from a year-long (ongoing) water diary study with over 500 households in across five sites in Kenya, Ethiopia and Bangladesh. The diversity of environmental, institutional and infrastructure risks in our study sites, including unreliable piped supply, drought, salinity, and river pollution, provide an interesting opportunity to explore the drivers, trade-offs and water security outcomes in both rural and urban settings. While recognizing the limitations of replicating such intensive methods at scale, we argue that such detailed data can complement existing methods in unmasking the socio-spatial inequalities and can support policy and practice through interdisciplinary evidence-based decision-making, particularly in contexts characterized by minimal, uncertain or absent data.

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