Shamali Rewari¹, Saba Mundlay², Esha Gupta²
¹BASIS Independent Silicon Valley; ²Water-to-Cloud (W2C), University of Chicago Trust in Delhi
We are in a global water crisis. Meeting the Sustainable Development Goal (SDG) of ensuring “safe water in sufficient quantities for all” is one of the most urgent and defining challenges of our times. Water quality is an invisible part of this crisis.
Water quantity challenges are often addressed, however water quality is just as important and is often overlooked. Unlike water quantity, perceptions of water quality differ greatly from scientifically measured water quality.
Too often, water insecure individuals are also invisible in our response to this crisis. The SDG is to work towards ensuring equitable progress towards “safe water in sufficient quantities for all.” Historically, progress towards this goal has been measured with a focus on state or regional water availability measures. But this scale is not granular enough, and water availability is not a sufficient measurement. The data layer should capture water quantity and quality, as well as high-resolution water insecurity measures: how water stress is experienced by communities, households, and individuals—including their ability adapt to water crises and be resilient to future ones.
The solution to the SDG requires combining measures of how water insecurity is experienced at the individual level (a social science discipline) with objective measures of water quality (a science/engineering discipline) into a single decision support framework. This unified datadriven approach can guide public water quality policies, interventions, and citizen action to be directed towards the individuals and communities that are most vulnerable.
The goal of this study was to develop such a framework, conduct a field study to implement it in a real-world scenario, and illustrate the value of the framework in prioritizing relief to the most vulnerable in a water crisis.