Addressing water scarcity in small and Indigenous communities is a complex challenge, but a promising new tool developed by a Columbia student aims to align water technology choices precisely with each community’s social and geographic realities.
- Tool uses social and physical data to rank water technologies
- Focus on rural and water-scarce communities including Indigenous groups
- Python-based model designed for accessibility by planners and policymakers
What happened
Dylan Dettloff, a sustainable development senior at Columbia, developed a decision-support tool using Python that helps identify suitable desalination technologies tailored to the needs of rural and underserved communities. He used a multi-criteria decision analysis framework to evaluate technologies based on social and physical characteristics of different locales.
Supported by a Collaborative Research Grant from the Columbia Climate School, Dettloff focused his project on evaluating solar still desalination and other water treatment technologies applied to communities such as the Navajo Nation and areas in India, Namibia, and Madagascar. His research incorporated inputs like location, community composition, and changing energy costs to create flexible rankings of technologies predicted to best fit each community.
Why it feels good
This tool addresses a significant gap in water technology implementation for smaller and Indigenous communities, often overlooked despite considerable global water scarcity affecting one in four people. By prioritizing community-specific data, the model helps ensure that water solutions are practical, accessible, and better matched to local realities, increasing the chances of successful adoption and impact.
Dettloff’s work demonstrates how thoughtful, data-driven approaches can empower communities and policymakers to make more informed, sustainable, and equitable decisions about water management—ultimately helping provide essential clean water where it is urgently needed.
What to enjoy or watch next
The next steps in Dettloff’s research include refining the tool’s outputs to be more visually intuitive and visiting the communities featured in his model to validate social data accuracy through direct engagement with local leaders. This community collaboration will help tailor solutions even further and build trust and understanding around the technology choices.
Looking forward, technologies such as electrodialysis, visited and studied during a trip to MIT, could be integrated into future versions of the tool. Continued research and development will help keep the decision framework adaptive to innovations and changing conditions, making it a valuable resource for those striving to solve water challenges worldwide.