Designing the Monitoring of Water-Related Sustainable Development Goals Based on Value of Information

Abstract:

The proposed Sustainable Development Goals (SDGs) represent an unprecedented international commitment to collective action and targeted interventions at global, regional, and national scales. Existing monitoring and data infrastructures are inadequate for producing the variety of environmental and socioeconomic information needed to ensure efficient and effective outcomes across the range of interlinked SDGs and targets. The scientific community needs to take a lead in developing new tools and approaches that, at reasonable cost, provide monitoring data of sufficient quality and spatial and temporal coverage to support informed decision making by diverse stakeholders.

The expanded SDGs related to water offer the opportunity to explore potential new monitoring approaches and data system architectures in a key sector, building on existing water monitoring capabilities and incorporating new technologies and methods. Since additional investments in monitoring will undoubtedly be limited, it is important to assess carefully the value of information produced by different options and their associated risks and tradeoffs. We review here the existing set of water monitoring systems, known gaps and limitations, stakeholder inputs on data needs, and the potential value of information in light of alternative water sector interventions. Of particular interest are opportunities to share investments in monitoring across sectors and stakeholders (e.g., public and private entities) and to identify where incremental improvements in water monitoring could have significant benefits for other SDGs (e.g., related to health, energy, agriculture, and climate change). Value of information is also driven by the numbers of people affected by decisions or able to take advantage of improved data, which implies the need not only to collect and archive data, but also to invest in making data accessible and usable to diverse and geographically dispersed users.