Making a Water Data System Responsive to Information Needs of Decision Makers

Abstract

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems—data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Stakeholder Engagement to Guide Decision‐Relevant Water Data Delivery

“…Focus group questions were derived from the preliminary interviews and guided by the statement following Cantor et al. 2018, 2021; “Who needs what data in what format to make what decisions”? To develop statements outlining user requirements, open‐ended questions were presented to the participants during the focus group in the following order: (1) decisions, (2) data format, (3) data type, and (4) data needs.…”

“…Oral consent was obtained to record focus groups and use a program to transcribe them for analysis (Creswell 2009). Focus group questions were derived from the preliminary interviews and guided by the statement following Cantor et al 2018Cantor et al , 2021"Who needs Two researchers were present during stakeholder engagement focus groups at all times. One researcher facilitated the focus group conversation while the other took notes and handled technical questions and managed feedback submitted in the group chat.…”

An internet for water: connecting Texas water data

“…Workshop participants received background information about recent efforts on the internet of water (Patterson et al 2017;Cantor et al 2018) and Texas water data security (Rosen et al 2017) in advance of the workshop. In addition to receiving advanced information, a portion of workshop participants met on the day immediately preceding the Connecting Texas Water Data Workshop in a roundtable discussion on the topic of "advancing the internet of water" in Texas.…”

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

“…Thus many observations are underutilized for ML despite large‐scale consolidation efforts such as the Water Quality Portal and GLORICH databases (Hartmann et al, 2014; Read et al, 2017), because it is labor‐intensive to harmonize and process data (Shaughnessy et al, 2019; Sprague et al, 2017). Recent efforts to make water data more broadly available and usable such as the U.S. Open Water Data Initiative and the California open water data system are essential for effective management and decision making (Blodgett et al, 2015; Cantor et al, 2021; Larsen et al, 2016). Because data preparation is one of the most time‐consuming aspects of model development, the development of benchmark datasets following FAIR (Findable, Accessible, Reusable, Interoperable) principles (Wilkinson et al, 2016), and the use of automated tools that make it easier to discover, synthesize, and assimilate data (e.g., Section 3.8) can accelerate adoption of ML approaches.…”