Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: Successes and challenges

Abstract

Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders-a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss *

Modeling Water Quality in Watersheds: From Here to the Next Generation

“…For modelers interacting with stakeholders, many authors have emphasized the importance of integrity and openness, including maintaining communication, building trust, being transparent, making assumptions clear, and maintaining neutrality ( Barnhart et al, 2018 ; Voinov & Gaddis, 2008 ). Stakeholder engagement should occur over the entire process of water quality modeling, from problem framing to model development, evaluation, and scenario analysis ( Badham et al, 2019 ; Hamilton et al, 2015 ).…”

“…Another important element when working with stakeholders is communicating uncertainty. Barnhart et al (2018) present best practices for communicating uncertainty throughout a stakeholder process, including selecting the methods for uncertainty analysis based on stakeholder knowledge and information gaps, providing clearly comprehensible graphs and depictions of uncertainty for all model outputs, and recognizing that certain stakeholder groups may be underrepresented or absent from discussions—which is a form of uncertainty—and providing a mechanism for these groups to become more active in the project. These best practices should be tested in water quality modeling.…”

Engaging Multiple Worldviews With Quantitative Decision Support: A Robust Decision‐Making Demonstration Using the Lake Model

“…Using quantitative decision support tools in analytic‐deliberative processes can increase the time, effort, and expense required, as well as creates barriers to participation for individuals who may lack comfort and skills with quantitative analysis. But in some cases with large uncertainties and complexities, such quantitative tools can prove crucial in helping participants to more clearly define their goals and objectives; assemble relevant information; identify possible courses of action; and to evaluate the consequences of those actions over many assumptions and objectives with an otherwise unobtainable speed, scale, and fidelity (Barnhart et al., 2018; NRC 2009; Wong‐Parodi, Mach, Jagannathan, & Sjostrom, 2020).…”

Machine Education: Designing semantically ordered and ontologically guided modular neural networks

“…development of methods for decision analysis). Participatory modelling has been applied in a variety of domains achieved a wide range of purposes, including decision making [14], deliberation and negotiation [15], futuristic system design [16], and to much less extent support formal education and curriculum design. Various participatory modelling frameworks exist that propose specific process steps and combinations of qualitative and quantitative modelling methods [12].…”