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

Autor: Barnhart BL; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Golden HE; United States Environmental Protection Agency, National Exposure Research Laboratory, Systems Exposure Division, Cincinnati, Ohio, 45268., Kasprzyk JR; University of Colorado Boulder, Civil, Environmental and Architectural Engineering, Boulder, Colorado, 80309., Pauer JJ; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, Minnesota, 55804., Jones CE; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Sawicz KA; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Hoghooghi N; United States Environmental Protection Agency, National Exposure Research Laboratory, Systems Exposure Division, Cincinnati, Ohio, 45268.; University of Georgia, School of Environmental, Civil, Agricultural and Mechanical Engineering, Athens, GA, 30602., Simon M; United States Environmental Protection Agency, National Risk Management Research Laboratory, Water Supply and Water Resources Division, Cincinnati, Ohio, 45268., McKane RB; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Mayer PM; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Piscopo AN; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, Rhode Island, 02882., Ficklin DL; Indiana University, Department of Geography, Bloomington, Indiana, 47405., Halama JJ; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Pettus PB; United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon, 97330., Rashleigh B; United States Environmental Protection Agency, National Risk Management Research Laboratory, Water Supply and Water Resources Division, Cincinnati, Ohio, 45268.
Jazyk: angličtina
Zdroj: Environmental modelling & software : with environment data news [Environ Model Softw] 2018 Nov; Vol. 109, pp. 368-379.
DOI: 10.1016/j.envsoft.2018.08.025
Abstrakt: 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 successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools.
Databáze: MEDLINE
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