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Uncertainty in critical source area predictions from watershed-scale hydrologic models.
Evenson, Grey R; Kalcic, Margaret; Wang, Yu-Chen; Robertson, Dale; Scavia, Donald; Martin, Jay; Aloysius, Noel; Apostel, Anna; Boles, Chelsie; Brooker, Michael; Confesor, Remegio; Dagnew, Awoke Teshager; Guo, Tian; Kast, Jeffrey; Kujawa, Haley; Muenich, Rebecca Logsdon; Murumkar, Asmita; Redder, Todd.
Afiliação
  • Evenson GR; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA. Electronic address: evenson.grey@epa.gov.
  • Kalcic M; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA; The Ohio State University Translational Data Analytics Institute, Columbus, OH, USA.
  • Wang YC; School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
  • Robertson D; U.S. Geological Survey, Upper Midwest Water Science Center, Middleton, WI, USA.
  • Scavia D; School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
  • Martin J; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA; The Ohio State University Sustainability Institute, Columbus, OH, USA.
  • Aloysius N; Department of Biomedical, Biological and Chemical Engineering, and School of Natural Resources, University of Missouri, Columbia, MO, USA.
  • Apostel A; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA.
  • Boles C; LimnoTech, Ann Arbor, MI, USA.
  • Brooker M; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA.
  • Confesor R; Heidelberg University, Tiffin, OH, USA.
  • Dagnew AT; Environmental Consulting and Technology, Inc., Ann Arbor, MI, USA.
  • Guo T; Heidelberg University, Tiffin, OH, USA.
  • Kast J; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA.
  • Kujawa H; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA.
  • Muenich RL; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA.
  • Murumkar A; Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, USA.
  • Redder T; LimnoTech, Ann Arbor, MI, USA.
J Environ Manage ; : 111506, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-33168300
ABSTRACT
Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or 'targeted' for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%-46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Tipo de estudo: Estudo prognóstico / Fatores de risco Idioma: Inglês Revista: J Environ Manage Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Tipo de estudo: Estudo prognóstico / Fatores de risco Idioma: Inglês Revista: J Environ Manage Ano de publicação: 2020 Tipo de documento: Artigo