Uncertainty in critical source area predictions from watershed-scale hydrologic models. (1st February 2021)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of environmental management. Volume 279(2021)
• Publication Date: 2021-02-01
• Subjects: Hotspots -- Targeting -- Prioritization -- Best management practices, (BMPs) -- SWAT -- SPARROW; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.111506
• Languages: English
• ISSNs: 0301-4797

Abstract

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-km 2 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. Highlights: A model ensemble was used to assess critical source area (CSA) uncertainty. CSA simulations varied substantially across models and were highly uncertain. Subwatersheds identified as CSAs across models may be prioritized for conservation. Model use for CSA identification would benefit from uncertainty analyses.