Can a simple water quality model effectively estimate runoff-driven nutrient loads to estuarine systems? A national-scale comparison of STEPLgrid and SPARROW. (April 2022)

• Record Type: Journal Article
• Authors: Montefiore, Lise R.; Nelson, Natalie G.
• Is Part Of: Environmental modelling & software. Volume 150(2022)
• Publication Date: 2022-04
• Subjects: Nonpoint source pollution -- Nitrogen -- Phosphorus -- Water quality modeling -- Eutrophication -- SPARROW -- STEPL; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105344
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study investigated whether a simple model could scale across watersheds and effectively predict runoff-driven nutrient loading as compared to a model with more complex process representation. A lumped model, the Spreadsheet Tool for Estimating Pollutant Load (STEPL), was adapted to use gridded data (STEPLgrid) and applied to 112 coastal watersheds across the Atlantic, Gulf, and Pacific coasts of the contiguous United States (U.S.) to estimate annual runoff-driven total nitrogen (TN) and total phosphorus (TP) loads. STEPLgrid outputs were compared to those of the SPAtially Referenced Regression on Watershed Attributes (SPARROW) model. Relative to SPARROW, STEPLgrid produced comparable estimates of TN and TP loads for most watersheds studied and its predicted loads were more similar to SPARROW for TN than TP. STEPLgrid was particularly effective at rank-ordering watersheds by TN and TP loads as compared to SPARROW, indicating that STEPLgrid was useful for relative comparisons across diverse watersheds. Highlights: STEPL was adapted to allow for its application with gridded data at large spatial scales (STEPLgrid). STEPLgrid and SPARROW estimates of total N and P loads were compared for 112 coastal watersheds. STEPLgrid estimates of total P loads were more similar to those of SPARROW than total N. STEPLgrid was particularly effective at rank-ordering watersheds by nutrient load. Simple models can be useful for comparing nutrient load scenarios across multiple basins.