Adapting a regional water-quality model for local application: A case study for Tennessee, USA. (May 2019)

• Record Type: Journal Article
• Authors: Hoos, Anne B.; Wang, Sherry H.; Schwarz, Gregory E.
• Is Part Of: Environmental modelling & software. Volume 115(2019)
• Page Start: 187
• Page End: 199
• Publication Date: 2019-05
• Subjects: Watershed models -- Nutrient source apportionment -- Decision support -- Nutrient change scenario -- Nitrogen -- Phosphorus; 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.2019.01.001
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We evaluated whether SPAtially Referenced Regression On Watershed attributes (SPARROW) models calibrated for two adjacent USA regions could be applied at the local scale to support management decisions for streams in Tennessee. Nutrient-source apportionment of load is important for this local-scale application and demands careful consideration of uncertainty in the calibrated coefficients. We used Gauss-Newton regression to test the published SPARROW models for constancy of coefficient estimates between calibration sites on streams within (n = 59) versus outside (n = 327) Tennessee and concluded source apportionment was unbiased for Tennessee streams. The SPARROW models were then applied without re-calibration to predict stream loads and source apportionment for Tennessee streams and to build tools for displaying model results and evaluating source-change scenarios. This approach may inform the adaptation of other regional-scale regression models for use to address water-resource management issues in smaller-scale watersheds. Highlights: Statistical watershed models have advantages for source apportionment applications. Unbiased source apportionment is verified using Gauss-Newton regression tests. We develop a method to correct bias in load estimates caused by heteroscedasticity. Tools to display stream load and evaluate source-change scenarios are presented.