A fully integrated NPS PI model in R: An option for coupling nonpoint source models and GIS. (April 2020)

• Record Type: Journal Article
• Authors: Su, Jingjun; Li, Xuyong
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: R language -- Fully integrated approach -- Geographic information system -- Nonpoint source modeling; 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.2020.104637
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The coupling of a geographic information system (GIS) and nonpoint source (NPS) models has significantly promoted NPS modeling and output visualization. This study reported an approach using R to fully integrate the GIS and a NPS Phosphorus indicator model, as well as to build an interactive web interface for the model". A case study in a semiarid northern China subwatershed showed that the proposed method was feasible, flexible and effective. Our experiences demonstrated that developing a fully-coupled GIS-NPS model system in R could simplify NPS modeling across computation platforms, promote modeling efficiency, implement dynamic simulations, enhance model inputs/outputs display and provide readily interactivity. We envision that the experiences could provide a promising option for NPS modeling. Highlights: Report a full integration of a non-point source Phosphorus indicator and the GIS in R. Full integration promoted modeling and visualization efficiency. R proves to be a promising option for non-point source pollution modeling.