Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis. (November 2017)

• Record Type: Journal Article
• Authors: Xu, Wenzhao; Collingsworth, Paris; Bailey, Barbara; Carlson Mazur, Martha; Schaeffer, Jeffrey; Minsker, Barbara
• Is Part Of: Environmental modelling & software. Volume 97(2017)
• Page Start: 72
• Page End: 85
• Publication Date: 2017-11
• Subjects: Water quality -- Rivermouth -- Plumes -- Dynamic -- Decision support -- Adaptive sampling; 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.2017.06.049
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper proposes a geospatial analysis framework and software to interpret water-quality sampling data from towed undulating vehicles in near-real time. The framework includes data quality assurance and quality control processes, automated kriging interpolation along undulating paths, and local hotspot and cluster analyses. These methods are implemented in an interactive Web application developed using the Shiny package in the R programming environment to support near-real time analysis along with 2- and 3-D visualizations. The approach is demonstrated using historical sampling data from an undulating vehicle deployed at three rivermouth sites in Lake Michigan during 2011. The normalized root-mean-square error (NRMSE) of the interpolation averages approximately 10% in 3-fold cross validation. The results show that the framework can be used to track river plume dynamics and provide insights on mixing, which could be related to wind and seiche events. Highlights: A geostatistical analyzing framework is developed for undulating sampling data. Hotspot and cluster analysis reveal river plume orientation and water mixing area. The framework supports near-real-time analysis and adaptive monitoring. The methods are implemented into an open source Web application in R.