Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

Wenzhao Xu; Paris D. Collingsworth; Barbara Bailey; Martha L. Carlson Mazur; Jeff Schaeffer; Barbara Minsker

doi:10.1016/j.envsoft.2017.06.049

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Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

Wenzhao Xu·Paris D. Collingsworth·Barbara Bailey·Martha L. Carlson Mazur·Jeff Schaeffer·Barbara Minsker

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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.

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Environmental Modelling and Software Article

Source-reported bounding extent: 43.058854606434494° to 44.10533762552548° latitude; -87.67364501953124° to -86.27288818359375° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Wenzhao Xu, Paris D. Collingsworth, Barbara Bailey, Martha L. Carlson Mazur, Jeff Schaeffer, Barbara Minsker. 2017. Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis. https://doi.org/10.1016/j.envsoft.2017.06.049

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