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

Xu, Wenzhao; Collingsworth, Paris; Bailey, Barbara; Carlson Mazur, Martha; Schaeffer, Jeffrey; Minsker, Barbara

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

2017
By Xu, Wenzhao ; Collingsworth, Paris ; Bailey, Barbara ; ...
Source: Environmental Modelling & Software, 97, 72-85

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Journal Title:

Environmental Modelling & Software
Personal Author:

Xu, Wenzhao ; Collingsworth, Paris ; Bailey, Barbara ; Carlson Mazur, Martha ; Schaeffer, Jeffrey ; ... More +

Xu, Wenzhao ; Collingsworth, Paris ; Bailey, Barbara ; Carlson Mazur, Martha ; Schaeffer, Jeffrey ; Minsker, Barbara Less -
NOAA Program & Office:

Sea Grant
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ILIN (Illinois-Indiana Sea Grant)
Description:

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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Ecological Modeling Environmental Engineering Software
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Environmental Modelling & Software, 97, 72-85
DOI:

https://doi.org/10.1016/j.envsoft.2017.06.049
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1364-8152
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Elsevier BV
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Journal Article
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urn:sha-512:44c0d4d030609c61d16e0ca1d09531024fc43e559d5e79f833510231588dc8e08e11ff949befee773905c50c319fea4c890ff53c9b93b59b8fbcaaab400a1314
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Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

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