Estimating cyanobacterial bloom transport by coupling remotely sensed imagery and a hydrodynamic model

The ability to forecast the transport of harmful cyanobacterial blooms in the Laurentian Great Lakes is beneficial to natural resource managers concerned with public health. This manuscript describes a method that improves the prediction of cyanobacterial bloom transport with the use of a preoperati...

Full description

Saved in:
Bibliographic Details
Published in:Ecological applications 2011-10, Vol.21 (7), p.2709-2721
Main Authors: Wynne, Timothy T, Stumpf, Richard P, Tomlinson, Michelle C, Schwab, David J, Watabayashi, Glen Y, Christensen, John D
Format: Article
Language:English
Subjects:
algal bloom
Artificial satellites
Centroids
Cyanobacteria
ecological forecasting
Ecological modeling
Ecosystem
Environmental Monitoring - methods
Forecasting models
Great Lakes Region
Harmful Algal Bloom
Hydrodynamics
Lake Erie, USA
Lakes
Laurentian Great Lakes
Microcystis
Microcystis aeruginosa
Microcystis spp
Modeling
Models, Biological
Pixels
remote sensing
Remote Sensing Technology
Satellite imaging
spp
Time Factors
time series
Time series forecasting
Water Movements
Weather forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The ability to forecast the transport of harmful cyanobacterial blooms in the Laurentian Great Lakes is beneficial to natural resource managers concerned with public health. This manuscript describes a method that improves the prediction of cyanobacterial bloom transport with the use of a preoperational hydrodynamic model and high temporal resolution satellite imagery. Two scenarios were examined from separate cyanobacterial blooms in western Lake Erie, USA. The first scenario modeled bloom position and extent over the span of 13 days. A geographic center, or centroid, was calculated and assigned to the bloom from observed satellite imagery. The bloom centroid was projected forward in time, and the projected position was compared to the final observed bloom centroid. Image pixels flagged as cyanobacterial bloom were compared between the initial image and the final image, and this was assumed as persistence. The second bloom scenario was modeled for a period of 12 days, and the results were framed in an ecological context in an effort to gain further understanding of cyanobacterial bloom dynamics. These modeling techniques can be incorporated into an operational forecasting system.
ISSN:1051-0761
1939-5582
DOI:10.1890/10-1454.1