A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Shiqi Fang; Dario Del Giudice; Donald Scavia; Caren E Binding; Thomas B Bridgeman; Justin D Chaffin; Mary Anne Evans; Joseph Guinness; Thomas H Johengen; Daniel R Obenour

doi:10.1016/j.scitotenv.2019.133776

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Sci Total Environ. 2019 Dec 10:695:133776. doi: 10.1016/j.scitotenv.2019.133776. Epub 2019 Aug 8.

Authors

Affiliations

¹ Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA. Electronic address: sfang6@ncsu.edu.
² Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA.
³ School for Environment and Sustainability, University of Michigan, 440 Church St., Ann Arbor, MI 48104, USA.
⁴ Water Science and Technology Directorate, Environment and Climate Change Canada, 867 Lakeshore Rd, Burlington, Ontario L7S 1A1, Canada.
⁵ Department of Environmental Sciences and Lake Erie Center, University of Toledo, 6200 Bayshore Drive, Oregon, OH 43616, USA.
⁶ F. T. Stone Laboratory and Ohio Sea Grant, The Ohio State University, 878 Bayview Ave, Put-in-Bay, OH 43456, USA.
⁷ U.S. Geological Survey, Great Lakes Science Center, 1451 Green Rd, Ann Arbor, MI 48105, USA.
⁸ Department of Statistics and Data Science, Cornell University, 1178 Comstock Hall, Ithaca, NY 14853, USA.
⁹ Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA.
¹⁰ Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA; Center for Geospatial Analytics, North Carolina State University, Campus Box 7106, Raleigh, NC 27695, USA.

PMID: 31426003
DOI: 10.1016/j.scitotenv.2019.133776

Abstract

Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June-October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.

Keywords: Algal biomass and extent; Harmful algal blooms; Lake Erie; Probabilistic estimates; Space-time geostatistical model.

MeSH terms

Environmental Monitoring / methods*
Harmful Algal Bloom*
Models, Statistical*
Water Pollution / statistics & numerical data*