Infrastructure for collaborative science and societal applications in the Columbia River estuary

Abstract

To meet societal needs, modern estuarine science needs to be interdisciplinary and collaborative, combine discovery with hypotheses testing, and be responsive to issues facing both regional and global stakeholders. Such an approach is best conducted with the benefit of data-rich environments, where information from sensors and models is openly accessible within convenient timeframes. Here, we introduce the operational infrastructure of one such data-rich environment, a collaboratory created to support (a) interdisciplinary research in the Columbia River estuary by the multi-institutional team of investigators of the Science and Technology Center for Coastal Margin Observation & Prediction and (b) the integration of scientific knowledge into regional decision making. Core components of the operational infrastructure are an observation network, a modeling system and a cyber-infrastructure, each of which is described. The observation network is anchored on an extensive array of long-term stations, many of them interdisciplinary, and is complemented by on-demand deployment of temporary stations and mobile platforms, often in coordinated field campaigns. The modeling system is based on finiteelement unstructured-grid codes and includes operational and process-oriented simulations of circulation, sediments and ecosystem processes. The flow of information is managed through a dedicated cyber-infrastructure, conversant with regional and national observing systems.

Conservation planning for freshwater–marine carryover effects on Chinook salmon survival

“…We also tested the salt intrusion length ( E ) as a covariate of the Columbia River estuary, and the volume ( V ) as a covariate of the Columbia River plume (Climatological Atlas for DB33, http://www.stccmop.org). Covariates E and V are products of the Virtual Columbia River (Baptista et al., ) that were computed from numerical simulations of 3D baroclinic circulation (Kärnä & Baptista, ). Because the migration timing through the estuary and coastal ocean was not observed, we used a 7‐day rolling mean right‐aligned to d for these marine covariates based on estimates reviewed in Dietrich et al.…”

Use of Highly Specific Molecular Markers Reveals Positive Correlation between Abundances of Mesodinium cf. major and Its Preferred Prey, Teleaulax amphioxeia, During Red Water Blooms in the Columbia River Estuary

“…Environmental data, including salinity, temperature, water velocity, chlorophyll a (chl a ) fluorescence, and phycoerythrin fluorescence (a putative proxy for cryptophyte chloroplast abundance assuming minimal cyanobacterial signal), associated with each water sample were acquired from the sensors of the research vessels’ conductivity‐temperature‐depth (CTD) package or from that of the SATURN monitoring station (Baptista et al. ). Phycoerytrin signal was quality controlled and corrected for turbidity bias as described in Baptista et al.…”

“…Phycoerytrin signal was quality controlled and corrected for turbidity bias as described in Baptista et al. (). Additional context for our molecular results was provided by salinity maps of the Columbia River coastal margin using the DB33 simulation database of the Climatology Atlas tool from the website of the Center for Coastal Margin Observation and Prediction (CMOP, stccmop.org/datamart/virtualcolumbiariver).…”

“…Environmental data, including salinity, temperature, water velocity, chlorophyll a (chl a) fluorescence, and phycoerythrin fluorescence (a putative proxy for cryptophyte chloroplast abundance assuming minimal cyanobacterial signal), associated with each water sample were acquired from the sensors of the research vessels' conductivity-temperature-depth (CTD) package or from that of the SATURN monitoring station (Baptista et al 2015). Phycoerytrin signal was quality controlled and corrected for turbidity bias as described in Baptista et al (2015). Additional context for our molecular results was provided by salinity maps of the Columbia River coastal margin using the DB33 simulation database of the Climatology Atlas tool from the website of the Center for Coastal Margin Observation and Prediction (CMOP, stccmop.org/datamart/virtualcolumbiariver).…”

Model-aided Lagrangian interpretation of non-synoptic estuarine observations

“…). 3D relocation of the observations was made possible by the “Virtual Columbia River” (Baptista et al , ), a high‐resolution numerical modeling system developed at the Center for Coastal Margin Observation and Prediction (CMOP). We specifically used simulations developed by Kärnä et al ().…”