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Frontiers of Earth Science

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Front. Earth Sci.    2015, Vol. 9 Issue (4) : 659-682    https://doi.org/10.1007/s11707-015-0540-5
RESEARCH ARTICLE
Infrastructure for collaborative science and societal applications in the Columbia River estuary
António M. BAPTISTA1,2(), Charles SEATON1,2, Michael P. WILKIN1,3, Sarah F. RISEMAN1,2, Joseph A. NEEDOBA1,2, David MAIER1,4, Paul J. TURNER1,2, Tuomas KÄRNÄ1,2, Jesse E. LOPEZ1,2, Lydie HERFORT1,2, V.M. MEGLER1,4, Craig McNEIL1,5, Byron C. CRUMP1,6,7, Tawnya D. PETERSON1,2, Yvette H. SPITZ1,6, Holly M. SIMON1,2
1. NSF Science and Technology Center for Coastal Margin Observation & Prediction
2. Oregon Health & Science University, Portland, Oregon 97239, USA
3. Oregon Health & Science University, Astoria, Oregon 97103, USA
4. Portland State University, Portland, Oregon 97201, USA
5. Applied Physics Laboratory of the University of Washington, Seattle, Washington 98105, USA
6. Oregon State University, Corvallis, Oregon 97331, USA
7. Formerly University of Maryland Center for Environmental Studies, Maryland 21613, USA
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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 finite-element 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.
Keywords estuaries      observations      numerical modeling      cyber-infrastructure      Columbia River     
Corresponding Author(s): António M. BAPTISTA   
Online First Date: 30 September 2015    Issue Date: 30 October 2015
 Cite this article:   
António M. BAPTISTA,Charles SEATON,Michael P. WILKIN, et al. Infrastructure for collaborative science and societal applications in the Columbia River estuary[J]. Front. Earth Sci., 2015, 9(4): 659-682.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-015-0540-5
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I4/659
Fig.1  The Columbia River estuary. The inset shows the full estuary, which is tidally influenced up to Bonneville Dam, and separates the states of Oregon (OR) and Washington (WA). The region of the estuary subject to ocean influence is contained in the larger map (maximum salinity intrusion is typically less than 45?km). Yellow squares represent interdisciplinary endurance stations of the SATURN observation network; dark and light cyan triangles represent current and historical physical endurance stations of the same network. Illustrative symbols represent areas of typical deployment of mobile platforms: kayak, gliders, and autonomous underwater vehicles (AUVs). The blue lines in the inset are endurance observation lines of the independent but synergistic Ocean Observing Initiative (NSF, 2005).
Fig.2  Ocean and river forcing have strong seasonal and inter-annual variability. Top panel: River discharge at Bonneville Dam for a 3-year period (red: 2010; green: 2011; blue: 2012), mapped against the 1996?2014 climatology (25th and 75th percentile in dark gray; maxima and minima in lighter gray). Second panel: Cumulative Coastal Upwelling Index (CUI; red: 2010; green: 2011; blue: 2012), mapped against 1996?2014 (in gray). Third and fourth panels: Freshwater nitrate and sediment loads, respectively, at SATURN-05 for 2010?2012 (darker shades represent more recent years). Bottom panel: Oxygen saturation at the mouth of the estuary (SATURN-02, in crimson shades); the South channel (SATURN-03, in blue shades); and the tidal freshwater (SATURN-05, in gray and black). Data for the bottom panel are for 2010?2012 as available; within a same color, darker shades represent more recent years.
Fig.3  Overview of the evolution of the endurance stations of the observation network, contextualized by ocean processes (via the El Niño-Southern Oscillation [ENSO] index) and river discharges at Bonneville Dam. For each station, periods of data collection are represented in black (scalar physical variables), red (vertical profiles of velocity) and green (interdisciplinary scalar variables). Numbers preceding physical stations correspond to numbering in Fig. 1.
Fig.4  Schematic representation of SATURN interdisciplinary endurance stations.
Fig.5  Equipped with a winched profiler, SATURN-01 offers a high-resolution characterization of the vertical structure of the water column. The variability of that vertical structure over a tidal day (May 17, 2012) is illustrated here. Flow at Bonneville Dam for the period is ~9,800?m3/s. From left to right and top to bottom: salinity, oxygen saturation, along-channel velocity, temperature, turbidity and cross-channel velocity.
Fig.6  Sensors deployed at the interdisciplinary SATURN endurance stations, colored by year of initial deployment.
Fig.7  Assessment of hypoxic conditions in the Washington continental shelf, based on a glider deployment for July 23?August 21, 2009. Left panel: 3D view of dissolved oxygen (DO). Middle panel: 3D view of hypoxia, where red is severe hypoxia (DO≤0.5?mL/L); yellow is mild hypoxia (0.5<DO≤1.4?mL/L); and green is oxygenated water (DO>1.4?mL/L). Right Panel: Plan view of glider path, showing the spatial extent of hypoxia at maximum dive depth. The Columbia River estuary is at the lower right (southeast) edge, with Willapa Bay and Grays Harbor to the north. See stccmop.org/datamart/observation_network/glider for additional glider data.
Fig.8  Salinity data (panels (b) and (e)) collected during an AUV mission in the North Channel of the estuary, in May 2012, compared with model results (panels (c) and (f)). Panels (d) and (g) show the horizontal extent of the corresponding AUV tracks, superimposed on modeled bottom salinity fields. As typical in our operations, AUV deployments were limited to periods of flood and high water slack (see panel (a)) for logistical reasons. Also as typical, AUV missions were coordinated with other assets: a vessel, occupying either station OC1 or OC2, and a bottom node (denoted NCBN1). SATURN-01 is also shown for geographic context.
Fig.9  Illustrative CMOP campaigns, showing water sample locations (colored circles). For context, temporary stations (red and yellow triangles) and selected endurance stations (red diamonds: interdisciplinary stations; red squares: active physical stations; hollow red squares: historical physical stations) are also shown. Bathymetry is shown in the background for all panels. Top panel: sampling pattern for monthly surveys to characterize Mesodinium spp. blooms, conducted in the M/V Forerunner or a NOAA vessel, the R/V Magister. Different colors refer to water collection lines along the South channel (cyan), across the entrance of the estuary (orange), in notable channels in Baker Bay (green and purple), at the mouth of Youngs Bay and at four endurance stations (red dots labeled SATURN-nn). Middle panel: a May 2012 campaign in the North Channel for characterization of the estuarine turbidity maxima. Green circles are sampling locations for the R/V Oceanus, and yellow circles represent acoustic nodes used for the navigation of AUVs. A bottom node was also deployed at NCBN1. Bottom panel: sampling patterns and stations for campaigns in Cathlamet Bay. The green line refers to M/V Forerunner transects, with three water-collection stations shown as green dots. Two temporary stations (yellow triangles) with bottom and surface expressions were also deployed. SATURN-04 (diamond) functioned as a field laboratory during this campaign. The acronym RM17 is at the approximate location of historical collection of water samples, which we re-visited as time allowed.
Fig.10  CMOP investigator Fredrick G. Prahl, from Oregon State University, operates a Fast Methane Analyzer at SATURN-04 during a land-based Cathlamet Bay campaign (Fig. 9, bottom panel).
Fig.11  Schematic of the Virtual Columbia River, which brings together consistent external forcing, coordinated codes (circulation, water age, sediment dynamics and biogeochemistry), data repositories and processing protocols, to support scientific understanding, management decisions and training activities. In the modeling box, arrows identify inter-model connectivity (with dashed arrows referring to activities in progress). Models are iteratively enhanced, a process informed by frequent assessment of simulation skill against field observations and scientific understanding.
Fig.12  Top view of illustrative computational grids of the Virtual Columbia River. The left panel shows the extent of the computational domain. The right panels are zooms on the chemical estuary of grids used in two specific simulation databases of circulation (named DB33 and DB22, respectively, with the former a newer generation simulation). The horizontal grid for the full domain consists of 109,000 triangles and 56,000 nodes for DB33, and of 39,000 triangles and 21,000 nodes for DB22. The corresponding three-dimensional grids have roughly 2.9 and 1.0?million prisms, respectively.
Fig.13  The Virtual Columbia River operational products include forecasts of circulation, posted daily at stccmop.org/datamart/virtualcolumbiariver/forecasts. Displayed here, for illustration, is a mid-ebb view of the predicted bottom salinity field in the estuary (top panel) for April 6, 2015, at 1:00AM Pacific Standard Time, complemented (bottom panels) by 3-day time series of salinity (left bottom), water levels (top right) and North-South winds (top left) at estuarine and plume stations, and of flow at Beaver Army dock (where SATURN-05 is located). Forecasts are typically done for the day of creation and for the two ensuing days.
Fig.14  A Climatological Atlas is one of the operational products of the Virtual Columbia River. Displayed here, for illustration, is the climatology of the salinity intrusion length (SIL) in the South channel of the estuary, based on a multi-year (1999?2014) simulation database. Shown are daily SIL statistics: average (in black), 25 and 75 percentiles (dark gray) and maxima and minima (lighter gray). Overlaid (in red) is SIL for 2011, a year characterized by exceptionally high discharges from mid-May through July (see Fig. 2). Because of its inverse relationship with river discharge, SIL in 2011 is extremely low for that period. See stccmop.org/datamart/virtualcolumbiariver/simulationdatabases/climatologicalatlas for more capabilities of the Climatological Atlas. Image is a screen capture of a Climatological Atlas session.
Fig.15  Data Near Here offers a convenient means to locate SATURN data sources within or near a region (here, the adjustable box in the map) and time period (here, 2011-09-25 to 2011-09-30), for a target variable or category (here, nutrients). This particular search located 50 sources that meet or are similar to the search criteria (including the endurance stations and cruises, shown in the map), ranked (and colored, from green to yellow and red) by how close they are to the criteria. The web interface displays information about those sources. The data identified can (in whole or part) be downloaded through files, or exported to Data Explorer. For access to Data Near Here, see stccmop.org/datamart/data_near_here. Image is a screen capture of a Data Near Here session.
Fig.16  Example of the exploratory use of the Data Explorer. The top panel shows, from a 1m-deep sensor, multiple CDOM fronts crossing SATURN-02 on April 16 and (more markedly) April 17, 2011. The middle panel shows salinities at 1?m (blue) and 35?m (red). The coupling of salinity and CDOM data suggest that the fronts are tied to the freshwater plume, and thus have a riverine source. The bottom panel shows that, while most fronts occur during ebb, the largest of the fronts occurs during flood—suggesting by its intensity the effective re-entrainment of water released from the river in the prior ebb or ebbs. Visualized but not shown: wind direction is approximately constant during the onset of the fronts, while wind speed has a temporary decrease. All panels are extracted directly from the Data Explorer web interface. See stccmop.org/datamart/observation_network/dataexplorer, for access to the Data Explorer. Image is a screen capture of a Data Explorer session.
Fig.17  Sensor data flow in real time from a wide variety of SATURN observing platforms to a central CMOP Database. Data can also flow in non real-time (non-RT) from certain CMOP platforms (e.g., ships or AUVs) or external sources. Regardless of their source, observations are imported, parsed and standardized using a common format. Researchers can access the data using diverse methods, which allow them to interactively view, combine and analyze data of interest, or to download selected data into specialized analysis tools for further study. See details in the text.
Fig.18  The importance of quality assessment is illustrated through a time series of phycoerythrin fluorescence at SATURN-03 (from the 2.4?m deep pumping port) for the summer of 2010. Colors represent data quality: PD0 (blue) and PD2 (red). Quality control corrected in this case a significant artifact due to turbidity, zeroed the data and removed data from an identified period of sensor fouling. Prior to correction, the data were difficult to interpret, with the artifact masking the phycoerythrin signal. Following quality control the phycoerythrin data clearly capture the dynamics of the bloom of Mesodinium spp. and when placed in the context of dissolved oxygen data (not shown) suggest that the bloom is responsible for local super-saturation of dissolved oxygen in the estuary. Image is taken from a Data Explorer session.
Fig.19  Data Near Here offers a convenient means to locate SATURN data sources within or near a region (here, the adjustable box in the map) and time period (here, 2011-09-25 to 2011-09-30), for a target variable or category (here, nutrients). This particular search located 50 sources that meet or are similar to the search criteria (including the endurance stations and cruises, shown in the map), ranked (and colored, from green to yellow and red) by how close they are to the criteria. The web interface displays information about those sources. The data identified can (in whole or part) be downloaded through files, or exported to Data Explorer. For access to Data Near Here, see stccmop.org/datamart/data_near_here. Image is a screen capture of a Data Near Here session.
Fig.20  Example of the exploratory use of the Data Explorer. The top panel shows, from a 1m-deep sensor, multiple CDOM fronts crossing SATURN-02 on April 16 and (more markedly) April 17, 2011. The middle panel shows salinities at 1?m (blue) and 35?m (red). The coupling of salinity and CDOM data suggest that the fronts are tied to the freshwater plume, and thus have a riverine source. The bottom panel shows that, while most fronts occur during ebb, the largest of the fronts occurs during flood—suggesting by its intensity the effective re-entrainment of water released from the river in the prior ebb or ebbs. Visualized but not shown: wind direction is approximately constant during the onset of the fronts, while wind speed has a temporary decrease. All panels are extracted directly from the Data Explorer web interface. See stccmop.org/datamart/observation_network/dataexplorer, for access to the Data Explorer. Image is a screen capture of a Data Explorer session.
Fig.21  Sensor data flow in real time from a wide variety of SATURN observing platforms to a central CMOP Database. Data can also flow in non real-time (non-RT) from certain CMOP platforms (e.g., ships or AUVs) or external sources. Regardless of their source, observations are imported, parsed and standardized using a common format. Researchers can access the data using diverse methods, which allow them to interactively view, combine and analyze data of interest, or to download selected data into specialized analysis tools for further study. See details in the text.
Fig.22  The importance of quality assessment is illustrated through a time series of phycoerythrin fluorescence at SATURN-03 (from the 2.4?m deep pumping port) for the summer of 2010. Colors represent data quality: PD0 (blue) and PD2 (red). Quality control corrected in this case a significant artifact due to turbidity, zeroed the data and removed data from an identified period of sensor fouling. Prior to correction, the data were difficult to interpret, with the artifact masking the phycoerythrin signal. Following quality control the phycoerythrin data clearly capture the dynamics of the bloom of Mesodinium spp. and when placed in the context of dissolved oxygen data (not shown) suggest that the bloom is responsible for local super-saturation of dissolved oxygen in the estuary. Image is taken from a Data Explorer session.
1 C R Anderson, R E Davis, N S Bandolin, A M Baptista, B M Tebo (2011). Analysis of in situ manganese (II) oxidation in the Columbia River and offshore plume: linking Aurantimonas and the associated microbial community to an active biogeochemical cycle. Environ Microbiol, 13(6): 1561–1576
https://doi.org/10.1111/j.1462-2920.2011.02462.x
2 A M Baptista (2006). CORIE: the first decade of a coastal margin collaborative observatory. In: Proceedings of MTS/IEEE Oceans '06. Boston, Massachusetts: IEEE, 1–6
3 A M Baptista (2015). The power of estuarine collaboratories: can lessons learned in the Columbia River inform a global vision? In: Proceedings of IEEE/OES Underwater Technologies 2015. Chennai, India: IEEE/OES, 1–7
4 A M Baptista, B Howe, J Freire, D Maier, C Silva (2008). Scientific exploration in the era of ocean observatories. Comput Sci Eng, 10(3): 53–58
https://doi.org/10.1109/MCSE.2008.83
5 A M Baptista, M Wilkin, P Pearson, P J Turner, C McCandlish, P Barrett (1999). Coastal and estuarine forecast systems: a multi-purpose infrastructure for the Columbia River. Earth Syst Monit, 9(3): 1–2, 4–5, 16
6 A M Baptista, Y L Zhang, A Chawla, M Zulauf, C Seaton, E P Myers III, J Kindle, M Wilkin, M Burla, P J Turner (2005). A cross-scale model for 3D baroclinic circulation in estuary-plume-shelf systems: II. application to the Columbia River. Cont Shelf Res, 25(7–8): 935–972
https://doi.org/10.1016/j.csr.2004.12.003
7 E B Barbier, S D Hacker, C Kennedy, E W Koch, A C Stier, B R Silliman (2011). The value of estuarine and coastal ecosystem services. Ecol Monogr, 81(2): 169–193
https://doi.org/10.1890/10-1510.1
8 C A Barnes, A C Duxbury, B A Morse (1972). Circulation and selected properties of the Columbia River effluent at sea. In: A T Pruter, D L Alverson, eds. The Columbia River Estuary and Adjacent Ocean Waters. Seattle: University of Washington Press, 41–80
9 D L Bottom, C A Simenstad, A M Baptista, J Burke, D A Jay, J Burke, K K Jones, E Casillas, M H Schiewe (2005). Salmon at river’s end: the role of the estuary in the decline and recovery of Columbia River salmon. NOAA Technical memorandum NMFS-NWFSC-68
10 S L Bräuer, C Adams, K Kranzler, D Murphy, M Xu, P Zuber, H M Simon, A M Baptista, B M Tebo (2011). Culturable Rhodobacter and Shewanella species are abundant in estuarine turbidity maxima of the Columbia River. Environ Microbiol, 13(3): 589–603
https://doi.org/10.1111/j.1462-2920.2010.02360.x
11 S L Bräuer, K Kranzler, N Goodson, D Murphy, H M Simon, A M Baptista, B M Tebo (2013). Dark carbon fixation in the Columbia River’s estuarine turbidity maxima: molecular characterization of red-type cbbL genes and measurement of DIC uptake rates in response to added electron donors. Estuaries Coasts, 36(5): 1073–1083
https://doi.org/10.1007/s12237-013-9603-6
12 B J Burke, J J Anderson, A M Baptista (2014). Evidence for multiple navigational sensory capabilities by Chinook salmon. Aquat Biol, 20(1): 77–90
https://doi.org/10.3354/ab00541
13 M Burla (2009). The Columbia River estuary and plume: natural variability, anthropogenic change and physical habitat for salmon. Dissertation for Ph.D Degree. Beaverton, Oregon: Oregon Health & Science University
14 M Burla, A M Baptista, E Casillas, J G Williams, D M Marsh (2010b). The influence of the Columbia River plume on the survival of steelhead (Oncorhynchus mykiss) and Chinook salmon (Oncorhynchus tshawytscha): a numerical exploration. Can J Fish Aquat Sci, 67(10): 1671–1684
https://doi.org/10.1139/F10-083
15 M Burla, A M Baptista, Y Zhang, S Frolov (2010a). Seasonal and interannual variability of the Columbia River plume: a perspective enabled by multiyear simulation databases. Journal of Geophysical Research. Oceans, 115(C2): C00B16
https://doi.org/10.1029/2008JC004964
16 A Chawla, D A Jay, A M Baptista, M Wilkin, C Seaton (2008). Seasonal variability and estuary-shelf interactions in circulation dynamics of a river-dominated estuary. Estuaries Coasts, 31(2): 269–288
https://doi.org/10.1007/s12237-007-9022-7
17 S DeLorenzo, S L Bräuer, C A Edgmont, L Herfort, B M Tebo, P Zuber (2012). Ubiquitous dissolved inorganic carbon assimilation by marine heterotrophic bacteria in the Pacific Northwest coastal ocean as determined by stable isotope probing. PLoS ONE, 7(10): e46695
https://doi.org/10.1371/journal.pone.0046695
18 R J Diaz, R Rosenberg (2008). Spreading dead zones and consequences for marine ecosystems. Science, 321(5891): 926–929
https://doi.org/10.1126/science.1156401
19 C A Durkin, S J Bender, K Y K Chan, K Gaessner, D Grunbaum, E V Armbrust (2013). Silicic acid supplied to coastal diatom communities influences cellular silicification and the potential export of carbon. Limnol Oceanogr, 58(5): 1707–1726
https://doi.org/10.4319/lo.2013.58.5.1707
20 W Evans, B Hales, P G Strutton (2013). PCO2 distributions and air–water CO2 fluxes in the Columbia River estuary. Estuar Coast Shelf Sci, 117: 260–272
https://doi.org/10.1016/j.ecss.2012.12.003
21 C S Fortunato, B C Crump (2011). Bacterioplankton community variation across river to ocean environmental gradients. Microb Ecol, 62(2): 374–382
https://doi.org/10.1007/s00248-011-9805-z
22 C S Fortunato, A Eiler, L Herfort, J A Needoba, T D Peterson, B C Crump (2013). Determining indicator taxa across spatial and seasonal gradients in the Columbia River coastal margin. ISME J, 7(10): 1899–1911
https://doi.org/10.1038/ismej.2013.79
23 C S Fortunato, L Herfort, P Zuber, A M Baptista, B C Crump (2012). Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J, 6(3): 554–563
https://doi.org/10.1038/ismej.2011.135
24 S Frolov, A M Baptista, M Wilkin (2008). Optimizing fixed observational assets in a coastal observatory. Cont Shelf Res, 28(19): 2644–2658
https://doi.org/10.1016/j.csr.2008.08.009
25 M Gilbert, J A Needoba, C Koch, A Barnard, A M Baptista (2013). Nutrient loading and transformations in the Columbia River estuary determined by high resolution in situ sensors. Estuaries Coasts, 36(4): 708–727
https://doi.org/10.1007/s12237-013-9597-0
26 V Green, N B Watts, K Wegner, M Thompson, A Johnson, T Peterson, A M Baptista (2013). Coastal margin science and education in the era of collaboratories. Current, 28(3): 27–31
27 L Herfort, T D Peterson, V Campbell, S Futrell, P Zuber (2011a). Myrionecta rubra (Mesodinium rubrum) bloom initiation in the Columbia River estuary. Estuar Coast Shelf Sci, 95(4): 440–446
https://doi.org/10.1016/j.ecss.2011.10.015
28 L Herfort, T D Peterson, L A McCue, B C Crump, F G Prahl, A M Baptista, V Campbell, R Warnick, G C Roegner, P Zuber (2011b). Myrionecta rubra population genetic diversity and its cryptophyte chloroplast specificity in recurrent red tides in the Columbia River estuary. Aquat Microb Ecol, 62(1): 85–97
https://doi.org/10.3354/ame01460
29 L Herfort, T D Peterson, L A McCue, P Zuber (2011c). Protist 18S rRNA gene sequence analysis reveals multiple sources of organic matter contributing to turbidity maxima of the Columbia River estuary. Mar Ecol Prog Ser, 438: 19–31
https://doi.org/10.3354/meps09303
30 L Herfort, T D Peterson, F G Prahl, L A McCue, J A Needoba, B C Crump, G C Roegner, V Campbell, P Zuber (2012). Red waters of Myrionecta rubra are biogeochemical hotspots for the Columbia River estuary with impacts on primary/secondary productions and nutrient cycles. Estuaries Coasts, 35(3): 878–891
https://doi.org/10.1007/s12237-012-9485-z
31 L Herfort, C Seaton, M Wilkin, K Seitz, J Lopez, M Smith, V Haynes, A M Baptista, H M Simon. Autonomous adaptive sampling of microbial processes in a dynamic estuary. Limnol Oceanogr Methods, (in press)
32 T Hey, S Tansley, K Tolle, eds. (2009). The Fourth Paradigm–Data-Intensive Scientific Discovery. Berlin, Heidelberg: Springer
33 B M Hickey, R M Kudela, J D Nash, K W Bruland, W T Peterson, P MacCready, E J Lessard, D A Jay, N S Banas, A M Baptista, E P Dever, P M Kosro, L K Kilcher, A R Horner-Devine, E D Zaron, R M McCabe, J O Peterson, P M Orton, J Pan, M C Lohan (2010). River influences on shelf ecosystems: introduction and synthesis. Journal of Geophysical Research, 115(2): C00B17
34 R Howarth, F Chan, D J Conley, J Garnier, S C Doney, R Marino, G Billen (2011). Coupled biogeochemical cycles: eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems. Front Ecol Environ, 9(1): 18–26
https://doi.org/10.1890/100008
35 HSBC (2012). Exploring the links between water and economic growth. A report prepared for HSBC by Frontier Economics: Executive Summary
36 IPCC (2014). Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, USA
37 P Kahn, L Herfort, T D Peterson, P Zuber (2014). Discovery of a Katablepharis sp. in the Columbia River estuary that is abundant during the spring and bears a unique large ribosomal subunit sequence element. MicrobiologyOpen, 3(5): 764–776
https://doi.org/10.1002/mbo3.206
38 T Kärnä, A M Baptista, J E Lopez, P J Turner, C McNeil, T B Sanford (2015). Numerical modeling of circulation in high-energy estuaries: a Columbia River Estuary benchmark. Ocean Model, 88: 54–71
https://doi.org/10.1016/j.ocemod.2015.01.001
39 J Lave, E Wenger (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press
40 Y Liu, P MacCready, B M Hickey (2009). Columbia River plume patterns in summer 2004 as revealed by a hindcast coastal ocean circulation model. Geophys Res Lett, doi: 10.1029/2008GL036447
41 J E Lopez, J Brown (2014). Improving the performance of the coastal ocean model SELFE. In: SIAM Conference on Parallel Processing for Scientific Computing, Portland, Oregon
42 J Lopez, A M Baptista, Y Spitz (2012). Modeling estuarine turbidity maxima in the Columbia River estuary. In: 2012 Columbia River Estuary Conference: New Scientific Findings and their Management Implications. Astoria, Oregon, US
43 H K Lotze, H S Lenihan, B J Bourque, R H Bradbury, R G Cooke, M C Kay, S M Kidwell, M X Kirby, C H Peterson, J Jackson (2006). Depletion, degradation, and recovery potential of estuaries and coastal seas. Science, 312(5781): 1806–1809
https://doi.org/10.1126/science.1128035
44 M A Maier, T D Peterson, J A Needoba, K Bartowicz, A M Baptista (2012). Phytoplankton seasonal dynamics and ecological interactions in the lower Columbia River. In: AGU Ocean Sciences Meeting, Salt Lake City, Utah
45 MEA (2005). Ecosystems and human well-being: current state and trends: findings of the Condition and Trends Working Group of the Millennium Ecosystem Assessment. Washington D C: Island Press
46 V M Megler, D Maier (2013). Data near here: bringing relevant data closer to scientists. Comput Sci Eng, 15(3): 44–53
https://doi.org/10.1109/MCSE.2013.38
47 J A Miller, D J Teel, W T Peterson, A M Baptista (2014). Assessing the relative importance of local and regional processes on the survival of a threatened salmon population. PLoS ONE, 9(6): e99814
https://doi.org/10.1371/journal.pone.0099814
48 J A Miller, D Teel, A M Baptista, C Morgan, M Bradford (2013). Disentangling bottom-up and top-down effects on survival during early ocean residence in a population of Chinook salmon. Can J Fish Aquat Sci, 70(4): 617–629
https://doi.org/10.1139/cjfas-2012-0354
49 NOAA (2014a), U.S. population living in coastal watershed counties, in NOAA’s State of the Coast
50 NOAA (2014b), The coast – our nation’s economic engine, in NOAA’s State of the Coast
51 NSF (2005). Ocean Observatories Initiative. Science Plan Report
52 OGE (2014). Our Global Estuary: U.S. National Workshop, October 21–23, 2013. Editor: M. Davis. Harbor Branch Oceanographic Institute, Harbor Branch, FL, USA
53 T D Peterson, R L Golda, M L Garcia, B Li, M A Maier, J A Needoba, P Zuber (2013). Associations between Mesodinium rubrum and cryptophyte algae in the Columbia River estuary. Aquat Microb Ecol, 68(2): 117–130
https://doi.org/10.3354/ame01598
54 L Pinto, A B Fortunato, A Zhang, A Oliveira, F E P Sancho (2012). Development and validation of a three-dimensional morphodynamic modelling system for non-cohesive sediments. Ocean Model, 57– 58(C): 1–14
https://doi.org/10.1016/j.ocemod.2012.08.005
55 K Rathmell, M Wilkin, P Welle, T Mattson, A M Baptista (2013). A Very Smart Kayak. Current: The Journal of Marine Education, 28(3): 32–33
56 P Robinson, A K Leight, D D Trueblood, B Wood (2013). Climate sensitivity of the national estuarine research reserve system. NOAA Special Report
57 C G Roegner, J A Needoba, A M Baptista (2011b). Coastal upwelling supplies oxygen-depleted water to the Columbia River estuary. PLoS ONE, 6(4): e18672
https://doi.org/10.1371/journal.pone.0018672
58 G C Roegner, C Seaton, A M Baptista (2011a). Climatic and tidal forcing of hydrography and chlorophyll concentrations in the Columbia River estuary. Estuaries Coasts, 34(2): 281–296
https://doi.org/10.1007/s12237-010-9340-z
59 C A Scholin (2013). Ecogenomic sensors. In: S A Levin, eds. Encyclopedia of Biodiversity. Waltham, Massachusetts: Academic Press, 690–700
60 C Seaton, P Turner J, T Kärnä, A M Baptista (2014). Columbia River channel improvement project. Post-project analysis. Final Report to the U.S. Army Corps of Engineers, Portland, Oregon: Oregon Health & Science University
61 S Simmons, K Casavant (2010). Historical waterborne commerce on the Columbia‒Snake River system: commodity movements up and down river, 1991–2010. Freight Policy Transportation Institute, Pullman, Washington, Washington State University
62 M W Smith, L Z Allen, A E Allen, L Herfort, H M Simon (2013). Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front Microbiol, 4: 120
63 M W Smith, L Herfort, K Tyrol, D Sucui, V Campbell, B C Crump, T D Peterson, P Zuber, A M Baptista, H M Simon (2010). Seasonal changes in bacterial and archaeal gene expression patterns across salinity gradients of the Columbia River coastal margin. PLoS ONE, 5(10): e13312
https://doi.org/10.1371/journal.pone.0013312
64 M W Smith, M Maier, D Suciu, T D Peterson, T Bradstreet, J Nakayama, H M Simon (2012). High resolution microarray assay for rapid taxonomic assessment of Pseudo-nitzschia spp. (Bacillariophyceae) in the field. Harmful Algae, 19: 169–180
https://doi.org/10.1016/j.hal.2012.07.003
65 Y H Spitz (2011). Ecosystem modeling of the Oregon shelf: everything but the kitchen sink. In: K Omori, X Guo, N Yoshie, N Fujii, I C Handoh, A Isobe, S Tanabe, eds. Interdisciplinary Studies on Environmental Chemistry—Marine Environmental Modeling & Analysis. TERRAPUB, 1–9
66 Y H Spitz, J R Moisan, M R Abbott (2001). Configuring an ecosystem model using data from the Bermuda Atlantic time series (BATS). Deep Sea Res Part II Top Stud Oceanogr, 48(8–9): 1733–1768
https://doi.org/10.1016/S0967-0645(00)00159-4
67 P J Statham (2012). Nutrients in estuaries—An overview and the potential impacts of climate change. Sci Total Environ, 434: 213–227
https://doi.org/10.1016/j.scitotenv.2011.09.088
68 J E Swalwell, F Ribalet, E V Armbrust (2011). SeaFlow: a novel underway flow-cytometer for continuous observations of phytoplankton in the ocean. Limnol Oceanogr Methods, 9(October): 466–477
https://doi.org/10.4319/lom.2011.9.466
69 J Towns, T Cockerill, M Dahan, I Foster, K Gaither, A Grimshaw, V Hazlewood, S Lathrop, D Lifka, G D Peterson, R Roskies, J R Scott, N Wilkens-Diehr (2014). XSEDE: accelerating scientific discovery. Computing in Science & Engineering, 16(5): 62–74
70 U.S. Entity (2013). Regional recommendation for the future of the Columbia River Treaty after 2024.
71 E Wenger (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press
72 Y Zhang, A M Baptista (2008). SELFE: a semi-implicit Eulerian Lagrangian Finite-element model for cross-scale ocean circulation. Ocean Model, 21(3–4): 71–96
https://doi.org/10.1016/j.ocemod.2007.11.005
73 Y Zhang, A M Baptista, E P Myers (2004). A cross-scale model for 3D baroclinic circulation in estuary-plume-shelf systems: I. formulation and skill assessment. Cont Shelf Res, 24(18): 2187–2214
https://doi.org/10.1016/j.csr.2004.07.021
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