Bayesian submerged oil tracking with SOSim: Inference from field reconnaissance data and fate-transport model output

2021 ◽  
Vol 165 ◽  
pp. 112078
Author(s):  
Chao Ji ◽  
James D. Englehardt ◽  
C.J. Beegle-Krause
Keyword(s):  
Transport Model ◽  
Model Output ◽  
Field Reconnaissance

How uncertainty in input and parameters influences transport model :output A four-stage model case-study

2015 ◽  
Vol 38 ◽  
pp. 64-72 ◽  
Author(s):  
Stefano Manzo ◽  
Otto Anker Nielsen ◽  
Carlo Giacomo Prato
Keyword(s):  
Transport Model ◽  
Stage Model ◽  
Model Output ◽  
Model Case

Geostatistical risk mapping with chemical transport model output and ozone station data

2004 ◽  
Vol 179 (2) ◽  
pp. 177-185 ◽  
Author(s):  
H. Wackernagel ◽  
C. Lajaunie ◽  
N. Blond ◽  
C. Roth ◽  
R. Vautard
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
Risk Mapping ◽  
Model Output ◽  
Chemical Transport Model ◽  
Station Data

Modeling Space–Time Dynamics of Aerosols Using Satellite Data and Atmospheric Transport Model Output

2011 ◽  
Vol 16 (4) ◽  
pp. 495-512 ◽  
Author(s):  
Catherine A. Calder ◽  
Candace Berrett ◽  
Tao Shi ◽  
Ningchuan Xiao ◽  
Darla K. Munroe
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Space Time ◽  
Model Output ◽  
Time Dynamics ◽  
Atmospheric Transport Model

A detailed contaminant transport model for facility hazard assessment in urban areas

1999 ◽  
Author(s):  
Bohdan Cybyk ◽  
Jay Boris ◽  
Theodore Young, Jr. ◽  
Charles Lind ◽  
Alexandra Landsberg
Keyword(s):  
Contaminant Transport ◽  
Hazard Assessment ◽  
Urban Areas ◽  
Transport Model

The Carbon:²³⁴Thorium ratios of sinking particles in the California Current Ecosystem 2: Examination of a thorium sorption, desorption, and particle transport model

2019 ◽  
Author(s):  
Michael Stukel ◽  
Thomas Kelly
Keyword(s):  
Organic Carbon ◽  
Water Column ◽  
Particulate Organic Carbon ◽  
Transport Model ◽  
California Current ◽  
In Situ Measurements ◽  
Power Law Distribution ◽  
Sinking Particles ◽  
Organic Carbon Concentration

Thorium-234 (234Th) is a powerful tracer of particle dynamics and the biological pump in the surface ocean; however, variability in carbon:thorium ratios of sinking particles adds substantial uncertainty to estimates of organic carbon export. We coupled a mechanistic thorium sorption and desorption model to a one-dimensional particle sinking model that uses realistic particle settling velocity spectra. The model generates estimates of 238U-234Th disequilibrium, particulate organic carbon concentration, and the C:234Th ratio of sinking particles, which are then compared to in situ measurements from quasi-Lagrangian studies conducted on six cruises in the California Current Ecosystem. Broad patterns observed in in situ measurements, including decreasing C:234Th ratios with depth and a strong correlation between sinking C:234Th and the ratio of vertically-integrated particulate organic carbon (POC) to vertically-integrated total water column 234Th, were accurately recovered by models assuming either a power law distribution of sinking speeds or a double log normal distribution of sinking speeds. Simulations suggested that the observed decrease in C:234Th with depth may be driven by preferential remineralization of carbon by particle-attached microbes. However, an alternate model structure featuring complete consumption and/or disaggregation of particles by mesozooplankton (e.g. no preferential remineralization of carbon) was also able to simulate decreasing C:234Th with depth (although the decrease was weaker), driven by 234Th adsorption onto slowly sinking particles. Model results also suggest that during bloom decays C:234Th ratios of sinking particles should be higher than expected (based on contemporaneous water column POC), because high settling velocities minimize carbon remineralization during sinking.

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