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We present a case study for Bayesian analysis and proper representation of distributions and dependence among parameters when calibrating process-oriented environmental models. A simple water quality model for the Elbe River (Germany) is referred to as an example, but the approach is applicable to a wide range of environmental models with time-series output. Model parameters are estimated by Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling. While the best-fit solution matches usual least-squares model calibration (with a penalty term for excessive parameter values), the Bayesian approach has the advantage of yielding a joint probability distribution for parameters. This posterior distribution encompasses all possible parameter combinations that produce a simulation output that fits observed data within measurement and modeling uncertainty. Bayesian inference further permits the introduction of prior knowledge, e.g., positivity of certain parameters. The estimated distribution shows to which extent model parameters are controlled by observations through the process of inference, highlighting issues that cannot be settled unless more information becomes available. An interactive interface enables tracking for how ranges of parameter values that are consistent with observations change during the process of a step-by-step assignment of fixed parameter values. Based on an initial analysis of the posterior via an undirected Gaussian graphical model, a directed Bayesian network (BN) is constructed. The BN transparently conveys information on the interdependence of parameters after calibration. Finally, a strategy to reduce the number of expensive model runs in MCMC sampling for the presented purpose is introduced based on a newly developed variant of delayed acceptance sampling with a Gaussian process surrogate and linear dimensionality reduction to support function-valued outputs.

Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Application of chemical dispersants is one option for combatting oil spills, dispersing oil into the water column and thereby reducing potential pollution to coastal areas. Efficiency of dispersant application depends on oil characteristics, sea and weather conditions. Potential environmental impacts must also be taken into account. Referring to the German Bight region (North Sea), we show how probabilistic Bayesian network (BN) technology can integrate all these aspects to support contingency planning. Expected effects of chemical dispersion on oil spill drift paths are quantified based on comprehensive numerical ensemble simulations. Ecological impacts are represented just in simplified terms focusing on nearshore seabird distributions. The intuitive and interactive BN summarizes expected benefits from chemical dispersion depending on where and under which weather conditions a hypothetical pollution occurs., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Maintaining the current state of ecosystem services from freshwater and marine ecosystems around the world is at risk. Cumulative effects of multiple human pressures on ecosystem components and functions are indicative of residual pressures that "fall through" the cracks of current industry sector management practices. Without an understanding of the level of residual pressures generated by these measures, we are unlikely to reconcile the root causes of ecosystem effects to improve these management practices to reduce their residual pressures. In this paper, we present a new modelling framework that combines a qualitative and quantitative assessments of the effectiveness of the measures used in the daily operations of industry sectors to predict their residual pressure that is delivered to the ecosystem. The predicted residual pressure can subsequently be used as an input variable for ecosystem models. We combine the Bow-tie analysis of the measures with a Bayesian belief network to quantify the effectiveness of the measures and predict the residual pressures., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In case of an oil spill, dispersant application represents a response option, which enhances the natural dispersion of oil and thus reduces coating of seabirds and coastal areas. However, as oil is transferred to the water phase, a trade-off of potential harmful effects shifted to other compartments must be performed. This paper summarizes the results of a workshop on the current knowledge on risks and benefits of the use of dispersants with respect to specific conditions encountered at the German sea areas. The German North Sea coast is a sensitive ecosystem characterised by tidal flats, barrier islands and salt marshes. Many prerequisites for a potential integration of dispersants as spill response option are available in Germany, including sensitivity maps and tools for drift modelling of dispersed and undispersed oil. However, open scientific questions remain concerning the persistence of dispersed oil trapped in the sediments and potential health effects., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Advances in offshore wind farm (OWF) technology have recently led to their construction in coastal waters that are deep enough to be seasonally stratified. As tidal currents move past the OWF foundation structures they generate a turbulent wake that will contribute to a mixing of the stratified water column. In this study we show that the mixing generated in this way may have a significant impact on the large-scale stratification of the German Bight region of the North Sea. This region is chosen as the focus of this study since the planning of OWFs is particularly widespread. Using a combination of idealised modelling and in situ measurements, we provide order-of-magnitude estimates of two important time scales that are key to understanding the impacts of OWFs: (i) a mixing time scale, describing how long a complete mixing of the stratification takes, and (ii) an advective time scale, quantifying for how long a water parcel is expected to undergo enhanced wind farm mixing. The results are especially sensitive to both the drag coefficient and type of foundation structure, as well as the evolution of the pycnocline under enhanced mixing conditions-both of which are not well known. With these limitations in mind, the results show that OWFs could impact the large-scale stratification, but only when they occupy extensive shelf regions. They are expected to have very little impact on large-scale stratification at the current capacity in the North Sea, but the impact could be significant in future large-scale development scenarios.

Remineralization and transformation of dissolved organic matter (DOM) by marine microbes shape the DOM composition and thus, have large impact on global carbon and nutrient cycling. However, information on bacterioplankton-DOM interactions on a molecular level is limited. We examined the variation of bacterial community composition (BCC) at Helgoland Roads (North Sea) in relation to variation of molecular DOM composition and various environmental parameters on short-time scales. Surface water samples were taken daily over a period of 20 days. Bacterial community and molecular DOM composition were assessed via 16S rRNA gene tag sequencing and ultrahigh resolution Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), respectively. Environmental conditions were driven by a coastal water influx during the first half of the sampling period and the onset of a summer phytoplankton bloom toward the end of the sampling period. These phenomena led to a distinct grouping of bacterial communities and DOM composition which was particularly influenced by total dissolved nitrogen (TDN) concentration, temperature, and salinity, as revealed by distance-based linear regression analyses. Bacterioplankton-DOM interaction was demonstrated in strong correlations between specific bacterial taxa and particular DOM molecules, thus, suggesting potential specialization on particular substrates. We propose that a combination of high resolution techniques, as used in this study, may provide substantial information on substrate generalists and specialists and thus, contribute to prediction of BCC variation.

The drift of marine litter in the southern North Sea was simulated with the offline Lagrangian transport model PELETS-2D. Assuming different source regions, passive tracer particles were released every 28 h within a nine-year period. Based on pre-calculated hourly wind and ocean current data, drift simulations were carried out forward and backward in time with and without the assumption of extra wind forces influencing particle movement. Due to strong variability of currents, backward simulations did not allow for the identification of particular source regions influencing given monitoring sites. Neither accumulation regions at open sea could be identified by forward simulations. A seasonal signal, however, could be identified in the number of tracer particles that reached the coastal areas. Both particle drift velocity and variability of drift paths further increased when an extra wind drift was assumed., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Phytoplankton blooms characterize temperate ocean margin zones in spring. We investigated the bacterioplankton response to a diatom bloom in the North Sea and observed a dynamic succession of populations at genus-level resolution. Taxonomically distinct expressions of carbohydrate-active enzymes (transporters; in particular, TonB-dependent transporters) and phosphate acquisition strategies were found, indicating that distinct populations of Bacteroidetes, Gammaproteobacteria, and Alphaproteobacteria are specialized for successive decomposition of algal-derived organic matter. Our results suggest that algal substrate availability provided a series of ecological niches in which specialized populations could bloom. This reveals how planktonic species, despite their seemingly homogeneous habitat, can evade extinction by direct competition.

Lagrangian passive tracer transport simulations covering the 46-year period 1958-2003 were utilized to compare the exposures of different parts of the German North Sea coast to ship-related chronic oil pollution. Assuming the spatial distribution of oil releases to be proportional to estimated ship traffic density, detailed drift reconstructions allowed for the reconstruction of wind-induced inter-annual variations in coastal pollution. For the winter months, a statistical relationship between simulated advective transports and prevailing sea surface pressure fields was established via Canonical Correlation Analysis. Wind effects were found to be more important for the northern (Schleswig-Holstein) than for the southern (Lower Saxony) part of the German North Sea coast. For Schleswig-Holstein, simulations showed consensus with beached bird survey data from this region. Proper identification of weather-driven inter-annual and spatial variations in monitoring data helps to avert misjudgments with regard to trends in the general level of chronic oil pollution.

Chronic oil pollution by illegal oil dumping in the North Sea is difficult to quantify. Beached, oil-contaminated sea birds, however, may be used as an indirect indicator. Reconstructing the drift of oil slicks and sea bird corpses in the southern North Sea for the period 1992-2003 by means of a two-dimensional numerical transport model driven by re-analysed weather data, we show with an example of two common sea bird species that the variability observed within the number of corpses registered during beached bird surveys for the German coast primarily reflects the inter-annual variability of prevailing weather conditions. This should be taken into account when interpreting the data. We propose normalisation of beached bird survey data based on numerical drift simulations to improve the recognition of trends in the level of chronic oil pollution.