This thesis presents a doctoral research where statistical concepts and techniques are applied to problems at the interface of marine and atmospheric processes. The research was conducted at the Statistics group of the Delft Institute of Applied Mathematics (TU Delft) and the Marine and Coastal unit of Deltares. The main objective of the work is to provide statistical tools to understand multi-dimensional climate and marine environmental datasets, as well as to offer ways for quantifying the uncertainties in the coastal ecological response that are driven by the climatic variation. Statistical quantification of uncertainties in data, models and predictions is therefore the central topic of the thesis. The research is built on open source data (in-situ and satellite measured as well as numerically modelled) from the Copernicus Marine Environment Monitoring Service, the Dutch Directorate-General for Public Works and Water Management (Rijkswaterstaat), the Royal Netherlands Meteorological Institute, and the Euro-CORDEX regional climate modelling experiment. It also uses the open source numerical modelling software Delft3D from Deltares. All other statistical models and algorithms developed during the research are published and available open source. The thesis starts by demonstrating the value of probabilistic predictions and uncertainty quantification for coastal ecosystems. That is done by constructing an ensemble modelling framework where certain chosen numerical model inputs and model process parameters are perturbed, to which the simulated coastal chlorophyll-a concentration is sensitive. The model perturbation was implemented using Latin Hypercube Sampling with Dependence (LHSD), and more than 150 ensemble members were produced using the Delft3D model. This ensemble prediction system is then compared to the deterministic model setup. A range of verification metrics that describe the goodness-of-fit, accuracy, reliability, and discrimination properties of both modelling experiments were computed. Apart from the verification metrics, the value of probabilistic predictions was also showcased by evaluating the benefit of having temporal and spatial estimates of uncertainty by producing ensemble band, predictive uncertainty intervals and standard deviations maps. In Chapter 3 of the thesis, we work towards the quantification of climate change induced uncertainties in coastal phytoplankton response. The first necessary step is a comprehensive data exploration and dimension reduction, which also provides a statistical underpinning of atmospheric variable selection for the climate impact studies conducted later in the thesis. Here a range of existing dimension reduction techniques are described and applied to seven atmospheric variables (air temperature, solar radiation, eastward wind, northward wind, air pressure, relative humidity, and total cloud cover) and the chlorophyll-a data at hand. These techniques are applied in a structured way to include spatial and temporal correlation, as well as functional features in the multi-dimensional data. The applied methods include Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares (PLS) Regression, multi-way models (PARAFAC, Tucker and N-PLS), Dynamic Factor Analysis (DFA), and Functional PCA. Room for dimension reduction in the atmospheric data was identified, underlying temporal patterns in the chlorophyll-a signal at different locations were revealed, structural similarities (characterized by a mean function and functional variation) in the Euro-CORDEX climate projections were found, and the most influential atmospheric variables (solar radiation and air temperature) were chosen. Building on these findings, we propose a way to quantify uncertainties in the climate scenarios that are used for the climate impact studies. The basis of this research step is the development of a stochastic climate generator, which is first tested on the solar radiation variable. This climate generator takes the existing Euro-CORDEX scenarios (a combination of Representative Concentration Pathways and Generic Circulation Model forcings) and enriches them by generating numerous new synthetic scenarios around them. These new generated scenarios are representative of the original ones due to the way the stochastic climate generator is constructed. The basis of the climate generator is a Bayesian multi-layered (hierarchical) model. In this model there are model parameters representing variation in the long term trend, seasonal amplitude, time shift, and additive residual. The generator estimates the distribution of each model parameter with Bayesian inference, and using data from all scenarios. Then, when sampling from the parameter distributions, numerous climate trajectories can be constructed. The climate generator is successfully tested on the solar radiation variable and the generated synthetic radiation projections are used in a demonstration study where uncertainties are further propagated to chlorophyll-a concentrations using the Delft3D numerical model. In the final research step of the thesis, this Bayesian stochastic generator is extended to air temperature. This way we have numerous (>100) radiation and temperature projections available to propagate climate induced uncertainties to coastal chlorophyll-a response once again, this time covering the entire 21st century. In order to translate the climate signal into chlorophyll-a response, we make use of a Bayesian structural time series model. This model follows a piecewise linear trend and continues to repeat its multi-seasonal behavior, learnt from the past data, and most importantly also includes linear effects of the two climate variables. For the training of this time series model, we construct a historical chlorophyll-a signal by fusing in-situ and satellite measurements. This fused signal helps us to take advantage of the more frequent satellite measurements while correcting them with the more accurate in-situ measurements that are also available for a longer historical period. The Bayesian structural time series model is then trained on the fused chlorophyll-a signal and used for long term projection, taking the generated radiation and temperature scenarios as regressors. Since our main interest is the phytoplankton spring bloom dynamics, as a last step we extract yearly spring bloom cardinal dates (beginning, peak, end) from the long-term chlorophyll-a projections using a non-parametric shape constrained method (log-concave regression). The final result is therefore the estimation of climate change induced uncertainty in the coastal phytoplankton spring bloom dynamics.@en

The EuroGOOS Coastal working group examines the entire coastal value chain from coastal observations to services for coastal users. The main objective of the working group is to review the status quo, identify gaps and future steps needed to secure and improve the sustainability of the European coastal service provision. Within this framework, our white paper defines a EuroGOOS roadmap for sustained “community coastal downstream service” provision, provided by a broad EuroGOOS community with focus on the national and local scale services. After defining the coastal services in this context, we describe the main components of coastal service provision and explore community benefits and requirements through sectoral examples (aquaculture, coastal tourism, renewable energy, port, cross-sectoral) together with the main challenges and barriers to user uptake. Technology integration challenges are outlined with respect to multiparameter observations, multi-platform observations, the land-coast-ocean continuum, and multidisciplinary data integration. Finally, the technological, financial, and institutional sustainability of coastal observing and coastal service provision are discussed. The paper gives special attention to the delineation of upstream and downstream services, public-private partnerships and the important role of Copernicus in better covering the coastal zone. Therefore, our white paper is a policy and practice review providing a comprehensive overview, in-depth discussion and actionable recommendations (according to key short-term or medium-term priorities) on the envisaged elements of a roadmap for sustained coastal service provision. EuroGOOS, as an entity that unites European national operational oceanography centres, research institutes and scientists across various domains within the broader field of operational oceanography, offers to be the engine and intermediary for the knowledge transfer and communication of experiences, best practices and information, not only amongst its members, but also amongst the different (research) infrastructures, institutes and agencies that have interests in coastal oceanography in Europe.@en

Coastal climate impact studies make increasing use of multi-source and multi-dimensional atmospheric and environmental datasets to investigate relationships between climate signals and the ecological response. The large quantity of numerically simulated data may, however, include redundancy, multi-colinearity and excess information not relevant to the studied processes. In such cases techniques for feature extraction and identification of latent processes prove useful. Using dimensionality reduction techniques this research provides a statistical underpinning of variable selection to study the impacts of atmospheric processes on coastal chlorophyll-a concentrations, taking the Dutch Wadden Sea as case study. Dimension reduction techniques are applied to environmental data simulated by the Delft3D coastal water quality model, the HIRLAM numerical weather prediction model and the Euro-CORDEX climate modelling experiment. The dimension reduction techniques were selected for their ability to incorporate (1) spatial correlation via multi-way methods (2), temporal correlation through Dynamic Factor Analysis, and (3) functional variability using Functional Data Analysis. The data reduction potential and explanatory value of these methods are showcased and important atmospheric variables affecting the chlorophyll-a concentration are identified. Our results indicate room for dimensionality reduction in the atmospheric variables (2 principle components can explain the majority of variance instead of 7 variables), in the chlorophyll-a time series at different locations (two characteristic patterns can describe the 10 locations), and in the climate projection scenarios of solar radiation and air temperature variables (a single principle component function explains 77% of the variation for solar radiation and 57% of the variation for air temperature). It was also found that solar radiation followed by air temperature are the most important atmospheric variables related to coastal chlorophyll-a concentration, noting that regional differences exist, for instance the importance of air temperature is greater in the Eastern Dutch Wadden Sea at Dantziggat than in the Western Dutch Wadden Sea at Marsdiep Noord. Common trends and different regional system characteristics have also been identified through dynamic factor analysis between the deeper channels and the shallower intertidal zones, where the onset of spring blooms occurs earlier. The functional analysis of climate data showed clusters of atmospheric variables with similar functional features. Moreover, functional components of Euro-CORDEX climate scenarios have been identified for radiation and temperature variables, which provide information on the dominant mode (pattern) of variation and its uncertainties. The findings suggest that radiation and temperature projections of different Euro-CORDEX scenarios share similar characteristics and mainly differ in their amplitudes and seasonal patterns, offering opportunities to construct statistical models that do not assume independence between climate scenarios but instead borrow information (“borrow strength”) from the larger pool of climate scenarios. The presented results were used in follow up studies to construct a Bayesian stochastic generator to complement existing Euro-CORDEX climate change scenarios and to quantify climate change induced trends and uncertainties in phytoplankton spring bloom dynamics in the Dutch Wadden Sea.@en

Spring phytoplankton blooms in the southern North Sea substantially contribute to annual primary production and largely influence food web dynamics. Studying long-term changes in spring bloom dynamics is therefore crucial for understanding future climate responses and predicting implications on the marine ecosystem. This paper aims to study long term changes in spring bloom dynamics in the Dutch coastal waters, using historical coastal in-situ data and satellite observations as well as projected future solar radiation and air temperature trajectories from regional climate models as driving forces covering the twenty-first century. The main objective is to derive long-term trends and quantify climate induced uncertainties in future coastal phytoplankton phenology. The three main methodological steps to achieve this goal include (1) developing a data fusion model to interlace coastal in-situ measurements and satellite chlorophyll-a observations into a single multi-decadal signal; (2) applying a Bayesian structural time series model to produce long-term projections of chlorophyll-a concentrations over the twenty-first century; and (3) developing a feature extraction method to derive the cardinal dates (beginning, peak, end) of the spring bloom to track the historical and the projected changes in its dynamics. The data fusion model produced an enhanced chlorophyll-a time series with improved accuracy by correcting the satellite observed signal with in-situ observations. The applied structural time series model proved to have sufficient goodness-of-fit to produce long term chlorophyll-a projections, and the feature extraction method was found to be robust in detecting cardinal dates when spring blooms were present. The main research findings indicate that at the study site location the spring bloom characteristics are impacted by the changing climatic conditions. Our results suggest that toward the end of the twenty-first century spring blooms will steadily shift earlier, resulting in longer spring bloom duration. Spring bloom magnitudes are also projected to increase with a 0.4% year⁻¹ trend. Based on the ensemble simulation the largest uncertainty lies in the timing of the spring bloom beginning and-end timing, while the peak timing has less variation. Further studies would be required to link the findings of this paper and ecosystem behavior to better understand possible consequences to the ecosystem.

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During the past decades, the aquaculture industry has developed rapidly, due to drop in wild fish catch. Water quality variables play major role in aquaculture operations, specifically seawater temperature has major impact on the metabolism of the fish species and therefore on the growth rate too. Since the fish farming business relies on the growth rate of the species to plan and operate the farm, seawater temperature becomes crucial information. With the availability of hydrodynamic modeling tools and global ocean information source such as Copernicus Marine Environment Monitoring Service (CMEMS), seawater temperature can be simulated for practically any coast with dynamic downscaling approach. However, the simulated data needs to be assessed for uncertainties for enabling informed decision making using such model predictions. In this paper, a coastal 3D hydrodynamic model aiming at simulating seawater temperature is developed for the southern Aegean Sea, Greece using the Delft3D Flexible Mesh modeling tool. Seawater temperature is impacted by atmospheric forces; therefore, uncertainties are assessed for seawater temperature using ensemble atmospheric forcing functions of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Spatial analysis of the uncertainty indicates regions of different seawater temperature behavior within the model domain. Seasonal behavior of the vertical temperature gradient suggests that farms need to adapt different operational strategies in different seasons to make best use of the seawater temperature. The application of CMEMS data along with ECMWF ERA5 ensemble atmospheric forcing members proves to be beneficial in analyzing the uncertainties both in spatial and vertical gradient of seawater temperature.

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Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.

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Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.@en

Traditionally, quantifying climate change induced uncertainty in ecological indicators requires stochastic simulation with a chain of physically-based models describing various processes such as hydrodynamics, waves, sediment transport and ecology. Such Monte Carlo based simulation on the entire model chain, especially with large sample size, is however computationally expensive and often unfeasible. In this paper, it was investigated how regression models can potentially replace physically-based models and predict chlorophyll-a concentration directly from meteorological variables. Since several correlated meteorological variables are used to estimate one ecological response variable, and thus a multi-collinearity problem is present, Partial Least Squares (PLS) regression is considered to be a favourable supervised technique. On the other hand, the climate change projection dataset at hand is multidimensional. This is due to the fact that it contains several variables which are not only varying over time but also over space (spatially distributed). Consequently, a multiway regression model should be applied which can account for the spatial dimension. The multiway PLS regression (N-PLS) algorithm is a promising candidate for this purpose. The N-PLS is an extension of the ordinary two-way PLS regression algorithm to multi-way data, where essentially the bilinear model of predictors is replaced with a multilinear model. In order to test its efficiency, the N-PLS algorithm was compared with other unsupervised and supervised, two-way and multi-way techniques using both synthetic and real datasets. The latter dataset consists of meteorological variables from KNMI (Royal Netherlands Meteorological Institute) and chlorophyll-a concentrations obtained from the Delft3D WAQ ecological model. Firstly, it was confirmed that supervised techniques should be favoured over unsupervised ones, due to their ability to include correlation to the response variable which reduces prediction error. Moreover, the results suggest that by applying multi-way methods improvements can be achieved in the prediction accuracy. The magnitude of these improvements is, however, case dependent. In conclusion, it was found that N-PLS, as a supervised multi-way method, is a promising regression model for the above mentioned purpose. Finally, due to the fast simulation time of the algorithm, it could be suitable for stochastic simulation with large sample size for the assessment of climate change induced uncertainty in coastal ecosystem indicators. Future work will focus on applying the fitted N-PLS model to EURO-CORDEX climate change projections and quantify related uncertainties in the Wadden Sea ecosystem. @en

Phytoplankton blooms in coastal ecosystems such as the Wadden Sea may cause mortality of mussels and other benthic organisms. Furthermore, the algal primary production is the base of the food web and therefore it greatly influences fisheries and aquacultures. Consequently, accurate phytoplankton concentration prediction offers ecosystem and economic benefits. Numerical ecosystem models are powerful tools to compute water quality variables including the phytoplankton concentration. Nevertheless, their accuracy ultimately depends on the uncertainty stemming from the external forcings which further propagates and complicates by the non-linear ecological processes incorporated in the ecological model. The Wadden Sea is a shallow, dynamically varying ecosystem with high turbidity and therefore the uncertainty in the Suspended Particulate Matter (SPM) concentration field greatly influences the prediction of water quality variables. Considering the high level of uncertainty in the modelling process, it is advised that an uncertainty estimate should be provided together with a single-valued deterministic model output. Through the use of an ensemble prediction system in the Dutch coastal waters the uncertainty in the modelled chlorophyll-a concentration has been estimated. The input ensemble is generated from perturbed model process parameters and external forcings through Latin hypercube sampling with dependence (LHSD). The simulation is carried out using the Delft3D Generic Ecological Model (GEM) with the advance algal speciation module-BLOOM which is sufficiently well validated for primary production simulation in the southern North Sea. The output ensemble is post-processed to obtain the uncertainty estimate and the results are validated against in-situ measurements and Remote Sensing (RS) data. The spatial uncertainty of chlorophyll-a concentration was derived using the produced ensemble spread maps. @en