Abstract
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 multidimensional 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 (insitu and satellite measured as well as numerically modelled) from the Copernicus Marine Environment Monitoring Service, the Dutch DirectorateGeneral for Public Works and Water Management (Rijkswaterstaat), the Royal Netherlands Meteorological Institute, and the EuroCORDEX 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 chlorophylla 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 goodnessoffit, 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 chlorophylla data at hand. These techniques are applied in a structured way to include spatial and temporal correlation, as well as functional features in the multidimensional data. The applied methods include Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares (PLS) Regression, multiway models (PARAFAC, Tucker and NPLS), Dynamic Factor Analysis (DFA), and Functional PCA. Room for dimension reduction in the atmospheric data was identified, underlying temporal patterns in the chlorophylla signal at different locations were revealed, structural similarities (characterized by a mean function and functional variation) in the EuroCORDEX 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 EuroCORDEX 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 multilayered (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 chlorophylla 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 chlorophylla response once again, this time covering the entire 21st century. In order to translate the climate signal into chlorophylla response, we make use of a Bayesian structural time series model. This model follows a piecewise linear trend and continues to repeat its multiseasonal 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 chlorophylla signal by fusing insitu and satellite measurements. This fused signal helps us to take advantage of the more frequent satellite measurements while correcting them with the more accurate insitu measurements that are also available for a longer historical period. The Bayesian structural time series model is then trained on the fused chlorophylla 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 longterm chlorophylla projections using a nonparametric shape constrained method (logconcave regression). The final result is therefore the estimation of climate change induced uncertainty in the coastal phytoplankton spring bloom dynamics.
The research is built on open source data (insitu and satellite measured as well as numerically modelled) from the Copernicus Marine Environment Monitoring Service, the Dutch DirectorateGeneral for Public Works and Water Management (Rijkswaterstaat), the Royal Netherlands Meteorological Institute, and the EuroCORDEX 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 chlorophylla 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 goodnessoffit, 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 chlorophylla data at hand. These techniques are applied in a structured way to include spatial and temporal correlation, as well as functional features in the multidimensional data. The applied methods include Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares (PLS) Regression, multiway models (PARAFAC, Tucker and NPLS), Dynamic Factor Analysis (DFA), and Functional PCA. Room for dimension reduction in the atmospheric data was identified, underlying temporal patterns in the chlorophylla signal at different locations were revealed, structural similarities (characterized by a mean function and functional variation) in the EuroCORDEX 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 EuroCORDEX 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 multilayered (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 chlorophylla 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 chlorophylla response once again, this time covering the entire 21st century. In order to translate the climate signal into chlorophylla response, we make use of a Bayesian structural time series model. This model follows a piecewise linear trend and continues to repeat its multiseasonal 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 chlorophylla signal by fusing insitu and satellite measurements. This fused signal helps us to take advantage of the more frequent satellite measurements while correcting them with the more accurate insitu measurements that are also available for a longer historical period. The Bayesian structural time series model is then trained on the fused chlorophylla 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 longterm chlorophylla projections using a nonparametric shape constrained method (logconcave regression). The final result is therefore the estimation of climate change induced uncertainty in the coastal phytoplankton spring bloom dynamics.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  30 Jun 2023 
Print ISBNs  9789463667005 
DOIs  
Publication status  Published  2023 
Keywords
 climate change
 uncertainty quantification
 coastal phytoplankton phenology
 Bayesian models
 data fusion
 multivariate analysis