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.
Original languageEnglish
Publication statusPublished - 2018
EventEGU General Assembly 2018 - Vienna, Austria
Duration: 8 Apr 201813 Apr 2018


ConferenceEGU General Assembly 2018
Abbreviated titleEGU 2018
Internet address

ID: 45688094