Christensen, J.P.A., Shetty, N., Andersen, N.R., Damgaard, C. & Timmermann, K. 2021. Modelling light conditions in Danish coastal waters using a Bayesian modelling approach. Model documentation. Aarhus University, DCE – Danish Centre for Environment and Energy, 48 pp. Scientific Report No. 422 http://dce2.au.dk/pub/SR422.pdf
Bayesian hierarchical models were developed to predict light limitation depth for eelgrass in Danish waterbodies as a function of land-based nitrogen and phosphorus loadings, physicochemical and climatic predictors. The objective of the model development was to support the Danish implementation of the Water Framework Directive (WFD) by providing tools applicable for estimating potential depth distribution of eelgrass and subsequent to calculate maximum allowable nutrient input (MAI) to the Danish coastal waters covered by the WFD.
The applied light limitation depth was calculated from light profiles as the maximum water depth, where at least 16% of surface light was available for benthic primary production from March to October. Bayesian single-station models for the light indicator were developed for 44 Danish water quality monitoring stations representing 41 waterbodies.
For the resulting set of Bayesian models we found that nutrient input was selected as predictor-variable in 23 of the models. As expected, we found a negative slope coefficient between nutrient inputs and depth of light limitation depth in all but 4 stations.
Model evaluation plots and performance statistics revealed that most of the models could capture the levels and year-to-year variation in the depth of light limitation depth reasonably well, indicating that the models can produce reliable predictions of light conditions in Danish coastal waters.