Climate change causes cities to deal with increased temperatures and more frequent weather extremes. Heat waves will occur more often, becoming a more prevalent issue in especially urban areas. The quantification of heat stress is a first step to define mitigation measures. For that purpose, a standardised method to assess the spatial influence of surfaces on the Physiological Equivalent Temperature (PET) was developed. This study aims to reshape this model into a statistical dependence model which is more flexible regarding missing data. To this end, we used a Non-Parametric Bayesian Network (NBPN). We created a model driven by both data and expert knowledge, that is capable of dealing with input data layers with a grid resolution up to 20 m. Results show that training the model with only 20 sample points did not affect the performance considerably, compared to using 2,000 data points. Inclusion of a layer with sky view factor mainly improves the estimation of observations in the tails of the distribution. The model predicts the PET with a Mean Absolute Error (MAE) of 1 to 2 ºC, dealing adequately with missing data layers. With this limited amount of necessary input, the NPBN in our study helps in standardising the assessment of heat stress outside the borders of the Netherlands. Also, our model offers a framework to make a first assessment regarding the effect of NBSs on heat stress.

Implementation of nature-based solutions (NBS) in flood defenses is hindered by a lack of probabilistic tools and design guidelines that can be used to assess spatial and temporal variability in these biophysical systems. It is well established that nature-based elements, such as vegetation, attenuate waves, capture sediment, strengthen the subsoil and invoke numerous ecological benefits. This thesis proposes a conceptual framework to design and assess hybrid flood defense (HFD) systems within the context of current Dutch design guidelines: `wettelijk beoordelings instrumentarium' (WBI). The framework builds a dynamic probabilistic tool (DPT) to assess the effect of temporal variability of NBS elements on wave loading. The framework was applied in a case study at Hellegatpolder to investigate temporal effects of nature-based elements on nearshore wave heights during storm conditions. Data was collected specifically for the idealized HFD system at Hellegatpolder. Vegetation and nearshore wave height data was unavailable and therefore, an autocorrelation function was developed to sample temporal vegetation data. A numerical XBeach model was constructed to model nearshore wave heights for a bare and vegetated foreshore, resulting in a uniform database. Development of a static and dynamic Bayesian network allowed dynamic probabilistic modelling of nearshore wave heights. Application-specific model settings combine normative hydraulic storm data with the database and probabilistic models. The developed DPT was applied to model nearshore wave heights for a bare and vegetated transect for each vegetative season.Numerical modelling using XBeach was applied to model 7 years of data with a temporal resolution of 30 minutes for both bare and vegetated foreshore scenarios. The numerical results conclude that vegetation at Hellegatpolder attenuates waves with an average of 54%, where wave attenuation was 1.7 times greater in July compared to December. The effect of bathymetry morphology on nearshore wave height was found insignificant for short time scales.A static Bayesian network (SBN) was built to model nearshore wave heights in a fixed point in time. The validated SBN was able to model nearshore wave heights with 90% accuracy. A dynamic Bayesian network (DBN), was created to model offshore hydraulic parameters time series. DBN achieved an accuracy of >85% for short time scales (<25 hours). Utilizing the DBN for long term modelling resulted in progression towards the mean value of the marginal distributions. Statistical validation of both models rejected the representation of the dependence structure using only a Gaussian copula.Application settings were defined in MATLAB to manipulate the database, probabilistic models and normative hydraulic storm conditions. The configured DPT was run to model nearshore wave heights for a bare and vegetated foreshore specifically at Hellegatpolder. During storm conditions average wave attenuation due to vegetation was 45%. Average wave attenuation results were thus lower than those available in the numerical dataset (54%). Moreover, wave attenuation was 3 times greater in summer compared to winter months. Furthermore, the DPT resulted in dynamic wave loading (i.e. varying through time) compared to the static WBI loading conditions. Resulting DPT wave heights were 82% lower during winter months than the normative loading defined by the WBI.These results show the effect of variability on wave attenuation. The percentages and factors are expected to differ for other locations. Nevertheless, the obtained results clearly illustrate the significance of temporal modelling of HFD systems. The method presents a novel conceptual framework to include the effects of NBS elements in the design and assessment of flood defenses. The conceptual framework, numerical and probabilistic models can be applied for other HFD systems, enabling engineers to assess flood defenses more realistically - a critical step in the implementation of NBS in design guidelines.