Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective.

Green Roofs (GRs) have proven to be a sustainable solution to stormwater management in urban areas. To boost their adoption at the large scale, there is a need to develop numerical models, which are accurate, computationally cheap, and as complex as needed to reproduce the hydrological behavior of GRs. Alternative conceptual and mechanistic approaches have been proposed and tested, however the most appropriate level of model complexity for GRs' analysis is still unknown. To cover this scientific gap, we provide a Bayesian comprehensive perspective of GR hydrological modeling, which includes a statistically rigorous Bayesian comparison of one conceptual and multiple Richards-based mechanistic GR models, and a probabilistic assessment of the information content of different observations. The analysis of the marginal likelihoods reveals that the conceptual and the unimodal van Genuchten - Mualem models are the most appropriate parameterizations, and that further layers of model complexity are not fully supported by the measurements. In addition to that, the estimated Kullback-Leibler divergences suggest that the measured volumetric water content outperforms the measured subsurface outflow and tracer concentrations in terms of informativeness, leading to the lowest model predictive uncertainty for the simulation of water fluxes. The findings of this study represent a first step to clarify the role of model complexity in GRs' analysis, and open new perspective on GRs' model-based experimental design.