Practice makes the model: A critical review of stormwater green infrastructure modelling practice. (1st June 2023) - Pons, Vincent; Abdalla, Elhadi Mohsen Hassan; Tscheikner-Gratl, Franz; Alfredsen, Knut; Sivertsen, Edvard; Bertrand-Krajewski, Jean-Luc; Muthanna, Tone Merete
Highlights: The modelling practice of 270 green infrastructure modelling study were reviewed. Limitation of methods and datasets need more attention. Uncertainty assessments are properly done in less than 20% of the studies. The framework STAMP was developed to align objectives and methods. Modelling tools should be improved to enhance the practices. Abstract: Green infrastructures (GIs) have in recent decades emerged as sustainable technologies for urban stormwater management, and numerous studies have been conducted to develop and improve hydrological models for GIs. This review aims to assess current practice in GI hydrological modelling, encompassing the selection of model structure, equations, model parametrization and testing, uncertainty analysis, sensitivity analysis, the selection of objective functions for model calibration, and the interpretation of modelling results. During a quantitative and qualitative analysis, based on a paper analysis methodology applied across a sample of 270 published studies, we found that the authors of GI modelling studies generally fail to justify their modelling choices and their alignments between modelling objectives and methods. Some practices, such as uncertainty analysis, were also found to be limited, despite their necessity being widely acknowledged by the scientific community and their application in other fields. In order to improve current GI modelling practice, the authors suggest the following: i) a framework, called STAMP, designed to promote the standardisation of the documentation of GI modelling studies, and ii) improvements in modelling tools for facilitating good practices, iii) the sharing of data for better model testing, iv) the evaluation of the suitability of hydrological equations for GI application, v) the publication of clear statements regarding model limitations and negative results. Graphical abstract: Image, graphical abstract