Digital Innovation to Accelerate the iMplementation Of Nature-based solutions for Decentralized wastewater treatment

Nature-based solutions (NBS) for wastewater treatment (WWT) are important technologies for climate change mitigation and water reuse practice. While stand-alone numerical models exist for such NBS none are capable for the integration planning and upscaling in urban water management tools. Therefore, model development and integration needs to be addressed by a holistic approach reaching from detailed process-based models to provide in-depth understanding, machine-learning approaches based on available data as support and the upscaling by surrogate models for planning tools and stakeholder discussions. Three objectives are defined (i) UPGRADE existing models by expanding the number of pollutants that can be modelled; (ii) CONNECT process-based and data-driven models to achieve better prediction; (iii) UPSCALE the developed approaches by creating surrogate models that can be either applied as stand-alone models for design purposes or included in decision support tools for urban water management. With the inclusion of the scientific community from the beginning, this model development has a high impact and will be widely used. Following the three objectives a clear path from process understanding and research-oriented model to the inclusion of experimental data towards the simplification by surrogate models is drawn. The emergence of hybrid modeling as a new tool in NBS for WWT is a long-term expected outcome that will allow scientists to consider phenomena not easy to conceptualize for their predictions. This also will close the gap between researchers working on numerical models, engineers and researchers carrying out experimental investigations The proposed data exchange format and willingness of researches to share their data this project provides a high impact on the field of research.