Modelling Nature-based Solutions for Improving Surface Water Quality: A Comprehensive Review and Synthesis

This paper presents a structured literature review of modelling studies assessing the effectiveness of Nature-based Solutions (NbS) for improving surface water quality. Using systematic searches in Scopus and Web of Science, 133 articles were analysed to identify global patterns in the drivers and consequences of water quality degradation, climate change impacts, and stakeholder involvement. Of these, 125 case study applications were assessed across five key aspects: (i) NbS terminology, (ii) modelling approaches used, (iii) distribution across modelled spatial scales, (iv) reported effectiveness for water quality and ecosystem services (ES), and (v) validation practices and experimental design of NbS effectiveness assessments. The review shows that agriculture is the primary driver of water quality degradation, primarily linked to eutrophication and biodiversity decline. Among modelling tools, the Soil and Water Assessment Tool (SWAT) is most widely used, applied in 40.8% of all case studies. Studies are predominantly conducted at local to basin scales, while only 1.7% are performed at the regional scale. Wetland restoration and construction emerge as key multifunctional NbS, occurring in 23.2% of studies and supported by a strong evidence base. Their effectiveness is generally positive but context-dependent, as one in four studies report variable outcomes in improving water quality. Although many studies incorporated data from stakeholders, their involvement was largely absent, with 59.4% of studies including no engagement. Methodological limitations were identified. Many modelling approaches oversimplify wetland and biogeochemical processes, overlook hydrological connectivity, and rely on limited or default parameter values, leading to high uncertainty and reduced transferability. Future research should enhance process representation, integrate uncertainty and climate change assessments, and link hydrological modelling with ES evaluation. Strengthening long-term monitoring and expanding stakeholder participation, particularly through community engagement and citizen science, can improve data availability and enhance policy relevance.