## 4 Discussion
Its main advantage is its ability to combine key factors that generate runoff--land use, soil type, and antecedent moisture conditions--into a single, empirically derived parameter. These inputs are often available in standardised forms, such as GIS-based land cover maps and urban planning documentation, making the method suitable for rapid assessments, preliminary designs, and decision-making in settings with limited data [87]. In line with [PERSON] and [PERSON]'s principle [88], which defines an optimal model as one that yields realistic results with minimal complexity and few parameters, the CN method offers a pragmatic solution. Although it is currently widely used for urban hydrology and stormwater management, it was originally developed for use in agricultural catchments [89]. This origin highlights its suitability for broader catchment-scale hydrological analysis, where diverse land cover, soil properties, and spatial variability interact. However, the method's broader analytical potential has increasingly been overshadowed by its dominant application in localised or urbanised contexts. This study aimed to reposition the CN method, presenting it not merely as a simplified urban runoff estimator, but as a practical, scalable, and adaptable tool for evaluating the hydrological response across entire catchments under current and future climate scenarios. Having explained the reason for choosing the CN number model, we finally incorporated SUDS scenarios into the model by adjusting the catchment's average curve number, as per [PERSON] et al. [90]. This simplifies model operation and scenario generation. As previously discussed, this approach does not allow for the local analysis of factors such as peak flow generation or attenuation. However, it provides a broader perspective on the impact on aggregated catchment processes. The discussion, then, moves on to the need for greater consideration of scale, focusing on the catchment as a whole to ensure a more holistic approach to managing hydrological resources. Another important example comes from the study by [PERSON] et al. [91], which analysed the effectiveness of SUDSs in an Italian urban context. The study showed that retrofitting SUDSs across approximately 8.3% of the study area significantly reduced total runoff and peak flows. Additionally, the proportion of nodes in the urban network in critical condition decreased from 52% to 24%. Finally, the paper by [PERSON] et al. [92] emphasised the importance of extensive SUDS implementation to reduce peak flows. Their study found that the scenario with the highest level of SUDS implementation (using a combination of infiltration and storage) produced the lowest peak flows, thus helping to limit the impact of heavy rainfall. Finally, building on the foundations of the previous study, we explored how SUDSs can be represented using the CN approach. To date, only one study appears to come close to our proposed approach. A recent study by [PERSON] et al. [93] presents a climate-resilient urban drainage model based on GIS and the SCS-CN method, and demonstrates its efficiency in ten summary points. While our approach is highly comparable to this existing study, a key limitation is the lack of analysis of future land use change (LULC), which could affect the long-term effectiveness of our model. Despite this, no other study appears to have proposed a similar methodology to ours for SUDS applications, highlighting the innovative nature of our model. We strongly recommend that future SUDS design phases be carried out using high-resolution, site-specific hydrological and hydraulic models. These models should consider the interactions between urban surface and subsurface drainage systems and natural watercourses to ensure a more complete and accurate assessment. Finally, we suggest that representing SUDS treatment trains--integrating multiple types of SUDSs to improve overall system performance--is a highly effective strategy [94].