## 4 Discussion
The catchment area must be considered an interconnected, dynamic network of interacting components. Localised or isolated interventions may fail to address hydrological imbalances across the entire system. In this context, SUDSs should be evaluated not only for their localised effects but also for their ability to restore and maintain natural hydrological equilibrium at catchment level. Previous studies have highlighted the need to understand the impact of specific aspects on SUDS performance, such as spatial distribution and timescales [26]. Recent studies have incorporated individual future scenarios to simulate SUDS performance (see, for example, [PERSON] [66]). However, these studies have only been applied to specific case studies, failing to propose a standardised methodology to address these gaps. Motivated by these gaps, this paper presents a structured methodology for analysing the potential influence of SUDS implementation at catchment scale in an evolving climate. Pooling data and analysing the range between maximum and minimum values provides a more meaningful understanding of SUDS effectiveness. In this study, we adopted the curve number (CN) method because we believe that its simple parameterisation will facilitate the practical application of the proposed methodology, particularly in urban contexts. Note that this method was developed in 1954 by the US Department of Agriculture's Soil Conservation Service (SCS) [42]. Historically, many authors have highlighted limitations, such as its design for single-event simulations and small to medium-sized catchments. Additionally, it does not account for complex hydrological processes such as subsurface flow, intra-storm rainfall variability, or dynamically changing soil moisture. Furthermore, assumptions such as fixed initial abstraction and sensitivity to land use and antecedent moisture conditions can introduce uncertainties, particularly in diverse, densely populated urban areas [67; 68; 69]. A wide range of rainfall-runoff models have been developed worldwide. Each model is designed to meet specific hydrological objectives, spatial resolutions, and constraints on data availability. These models can be broadly grouped into four categories: empirical, conceptual, physically based, and data-driven approaches [70; 71; 72]. * Empirical models, such as the rational method, the Horton infiltration model [73], the curve number method [74], the Green-Ampt method [75], and ACRU [76], rely on simplified relationships derived from observed data. While these models are easy to implement and interpret, they may lack robustness when transferred to catchments with varying physical characteristics. * Physically based models, including SHE [77], SWAT [78], and IHDM [79], attempt to simulate hydrological processes through governing equations of mass and momentum conservation. These models allow for fine-resolution process representation, but demand extensive, high-quality input data (e.g., soil hydraulic properties and detailed DEMs) and significant computational resources, which often limits their applicability in small urban catchments or regions with limited data [80]. * Conceptual models, such as the HBV model [81], the GR4J model [82], and the Stanford watershed model [83], conceptualise catchments using simplified storage and flow components. They balance physical realism and operational simplicity, but generally require careful calibration and may be sensitive to input uncertainty. * Data-driven models, particularly those using machine learning (ML) or deep learning (DL) techniques, are being used more and more in hydrology [84]. These models can learn complex nonlinear relationships from data without requiring explicit physical equations. However, their reliance on long-term historical data and limited interpretability restrict their use in regulatory and planning contexts [85]. Despite its conceptual simplicity, the curve number (CN) method remains one of the most effective and widely adopted techniques for estimating surface runoff [86].