## 2 Materials and Methods
### Data Sourcing and Processing  
Through a systematic quantification of supply and demand indicators, this study identifies mismatches in GSI allocation in the GBA, enabling more targeted resource distribution. Meteorological and hydrological datasets, including rainfall and streamflow records, were sourced from governmental archives and local reports. All spatial data were processed and geo-referenced using ArcGIS, with the regional hydrological network comprising approximately 2596 watercourses (see Figure 1). The assessment of GSI in this study was anchored in a comprehensive set of supply and demand indicators. Data was sourced from established governmental and scientific repositories to ensure validity. Supply-side variables included parks, vegetation cover, impervious surface rate, road networks, and waterway density (Table 1). To ensure data compatibility across formats, all spatial indicators were aggregated at the district level. Raster datasets (e.g., vegetation cover, impervious surface ratio) were averaged over each administrative unit. Polygon features (e.g., road and waterway networks) were converted to length density (km/km2). Point-based features (e.g., parks and medical facilities) were expressed as counts per km2. This standardized spatial resolution enabled integration into a unified indicator framework for supply and demand assessment. Due to the limitations in polygonal land parcel data, parks were represented by point features from OpenStreetMap and counted per district. While this simplifies spatial representation, it is acknowledged that using area-based metrics would provide more accurate GSI supply estimations. These factors capture the biophysical capacity of each district to manage stormwater through retention, infiltration, and conveyance mechanisms. Demand-side variables covered urban flood risk, GDP, infrastructural facilities (e.g., transport and healthcare, as well as science and education institutions), historical heritage, and vulnerable population demographics (e.g., female, children, elderly, and low-education groups), as shown in Table 2.  
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Urban green spaces, such as parks, can function as buffers to absorb excess runoff and reduce peak discharge [23], provided they meet essential hydrological and design specifications. These include sufficient pervious surface area, soil infiltration capacity, vegetation cover, slope control, and surface detention features. The effectiveness of such spaces in stormwater attenuation is well captured in hydrodynamic models like the Storm  
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Water Management Model (SWMM), which simulate interactions between precipitation, catchment characteristics, and drainage response. Vegetation cover enhances natural infiltration [21], while high impervious surfaces ratios are consistently associated with reduced absorptive capacity and greater flood risk [24]. Likewise, the density and configuration of roads and drainage networks shape stormwater conveyance efficiency and influence overall GSI functionality [25]. In terms of demand, population density and social vulnerability are key drivers of GSI needs [26]. Districts with a high concentration of children, elderly, or under-educated residents face elevated flood risks due to their reduced adaptive capacity--such as limited mobility, lower awareness of disaster protocols, and economic vulnerability--which constrains their ability to prepare for, respond to, and recover from flood events. Furthermore, areas rich in cultural or historical assets require robust GSI measures to protect against flood damage [17; 27]. Economic variables, particularly GDP, influence both exposure levels and the institutional capacity to implement GSI [28; 29]. To ensure consistency across diverse indicators, data were standardized using a min-max normalization technique [30].