## 4 Discussion  
This study presents a comprehensive hydraulic modeling framework for the water distribution network of the city of Farsala, integrating CAD-based digitization, GIS spatial data, and real-time SCADA telemetry under conditions of significant data scarcity. The novelty of the work lies in the methodology's adaptability to incomplete or outdated datasets, demonstrating that a reliable and operationally useful model can still be developed when traditional records and infrastructure documentation are lacking. By synthesizing digital mapping, geospatial analysis, and telemetry-informed calibration, this study sets a precedent for under-resourced utilities, echoing the findings of [24] on the utility of virtual modeling platforms to digitize previously unmapped networks. Similar challenges are identified in [23], which highlighted the limitations in data availability for small municipalities and proposed integrated modeling approaches under constrained conditions. The modeling process, anchored in AutoCAD digitization and enhanced with GIS elevation data and satellite imagery, allowed for the first complete mapping of Farsala's network since its initial construction. The identification and georeferencing of the key infrastructure components, such as pressure pipes, laterals, tanks, hydrants, and valves, produced a high-fidelity spatial model that both improved visualization and served as the basis for hydraulic simulation. These practices align with recommendations by [PERSON] [2; 25] regarding GIS-based data integration for smart water infrastructure. The performance metrics obtained in this study, with Nash-Sutcliffe efficiency (NSE) of 0.841, volumetric efficiency of 0.9998, R\({}^{2}\) of 0.9002, MAE of 0.049, and RMSE of 0.058, demonstrate the model's high accuracy in replicating the real-world behavior of the Farsala water distribution system. When compared to the results of [12], which reported an NSE of 0.82 in simulations of leakage dynamics, and [29], which obtained an NSE of 0.85 in calibrating potable water networks using WaterGEMS and SCADA data, the performance of this study was comparable and within the upper range of acceptable accuracy thresholds. Similar values were also reported in [8], where Bayesian calibration incorporating historical demand data yielded NSE values exceeding 0.80, further validating that such levels of accuracy are indicative of reliable hydraulic models. Therefore, the results of this study fall well within the range of accepted values for urban water distribution system simulations and confirm that even under significant data limitations, it is possible to develop robust and reliable hydraulic models. Figure 9: Leakage hotspot identification (GIS-based leakage detection map with color-coded severity zones). The zoning of the network into seven pressure areas and the introduction of DMAs based on elevation and demand patterns reflect the current best practices in pressure management and demand allocation, as also discussed in [7; 8], which emphasized the importance of demand pattern calibration in water resource planning. The critical modeling decision to simulate the system beginning from the reservoir rather than from boreholes helped overcome the well-known limitations of groundwater abstraction modeling in WaterGEMS, a challenge similarly noted in [4; 18]. Despite these strengths, the results also reveal significant systemic weaknesses. The network operates at consistently high pressures (>900 kPa in many zones), exceeding the standard design limits and increasing the risk of pipe failures, leakage, and customer-side damage, an issue reinforced in [10] in relation to critical pipe identification, and in [36] in relation to pressure-induced leakage dynamics. The analysis of leakage hotspots further validates this concern, identifying flow losses of up to 1.2 m\({}^{3}\)/day at individual junctions, particularly in high-pressure or older pipe segments, consistent with patterns observed in [11], which used SCADA-informed pressure simulations to detect similar anomalies.