Analysis and Conclusions – Virtual City Model

Evaluation

In this project, the design of the surface water management system is fundamentally based on a scenario-driven approach, where two primary rainfall conditions—Normal Rainfall and Heavy Rainfall—are defined as the main design criteria. This distinction constitutes the core logic of the system and directly influences both the structural configuration and the functional behavior of its components.

The evaluation of the results indicates that the developed ontology successfully formalizes this scenario-based design logic. Core system components, such as drainage channels, gutters, storage basins, pumps, and photovoltaic panels, are modeled as permanently active elements that operate under normal rainfall conditions. In contrast, overflow-related components, including the overflow channel and the culvert, are explicitly linked to the heavy rainfall scenario. This clear separation allows the system behavior under routine and extreme conditions to be conceptually distinguished and consistently analyzed.

The Dynamo model complements this conceptual framework by implementing the same dependency logic in a parametric manner. Changes in rainfall intensity directly affect runoff generation, storage demand, pump capacity, and ultimately the activation of overflow mechanisms. The close alignment between the ontology and the Dynamo model demonstrates that the project successfully translates a semantic, scenario-based design concept into a coherent computational representation.

Limitations

Despite its conceptual strengths, the project is subject to several limitations that arise primarily from intentional modeling simplifications. The most significant limitation is the reduction of rainfall behavior to two discrete scenarios—normal and heavy rainfall—without explicitly modeling intermediate or transitional conditions. While this binary classification effectively clarifies the design logic, it does not capture the continuous variability of real rainfall events.

Furthermore, the Dynamo implementation relies on simplified and static relationships, such as constant flow velocities and steady-state comparisons between inflow, storage capacity, and pump discharge. Temporal dynamics, including the time-dependent filling of the storage basin, delayed pump response, and fluctuating rainfall intensity, are not explicitly simulated. As a result, the numerical outputs should be interpreted as indicative design guidance rather than detailed hydraulic solutions.

Additionally, the definition of rainfall scenarios is based on a specific climatic context, such as typical and extreme precipitation conditions in Berlin. Consequently, direct application of the model to other geographical regions would require adjustment of scenario parameters. Nevertheless, the underlying ontology-based framework remains transferable and adaptable to different climatic settings.

Discussion

The results of this project demonstrate the effectiveness of a scenario-based design methodology for the conceptual modeling of integrated engineering systems. By distinguishing between normal and heavy rainfall as explicit design scenarios, the system is structured around functional priorities rather than purely numerical thresholds. This approach enables a clear understanding of which components are essential for routine operation and which are required solely to ensure resilience under extreme conditions.

The ontology plays a crucial role in formalizing this logic by providing a structured semantic representation of components, functional roles, and scenario dependencies. This ensures that overflow-related elements are not treated as permanently active system parts but are instead activated only when specific environmental conditions are met. Such a representation supports transparent reasoning about system behavior and reduces ambiguity in the interpretation of design decisions.

The Dynamo model further illustrates how this semantic structure can be translated into a parametric design workflow. By linking rainfall intensity to system capacities and triggering overflow activation only under heavy rainfall conditions, the model highlights the practical implications of the scenario-based design. This combined ontology–Dynamo approach is particularly valuable during early design stages, where comparative analysis and conceptual clarity are more critical than detailed numerical precision.

Overall, the project demonstrates that integrating ontology-based modeling with simplified parametric tools can provide a robust framework for understanding, evaluating, and communicating the behavior of complex water management systems under varying environmental scenarios.