Urban Drainage Systems (UDSs) are increasingly challenged by ageing infrastructure, rapid urbanization, andthe impacts of climate change. Nature-Based Solutions (NBSs), such as green roofs and swales, can enhanceUDS performance and resilience, yet traditional Asset Management (AM) approaches primarily focus on greyinfrastructure. To ensure optimal functionality, both grey and blue green components must be systematicallymonitored and maintained. This study presents a Digital Twin (DT)-based framework for the rapid assessmentof NBS conditions through continuous model updating. The proposed method employs a ProportionalIntegral Derivative (PID) controller to dynamically adjust simulation model parameters (e.g., surfaceroughness, hydraulic conductivity) based on real-time sensor observations. Parameter changes are interpretedas indicators of NBS condition, serving as early warnings for maintenance or intervention. The methodologywill be tested on a section of the Belgrade UDS, focusing on a hypothetical green roof scenario. Water leveldata collected from the nearby sewer network will be assimilated into the model to update NBS parameters.The results aim to demonstrate the potential of DTs as effective asset management tools for monitoring andmaintaining hybrid urban drainage systems.