Interoperability Strategies for Process-Based Models in Digital Water Twins: An Architectural and Framework-Based Synthesis

Digital Water Twins (DWTs) are increasingly adopted to support sustainable and efficient management of complex water systems. While process-based models form the analytical core of the DWTs, the literature lacks a coherent, model-centric synthesis that links DWT architectures and operational objectives to explicit interoperability requirements and systematic framework evaluation. Here we synthesize the literature to distil interoperability requirements for process-based models within multi-scale DWT architectures and to evaluate established interoperability frameworks (i.e., BMI/CSDMS, OpenMI, HydroCouple, ESMF and OMS3) against operational demands. The synthesis identifies core interoperability requirements for process-based DWTs, including semantic consistency, modular coupling, coordinated time–space execution, operational robustness, and traceability.  Comparative analysis shows that framework suitability is primarily determined by where coupling and semantics sit in the DWT architecture. BMI/CSDMS favours rapid model wrapping and flexible model–data exchange. OpenMI and HydroCouple support tighter, time-synchronized and spatially explicit coupling, making them suitable for operational and high-performance contexts. ESMF excels at parallel coupling and regridding for Earth-system–scale applications but requires substantial integration effort. OMS3 is best suited to modular, calibration-focused workflows, with more limited applicability to spatially detailed or HPC-driven models. Consistent with these trade-offs, application studies continue to rely on ad-hoc pipelines, while successful large-scale initiatives converge on layered interoperability strategies that combine lightweight interfaces, targeted runtime couplers, and platform services. These syntheses establish a model-centric evaluation basis for selecting and combining interoperability frameworks and provide actionable guidance for designing scalable, robust, and decision-relevant DWTs.  

 

Acknowledgement

This research has been conducted with Flagship Programme funding granted by the Research Council of Finland for Digital Waters Flagship (decision no. 359248).