Development of an open-source analytical Digital Twin framework for environmental modelling and management

Abstract

Abstract. Environmental Digital Twins are dynamic virtual representations of physical environments that support real-time monitoring, scenario modelling, and community decision-making, that hold significant promise for addressing complex environmental challenges. Yet the development of each new system typically requires substantial duplicated engineering effort, and open-source frameworks specifically designed for environmental applications remain scarce. We present the Environmental Digital Data Intelligence Engine (EDDIE), a free and open-source framework (AGPL-3.0) for building environmental Digital Twins, developed at the Geospatial Research Institute Toi Hangarau. EDDIE assembles proven free and open-source geospatial (FOSS4G) components including PostGIS, GeoServer, TerriaJS, and a Python processing stack into a modular, containerised architecture. A plugin-based module system allows domain-specific environmental models to be registered and orchestrated within a shared infrastructure, while OGC-standard service interfaces (WPS, WFS, WMS) ensure interoperability with external tools and other Digital Twin instances. EDDIE underpins three operational applications: the Flood Resilience Digital Twin (FReDT), which automates compound flood inundation modelling under climate change scenarios; the Ōtākaro Digital Twin, an environmental monitoring and modelling platform for the Ōtākaro/Avon River developed with hapū Ngāi Tūāhuriri and Christchurch City Council; and Te Awarua Kai Ora, a community hydrodynamic modelling and story-mapping platform for Te Awarua / Porirua Harbour. We describe the EDDIE architecture, present performance benchmarks for FReDT, and discuss EDDIE’s potential as a reusable community framework for the open environmental Digital Twin ecosystem.