Digital Twins are software systems that provide dynamic virtual representations of physical systems(1), enabling modelling and visualisation, with automated data exchange and analytics being key attributes. These systems are enabling the development of smart cities(2) and may also represent the natural environment(3–5). Common use cases for Digital Twins are to monitor and control manufacturing lines or smart cities, but in environmental applications they are less common. Digital Twins can be used to automate and connect computer models of the environment, enabling on-demand simulations or ingestion of model outputs in planning.

A key example of an environmental Digital Twin is the the EU's “Destination Earth” system, which is being developed as a Digital Twin for climate services, to facilitate access to weather and climate models which can be used for impact studies(6). Physics-based Digital Twins such as this will revolutionise access to and use of numerical model predictions. By connecting systems together through open-data and standards, a “Digital Twin web” will be created, powered by rapidly growing data and distributed cloud computing(7). Yet the development of each component remains challenging.

In this work, we describe the development of the Environmental Digital Data Intelligence Engine (EDDIE), an open-source framework for creating environmental Digital Twins. The concept of EDDIE is that it acts as a core engine which manages the ingestion and processing of spatial and other data, provides a modularised framework for running environmental models from these data, orchestrates them and ingests their results, and provides an (optional) web-based user interface and visualisation system. EDDIE is based on APIs, meaning that is it possible to connect two or more instances of EDDIE (or other Digital Twins) to share data and environmental models. For example, these Digital Twins can represent multiple different domains, such as hazard assessment, environmental monitoring, and community and urban planning. Here we describe the EDDIE system and provide some application examples.

EDDIE and its open-source module implementations help developers of novel Digital Twins by providing a structure to follow, and providing library functionality for key spatial data handling processes. A dashboard of existing spatial data becomes trivial to setup and fetching and combining open data for analysis becomes simpler by following existing workflows and patterns.

An application using EDDIE is comprised of multiple containers working together to form a web application. Key containers include PostGIS, GeoServer, TerriaJS and the EDDIE backend and processing containers. EDDIE’s Python library is used in the backend to prepare data and keep them up to date if required. When a model scenario is requested, the Python library is used within domain-specific modules to gather and process data to generate predictive outputs. TerriaJS is the typical frontend for an EDDIE application, allowing 3D visualisations as well as the ability to request model scenarios to be run. These requests use the OGC Web Processing Service standard, and return JSON results that are valid TerriaJS catalog items. This allows requests to use existing tooling with standardised inputs, with results that can be used in further processing scripts or can be automatically displayed on the web. The standard front-end for EDDIE applications is TerriaJS, with the backend containers able to expose detailed dynamic catalogs. These catalogs can also be used by other independent Digital Twins, enabling them to use all functionality available to create more powerful ecosystems of Digital Twins.

EDDIE is used in active research projects for multiple distinct Digital Twins developed by the Geospatial Research Institute Toi Hangarau. EDDIE was born from the Flood Resilience Digital Twin (FReDT), focused on automated prediction of flood risk and collation of data for impact analysis. Currently, FReDT allows users to select parameters relating to climate change to assess how sea-level rise and increased storm intensity may change flood inundation risk. Ongoing developments are focused on working with communities to develop nature-based solutions to reduce flood impact, while allowing them to trial many different scenarios using the web interface. The core modules were extracted from FReDT to be able to be reused to construct novel environmental Digital Twins, and this core has formed EDDIE.

From there, EDDIE was used as the basis for the Ōtākaro Digital Twin, a prototype environmental platform for monitoring the health of the Ōtākaro/Avon River in Christchurch, New Zealand. This Digital Twin was created in collaboration with Ngāi Tūāhuriri and Christchurch City Council. Modelling available within the platform currently focuses on the MEDUSA 2.0 stormwater pollutant runoff model using user-inputted rainfall event parameters, and potential future modelling may include linking this to rainfall gauge telemetry.

Most recently, EDDIE was the core framework used to create Te Awarua Kai Ora, a platform for Te Awarua / Porirua Harbour. This platform collates data from open data sources relating to the harbour, presents spatial data on environmental sampling, and allows the Porirua community to understand a flow model of the harbour created at the Geospatial Research Institute Toi Hangarau in collaboration with PHF Science. People can create story maps to describe the environmental data, as well as interact with overviews and detailed plots of flows within the harbour to understand how the catchment, streams, tide and rain contribute to sedimentation, flushing, or contaminant buildup.

Current and near-future developments of EDDIE include optimisations and templates for cloud deployments. Focusing on facilitating cloud deployments allows for dynamic scaling to occur, allowing for large amounts of processing power to be accessed for only the short amount of time needed. This will be invaluable for FReDT allowing us to run many proposed scenarios at once for communities. EDDIE was built on a containerised architecture, and these additional developments will remove barriers to deploying new EDDIE projects.

EDDIE provides a framework for building environmental Digital Twins with interoperable standards. This framework will help adoption of new Digital Twins and strengthen the community ecosystem of environmental Digital Twins. This will enhance access to data and insights for communities, for planning, for decision making and for research.

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Open-source software was used to develop water flow and tracing models of Te Awarua o Porirua (Porirua Harbour), Aotearoa-New Zealand. To facilitate model outputs’ interpretation by the local community, an opensource dashboard was developed together with 3D visualisations of the data. All developed products remained with the community.

Background

Flood inundation models are an essential component of flood hazard assessment, emergency management, infrastructure planning, and climate adaptation. Most contemporary two-dimensional flood models operate on structured raster grids or unstructured meshes and typically require repeated conversion between raster, vector, and computational representations throughout the modelling workflow. While these approaches are mature and widely adopted, they can introduce complexity in data management and interoperability. Discrete Global Grid Systems (DGGS) provide an alternative spatial framework based on hierarchical tessellations that support globally consistent indexing, multi-resolution analysis, and standardised spatial referencing. Although DGGS have been widely applied in Earth observation, geospatial analytics, and environmental data management, comparatively little work has explored their use as the primary computational mesh for hydrodynamic simulation.
This paper presents FloodA5, an open-source flood modelling framework built on the A5 equal-area pentagonal DGGS. The A5 DGGS provides an equal-area hierarchical tessellation of the Earth composed of pentagonal cells. At any given resolution, all cells possess identical area, while refinement follows a strict parent–child hierarchy in which each cell subdivides into five children. Interior cells possess a uniform five-neighbour topology, and compact hierarchical identifiers provide efficient indexing and storage. These properties make the grid attractive for hydrodynamic modelling because water storage calculations can be performed directly from cell area and depth, while the hierarchical structure provides a potential pathway towards future adaptive multi-resolution simulations.

Methods

FloodA5 was developed to investigate the feasibility of performing flood inundation modelling within a fully DGGS-native workflow, maintaining a single spatial representation from mesh generation through terrain processing, simulation, storage, and visualisation. The framework is implemented primarily in Julia, with DGGS operations provided through the pya5 ecosystem via a lightweight Python interoperability layer. FloodA5 integrates mesh generation, digital elevation model (DEM) processing, hydrodynamic simulation, sub-grid terrain representation, visualisation, and data storage within a unified software architecture.
FloodA5 currently supports two hydrodynamic formulations. The standard solver applies the inertial shallow-water approximation of Bates et al. (2010) on the A5 mesh. Because A5 cells form a non-orthogonal polygonal grid, a first-order correction based on the angle between the edge normal and the cell-centre connection vector is applied when calculating water-surface gradients. The framework also includes an optional Sub-Grid Sampling (SGS) formulation intended to represent terrain variability below the computational mesh resolution. Rather than storing a single representative elevation for each cell, the SGS approach derives hypsometric relationships from high-resolution DEM samples and pre-computes volume–elevation, wetted-area, hydraulic-radius, conveyance, and edge-sill relationships. During simulation, water storage is tracked as volume and converted to water-surface elevation through inversion of the hypsometric curves, while flow routing uses hydraulic properties derived from the pre-computed SGS tables.
The framework was evaluated using two synthetic benchmark problems and a real-world flood case study. The first benchmark consisted of a point-source injection on a flat domain. Under isotropic conditions, the resulting inundation pattern should be circular and therefore provides a simple test of directional bias. The second benchmark consisted of a planar slope intersected by a perpendicular embankment. This benchmark was designed to evaluate routing behaviour under a known flow direction and assess the ability of the SGS formulation to represent sub-cell topographic barriers. A larger-scale evaluation was performed using the January 2005 Carlisle flood event, using the same domain configuration and inflow hydrographs employed in previous LISFLOOD-FP studies. Three FloodA5 configurations were tested: a resolution 18 standard solver (approx. 22 m cell spacing), a resolution 20 standard solver (approx. 5.6 m cell spacing), and a resolution 18 SGS solver. Results were compared against a 5 m LISFLOOD-FP reference simulation using inundation extent intersection-over-union (IoU) and depth RMSE metrics.

Results

The synthetic benchmarks demonstrated that physically plausible flood propagation can be simulated on the A5 DGGS. In the point-source benchmark, the resulting inundation pattern exhibited a high Polsby–Popper circularity score of 0.96, indicating near-circular expansion. However, visual inspection revealed a preferred northwest–southeast propagation axis, suggesting the presence of directional routing bias. The planar slope benchmark provided stronger evidence of this behaviour, with the flood wave deviating approximately 30–60 degrees from the expected downslope direction. These results indicate limitations in the current treatment of non-orthogonal gradients and suggest that more sophisticated gradient reconstruction approaches may be required for accurate routing on pentagonal meshes.
The SGS benchmark revealed a second important limitation. Although the SGS formulation successfully represented sub-cell elevation variability, it failed to reproduce the hydraulic effect of an embankment located entirely within individual cells. Water was able to pass through the barrier because the current SGS representation preserves elevation distributions but not the spatial arrangement of topographic features. This finding highlights a potential limitation of storage-based sub-grid approaches when internal barriers are important controls on flow routing.
Comparison with the Carlisle reference simulation demonstrated that useful flood simulations can nevertheless be produced using the current implementation. The Resolution 18 standard solver produced the closest correspondence with the LISFLOOD-FP reference, achieving an IoU of 0.67 and a depth RMSE of 1.17 m. While these results indicate only reasonable rather than strong agreement, they demonstrate that a DGGS-native flood model can reproduce broad inundation patterns within a real-world catchment. Interestingly, neither increased resolution nor the SGS formulation improved performance, suggesting that numerical formulation errors currently dominate resolution-related effects.
Computational performance was also evaluated. A Resolution 18 mesh containing approximately 30,000 cells was generated in approximately two minutes and completed a 120-hour flood simulation in 39 minutes on a workstation-class AMD Ryzen Threadripper system. These results indicate that DGGS-native flood modelling can be performed efficiently without specialised high-performance computing infrastructure.

Conclusions

The results demonstrate the feasibility of hydrodynamic flood modelling on an equal-area pentagonal DGGS and establish FloodA5 as one of the first open-source frameworks to provide a complete DGGS-native flood-modelling workflow. At the same time, the synthetic benchmarks identify important methodological challenges, particularly in relation to non-orthogonal gradient treatment and sub-grid representation of internal topographic barriers. These findings should not be interpreted as limitations of DGGS-based modelling in general. Rather, they highlight the importance of numerical formulations specifically designed for non-orthogonal polygonal meshes. Beyond flood modelling, FloodA5 illustrates the broader potential of DGGS-native environmental simulation frameworks and provides an open-source platform for future research into multi-scale environmental modelling, integrated geospatial analysis, and environmental digital twins.