# A conceptual architecture for AI-assisted Digital Twins in natural resource management

Félix Iglesias, Frédéric Ros, Vy-Thuy-Lynh Hoang, Laurence Gourcy, Jean-Sébastien Moquet, Véronique Daële, Sébastien Dupraz

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Félix Iglesias, Frédéric Ros, Vy-Thuy-Lynh Hoang, Laurence Gourcy, Jean-Sébastien Moquet, et al. A conceptual architecture for AI-assisted Digital Twins in natural resource management. Ecological Informatics, 2026, 94, pp.103635. 10.1016/j.ecoinf.2026.103635. hal-05495952

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# A conceptual architecture for AI-assisted Digital Twins in natural resource management

Félix Iglesias<sup>a, b,*</sup>, Frédéric Ros<sup>b</sup>, Lynh Hoang Vy Thuy<sup>c</sup>, Laurence Gourcy<sup>c</sup>,
Jean-Sébastien Moquet<sup>d</sup>, Véronique Daële<sup>e</sup>, Sébastien Dupraz<sup>c</sup>

## ARTICLE INFO

Dataset link: https://doi.org/10.48436/b615a-vgd97

**Keywords:**

Digital twins

Natural resource management

Artificial intelligence

Data architecture

Environmental monitoring

## ABSTRACT

The management of natural resources is increasingly critical and challenging due to complex interactions among environmental, industrial, and societal processes. Traditional approaches often fail to integrate heterogeneous data, limiting predictive and decision-support capabilities. This study presents a conceptual architecture for an Artificial Intelligence (AI)-assisted Digital Twin (DT) of the Centre-Val de Loire region, designed to unify time-dependent multi-source data. Based on the ENVRI Reference Model, it covers Science, Information, Computational, Engineering, and Technology layers, defining standardized data exchange, communication protocols, and prototype functionalities. A proof of concept FIWARE implementation supports ingestion, monitoring and analytical services for piezometric and meteorological data, exemplified through groundwater dynamics in the Beauce aquifer. It integrates daily observations from 53 piezometric stations over more than five years, managing approximately 2.8 million records in a containerized environment.

Results show that the proposed DT architecture can enhance sustainability-oriented decision making, integrating heterogeneous data and predictive analyses while enabling collaboration across scientific and technical domains. Its modular design offers a replicable template for future AI-assisted environmental DTs, scalable to larger regions. Hence, this work illustrates how DTs can improve environmental monitoring and understanding, providing a pathway toward resilient, data-driven management of natural resources.

## 1. Introduction

Despite the dependence of modern societies on natural resources, their future availability and real costs are widely ignored. Such an issue is progressively becoming a major concern in political and executive agendas. Ahmed et al. (2020) study the relationship between natural resources, human capital, urbanization, and the ecological footprint in China from 1970 to 2016, concluding that the interaction between urbanization and human capital decreases environmental degradation, while highlighting the need to strengthen laws and initiatives related to minerals, soil, and water usage and pollution. Educated human capital is key to this purpose, as it stimulates energy efficiency and environment-friendly technologies (Zafar et al., 2019). In the same vein, the risk of natural resource shortages encourages the development of plans for mitigating the effects, as well as the use of new technological paradigms to improve knowledge, prevention and response (George

et al., 2015). Recommendations and initiatives promoting integrated and systemic environmental governance approaches have been consistently upheld over the last decade, with recent emphasis on aligning socio-economic development with ecological sustainability and on the need for coordinated decision-making frameworks at regional scales (Shang et al., 2025).

A technological concept that has gained increasing prominence is the Digital Twin (DT). Jones et al. (2020) define DT as a digital representation of an active, unique object or system that encompasses a set of features and behaviors through models and data within one or multiple life-cycle phases. The purpose of a DT is to improve the understanding of the nature behind its real counterpart, to monitor its state and predict its evolution, but also to test the impact of strategies and changes without affecting the real twin. The potential of DTs lies in its holistic embodiment, i.e., the combination of domain knowledge

<footnote><sup>a</sup> CN Group, Institute of Telecommunications (ITC-ETIT), TU Wien, Vienna, Austria</footnote>

<footnote><sup>b</sup> PRISME, Polytech Orléans, University of Orléans, Orléans, France</footnote>

<footnote><sup>c</sup> French Geological Survey (BRGM), Orléans, France</footnote>

<footnote><sup>d</sup> Institut de Combustion, Aérothermique, Réactivité et Environnement (ICARE), UPR3021, CNRS, Orléans, France</footnote>

<footnote><sup>e</sup> Institut des Sciences de la Terre d'Orléans (ISTO), UMR7327, University of Orléans/CNRS/BRGM - OSUC, Orléans, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: felix.iglesias@tuwien.ac.at (F. Iglesias).</footnote>
and the integration of multiple data assets (de Koning et al., 2023). Hence, DTs are deemed promising for optimizing sustainability and the use of natural resources, giving rise to emerging movements such as the DestinE program of the European Commission, whose goal is “to develop a dynamic, interactive, multi-dimensional, and data-intensive replica of the Earth (system), which would enable different user groups (public, scientific, private) to interact with vast amounts of natural and socio-economic information” (Nativi et al., 2021). However, the application of DTs for global sustainability is still in its infancy (Tzachor et al., 2022), partly due to the absence of established methodologies (de Koning et al., 2023). While the current technological paradigm increasingly promotes the integration of AI to enhance environmental decision support from an analysis-driven perspective — as exemplified by recent approaches such as Song et al. (2025) — DTs provide the architectural concept required to support a holistic representation across multiple domains.

This paper introduces the methodological design for developing a DT of natural resources in the Centre-Val de Loire region, France. Such a DT falls within the framework of the JUNON program (BRGM, 2022), whose overarching goal is to establish a digital research center of the Centre-Val de Loire environment encompassing agriculture, urban development, forestry, and river ecosystems. JUNON, spearheaded by BRGM — the French geological survey — brings together multiple research organizations including CNRS (French National Center for Scientific Research), INRAE Centre Val de Loire, INSA CVL, the University of Orléans, and the University of Tours. Additionally, private companies such as ATOS, AnteaGroup, and SDEC, as well as non-academic partners including the DREAM water and environment competitiveness cluster, GABOR 45, the Agreen Tech Valley cluster, Orléans-Val de Loire Technopôle, and LE STUDIUM IAS, are involved. A preliminary overview of this work was presented at DS-LB 2024 (Iglesias et al., 2024). Here we present the complete study, including detailed methodology and a functional proof of concept (PoC).

**Main contribution.** This work contributes to advancing the development of DTs for environmental and natural resource management by introducing a complete methodological framework and a functional prototype applied to the Centre-Val de Loire region. The architecture is designed to integrate sensorics data with modern data analytics and machine learning (ML). The main novelty of this study lies in the integration of the ENVRI Reference Model (ENVRI RM) with regional data ecosystems and domain-specific knowledge to design a sustainable and interoperable DT architecture. Through this approach, we show how the ENVRI RM can be operationalized in practice, bridging the gap between conceptual models and real-world implementation.

**Alignment with policy agendas.** Beyond its technical contributions, the proposed AI-assisted DT architecture is well aligned with current global sustainability and environmental agendas. International frameworks such as the UNFCCC process and forthcoming COP milestones, the UN Decade on Ecosystem Restoration, the New York Declaration on Forests, and the Convention on Biological Diversity consistently highlight the need for transparent, interoperable, and trustworthy infrastructures to support environmental monitoring, policy evaluation, and accountability of climate and restoration actions (Friedman et al., 2022; McElwee and Harrison, 2025). In this context, DTs offer an enabling architecture that integrates heterogeneous environmental observations and supports traceable, evidence-based decision making. The proposed design is modular, standardized, and reproducible, supporting long-term monitoring and adaptable to regional and multi-sector contexts.

**Paper structure.** The remainder of this paper is organized as follows. Section 2 reviews the main precedents of Earth DTs. It also introduces the ENVRI RM (Section 2.1), some previous approaches for natural resources at regional scale (Section 2.2), as well as the specific features of the Centre-Val de Loire's natural resources and their environmental and socio-economic relevance (Section 2.3). Section 3 describes the architecture of our DT prototype, covering organizational

aspects and use cases (Section 3.2), data sources and envisioned assets (Section 3.3), operational schemas (Section 3.4), engineering components (Section 3.5), and selected technologies for DT implementation (Section 3.6). Section 4 presents an example of modeling the system architecture in compliance with the ENVRI RM. Section 5 details the PoC. Finally, Section 6 concludes the paper, wrapping up the lessons learned and highlighting the main insights.

## 2. Digital Twins of the Earth

The concept of “Digital Earth” has been present in the research landscape since the end of the last century. In their *Manual of Digital Earth*, Guo et al. (2019) investigate global and regional responses to the Digital Earth initiative and provide an examination of all related and last-generation technologies while exploring potential contributions to achieving the Sustainable Development Goals (SDGs).

Approaching the concept of DT, Nativi et al. (2021) take DestinE as an example to delve into the design of Digital Ecosystems for the development of “Digital Earth Twins”. DestinE seeks to construct a precise digital replica of the Earth on a global scale. This replica will serve to monitor, simulate, and forecast the interplay between natural phenomena and human activities with the dual goals of advancing digital transformation and mitigating environmental degradation for the future Europe. The cited work acknowledges the extreme challenge of the task and, by exploring architectural principles and designs, arrives at a set of recommendations and best practices.

In spite of the difficulties, large-scale projects related to the ideas of Digital Earth and Digital Ecosystems have been emerging and improving over the last two decades. For instance, the EVER-EST Horizon 2020 project (Glaves and Albani, 2016) develops a virtual research environment (VRE) where Earth scientists can maintain, share, contextualize, combine, analyze, and visualize their work and publications. Another impressive initiative is NASA's Prediction Of Worldwide Energy Resources (POWER) project (Zhang et al., 2009), which began in 2003 with the goal of providing global solar and weather data to assist renewable energy, building energy efficiency, and optimization of agricultural and industrial development.

These initiatives align with the creation of DTs of the Earth, and are essential as part of their technological foundation, but the implementation of DTs at local or regional scales is still marginal, except for a few emerging projects like the DT of the Alps (Malet et al., 2024), or DT design proposals for specific natural resources (Yang et al., 2024). DT researchers and developers are regularly challenged by strong technical, organizational, and methodological barriers, including the lack of common standards, definitions and maturity models, and especially the absence of interoperability across DT systems (Klar et al., 2024). In fact, Klar et al. propose a domain-independent maturity framework that shows how most DTs remain at early stages of development, only reaching their full potential when assessed in interaction with other systems rather than in isolation. In the ecology field, de Koning et al. (2023) identify some of these barriers that are extensible to Earth sciences, namely:

1. The lack of common methodologies and standards to build tools and workflows, forcing experts to design them from scratch.

2. The multiple options, protocols, infrastructures, and platforms for data collection, integration and preservation, which have a decisive impact on the functionality, deployment and usability of the final DT.

3. No existing guidelines to determine what information should be included and what is dispensable, nor at what scale. This ends up being established according to the resources available and the use cases.

4. The complexity inherent to the size of the system, whose uniqueness and coherence are inversely proportional to the size and multiplicity of the data resources.
5. And the dependence — both in terms of quality and continuity of service — on the entities and elements that supply data, whether they are devices, persons or entities.

Beyond Earth system science, notable progress in DT research has been achieved in industrial and infrastructural domains, where hybrid modeling and AI-driven feedback enhance predictive and operational performance. For instance, Li et al. (2025) propose a degradation DT for rolling bearing systems, coupling physical fault evolution with data-driven dynamics, while Xu et al. (2025) develop an adaptive routing scheme for low Earth orbit constellations, where DT and neural optimization enable real-time system optimization. These advances show how the DT paradigm increasingly integrates intelligent, physics-informed, and adaptive mechanisms—principles equally relevant to environmental and natural resource management.

On the other hand, there are common aspects in most studies of the Earth and its natural resources, whether for scientific, industrial or socio-economic management purposes. The evolution of natural resources is a complex process that spans various temporal and geographic scales. Williams (2011) notes a strong analogy between the management of natural resources and the characteristics of scientific investigation, which can be summarized in common steps:

1. Formulating hypotheses to describe phenomena.

2. Developing models consistent with these hypotheses.

3. Monitoring real indicators.

4. Comparing actual and predicted values to test hypotheses and enhance understanding.

Both the described limitations and the common methodological patterns suggest that the use of reference models can be highly beneficial in the design of the underlying architectures for large digital projects.

## 2.1. Reference models

Within the presented conceptual framework, considering the aforementioned precedents and limitations, it is possible to design DTs for managing regional natural resources. An effective approach to address this challenge is to adopt mechanisms that have already proven their usefulness and effectiveness. For example, in the industrial environment, the Reference Architecture Model Industry 4.0 (RAMI 4.0) (RAMI contributors, 2016) is the most widely accepted “architectural map”, solving challenges of complexity, technological fragmentation, and scalability. Its adoption has accelerated industrial digital transformation by providing clarity in a constantly evolving technological environment. Recently, the RAMI 5.0 version has evolved this approach by integrating emerging technologies such as Artificial Intelligence (AI) and Internet of Things (IoT), while also enhancing the scalability and interoperability of industrial systems. Derived from RAMI 4.0 is the ENVRI Reference Model or ENVRI RM (de la Hidalga et al., 2020), which is the equivalent of RAMI 4.0 for large digitization projects in environmental research (“ENVRI” stands for “ENVironmental Research Infrastructures”).

The architectural design of our DT (Section 3) is based on the approach outlined by the ENVRI RM. While there exist precedents of using reference models for developing DTs in the industry — for instance, Steindl et al. (2020), and in domains related to environmental applications such as renewable energies (Wiens et al., 2024) — environmental DT initiatives have so far relied mainly on large-scale or thematic frameworks rather than explicit reference models. Examples include the FAIR Digital Twins paradigm, which focuses on FAIR data principles and composable digital objects (Peters and Schindler, 2024), the European DestinE and DT-GEO projects developing Earth and geohazard twins, and the GEOSS and related initiatives promoting geospatial DT ecosystems (Nativi et al., 2021; Folch et al., 2023). To the best of our knowledge, none of these efforts have explicitly adopted a reference model such as the ENVRI RM to structure and guide the

layered architectural design. Hence, our working hypothesis is that architectural designs supported by appropriate reference models provide a natural foundation for creating DTs that can address the needs of both scientific communities and non-scientific entities, including public and private organizations that depend on the evolution of local natural resources.

## 2.2. Precedents of DTs for regional natural resources and agriculture

As mentioned earlier in this section, DTs are emerging as a powerful technology for integrating data and modeling complex environmental systems. They have significant potential for water management, crop optimization and climate change adaptation. However, it should be noted that adoption remains limited in practice and most studies focus on agricultural applications rather than holistic regional natural resource management. For instance, Silva et al. (2023) conduct a systematic review of the literature in search of DTs applied to precision agriculture. The authors highlight the limited adoption and scale of these initiatives compared to industrial applications, and that in the period explored they have not found a single study “on the use of digital twins for improving soil quality within a context of predictive model control”. The recent surveys (Purcell and Neubauer, 2023; Subeesh and Chauhan, 2025) provide a fairly comprehensive overview of the use of DTs in agriculture. They address different proposals, the potential of DTs, their limitations, and future directions. In Table 1 we show main and recent precedents of natural resource DTs. In terms of their technological basis, options (Alves et al., 2019, 2023) are the most similar to our proposal. However, note that none of the listed works rely on the ENVRI RM for architectural design, which distinguishes our approach from existing studies.

## 2.3. Natural resources of the Centre-Val de Loire

The JUNON program aims to create a network of interconnected DTs to represent the Centre-Val de Loire's natural resources, initially focusing on water, air (climate), and soil. The territory's strong dependence on and interaction with water and land makes DTs a natural tool for ensuring sustainable management in the medium and long term.

The Centre-Val de Loire features a rich mosaic of landscapes, including rivers, farmlands, and forests, extending across the central European plain. It hosts exceptional assets such as the Beauce aquifer (the largest in Europe), the Sologne wetlands (Europe's largest terrestrial Natura 2000 site), and the Orléans forest (the largest state forest in mainland France). Recognized by UNESCO for its cultural and ecological value, the region is also the leading cereal-producing area in France and Europe.

Yet, these resources face growing pressure. The Beauce aquifer, for instance, is vital for agriculture, industry, and drinking water, but suffers from overexploitation and pollution from agricultural and industrial activities. Without careful management, declining water levels and contamination by nitrates and pesticides could jeopardize its future. This combination of high value and high vulnerability underlines the importance of digital tools that can integrate data, simulate scenarios, and guide informed decisions for sustainable resource use.

# 3. Architecture of the Digital Twin prototype

The reasons for conforming our DT prototype architecture to ENVRI RM are already given by de la Hidalga et al. (2020):

> By adopting ENVRI RM as an integrative approach, the environmental research community can secure interoperability between infrastructures, enable reuse, share resources, experiences and common language, reduce unnecessary duplication of effort, and speed up the understanding of research infrastructure systems.
<table><caption>**Table 1**
Comparison of representative environmental DT initiatives.</caption><thead><tr><th></th><th>Summary (Domain, Objective, Technologies, Limitations)</th></tr></thead><tbody><tr><td>Malet et al. (2024) — Alps region</td><td><strong>Domain:</strong> Alpine water resources & geohazards. <strong>Objective:</strong> DT for the Alps to simulate water-related processes and adapt to climate change. <strong>Base tech:</strong> Earth Obs. data analytics (EO), ML, hydrology & geohazard modeling, geospatial platforms. <strong>Limitations:</strong> Demonstrator stage; complex physics integration; calibration in alpine conditions; heterogeneous high-res data.</td></tr><tr><td>Yang et al. (2024) — Lower Yangtze-Taihu Basin as use case</td><td><strong>Domain:</strong> River-basin management for water & disaster risk. <strong>Objective:</strong> Framework for full-scale DTs of river basins for sustainable, equitable water management and disaster mitigation. <strong>Base tech:</strong> Data hub, model hub (physics + data), cloud computing, multi-scale integration, stakeholder UI. <strong>Limitations:</strong> Evolving data infrastructure; partial model integration; need for common standards; uneven digital capacity; transboundary collaboration.</td></tr><tr><td>Fuertes et al. (2020) — city of Valencia</td><td><strong>Domain:</strong> Water distribution management. <strong>Objective:</strong> Develop/exploit a DT for real-time monitoring, planning and operation of urban water networks. <strong>Base tech:</strong> Hydraulic modeling (EPANET), Geographic Information System, big data integration, optimization algorithms, smart sensors and actuators. <strong>Limitations:</strong> Continuous calibration; complex models; ongoing work to couple detailed and strategic models, and to extend to water quality.</td></tr><tr><td>Escribà-Gelonch et al. (2024) — generic model, no region-specific</td><td><strong>Domain:</strong> Crop lifecycle management (soil, crop, postharvest, infrastructure). <strong>Objective:</strong> Critical review of DT tech and orchestration needs across crop lifecycle. <strong>Base tech:</strong> EO, IoT, orchestration frameworks, ML, decision-support systems. <strong>Limitations:</strong> Data/model heterogeneity; orchestration complexity; interoperability issues; development cost and scalability issues.</td></tr><tr><td>Camps et al. (2024) — Urgell channel region</td><td><strong>Domain:</strong> Irrigated agriculture, water resources. <strong>Objective:</strong> Develop AI4WATER DT to model fluxes and optimize water use. <strong>Base tech:</strong> EO, in-situ soil probes, meteorological stations, GPR rover, ML. <strong>Limitations:</strong> Calibration/validation ongoing; multi-source integration challenges.</td></tr><tr><td>Alves et al. (2023) — simulated area, weather data from São Bernardo do Campo, Brasil</td><td><strong>Domain:</strong> Smart farming, irrigation. <strong>Objective:</strong> DT for irrigation management to improve water efficiency. <strong>Base tech:</strong> FIWARE-based IoT platform, discrete-event simulation (Siemens Plant Simulation), OPC UA, FAO-56 (Penman-Monteith), Fuzzy Inference System. <strong>Limitations:</strong> Evaluated only in a virtual farm scenario; real-world deployment requires hardware/software adaptation; scalability and real-time operation in large farms not validated.</td></tr><tr><td>Alves et al. (2019) — generic model, no region-specific</td><td><strong>Domain:</strong> Farm cyber-physical-system (CPS), soil and water. <strong>Objective:</strong> DT prototype for smart farming, soil probes and IoT platform to monitor and visualize farm parameters. <strong>Base tech:</strong> Soil-sensing hardware (DHT22, BH1750, DS18B20, CSMv1.2), IoT platform (SWAMP, FIWARE), dashboards (Grafana), cloud integration, CPS architecture. <strong>Limitations:</strong> Prototype stage; limited sensors deployed; full CPS integration and automated control not yet implemented; scalability untested; field deployment partial.</td></tr><tr><td>Ghandar et al. (2021) — Shenzhen, China, lab aquaponics as PoC</td><td><strong>Domain:</strong> Urban agriculture, aquaponics. <strong>Objective:</strong> DT-based decision support system to plan/coordinate production. <strong>Base tech:</strong> CPS (IoT sensors/actuators), ML, agent-based modeling, Dynamic Data-Driven Application Systems (DDDAS). <strong>Limitations:</strong> Small-scale trial; requires periodic recalibration; scalability and multi-actor adoption untested.</td></tr></tbody></table>

In fact, the ENVRI RM was created to address four major issues in the field (de la Hidalga et al., 2020):

(i) duplication of efforts to solve similar problems;
(ii) lack of standards to harmonize and accelerate development, and bring about interoperability; (iii) a large number of data models and data information systems within the domain, and (iv) a steep learning curve for integrating complex research infrastructure systems.

From a practical perspective, the ENVRI RM helps to understand how to think about the system to be developed, while consolidating a global vision and detecting aspects that are critical, problematic or simply overlooked during previous phases related to the conceptualization and the original proposal. The spirit of the ENVRI RM is to facilitate easy and rapid design of architectures and prototypes, favoring modularization and iterative refinement. It seeks to simplify what is complex by establishing independent but interrelated perspectives that cover the different levels of design, management and operation. It considers three mechanisms for modeling in order to describe a complex system as a constellation of interlinked viewpoints. They are:

* *Object models*, which can represent abstract entities (measurement, data set, metadata, system, service), physical entities (sensor, server, network) and social entities (institution, research group, researcher).

* *Viewpoint specifications*, which divides the overall conceptualization of the system into several interrelated layers. The aim is to eliminate complexity and facilitate design.

* *Correspondences*, that support project consistency by establishing a mapping where elements of one viewpoint are connected to elements of other viewpoints.

Fig. 1 shows the five viewpoints adapted to the context of JUNON. We can briefly describe their coverage:

<figure><img src="image_3.png" /><figcaption>Fig. 1. Diagram showing the five viewpoints of the ENVRI RM in JUNON.</figcaption></figure>

* **Science** (Organization), which involves aspects related to participating institutions, research and technical teams, the organizational chart, human capital, stakeholders, overall purposes of the system, end users for whom it is intended, social context, policies and constraints, etc.

* **Information** (Data), covering aspects related to main data assets used and generated in the project (description and interpretation), as well as their sources and complete lifecycle.

* **Computational** (Functions), addressing data analysis and processing units, which embraces interfaces, functionalities, logical behavior of modules, operations, applications, services, etc.

* **Engineering** (Distribution), which deals with hardware and software requirements, physical and logical equipment, networks, servers, containers, engineering structures, middlewares, client devices, nodes, etc.
* **Technology** (Implementation), focused on specific technologies and standards and describing dependencies between design and implementation, also considering constraints due to the incorporation of specific technologies and systems that already exist or must be necessarily used.

The five ENVRI RM viewpoints should not be interpreted as functional layers of the architecture. Instead, they represent transversal dimensions that collectively describe how the system's conceptual, computational, and technological aspects are aligned to support interoperability and reuse.

## 3.1. Modeling viewpoints

The ENVRI RM proposes to build models of viewpoints by means of Unified Modeling Diagrams (UML), but it is not limited to such types of schema. This can be beneficial already from an organizational perspective, but it is even more obvious at the other viewpoint levels. For instance, in such a context it would allow communicating in a clear and structured way how the organization works, how its components are arranged and how they interact with each other. More broadly, in general these UML models enable a quick understanding of how the system is divided into parts and how these parts exchange information and interoperate.

Furthermore, when stringent requirements for automated processing and data integration are present, it can be advantageous to transform or interpret complex UML diagrams in terms of ontologies. Ontologies add semantic and dynamic value to diagrams, serving as a machine-processable bridge between subsystems, and can even integrate human roles with technological components. They also enable the automatic evaluation of the diagrams themselves and support more flexible and reusable documentation. An example of converting UML into an ontology can be found in the work of Vo and Hoang (2020). Particularly for Earth Science, Ludäscher et al. (2006) suggest that scientific data integration can benefit from semantic extensions consisting of increasing database mediators and refining query evaluation algorithms by leveraging shared ontologies.

Finally, modeling viewpoints by diagrams independently is not enough; a higher-order, cross-cutting modeling methodology is required to contextualize and consolidate the models developed for the different viewpoints. For this purpose, the ENVRI RM proposes a recursive method consisting of five steps: (a) identification, (b) modeling, (c) refinement, (d) review-revision, and (e) mapping. We address the reader to de la Hidalga et al. (2020) for further details about this methodology.

In Section 4, we show a simple example of modeling using UML diagrams, but first, we describe the elements that constitute and identify the architecture of the JUNON DT prototype from a global perspective.

## 3.2. Organization and use cases

As conceived in the JUNON program, the planned DT is intended to be a digital replica of regional natural resources supported by an existing solid sensory base (partially controlled or maintained by the integrating organizations) to serve the interests of local public and private entities. To this end, JUNON divides natural resources into four individual representations (or sub-DTs): water quantity, water quality, air quality and soil-GHG flux ("GHG" stands for "Green House Gases"). These sub-DTs are connected and interrelated to build a higher-level DT that embodies the digital replication of regional natural resources as a whole. To make such a conceptual approach a reality requires the participation of different teams of experts. Fig. 2 shows the main teams collaborating in the JUNON program and provides a synthesized notion of their role and connections. They are:

* Team JANEAU, organized into sub-teams that are responsible for water DTs. That is, providing key data, knowledge and models for water-level management, control, visualization and prediction in the Beauce area (Water Quantity DT), and performing water quality assessments in the same area (Water Quality DT).

* Team JANUSS, also formed by sub-teams that are responsible, on the one hand, for monitoring, modeling and predicting the quality and state of the air in the Centre-Val de Loire region, covering meteorological aspects but also the concentration and emission of pollutants (Air Quality DT). On the other hand, they provide measurements, models and information related to emission of the nitrous oxide (N₂O) and other GHG by soils of the Centre-Val de Loire (Soil-GHG Flux DT).

* Team DATA takes over data integration, platforms, infrastructure, storage and services so that data can be accessed, used and combined seamlessly, comprehensively and accurately.

* Team PREDICTION develops algorithms, models and advanced ML solutions to address the complexity of data scenarios. It handles analysis methods and pipelines.

* Team DIGITAL TWIN takes care of the overall architecture and conceptual design, harmonizing the constituent parts, defining functionalities, top-down AI, designing the common framework and testing prototypes. It handles methodologies and workflows.

* MANAGEMENT, supervising the work and interoperation between the different teams in the achievement of milestones and goals.

### 3.2.1. Use cases

The DT prototype is designed to potentially satisfy the following cases, covering both scientific research and non-scientific investigation goals:

1. *Understanding the impact of NH₃ on atmospheric chemistry and particulate formation.* NH₃ significantly contributes to the formation of particulate matter in the atmosphere, leading to visibility degradation and nitrogen deposition in sensitive ecosystems, thus affecting environmental and public health. Gong et al. (2013) show how atmospheric NH₃ plays a decisive role in the increase of measured particle mass and measured/modeled particle concentrations. Consequently, the rise in NH₃ emissions has adverse effects on environmental and public health, as well as climate change. Hence, the relevance of studying and understanding the sources, deposition, and atmospheric dynamics of NH₃ (Behera et al., 2013).

2. *On the correlation of NH₃ measurements and emissions.* Along the same line, studying the correlation between NH₂ sources and measured concentrations can help understand the dynamics of its dispersion and detect predominant elements in pollution and factors that affect its accumulation (Perrino et al., 2002).

3. *Quantifying the damage of long-term ozone and PM₁₀ exposure to human health.* Long-term exposure to ambient pollutants can significantly contribute to or even cause various chronic diseases. However, accurately studying and spatially estimating long-term exposure at residential areas based on field observations is really challenging — Air Quality agencies often face problems related to missing data stemming from the sparse monitoring network across space and inconsistent recording periods at different monitoring stations (Yu et al., 2009). The envisioned DT can provide an excellent background to collect, study, model and estimate/quantify the impact of long-term exposure to ozone and PM₁₀ in terms of disability-adjusted life years (Van Zelm et al., 2008).

4. *Viability for farming.* In Beauce the main use of water is irrigation for farming. On average, current abstraction stands at around 200 million cubic meters, fluctuating with the climate. The agricultural census conducted in 2010 surveyed 6543 km² of
<figure><img src="image_4.png" /><figcaption>Fig. 2. Teams in JUNON and their roles for the design and development of the DT prototype.</figcaption></figure>

utilized agricultural land, covering 68% of the region and a total of 6044 farms. The census revealed that 53% of agricultural land is equipped for irrigation. This figure largely surpasses the 22% recorded in the other four major French regions where irrigation is prevalent, and the mere 9% for the rest of France (Verley, 2020). Controlling, calculating and predicting production and costs based on type of crop and water-availability is considered of utmost importance. This can be tackled by models involving actors, objects and natural resources (Pluchinotta et al., 2018).

5. *Prediction of droughts.* Connected to the previous use case, droughts are viewed as complex events influenced by multiple variables. Therefore, predicting them benefits from the DT multidisciplinarity. To mitigate uncertainty arising from changing climate patterns and enhance long-term forecasting accuracy, further research is essential to comprehend the dynamics related to various climatological, oceanic, meteorological, and hydrological parameters, alongside local factors such as regional water demand. In this context, probabilistic frameworks and advanced ML techniques for modeling time series temporal dynamics become crucial components with the potential to better capture abrupt shifts in drought patterns. In this line, conducting spatio-temporal drought analyses based on factors like duration, severity, area, and interarrival time is vital for both short-term and long-term water management strategies (Mishra and Singh, 2011). DT predictions will be available for the Regional Water Authorities (DREAL, SAGE) to suggest and evaluate control scenarios, restrictions, recommendations and limits on future water use by the different regional sectors (Verley, 2020).

6. *Prediction of floods.* Floods (also river overflows and landslides) are intricately connected to droughts in terms of prediction and modeling. Forecasts for floods, droughts, and rapid transitions between the two are necessary across various time scales to devise management strategies aimed at reducing adverse societal and economic consequences. Challenges common to both drought and flood prediction can be split into four interrelated areas: data, understanding of processes, modeling/prediction, and humans-water interactions. In their scientific review, Brunner et al. (2021) identify potential approaches for tackling these challenges that align perfectly with the objectives and specifications of our DT. In the authors’ own words: “limited data availability is one of the most important challenges because it diminishes our ability to improve process understanding and to develop reliable models”.

7. *Viability of forest extension.* The Orleans Metropole would like to extend the forest in the eastern part of the Beauce aquifer as a nature based solution against flooding. The relationship between forests and water resources is widely recognized, although the interactions among their components are only partially understood. There is ongoing concern about the impact of forestry practices, especially harvesting, on major flooding events, which is a recurring issue in forest and watershed management. Data availability is also often limited for addressing specific research inquiries; consequently, models are frequently utilized to explore additional questions by extrapolating interpretations from existing combined datasets (Eisenbies et al., 2006).

8. *Understanding pollutant migration between different soil layers.* Environmental issues challenge both sustainability and biodiversity. To this end, scientific efforts are focused on understanding the mechanisms of environmental degradation (Thouin et al., 2022). In particular, the Beauce aquifer holds immense importance within the biosphere, serving as a pivotal storage medium for supplying water to plants and the atmosphere. Additionally, it plays a crucial role as a regulatory agent in the transmission of recharging water and contaminants from the land surface to groundwater. The interest in the vadose zone has been mainly due to public concerns about protecting groundwater for drinking and agriculture (Arora et al., 2019).

9. *Development of models and simulations of pollutant migration and removal techniques.* This means simulating the polluted environment on a small or intermediate scale, from the soil surface down to the groundwater table. The development of mathematical and ML models allows the development of increasingly accurate simulations. This produces quantitative explanations of how ecosystems function and enables us to hypothesize about their response to diverse factors (Mertz et al., 2021). By means of simulations, we can predict the effects of environmental changes, human activities, and ecological management strategies. This also enables the design and development of plans and techniques to clean up or stabilize pollutants (Trellu et al., 2015).

10. *Evaluating the impact of ecological products and strategies.* Experimental setups feeding the DT also enable testing and evaluating “environmentally friendly” pesticides, biocomposites, and alternatives to chemical pesticides (Huang et al., 2018).

11. *Understanding nitrous oxide emissions*, meaning quantifying N₂O emissions to better understand what generates these emissions, with a particular focus on agriculture, i.e., to study what affects the N₂O emissions (fertilization, rain and weather conditions, type of soil, artificial drain) (Gu et al., 2013).

12. *Generation and evaluation of hypotheses and strategies for reducing GHG emissions*, with the objective of providing well-informed guidance to decision-makers regarding the impact of agriculture on climate. Additionally, we aim to enhance understanding of animal and tree biology, animal health, sustainable livestock management, forests, and soils (Hristov et al., 2013). At the same time, the DT offers a suitable platform for evaluating such recommendations, hypothesis and strategies (Pellerin et al., 2013), e.g., in terms of local and global disturbance (climate changes, local anthropogenic actions, etc.) on wetland GHG emissions. Wetlands and peatlands are considered major regional contributors to the natural GHG balance in the Centre-Val de Loire. A better assessment of their impact would be a key factor in managing them more effectively (D'Angelo et al., 2021; Ma et al., 2024).

13. *Modeling reactive nitrogen losses.* Agriculture has intensified by supplying crops with more synthetic mineral nitrogen. This increase leads to more reactive nitrogen (Nr) being released into the environment, including ammonia (NH₃), nitrous oxide (N₂O), nitrogen oxides (NOₓ), nitrate (NO₃⁻), and ammonium
(NH₄⁺). This can harm agroecosystems, causing soil, water, or air pollution, GHG emissions, and loss of biodiversity. Nitrogen loss occurs through many interacting processes, integrated models help evaluate initiatives to reduce it (Franqueville et al., 2018).

### 3.3. Data sources and abstractions

The DT prototype aims to establish a conceptual and methodological basis covering aspects related to infrastructure, integration, interoperability, functionality and objectives. A group of use cases and initial data sources are fixed, but under a structural design that allows the natural extension and modification of the DT in terms of its components, attributes and applications. Here we present preliminary data resources:

* **LIG'AIR** (Lig'Air, 2024) provides access to air quality data through APIs (Application Programming Interfaces). Planned data for the DT prototype are:

- (hourly) *concentrations of pollutants* collected from 25 measuring stations in the region (NOₓ, PM₁₀, PM₂.₅, O₃, NH₃).

- (hourly) *concentrations of Black Carbon* collected from 2 measuring stations.

- (annual, seasonal) *emissions of pollutants and GHG* (up to 44 pollutants) by sectors (agriculture, industry, transportation, etc.).

- (daily) *ATMO* index, i.e., three-day scope air quality predictions/evaluations.

* **MÉTÉOFRANCE.** An API for accessing data from French weather stations (RESO40, WMO) is supported by the Infoclimat network (StatIC) and Météo-France (SYNOP) (Infoclimat and Météo-France, 2024). The API allows downloading data in CSV/JSON or to request it automatically via a private key. Planned data sources for the DT prototype are:

- (3-hour sampling) *temperature, pressure, humidity, dewpoint, solar radiation, wind (mean), wind (gust), wind (direction), rain (3 h), rain (1 h)*.

Moreover, **SAFRAN** meteorological models are also used, which provide consistent atmospheric data with high spatial and temporal resolution. They were originally applied for avalanche hazard, but also for hydrological monitoring and estimation throughout France. The DT is primarily fed with:

- (daily) *effective rainfall*.

- (daily) *potential evapotranspiration*.

* **ERA5** is a detailed record of global weather patterns, part of the Copernicus Climate Change Service (C3S). The Climate Data Store (CDS) API is a tool for accessing ERA5 databases. Data are broken down into grids covering an area of 30 km per square. It gives insights into the atmosphere, land, and oceans, using 137 layers from the ground up to 80 km high (Copernicus Climate Change Service (C3S), 2024). Planned data sources for the DT prototype are:

- (hourly) *precipitation*.

- (hourly) *evapotranspiration*.

Additionally, the DT will also use satellite data from Sentinel-5P L2, provided by the European Space Agency (ESA), and related to crucial aspects of air quality and atmospheric composition, e.g., CH₄, CO, HCHO, NO₂, SO₂.

* **ADES.** Hub'Eau provides Representational State Transfer (REST) APIs for accessing data in common formats (CSV, JSON, GeoJSON). Particularly, the "Piezometry" API data comes from ADES (national portal for access to groundwater data) and covers piezometry measurements (water level in groundwater) of the whole France (Hub'Eau, 2024). Data sources for the DT prototype are:

- (hourly/daily) *piezometric*, groundwater levels.

* **JUMP (BRGM).** The CRCEN JUMP project sets up highly controlled experimental conditions to analyze how water and contaminants pass through the soil. Sensor measurements are taken from a lab-column soil by core sampling for physico-chemical and microbiological analyses (microbial quantification and diversity: Mircoresp, DNA quantification, qPCR 16S, degradation genes) of the different levels. Data are accumulated on BRGM servers and accessed through private APIs. Data sources for the DT prototype are:

- (hourly) *humidity and temperature* of the experimental setup.

- (weekly) experimental *measurements and analysis*: pH, Eh, dissolved O₂, conductivity, major cations and anions, soil gases, dissolved organic carbon/nitrogen, pesticide concentration (glyphosate, atrazine and metolachlor).

- (seasonal, yearly) *quantification and microbial diversity, biodegradation*.

* **Info&Sols (INRAE, OS²).** Info&Sols develops statistical and geo-statistical approaches for the spatial analysis of soil organization and functioning. Among diverse initiatives, it implements innovative geostatistical techniques to extract information from sensor data to reveal map soil properties. Through the OS² — Observatoire Spatialisé Orléanais des Sols, 20 km2, Centre-Val de Loire Region (Grossel et al., 2016) — the characterization of N₂O emissions has been performed since 2009 at 2 to 9 places. Automatic chambers will be installed in one field and provide high frequency measurements. Planned data sources for the DT prototype are:

- (4-hour sampling) *N₂O flux*.

- (2-hour sampling) *meteorological data*: air temperature, precipitations, wind.

- (2-hour sampling) *soil status measurements* (water content and temperature).

- *Soil characterization* database.

* **PESAT-SNO Tourbières.** PESAT (OSUC-Univ. Orléans) and SNO Tourbières observatory (OZCAR res. infras. - CNRS, https://www.sno-tourbieres.cnrs.fr/) acquire data from observations and experiments to estimate the effect of global and local anthropogenic disturbance on peatland carbon cycle, especially CO₂ and CH₄ (Gogo et al., 2021). In the peatland selected for the DT (La Guette, Nb, Sologne), monitored parameters are:

- (hourly) 14 *meteo-soils variables*: photosynthetic photon, soil heat flux, etc.

- (hourly) 5 *GHG values* throughout eddy-covariance flux tower.

- (hourly) 5 *biogeochemistry parameters*: conductivity, pH, DOC, etc.

- (hourly) 2 *hydrology features*: water table depth, water outflow rate.

- (seasonal, yearly) *biology feature*: green area index.

Fig. 3 shows data sources referenced by acronyms or abbreviations. This same figure also links data assets to elements of the Organization viewpoint.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Overview of the DT showing represented natural resources, data sources, functional models and use cases.</figcaption></figure>

### 3.3.1. Generated models

Building on the data sources described above, the DT prototype automatically generates and adapts a variety of models. These models provide interpolation and extrapolation capabilities (from measurement points to spatial areas), as well as prediction functionalities (from past observations to future values). They can also construct maps that may be benchmarked against external references and thresholds, and further enriched through integration with knowledge bases. The initial set of models under development in the DT prototype includes: (1) *air quality and pollutant concentration models*, (2) *water-level models*, and (3) *N₂O soil emission models*. In addition, the prototype incorporates (4) a *knowledge base* of pollutants and soil properties, and (5) a suite of numerical and non-linear *water/soil models* to characterize pollutant transport and filtration dynamics in soil and water.

A representative modeling challenge faced by the DT — whether for descriptive analysis, clustering, or predictive tasks — is the handling of multivariate time series collected from diverse geolocated yet interrelated measurement points (Mbouopda et al., 2023).

### 3.3.2. Temporal consistency of data streams

In the DT prototype, temporal coherence across heterogeneous sources (e.g., hourly, daily, or seasonal) is ensured during ingestion by a data management layer (see Section 4.4) through a uniform sampling policy that harmonizes observation intervals. Basic integrity checks are performed prior to database storage, but, in principle, no interpolation or resampling is applied at this stage, as such operations depend on the analytical context. Applications consuming the data may subsequently perform temporal alignment, aggregation, or interpolation as required by specific use cases.

## 3.4. Operational schema

From a broad perspective, the DT prototype can be described in terms of its main *functions* and how these are conceptually arranged into blocks that perform processing, applications, or services relevant to research activities. Broadly speaking, the computing objects of the DT can be grouped into two categories:
* Elements that generate and apply prediction or interpolation models, as well as tools for visualizing, contrasting, and interpreting historical data and accumulated knowledge.

* Tools that support a set of interdisciplinary digital goals, oriented both to scientific research and to industrial/social exploitation.

These functions are performed at two different levels depending on whether or not there is interaction with DT users. In general terms, we refer to them as front-end and back-end operations.

## FRONT END

At the user-facing level, the DT exposes three main layers:

1. **Visualization Layer**: reporting of the current state of natural resources. This is the public face of the DT prototype (dashboards, plots, applications and information).

2. **Applications Layer**: a collection of workflows, services and analyses as dedicated environments or embeddable widgets; in the DT prototype, these align with defined use cases.

3. **Data Layer**: access to data assets (and models or other information resources) through query builders with comprehensive configuration. This layer provides full data downloads to support complex scientific workflows that may use the DT primarily as a data provider.

## BACK END

In the background, the DT prototype encapsulates computing processes in three functional layers:

1. **Face**: operations that query main databases.

2. **Limbs**: operations triggered by users that query external data sources.

3. **Back**: initialization, refresh and maintenance processes.

These layers define how functionality is logically organized before considering their technical implementation. Fig. 4 offers a view of distribution components. Note that the DT depends on both internal and external data sources (we mean “external” with regard to JUNON partners and their owned resources). External data may be used on demand or ingested into internal repositories. In all cases, data are *validated* (consistency, compatibility and compliance checks) after normalization, merging or summarization steps.

### 3.5. Engineering components

At this level, we refer to subsystems, networks, infrastructure, servers and other physical components, as well as to software considered as units beyond their purely functional role (e.g., a data integration platform as a system).

#### 3.5.1. Data integration

A data architecture defines how data is managed across the DT — from collection to consumption — including transformation, storage and distribution. Closely related, data integration combines data from diverse sources to form a unified system view aimed at analytical and operational use. Scientific data is typically more complex than business data; hence, standard integration solutions may not suffice for the DT at scale. Application requirements and scientific goals should drive architectural choices rather than criteria unrelated to scientific functionality (Ludäscher et al., 2006).

In non-scientific domains (e.g., market, logistics, industry), the traditional objective of data integration is to offer a global view over multiple sources within a single domain. In scientific applications, however, part of the work is to determine not only which sources are needed but also how they must be brought together for experiments and analyses (Ludäscher et al., 2006). This calls for architectures and

enabling technologies flexible enough to support advanced querying, indexing and data representations (Lapatas et al., 2015).

Therefore, from an engineering perspective, having an agile and versatile data integration platform that provides effective and efficient support for computational elements is key. In addition to the planned uses, data exchange patterns, formats, and protocols are also decisive factors to take into account in this regard.

#### 3.5.2. Exchange patterns

The DT prototype adopts three architectural patterns depending on data characteristics and operational constraints:

1. **APIs**: used for external services and heterogeneous data sources that are frequently queried and subject to change. These Web Service interfaces offer flexibility in data formats and configurations, enabling robust application-to-application communication.

2. **File Transfer**: employed for large data transmissions, platform updates, or versioned data deliveries.

3. **ETL (Extract-Transform-Load)**: applied to core databases and warehouses to guarantee validation, structural integrity, and compliance with predefined schemas.

Additionally, a Streaming Data architecture is generally required when multiple sources continuously push data to a receiver that must promptly process, analyze, and disseminate information in near real-time (e.g., network traffic monitoring). In our case, although most DT data are timestamped, our prototype does not adopt a streaming paradigm, since envisioned use cases currently operate at timescales no shorter than daily/hourly resolution.

#### 3.5.3. Formats and protocols

Consistent with the exchange patterns, the DT prototype primarily relies on CSV, JSON, and NetCDF formats, while also supporting other structured and unstructured artifacts (e.g., images, n-dimensional matrices, graphs, shapefiles, container files).

* CSV is a lightweight text format organizing data in rows and columns. Within the DT, it is typically used to represent time series of multiple variables for a given geographical location.

* JSON provides greater structural flexibility, embedding metadata and syntactic markers. In the DT, it often carries arrays or time series similar to CSV, but with explicit contextual information — such as location — encoded within the data.

* NetCDF is a binary format designed for array-oriented scientific data. It efficiently supports complex multi-dimensional structures (e.g., 2D/3D spatial grids), making it suitable for geospatial and environmental datasets.

Regarding Communication Protocols, APIs operate mainly over Hypertext Transfer Protocol Secure (HTTPS), file transfers prioritize security through File Transfer Protocol Secure (FTPS), while both HTTPS and FTPS are also preferred for ETL connectivity.

#### 3.5.4. Distribution architecture

Fig. 4 (left) provides a schematic operational view of the DT prototype at the distribution level. A dedicated DATA Network hosts the core components, including repositories of analysis results, aggregated and precomputed data, validated models, and high-quality benchmarks. It also supports scheduled data tasks, advanced analyses, and resource-intensive AI processes (e.g., Deep Learning training, clusters, Graphics Processing Units (GPUs), specialized hardware). Within this network, a server for external access, databases, and integration-control services are deployed, enabling both internal and external interactions with JUNON through APIs (primarily REST). Downstream, each sub-DT (DT1, DT2 and DT3 in Fig. 4) connects via these APIs and maintains its own network, repositories, and computational resources.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Diagram illustrating distribution components of the DT prototype. The DATA network is responsible for maintaining and processing data from both internal (JUNON) and external sources via APIs. Users primarily interact with aggregated and preprocessed data through the DATA network using Face operations, while access to the original data sources is provided through Limb operations.</figcaption></figure>

Connecting with Section 3.4, from an engineering perspective the Back End processes determine data exchange patterns and structural components. These can be grouped into three categories, consistently mapped to the DT distribution layer (Fig. 4):

1. **DT-face:** Operations that query the main DT databases are mapped to local database servers and ETL jobs within the DATA network, typically executed between the visualization server and DT-face warehouses (ETL over HTTPS).

2. **DT-limbs:** User-triggered operations accessing external services are implemented via secure REST APIs (HTTPS); for very large data transfers, FTPS may be employed.

3. **DT-back:** Initialization, refresh, and maintenance operations are supported by scheduled tasks, container orchestration, and system services. Background updates and provisioning include both minor changes via REST APIs and major updates through File Transfer, while ETL governs cleaning, processing, and updating of DT-face databases, thus integrating the three patterns above.

### 3.6. Selected technologies

The design of the DT prototype builds on the technologies presented in Fig. 5. When selecting technologies, it is essential to account for dependencies between conceptual designs and their actual implementation, as well as potential constraints arising from budgetary limitations or from non-technical requirements (e.g., internal policies or strategic decisions) that mandate the use of specific platforms or legacy solutions. In the current DT prototype, however, the technology stack has been chosen primarily on technical grounds — namely quality, suitability, and flexibility — while also fulfilling the requirement to rely on public, open-source, and standardized technologies with broad support and acceptance within technical and scientific communities. Main technologies used in our DT prototype are:

* **FIWARE** is a data integration platform promoted at the European level. It is fully open-source, publicly available, and free of charge, providing standardized APIs and an architecture particularly suited for IoT environments where data is dynamically generated. Its broad adoption and extensive ecosystem make it a reliable foundation for DT development. A detailed analysis of FIWARE's features is provided by Cirillo et al. (2019).

* **MongoDB** is a source-available, document-oriented NoSQL database that stores data as JSON-like documents with dynamic schemas. It facilitates integration of large volumes of semi-structured or unstructured data, particularly in evolving IoT environments. Although it does not natively support ontologies or Linked Data, it can host JSON-LD or RDF triples and interoperate with external tools for semantic processing.

* **NGSI-LD** (Next Generation Service Interface-Linked Data) is a standard developed by ETSI for managing and exchanging context information in a structured and interoperable way, targeting domains such as smart cities, IoT, and DTs. It defines data model templates for structuring contextual elements and has been explicitly conceived for DTs. NGSI-v2 represents a simplified version of this specification.

* **Docker** is a containerization technology that encapsulates software components, decoupling them from the underlying infrastructure. It enables modular and simplified management, reproducibility, resource sharing, rapid prototyping, scalability, and effective integration testing, making it well-suited for data integration projects.

* **Python** is a high-level programming language with wide adoption across web development, data science, AI, and automation. Its active community and extensive ecosystem of libraries facilitate the implementation of complex tasks and accelerate prototyping.

* **Plotly-Dash** is a Python-based framework for building interactive web applications and dashboards, particularly for data visualization. It allows the development of custom interfaces and dynamic
<figure><img src="image_7.png" /><figcaption>Fig. 5. Technologies grounding the DT prototype.</figcaption></figure>

Simple Water/Meteo-DT Query - Science Viewpoint

<figure><img src="image_8.png" /><figcaption>Fig. 6. Example of modeling the Scientific Viewpoint with UML diagrams.</figcaption></figure>

plots without requiring expertise in JavaScript or other web development tools.

## 4. UML modeling example

In this section, we illustrate how the ENVRI RM recommendations can be applied to model the architecture of a DT prototype. We present a multi-viewpoint representation to highlight how the same system is perceived differently across the Science, Information, Computational, Engineering, and Technology layers. For clarity, the example is scoped to a selected part of a representative use case from the PoC described in Section 5, leaving aside elements that are not necessary to understand the example. This focused view is sufficient to demonstrate how the DT prototype is expressed through distinct viewpoints and how UML supports the abstraction of each layer.

### 4.1. *Science Viewpoint*

We model a very basic *use case* and we call it **Simple Water/Meteo-DT Query**. Here, a Scientific User accesses the DT to query water levels

and meteorological phenomena in the region. Fig. 6 shows the diagram, which comprises necessary *objects* to understand the process from the Science Viewpoint. The “Scientific Concept” labels will serve us as *correspondences* to connect viewpoints.

### 4.2. *Information Viewpoint*

From the point of view of the information handled by the system, we define different data entities necessary to satisfy the example use case. The Scientific User consults the DT using a UserQuery with details related to the period they want to consult (start, end), the location in the region (*geolocation*), the sampling frequency (*sampling_freq*), and the specific variables related to water levels and meteorological information (*variable selection*). Based on the UserQuery, the DT will return aggregated time series (identified with the WaterLevelTS and MeteoTS objects). Internally, the DT defines Data Entities that link metadata with sensor units of water levels and meteorological phenomenon (MeasurementPoint, WaterLevelSensor, MeteoSensor). Fig. 7 shows the corresponding UML model.
## Simple Water/Meteo-DT Query - Information Viewpoint

<figure><img src="image_9.png" /><figcaption>Fig. 7. Example of modeling the Information Viewpoint with UML diagrams.</figcaption></figure>

### 4.3. Computational Viewpoint

The Computational Viewpoint breaks down functionality into conceptual components (services, processes, modules) and how they communicate with each other, thus modeling the functions that exist and how they are orchestrated. In our example, we have defined a few data processes: Initialization, Bulk Load, Scheduled Ingestion, and Data Query. These meet the following requirements:

* Initialization creates Data Entities and subscribes some variables (e.g., groundwater levels) in dynamic management to allow subsequent aggregation into time series. It then calls Bulk Load.

* Bulk Load calls the Data Integration service to populate databases with historical information. In principle, Bulk Load is only used during Initialization, as well as during database maintenance and corrective procedures.

* Scheduled Ingestion is run at regular intervals (e.g., once a day) to update databases. This is also carried out through the Data Integration process.

* Data Query represents a user query to obtain aggregated data.

The Data Integration process comprises obtaining raw data from external sensor sources (Water Data Ingestion and Meteo Data Ingestion), preprocessing it, normalizing and transforming it into standard data models (Data Transformation), and storing it in databases (Data Storage). Fig. 8 shows the corresponding UML model.

### 4.4. Engineering Viewpoint

The Engineering Viewpoint specifies how the hardware and software components are distributed in the DT prototype and how they interact. In our example, two networks are defined: an Internal Network and an External Network. The External Network provides access to authoritative data sources through the Water Data API and the Meteo Data API. Within the Internal Network, the Dashboard hosted on the Web Server enables user interaction and visualization. The Data Manager hosts several components:

* The Matching Component, which ensures that the list of measurement points available in the DT matches the official authorized sources, provides appropriate metadata and identification information, and thus enables the creation of Data Entities.

* The MeasurementPoints Manager, which creates and updates entities such as MeasurementPoints, WaterLevelSensors, and MeteoSensors, and executes the configuration that allows certain variables to be aggregated over time.

* The Water Data Manager and Meteo Data Manager, which retrieve data from the respective external APIs.

* The Database Queries, which manages read/write operations and other interactions with internal databases.

The Data Integration Network enables smooth data exchange within the DT system, primarily through the Context Broker, which manages context entities and feeds the internal storage. This is achieved mainly by orchestrating the Aggregation Writer (which forwards aggregated data) and the Aggregation Queries (which exposes historical data). Finally, the Database Server hosts the Database Storage, which stores both raw and aggregated data.

Fig. 9 illustrates the overall data flow across components, from external sources to internal storage and queries.

### 4.5. Technology Viewpoint

The Technology Viewpoint specifies concrete technologies and deployment platforms supporting the DT prototype. In the PoC (Section 5), the entire Internal Network is deployed on a single machine, with each component containerized using Docker. The Web Server hosts the Dashboard, implemented in Plotly, Dash, and Python. The Data Manager Server runs Python-based components: the Matching Component, MeasurementPoints Manager, Water Data Manager, Meteo Data Manager, and Database Queries. Additionally the Water Data Manager and the Meteo Data Manager adapt to NGSI-v2 smart information models. The Data Integration Network is based on FIWARE, including the Context Broker (Orion), Aggregation Writer (Cygnus), and Aggregation Queries (STH-Comet). The Database Server relies on MongoDB for the Database Storage. The external network components correspond to the Water Data API (Hubeau Connector) and the Meteo
Simple Water/Meteo-DT Query - Computational Viewpoint

<figure><img src="image_10.png" /><figcaption>Fig. 8. Example of modeling the Computational Viewpoint with UML diagrams.</figcaption></figure>

Simple Water/Meteo-DT Query - Engineering Viewpoint

<figure><img src="image_11.png" /><figcaption>Fig. 9. Example of modeling the Engineering Viewpoint with UML diagrams.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Fig. 10. Example of modeling the Technology Viewpoint with UML diagrams.</figcaption></figure>

Data API (Safran Connector). This viewpoint thus makes explicit the technological stack and deployment choices underlying the logical and engineering specifications. Fig. 10 summarizes the mapping between architectural components and their supporting technologies.

## 5. Proof of concept

This PoC was designed as a small-scale implementation to validate key components and assumptions of the architecture described in Section 3. It adheres to the same conceptual and methodological criteria defined in the proposed DT architecture, serving as a preliminary evaluation of its feasibility and internal consistency. Since the POC is also based on the ENVRI RM, it inherits its technological principles and interoperability patterns. However, its scope is deliberately constrained: the entire setup is deployed locally in dockerized modules, enabling controlled testing of data exchange and communication processes without addressing large-scale networking aspects beyond the containerized FIWARE-based environment. Hence, the PoC is deployed in dockerized modules that can be executed on a single machine.

Both in the architecture and in the PoC, we opted to use dockerized components, as containerization is increasingly adopted in data integration platforms and infrastructures across both science and industry. It effectively mitigates one of the main barriers to scientific reproducibility by encapsulating all runtime dependencies, configurations, and inter-service communications into portable and version-controlled environments. This ensures that identical workflows can be reliably executed across different machines and time periods, regardless of software changes or system evolution.

The FIWARE-based network includes the following components: Data Manager, Scheduler, Web Application, MongoDB, Orion Context Manager, Cygnus, and STH-Comet (Fig. 11).

The PoC integrates external repositories — MÉTÉOFRANCE-SAFRAN, ADES, and Folium (Folium contributors, 2015) — via their official APIs. It supports multiple use cases in which the DT prototype collects and processes data from 53 piezometric sensors strategically located across the Centre-Val de Loire. These monitoring sites, selected by BRGM experts, are critical to understanding the hydrological functioning and long-term evolution of the region. The sensors provide daily measurements of aquifer water levels (ADES source). These observations are combined with estimates of potential evaporation, total precipitation, and air temperature at 2 m height for the same locations (MÉTÉOFRANCE-SAFRAN source).

By default, the repository database (Iglesias Vázquez, 2025) contains data spanning from January 2020 to July 2025.

Observations are persisted in two MongoDB databases. The first, **orion-junon**, stores metadata in 2 collections (152 documents, ~0.17 MB), while the second, **sth-junon**, contains 2.8 million documents across 194 collections (~8.2 GB), including both raw measurements and aggregated time series. Data retrieval through Orion and Cygnus exhibited negligible latency in our local tests, with delays dominated by analytics operations (e.g., ML-based forecasting, model training) rather than database access. Although large-scale quantitative scalability tests were beyond the scope of this work, the architecture relies on proven scalable tests (e.g., Orion Context Broker interacting with MongoDB via Cygnus Martínez et al., 2016), ensuring adaptability to larger deployments. Users can extend the temporal coverage to include earlier or more recent periods.

Before storing data in MongoDB databases, external data sources are transformed by the Data Manager to ensure schema harmonization and semantic compatibility with the FIWARE ecosystem. In this PoC, the harmonization follows the NGSI-v2 information model and associated Smart Data Models, which define standardized attribute names, units, and entity structures for interoperability. Although NGSI-LD ontologies and ISO 19115 metadata descriptors are not yet enforced, the current design maintains full compatibility for future adoption at ingestion. Listing 1 shows an example of raw data obtained from the ADES API, while Listing 2 illustrates how the same data is transformed prior to ingestion.

<caption>Listing 1: Example of piezo-data collected from ADES</caption>

```json
{
  "code_bss": "02558X0034/P",
  "urn_bss": "http://services.ades.eaufrance.fr/
    pointeau/02558X0034/P",
  "date_mesure": "2020-01-01",
  "timestamp_mesure": 1577919600000,
  "niveau_nappe_eau": 132.16,
  "mode_obtention": "Valeur mesur\'ee",
  "statut": "Donn\'ee contr\'ol\'ee niveau 2",
  "qualification": "Correcte",
  "code_continuite": "2",
  "nom_continuite": "Point li\'e au point pr\'ec
    \'edent",
  "code_producteur": "293",
  "nom_producteur": "Service G\'eologique R\'
    \'egional Centre (293)",
  "code_nature_mesure": "N",
  "nom_nature_mesure": "Naturel",
  "profondeur_nappe": 19.38
}
```
<figure><img src="image_13.png" /><figcaption>Fig. 11. FIWARE network and dockerized components in the JUNON DT prototype PoCv4.0.</figcaption></figure>

<caption>Listing 2: Example of piezo-data used in the FIWARE network</caption>

```json
{
    "id": "urn:ngsi-ld:WaterLevelMeasurement:02558
           X0034_P",
    "type": "WaterLevelMeasurement",
    "groundwater_depth": {
        "type": "Float",
        "value": 19.38,
        "metadata": {
            "TimeInstant": {
                "type": "DateTime",
                "value": "2020-01-01T00
                        :00:00.000Z"
            }
        }
    },
    "groundwater_level": {
        "type": "Float",
        "value": 132.16,
        "metadata": {
            "TimeInstant": {
                "type": "DateTime",
                "value": "2020-01-01T00
                        :00:00.000Z"
            }
        }
    },
    "refMeasurePoint": {
        "type": "Relationship",
        "value": "urn:ngsi-ld:MeasurePoint
                    :1.754-48.422",
        "metadata": {}
    }
}
```

Folium is employed to generate interactive geographic maps. The DT prototype provides services for: (a) historical data visualization, (b) time series forecasting with or without exogenous inputs, (c) clustering of multivariate time series, and (d) spatial interpolation of two-dimensional water-level maps.

Advanced ML methods have been integrated into these services, including, among others, predictive time series analysis through the Darts package (Herzen et al., 2022), with nonlinear autoregressive models with exogenous variables (NARX) using Random Forest, XG-Boost, or Linear Regression (Maçaira et al., 2018), Auto-ARIMA (Azul Garza and Max Mergenthaler Canseco, 2022), Prophet (Taylor and Letham, 2018), TiDE Neural Network (Das et al., 2024), and LSTM Neural Network (Hochreiter and Schmidhuber, 1997). Clustering capabilities include last-generation parameter-robust and self-adjusting algorithms such as CLASSIX (Chen and Güttel, 2024) and SDOclust (Iglesias Vázquez et al., 2025), as well as time-series-specific approaches like

k-means with Soft and Barycenter Averaging Dynamic Time Warping (DTW) metrics (Anh and Thanh, 2015).

As an illustration, Fig. 12 shows a high-resolution view of an interpolated groundwater-level map of the Beauce Aquifer on August 10, 2023. This view was obtained using a dedicated service of the PoC Web Application for comparing water-level distributions across different dates.

Another example of an included service in the PoC is given in Figs. 13 and 14. Fig. 13 shows the clustering configuration (left) for multivariate time series, spanning over longer than five years with daily sampling. Each measurement point is represented by the four signals: groundwater_level, temperature_oC, potential_evapo-transpiration_mm, and liquid_rainfall_mm. The workflow first applies univariate clustering using SoftDTW_kmeans with $k = 5$. The outcomes are then contextualized and projected into a two-dimensional space using t-SNE (T-distributed Stochastic Neighbor Embedding), and a final clustering step is performed with SDOclust, which does not require specifying $k$. The discovered clusters are also identified by color in the regional map (Fig. 13, right). Fig. 14 shows the centroid after selecting a specific cluster and variable for exploration. Since DTW is used as a metric, the clustering criterion emphasizes shape similarity over absolute magnitude. To clarify how this service integrates into the DT architecture, Fig. 15 presents a conceptual end-to-end data pipeline, spanning from data ingestion to dashboard-based visualization. Data ingestion is performed either on a scheduled basis (Scheduler) or at initialization and update times (Data Manager), whereas data querying is executed on demand via the Web Application. The diagram is intended as an illustrative example of the PoC that reflects the flexibility of the proposed architecture and, consequently, does not impose any methodological constraints.

Finally, Fig. 16 shows an example of the prediction service integrated in the PoC, where the Prophet algorithm is used to predict the water level (groundwater_level) at station 02913X0024/P (FRANCOURVILLE) over a 30-week horizon using rainfall (liquid_rainfall_mm) and temperature (temperature_oC) as endogenous inputs.

The PoC is publicly accessible and fully documented to ensure reuse and reproducibility via a DOI-citable repository (Iglesias Vázquez,
## Simple Kriging Contour Map

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** View of an interpolated groundwater-level map of the Beauce Aquifer on August 10, 2023, generated by the PoC Web Application. The visualization corresponds to the PoC service used for comparing groundwater-level distributions across different dates.</figcaption></figure>

## Clustering

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Screenshot of the JUNON DT prototype (PoCv4.0) Web Application illustrating an example of multivariate time series clustering. Left: configuration parameters, including time span, sampling rate, selected variables, analysis method, and distance metric. Right: geographical placement of measurement points in spatial coordinates, colored by cluster assignment (5 clusters and outliers shown in black). Clusters are obtained by combining K-means with DTW, t-SNE embedding, and SDOclust.</figcaption></figure>

2025). It is important to note that the services currently implemented in this PoC are not intended as fully optimized solutions for end-user applications, but rather as demonstrative modules designed to validate the feasibility of the overall approach at the architectural and communication levels. They provide a functional environment to test data flows, orchestration, and interoperability across components, ensuring that the foundational layers of the DT can operate reliably. This stage confirms the viability of the concept and paves the way for a subsequent phase in which the services will be refined, extended, and tailored to specific use cases with greater detail and rigor.

## 6. Conclusion

In this article, we have presented a conceptual architecture for Artificial Intelligence (AI)-assisted Digital Twins (DTs) in natural resource management, illustrated through a prototype focused on the Centre-Val de Loire. Elements belonging to the Science, Information, Computational, Engineering, and Technology layers have been described, following the ENVRI RM methodology. We also demonstrated the implementation of a dockerized and reproducible proof of concept (PoC) based on FIWARE, showing how a functional DT can be deployed at a small scale.
<figure><img src="image_17.png" /><figcaption>Fig. 14. Screenshot of the JUNON DT prototype (PoCv4.0) Web Application showing the centroid time series of groundwater level for the discovered Cluster 1 under the setup described in Fig. 13. The x-axis represents time with daily sampling (displayed at yearly resolution), while the y-axis shows groundwater level in meters above sea level (m.a.s.l.).</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 15. Example of high-level end-to-end dataflow of the DT architecture. It illustrates the integration of clustering analytics as an on-demand service. The diagram depicts the progression from data ingestion to visualization across four phases (1–4) and four architectural levels: Data ingestion, Data access, Representation & Analytics, and Visualization.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Fig. 16.** Screenshot of the JUNON DT prototype (PoCv4.0) Web Application illustrating groundwater-level prediction using the Prophet model, supported by the endogenous variables *rain* and *temperature*, at station 02913X0024/P (FRANCOURVILLE) over a 30-week forecast horizon. The plot displays the observed and predicted groundwater-level time series, together with error and distance metrics shown in the lower part.</figcaption></figure>

In short, **main contributions** of this work are: (i) a conceptual architecture for environmental DT aligned with the ENVRI RM framework; (ii) a dockerized, FIWARE-based PoC demonstrating practical feasibility; and (iii) a set of services illustrating its potential in water resource management.

The proposed design highlights how DTs can support sustainability-oriented decision making by integrating heterogeneous data, enabling predictive modeling, and fostering collaboration across scientific and technical domains. By providing an open, extensible, and interoperable prototype, we pave the way for further research on scalable, AI-assisted architectures for environmental DTs. Moreover, the case of the Beauce aquifer illustrates the urgent need for advanced monitoring and management of vulnerable water resources. A DT of this aquifer would allow simulating future scenarios, supporting real-time monitoring, and guiding informed strategies to ensure its long-term sustainability.

Concerning **open issues and future steps**, despite the advances achieved, several limitations remain — particularly regarding data availability, model scalability, and stakeholder integration — that must be addressed in subsequent developments. Future efforts will therefore focus on extending the prototype's coverage through co-design with regional stakeholders (scientists, farmers, and public agencies), developing tailored AI services, and enhancing the DT's usability through improved dashboards and storytelling approaches. The current services, mainly focused on water quantity, will be complemented by components addressing water, air, and soil quality, as well as their interrelations. Building upon these preliminary designs, future work will include pilot deployments and pre-production testing in dedicated infrastructures.

The **societal relevance** of AI-assisted DTs for natural resource management extends beyond technological innovation, addressing pressing global sustainability challenges. International frameworks — including COP30 under the UNFCCC, the UN Decade on Ecosystem Restoration, and the Convention on Biological Diversity — underline the urgent need for transparent and reliable digital initiatives to monitor environmental change, evaluate interventions, and ensure accountability. In this context, the DT architecture presented in this work illustrates how standardized and reproducible digital infrastructures can contribute to the integrity and efficacy of environmental and climate-related projects at regional and broader scales.

### CRediT authorship contribution statement

**Félix Iglesias:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Conceptualization.
**Frédéric Ros:** Writing – review & editing, Supervision, Conceptualization.
**Lynh Hoang Vy Thuy:** Writing – review & editing, Software, Formal analysis.
**Laurence Gourcy:** Writing – review & editing, Resources, Conceptualization.
**Jean-Sébastien Moquet:** Writing – review & editing, Conceptualization.
**Véronique Daële:** Writing – review & editing, Conceptualization.
**Sébastien Dupraz:** Writing – review & editing, Project administration, Conceptualization.

### FAIR compliance

This work adheres to the FAIR data principles. It will be published with a DOI and archived in HAL, ensuring findability and long-term accessibility through OAI-PMH and OpenAIRE standards. The complete proof of concept (Iglesias Vázquez, 2025) provides open documentation, datasets, and ISO-compliant metadata for reproducibility. Interoperability is achieved through alignment with the ENVRI RM and the adoption of Smart Data Models, NGSI-LD, NGSI-v2, and open-source technologies such as FIWARE, Python, Docker, and MongoDB, enabling seamless integration across heterogeneous data sources. The provided containers and openly available components further ensure software and data reusability.

### Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT in order to refine the intelligibility of the text. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

### Funding

This work has received support under the JUNON Program, with the financial support of the Région Centre-Val de Loire (France).
Declaration of competing interest

The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.

Acknowledgments

The authors acknowledge TU Wien Bibliothek, Austria for financial support through its Open Access Funding Programme. Additionally, we thank the LE STUDIUM Institute for Advanced Studies in the Loire Valley for fostering a stimulating environment for scientific exchange, and particularly Sophie Gabillet for her excellent management.

Data availability

Data and codes available in: Iglesias Vázquez, F. (2025). Proof of Concept: JUNON Digital Twin (v1.4d). TU Wien. https://doi.org/10.48436/b615a-vgd97.

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