Abstract
The Horizon Europe interTwin project is developing a highly generic yet powerful Digital Twin Engine (DTE) to support interdisciplinary Digital Twins (DT). Comprising thirty-one high-profile scientific partner institutions, the project brings together infrastructure providers, technology providers, and DT use cases from Climate Research and Environmental Monitoring, High Energy and Astro Particle Physics, and Radio Astronomy. This group of experts enables the co-design of the DTE Blueprint Architecture and the prototype platform benefiting end users like scientists and policymakers but also DT developers. It achieves this by significantly simplifying the process of creating and managing complex Digital Twins workflows. There are 6 use cases co-designing and validating the interTwin DTE in Climate Reaearch and Environmental Monitoring, ranging from Early Warning for floods and droughts to climate impact assessment, The DTs exploit one of the most promising applications of digital twins of the Earth, the simulation of user-defined what-if scenarios by allowing the selection of a high number of different input datasets, models, models’ parameters, regions and periods of time. This talk will highlight in more technical detail the implementation of drought early warning DT and the utilized components from the digital twin engine. Motivated by the goal of contributing to climate change adaptation measures and recognizing the importance of seasonal forecasts as a crucial tool for early warning systems and disaster preparedness, we are developing a hydrological seasonal forecasting digital twin for the Alpine region to tackle the critical challenge of drought risk management. The analysis of historical observations shows that the pattern and intensity of precipitation and temperature trends are changing over the European Alpine region (Brunner et al. 2023), with important consequences for the management of water resources in the Alpine and downstream basins. The modelling workflow of the proposed forecasting system is based on the integration of physical-based models, artificial intelligence, climate forcings and satellite-based estimates. We believe that the complexity of such workflow can effectively showcase the benefits of developing a digital twin. The project emphasizes reproducibility and portability, adhering to the principles of FAIR (Findable, Accessible, Interoperable, Reusable) and open science to ensure transparency, usability, and widespread applicability of the results. All software components are built as new open-source software (https://github.com/orgs/interTwin-eu/ ) or contributing to existing open-source projects. To achieve these goals, we adopt cutting-edge technologies widely recognized within the Earth Observation (EO) and environmental modeling communities. The openEO API, a standardized interface for processing large geospatial datasets, enables seamless integration of remote sensing data, while the SpatioTemporal Asset Catalog (STAC) API facilitates efficient data discovery and management. Together, these technologies form the backbone of our data pipeline, enabling scalable and efficient workflows. A distinguishing feature of our approach is the use of containerized workflows, implemented using the Common Workflow Language (CWL) (Amstutz et al. 2018). CWL provides a standardized, flexible framework for defining and executing computational workflows, ensuring consistency and repeatability across different computing environments. However, the integration of CWL with APIs like openEO and STAC in the Earth Observation domain presents unique challenges. Real-world examples of such integrations are sparse, requiring us to pioneer innovative solutions that bridge these technologies. This involves addressing complexities in workflow orchestration, data handling, and inter-API communication to build a robust and interoperable system. The ITwinAI core module of intertwin allows seamless integration of data driven modelling with our workflows. It enables development and deployment of complex deep learning models in scalable HPC and cloud environments. The DT is deployable using standard TOSCA templates and utilizes High-Performance Computing (HPC) instances to accommodate the computational demands of large-scale simulations and data processing. This deployment ensures scalability, enabling the system to handle extensive datasets and support a diverse range of applications. By leveraging distributed computing resources, we aim to create a responsive and adaptive framework capable of addressing dynamic environmental challenges. The interTwin project represents a significant step forward in the application of Digital Twins to environmental monitoring and prediction. By integrating state-of-the-art technologies with open science principles, we aim to deliver a powerful tool for drought prediction that is not only accurate and reliable but also accessible to researchers and policymakers. Our work paves the way for broader adoption of Digital Twin technologies in the Earth Observation community, offering a replicable and scalable model for tackling global environmental issues. In doing so, we hope to contribute to the development of resilient and sustainable systems capable of mitigating the impacts of climate change and environmental degradation. Amstutz, P. (Ed.), Crusoe, M. R. (Ed.), Tijanić, N. (Ed.), Chapman, B., Chilton, J., Heuer, M., Kartashov, A., Leehr, D., Ménager, H., Nedeljkovich, M., Scales, M., Soiland-Reyes, S., & Stojanovic, L. (2016). Common Workflow Language, v1.0. figshare . https://doi.org/10.6084/m9.figshare.3115156.v2 Brunner, M. I., Götte, J., Schlemper, C., & van Loon, A. F. (2023). Hydrological Drought Generation Processes and Severity Are Changing in the Alps. Geophysical Research Letters, 50(2). https://doi.org/10.1029/2022GL101776