interTwin Use Case

A Digital Twin for Flood Early Warning in coastal and inland regions

Developing the components to set up a Digital Twin for flood early warning in coastal and inland regions, with a focus on the generation of flood risk maps that trigger early warning alerts when floods are predicted


The urgency for effective flood early warning systems has intensified lately due to escalating extreme weather patterns driven by human-induced climate change that could be recently observed in Europe and worldwide. These systems are not a luxury but a vital and cost-effective tool, yielding a substantial return on investment by saving lives and minimizing economic losses.


While existing early warning systems have proved successful in reducing fatalities and damages, significant gaps remain, particularly in small island developing states and least developed countries. Merely half of the countries globally possess adequate multi-hazard early warning systems, with even fewer having regulatory frameworks linking warnings to emergency plans.


Bridging these gaps necessitates robust global observing systems, comprehensive flood modeling, and integration with various tools and data, a complex and time-consuming process that nonetheless significantly protects lives and livelihoods from natural hazards like floods. The challenge lies in deploying flood early warning systems, which requires the setup and integration of complex compound flood models with hydrological models, impact assessment tools, and the necessary local and global data. Developing and validating such models can be a time-consuming task, even for expert users, posing a formidable obstacle to the widespread implementation of these critical systems.


The Global Status Report (2022) underscores that countries with extensive early warning coverage experience eight times fewer disaster-related deaths, underscoring the critical role of such systems in bolstering resilience. A 24-hour warning before a hazardous event, as advocated by the Global Commission on Adaptation, can further reduce ensuing damages by 30 percent.



In interTwin, we are actively engaged in the development of thematic and core modules for the Digital Twin Engine (DTE)  regarding flood early warning and flood impact estimation.. This technology will fast-track the implementation and validation of complex compound flood models, addressing a significant challenge in providing flood early warning systems for all.


The ultimate goal is to create capabilities to easily set up digital twins supporting flood early warning in both coastal and inland regions anywhere on Earth.


Our approach utilises Deltares’ Super-Fast INundation of CoastS (SFINCS), an efficient dynamic compound flood model combined with Wflow, Deltares’ solution for modelling hydrological processes. Their integration ensures a comprehensive and accurate representation of flood dynamics, and combined with satellite derived flood maps developed at TU Wien, the flood early warning system’s accuracy and reliability will be further enhanced.


interTwin’s Digital Twin Engine (DTE) will offer a significant leap forward in the implementation of flood early warning systems anywhere on Earth. It will facilitate easy, rapid, and streamlined deployment of flood early warning digital twins, aligning with the United Nations Secretary-General’s urgent call for the establishment of life-saving early warning systems worldwide within the next four years. Through interTwin’s innovative approach, we are at the forefront of harnessing digital technology to mitigate the impacts of flooding and enhance global disaster resilience.



An example flood simulation for Humber, UK, using SFINCS with XYZ input conditions.

The interTwin Digital Twin Engine gives experts the ability to quickly set up and use complicated models and satellite data processing pipelines, allowing stakeholders to keep a close eye on floods and predict their behavior. By offering easy-to-access open-source tools and smooth integration with processing platforms, we're taking steps towards making flood early warning systems more accessible and effective.

Björn Backeberg (Deltares, senior advisor/researcher)

More background

TU Wien has published a  dataset from the Pakistan flood in 2022 for the EC-JRC funded project “Global Flood Monitoring”.

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (, within a dedicated project by the Join Research Centre (JRC) of the European Commission. Open use is granted under the CC BY 4.0 license.

End of summer 2022 Pakistan was hit by one of the most severe floods in decades. The event was covered by multiple satellite-based emergency services, including the Copernicus Emergency Management Service (CEMS) global flood mapping (GFM) component. As part of the project’s consortium, the Technische Universität Wien (TU Wien) developed a dedicated flood mapping algorithm (Bauer-Marschallinger et al. 2022) using the Synthetic Aperture Radar (SAR) satellite Sentinel-1 as an input. The published dataset contains the results of the TU Wien algorithm for the time period August 10 to September 23, 2022 and the covered area is located in the southern part of Pakistan. Besides the binary flood maps, the dataset contains retrieved statistics aiming for presenting the impact of the event as seen from satellite data. With the publication of this dataset, we want to share timely results of our algorithm and support further studies about the event.