How could a digital twin predict water-based natural disasters?

Have you heard of a digital twin? Scientists have demonstrated the use of next-generation satellite data and advanced modelling to build virtual replicas of the terrestrial water cycle. This new technology can track water resources and create detailed simulations of flooding and other extreme events.

Have you heard of a digital twin? Scientists have demonstrated the use of next-generation satellite data and advanced modelling to build virtual replicas of the terrestrial water cycle. This new technology can track water resources and create detailed simulations of flooding and other extreme events.

The water cycle looks simple in theory. However, human impacts, climate change, and complicated geography mean that floods and droughts remain hard to predict in practice. To model water on Earth, you need incredibly high-resolution data across an immense expanse. Researchers also need modelling sophisticated enough to account for everything from snowcaps on mountains to soil moisture in valleys. Now, scientists funded by the European Space Agency have made a tremendous step forward by building the most detailed models created to date.

“Simulating the Earth at high resolution is very complex. Basically, the idea is to first focus on a specific target,” said Dr Luca Brocca of the National Research Council of Italy, lead author of the article published in Frontiers in Science. “That’s the idea behind what we have developed. It’s a series of digital twin case studies for the terrestrial water cycle in the Mediterranean Basin. We aim to create a system allowing non-experts, including decision-makers and citizens, to run interactive simulations.”

A test environment for the planet

In engineering, a digital twin is a virtual model of a physical object. It can be tested to destruction without doing real damage. A digital twin of the Earth, constantly updated with new data, would allow us to simulate best and worst-case scenarios, assess risks, and track the development of dangerous conditions before they occur. Such information is vital for sustainable development and protecting vulnerable populations.

To build their digital twin models, Brocca and his colleagues harnessed extraordinary volumes of satellite data. They combined new Earth observation data that measures soil moisture, precipitation, evaporation, river discharge, and snow depth. This newly available data is crucial to the development of the models. It includes measurements taken much more frequently across space and time: as often as once a kilometre and once an hour.

This higher-resolution data creates a more detailed picture, like a screen with more pixels. The scientists used this data to develop their modelling and then integrated it into a cloud-based platform that can be utilised for simulations and visualizations. This is the ultimate goal: an interactive tool anyone can use to map risks like floods and landslides and manage water resources.

“This project is a perfect example of the synergy between cutting-edge satellite missions and the scientific community,” said Brocca. “Collaborations like this, coupled with investments in computational infrastructures, will be crucial for managing the effects of climate change and other human impacts.”

Helping people plan the future

The scientists began by modelling the Po River valley, then expanded the digital twin to other parts of the Mediterranean basin. Upcoming projects plan to expand to cover Europe, and future collaborations will allow the same principles to be applied worldwide.

“The story started with an initiative from the European Space Agency,” said Brocca. “I said we should start from something we know very well. The Po River valley is very complex. The Alps and snow are difficult to simulate, especially in irregular and complex terrain like mountains. Then there is the valley with all the human activities – including industry and irrigation. Then we have a river and extreme events, such as floods and drought. And then we moved to the Mediterranean, a good place to investigate extreme events for too much and too little water.”

The platform’s primary use case is to enhance flood and landslide prediction and optimize water resource management. More granular data and more sophisticated modelling will be needed to make this work better on a more local level. For instance, to maximize the potential of a digital twin for agriculture, data resolution should be measured in tens of meters, not hundreds.

Known unknowns

Additional challenges persist. These include delays in transferring satellite data to the model, more ground observations to validate satellite data, and the increasing complexity of the algorithms needed to handle the data. Furthermore, no model is perfect, and satellite data can contain errors. Uncertainties must be characterised appropriately to give users an accurate picture of the model’s reliability. According to Brocca, artificial intelligence and machine learning will be pivotal in overcoming these challenges by enhancing data analysis, collection, processing speed and streamlining data quality assessment.

“The collaborative efforts of scientists, space agencies, and decision-makers promise a future where Digital Twin Earths for hydrology provide invaluable insights for sustainable water management and disaster resilience,” Brocca concluded.

The paper, “A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations,” can be be read here: https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1190191/full

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