Call for abstracts EGU25 session HS4.10: Recent advances in (hybrid) hydrological forecasting using physically-based and machine learning models

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Hauswirth, S.M. (Sandra)

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Nov 20, 2024, 7:01:37 AM11/20/24

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Dear HEPEXers,

Are you working on topics related to (hybrid) hydrological forecasting? Or would you like to learn about it at the next European Geosciences Union (EGU) General Assembly 2025, which will take place from 27th April – 2nd May 2025 in Vienna, Austria?

Then we are pleased to invite you to submit your abstract to our session on ‘HS4.10 Recent advances in (hybrid) hydrological forecasting using physically-based and machine learning models’. Abstract deadline is the 15th of January 2025 at 13:00 CET!

For more information (also see below) and link to submission: https://meetingorganizer.copernicus.org/EGU25/session/51576

Looking forward to your abstracts and meeting you, virtually or on-site in Vienna!

On behalf of the conveners,

Kind regards,

Sandra Hauswirth
Postdoctoral Researcher

Department of Physical Geography, Faculty of Geosciences

Utrecht University

Note: apply for financial support by submitting your abstract by 2 December 2024, 13:00 CET (https://www.egu25.eu/guidelines/supports_and_waivers.html)

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Session description:

HS4.10: Recent advances in (hybrid) hydrological forecasting using physically-based and machine learning models

Convener: Sandra Margrit Hauswirth | Co-conveners: Hamid Moradkhani, Ilias Pechlivanidis, Louise Slater

In recent years, there has been a strong increase in the use of machine learning techniques to enhance hydrological simulation and forecasting. These methods are receiving growing attention due to their ability to handle large datasets, combine different sources of predictability, increase forecasting skill and minimize the effect of biases, as well as enhance computational efficiency. Furthermore, the range of implementations is broad, from purely data-driven forecasting systems to hybrid setups, combining both physically-based models and machine learning techniques, from large to local scales as well as different time horizons. These all allow forecasters to address and cover various aspects and processes of the hydrological cycle, including extreme conditions (floods and droughts), which are important for water resources and emergency management.

This session aims to highlight and bring together recent efforts in hydrological forecasting, using machine learning based techniques and/or hybrid approaches. Contributions are welcome showcasing examples of model developments (ranging from implementations to operational setups), studies ranging from local to global scales and across different time horizons (short-, medium- and long-term), as well as studies showcasing the efforts data-driven/hybrid approaches to tackle challenges in hydrological forecasting. We particularly welcome talks that reach beyond the description of machine learning architectures to uncover physical and human-induced processes, account for uncertainties, generate novel insights about hydrological forecasting, or support efforts in reducing common forecasting difficulties.