Background
Understanding and accurately simulating the terrestrial water cycle is essential for predicting hydrological extremes, managing water resources, and assessing the impacts of climate change. Land-surface and hydrological processes also help constrain the atmosphere and, through their longer memory, can enhance the reliability of forecasts at longer lead times. Physically based hydrological and land-surface models underpin many operational forecasting systems, yet they often struggle to represent nonlinear interactions, parameter uncertainty, and heterogeneous and scale-dependent land-surface processes—particularly at the spatial and temporal scales required for real-time decision-making.
Recent advances in machine learning (ML) provide complementary pathways to improve hydrological and land-surface prediction. These include fully data-driven ML models trained directly from observations, hybrid approaches that combine ML with process-based models, and physics-informed methods that embed physical constraints into learning algorithms. Together, these approaches offer new opportunities for scalable, computationally efficient, and observation-driven prediction systems suitable for operational and pre-operational use. In addition to improving predictive components, ML also enables advances in data assimilation, which can help integrate emerging satellite and in situ datasets into forecasting systems more effectively and at reduced computational cost.
Workshop overview
The workshop will bring together experts in machine learning, hydrology, and land-surface modelling to discuss the development and use of ML-based and ML-enhanced forecasting systems. It will focus on datasets, methods, applications, and evaluation strategies for machine-learning approaches, covering fully data-driven, hybrid, and physics-informed models. Contributions will span both global and regional perspectives, addressing ML-based prediction of streamflow, floods, soil moisture, and key land-surface processes, with an emphasis on robustness, scalability, and operational relevance.
Discussions will address scientific and operational challenges such as data availability and latency, robustness under extremes and non-stationarity, uncertainty quantification, evaluation and benchmarking, and the transferability of ML models across regions, climates, and time scales. By explicitly linking observation-driven ML approaches with operational requirements, the workshop aims to inform the development of the next generation of hydrological and land-surface forecasting systems.
Expected outcomes
-
A synthesis of emerging ML methods and use cases for hydrological and land-surface modelling.
-
Community recommendations for data standards, benchmarking, and evaluation practices.
-
Identification of key research priorities and future directions.
-
Strengthened collaboration between the hydrology, land-surface, and ML communities, including ECMWF Member States.
Format
This workshop is designed exclusively for in-person attendance. There will be no virtual or online attendance option available. We look forward to welcoming attendees on site and engaging face-to-face throughout the event.
The workshop will comprise of invited keynote lectures, contributed oral presentations, poster sessions, and moderated discussions. A half-day session will focus on emerging methods and practical demonstrations.
Attendance
The workshop will take place at ECMWF's headquarters in Reading, UK over 2.5 days from 3 to 5 of November. Participation is open to researchers, operational scientists, and practitioners working in hydrology, land-surface modelling, Earth system science, and machine learning. Priority will be given to registrants from organisations in ECMWF's Member States.
Whilst there are no registration fees, please note that we are unable to provide funding to support attendance. Participants are expected to cover their own travel, meal, and accommodation costs.
If you would like to attend, please complete the registration form before the closing date (see timeline below).
Call for abstracts
Oral and poster presentations contributions are invited across the key topics. If you wish to make a contribution, please complete the abstract submission form before the deadline (see timeline below).
Timeline
1 March 2026: Registration and abstract submission open
26 June 2026: Abstract submission deadline
31 July 2026: Notification of abstract acceptance
30 September 2026: Registration closes