TRAINING
Post-Conference Trainings (10–11 July, 2026)
A two-day specialized training will be offered after the conference, led by international experts.
Each training session is scheduled for one day, therefore a participant may attend one or two sessions.
The participation fee for each session is €50. All requests to participate will only be considered once the fees have been paid.
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Fill out the form below to request access to a training session:
Note : Please note that submission for the conference is mandatory to benefit from the training. Training-only requests will not be processed.
Request accessTraining Sessions Overview
Day 1-Session 1
Integrated Remote Sensing and Multivariate Statistical Techniques for Hydrological Process Monitoring
This course aims to provide participants with the knowledge and practical skills to integrate remote sensing and multivariate statistical techniques for hydrological process. It covers the use of satellite data and ground observations to assess water cycle components such as rainfall, runoff, evapotranspiration, and groundwater dynamics. Through practical sessions, participants will learn how to apply statistical and data-driven approaches-including principal component analysis (PCA), correlation analysis, and regression modeling, to extract meaningful relationships among hydrological variables and detect trends or anomalies.
Day 1-Session 2
Machine Learning for Hydrological Impacts Mapping
This course introduces the application of Machine Learning (ML) techniques for mapping and predicting hydrological and environmental impacts such as soil erosion, flood susceptibility, and groundwater variations. Participants will learn how to collect, preprocess, and integrate geospatial and remote sensing data into predictive models. Through practical exercises, the course will demonstrate how ML algorithms, particularly Random Forest and other ensemble methods, can be used to generate high-resolution impact maps and support sustainable water and land management. The training emphasizes the methodological workflow: data preparation, feature selection, model training, validation, and map generation. By the end of the course, participants will be able to design and implement ML-based mapping workflows adapted to hydrological and environmental challenges in data-scarce regions.
Pr. Essam Heggy
Research scientist at the Electrical and Computer Engineering Department at the University of Southern California and an affiliate of the Earth Science Division of the NASA Jet Propulsion Laboratory
More detailsDay 1-Session 3
Application of polarimetric SAR and interferometric techniques to investigate the temporal evolution of hydrodynamic properties in watershed systems.
This course provides a rigorous treatment of Synthetic Aperture Radar (SAR) theory, sensor systems, and quantitative analysis techniques for Earth observation with a specific focus on its application for monitoring major river basins. The course covers the physical principles of microwave remote sensing, radar backscatter mechanisms, imaging geometry, polarization, speckle, radiometric calibration, and spatial resolution considerations. Students will evaluate SAR system parameters—including wavelength, polarization, incidence angle, orbit characteristics, and temporal sampling—and assess their suitability for understanding the hydroclimatic evolution and environmental changes in major river basins. Emphasis is placed on interpreting SAR backscatter signatures to discriminate surface properties and land cover types, including vegetation structure, soil moisture, open water, and inundation extent. The course will also introduce interferometric SAR (InSAR) processing workflows, including co-registration, interferogram formation, phase unwrapping, and error sources (e.g., decorrelation, atmospheric artifacts, and orbital inaccuracies). Students will analyze interferometric phase to quantify changes in surfaces and land cover associated with hydroclimatic drivers.
Pr. Christel PRUDHOMME
Expert in global hydrology
The European Centre for Medium Range Weather Forecast
Reading, UK
Mr. Mohamed AZHAR
Associate hydrologist at ECMWF, hydrological data analysis
Day 1-Session 4
Hydrological forecasting and the Global Flood Awareness System (GloFAS) from the Copernicus Emergency Management Service
This course introduces the use of ensemble hydrological forecasting for flood risk prediction, based on the Global Flood Awareness System (GloFAS) from the Copernicus Emergency Management Service of the European Commission. The training is designed to be interactive and hands-on, alternating between presentations and practical exercises using the GloFAS Interactive System and Jupyter notebooks. Participants will learn how GloFAS global hydrological forecast are generated, how the forcast products portfolio can be used for example to prepare flood forecast bulletins for disaster risk reduction, what type of data are freely available for operational and research use and how they can be accessed. Throughout the session, participants will practice mapping gauge stations to the GloFAS river network, accessing, downloading, and analysing GloFAS data from the Copernicus Early Warning Data Store (EWDS), and assessing the forecast performance. By the end of the course, the participants will be able to design and implement their own workflow to produce hydrological forecast products tailored to their needs from the GloFAS predictions.
Pr. Bertil NLEND
Senior Lecturer at University of Douala. Vice-Chair of the IAHS Africa Reginal
Cameroun
Day 2-Session 5
Isotopic Tracers in Hydrology
This course aims to introduce the fundamental principles and applications of isotopic techniques in hydrology. Learners will explore how stable and radioactive isotopes serve as natural tracers to understand the water cycle and related processes at different spatial and temporal scales. For instance, how isotopic signatures are used to investigate surface–groundwater interactions, response of rivers to rainfall input, evaporation processes, residence times, and contaminant transport and sources. All these points will be addressed for capacity building in the use of geochemical tracers for solving hydrological problems.
Day 2-Session 6
Satellite Radar Altimetry for Monitoring Ungauged Rivers
This course provides the specialized skills to measure water levels in remote, ungauged rivers using satellite radar altimetry. You will learn to process and analyze satellite data to create hydrologic insights where ground-based monitoring is impossible. The curriculum covers the core principles of altimetry, the technique for establishing and validating Virtual Stations (VS) at river crossings, and the methods to transform raw data into reliable water level time series. Empower yourself to perform discharge estimation and robust hydrologic analysis in any basin, anywhere.
Day 2-Session 7
Deep Learning for Hydrological Time Series Prediction
This course provides a practical introduction to the use of Deep Learning, particularly Long Short-Term Memory (LSTM) networks, for the prediction of hydrological time series. It is designed for professionals, PhD candidates, and students with basic knowledge of AI and Python. At first, participants will explore the key challenges and fundamental concepts of time series modeling, as well as the reasons for applying neural networks in hydrology. Then, they will learn the complete theoretical approach, including data preprocessing, LSTM model construction, and performance evaluation using indicators such as R², RMSE, and NSE. Finally, a guided hands-on session will allow participants to apply these concepts to a real case study using a Python notebook (Google Colab), with result visualization, interpretation, and manual optimization. By the end of the training, participants will be able to design, train, and validate a simple LSTM model while understanding its limitations and potential improvements.
Dr. Laurent FROIDEVAL
Researcher specializing in airborne LiDAR, drone based mapping, and photogrammetry
CNRS France
Day 2-Session 8
LiDAR and Photogrammetry for Terrain Digitization: Instrumentation, Measurement, and Modeling
One-day introductory course on terrain digitization using airborne LiDAR and SfM photogrammetry. It covers measurement principles, mission planning and georeferencing, point-cloud processing workflows (QA/QC, DTM/DSM generation, basic change detection), multi-sensor data fusion, and an overview of information extraction approaches including ML-inspired methods. Concepts are illustrated through demos and case studies (drone/airborne surveys; coastal and hydrology-oriented applications). Depending on logistics, guided hands-on manipulation may be proposed.