01.05.2021 - 30.04.2024

Wider research context: Hydrological models are essential tools in water resources assessment and management. Advanced computational algorithms can simulate the relevant physical processes and form the feedback mechanism across a wide range of spatial and temporal scales. However, a bottleneck of these models is the lack of environmental observations to calibrate model parameters and to assess the robustness of model predictions. The relevant physical processes and heterogeneity of hydrological catchments need to be integrated in hydrological models as a basis for reliable model predictions. A major challenge in this endeavour is identifying the observation data with the highest information content to constrain model parameters. Unfortunately, neither in-situ networks nor remote sensing alone can provide sufficient information to capture the high spatial and temporal variability of hydrological processes. Recently, downscaling frameworks have been developed, building robust models between coarse scale products and high-resolution covariates using in-situ measurements.

Objectives: WATERLINE will employ multi-source information from remote sensing, historical data, in-situ data from meteorological networks as well as crowdsourced measurements to improve hydrological models and their predictions.

Approach: The WATERLINE concept will be implemented through the development of a web services tool with three modular applications, targeting a) use by scientists (data access, downscaling, filtering, uncertainty analysis, modelling applications), b) use by non-technically trained stakeholders, providing enhanced visualization outputs, in the form of maps, graphs, indices enhanced with Augmented Reality and Virtual Reality functionalities, and c) use by a random user through crowdsourcing app, which enables any user to report about any hydrological-related event and its severity using location-based service and textual input, which is then considered as an additional source of information for modelling and forecast estimation. User groups will be actively involved in the development of the web-based interfaces to ensure the usability and adoption of the outcomes by relevant user communities.

Innovation: WATERLINE will improve the efficiency and robustness of hydrological models through strategic integration of variables covering different spatial and temporal scales. Furthermore, we will optimize the computational performances to provide near real-time and short-term predictions of various hydrological states with unprecedented spatial detail. Improved representation of soil moisture, groundwater levels and recharge, stream discharge, and evapotranspiration can significantly advance the sustainable management of water resources for a wide range of stakeholders.

Primary researchers involved: The consortium is led by Democritus University of Thrace, Greece, and consists of researchers from Austria, Finland, Poland and Switzerland.






  • Fonds zur Förderung der wissenschaftlichen Forschung (FWF) (National) Programm Joint Projects Internationale Programme Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Fördergeber Typ Forschungsförderungsinstitutionen Ausschreibungskennung CHIST-ERA 2019


  • Energy and Environment


DatenassimilationData Assimilation
FernerkundungRemote Sensing

Externe Partner_innen

  • Democritus University of Thrace
  • University of Oulu - OULUN YLIOPISTO
  • University of Science and Technology Krakow
  • Universite Neuchatel
  • Digital Innovations