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Hydrological models are an essential tool for water resources assessment and management. Advanced computational algorithms are capable of simulating 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. WATERLINE will employ multi-source information from remote sensing, historical data, in-situ data from meteorological networks as well as crowdsourced data to improve hydrological models and their 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 ancillary variables using in-situ measurements. The lack of in-situ measurements to train such models can be overcome by the growing availability of crowdsourced observations. 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.
The WATERLINE concept will be implemented through 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) crowdsourcing of hydrological information where a random user can 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, such as farmers, water authorities, fire brigade services, entrepreneurs in tourist, agricultural, industrial sector 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.

Call Topic: Novel Computational Approaches for Environmental Sustainability (CES), Call 2019
Start date: (36 months)
Funding support: 1 379 116 €