The interdisciplinary research in WATERLINE will focus on new methods: 1) to improve model input data by dealing with the uncertainty of measurements and 2) to optimize hydrological models along with methods to include model uncertainties for improved model forecasts.

Project information

Project duration


Funded by

Research Council of Finland

Funding amount

449 595 EUR

Project coordinator

Environmental Engineering Department, Democritus University of Thrace, Greece

Project description

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. 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. 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. 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.