Earth Observation (EO) is a common name for satellite based remote sensing of the states of Earth surface systems. EO is available for many components in the water cycle with various temporal and spatial resolution and accuracy – for instance precipitation, evaporation, soil moisture, snow cover, water levels, and flooded.
The usefulness of EO data to improve hydrological information has often been shown to be limited, either due to high uncertainty or overlapping information content with other data integrated in the hydrological models. At the same time, there is an increasing requirement for higher resolution and more spatial coverage in hydrological information.
There is thus need to develop and improve the utilization of EO in hydrological modelling and forecasting. To do so, we need to improve our knowledge about the properties and uncertainties in the EO data and in the methods for integrating the EO data in the hydrological models.
Develop understanding of properties and information content in EO through studies in areas with large amount of ground based reference data.
Development of hydrological models and analysis of on-going and future hydrologic change combining EO data and hydrological models, with focus on the Arctic region and Africa.
Improved hydrological forecasting using EO data assimilation, with focus on Europe and Sweden
How are our decision support data changed by integration of EO data in hydrological models?
How can EO data contribute to improved knowledge about hydrological system state and change?
Data assimilation (DA) are methods for initializing model states using observations to improve short term and longterm forecasts, or for combing models and observations for descriptions of historical periods (re-analysis). DA will be and importan method in the development of improved utilization of EO data in hydrological modelling and forecasts, but also for innovative use of ground-based in-situ data, for instance snow and river discharge observations. The challange for DA is to develop effective methods that take into account observation uncertainties, especially in terms of spatial and temporal representation.
Development of methods for assimilation of EO and in-situ data in hydrological models
Contribute to improved integration of hydrological information in meteorological models through collaboration on EO and DA.
How can DA contribute to improved hydrological forecasts, and to improved knowledge about the state and change of hydrological systems?