Periodic Reporting for period 1 - HYPOS (HYdro-POwer-Suite)
Reporting period: 2019-12-01 to 2021-02-28
An online Decision Support Tool (DST) for appropriate environmental and economic investment planning and monitoring based on Earth Observation (EO) technologies and modelling for the hydropower industry will be developed in the framework of the H2020 funded HYPOS project. The project started in December 2019 and will last two-and-a-half-years. The consortium partners of the project represent the full range of relevant players for a successful integration of EO data combined with modelling information such as service providers (EOMAP, SMHI), public and independent research institutions (CNR, NTNU), integrators and users (STUCKY).
The DST will provide essential assets for hydropower developers, managers and decision makers by bringing together high-quality satellite-based measurements for historic time periods, up-to-date modelled hydrological parameters with nowcasting on various orderable levels of detail and available in-situ data for integrated baseline and environmental impact assessments.
This new tool significantly contributes on a trans-national as well as on a global scale, with independent, standardized and consistent information over a wide range of different water bodies and spatial scales. Substantial Blue Footprint analysis are enabled based on sophisticated and state-of-the-art algorithms and methodology featuring sustainable long-term monitoring solutions. Knowledge will be gained about essentially needed parameters, such as inflow/outflow volumes, sedimentation, evaporation rates, bank and bed erosion, phytoplankton and higher aquatic vegetation development. The tool answers the most crucial faced issues on different levels, starting with a global perspective in medium temporal and spatial resolution and will range up to local high and very high-resolution demands.
The front end of the HYPOS Decision Support Tool will integrate the functionalities to access data and provide assessments through private user accounts. With this online platform, the end user can easily setup individual projects, covering reservoir subsections or whole catchment areas, aggregate data and model runs, visualize model data within the eoApp web application and improve their accuracy.
The business model and service integration will be showcased for a set of hydropower applications in Switzerland, Georgia and Albania, through a close collaboration with its operators. Here, it provides support for the monitoring of sedimentation levels, both upstream and downstream of the reservoirs and river sections, and supply data for flushing events in high spatial and temporal resolution. With the analysis of historic satellite datasets of the Copernicus mission data from Sentinel-2A/B or USGS missions of Landsat 5,7 and 8, baselines of total suspended matter concentrations are calculated. Furthermore, based on the analysis of transects across reservoir or river sections, sedimentation flows and sediment loads are estimated, e.g. used for planning in commissioning of hydropower plants.
To ensure that the HYPOS services meet the requirements of a wide set of hydropower stakeholders, several dedicated activities are planned during the upcoming phases of the project. For example, the different integration examples will be demonstrated to customers in specific workshops and trainings on an international level.
Throughout HYPOS, EOMAP will further improve cutting-edge algorithms for harmonized multi-sensor high resolution water quality products. Especially the automated adaptation of the changing specific inherent optical properties (SIOPs) of water constituents will be improved. According to Brando et al. (2012 ) to address the challenges of the parameterization of physically based algorithms in optically complex waters, an adaptive implementation which iterates over a limited number of SIOPs is improving the accuracy the retrieval of water quality parameters.
The adjacency correction shall be part of the coupled inversion process, retrieving aerosol properties, adjacency impact and in-water optical properties in one step. An improved sunglitter detection and correction algorithm shall be investigated, and multi-sensor-fusion technologies will be developed to improve both the spatial and temporal resolution of products and the product consistency.
HYPOS will allow expanding the existing operational setups beyond the state-of-the-art with assimilating EO for water quality modelling. Within HYPOS, EO will be used to automatically update the model initial conditions using data assimilation techniques in a continuous way. Data assimilation provides substantial skill by reducing the initial conditions uncertainties.
Exploration of the three data streams (i.e. modelling data, EO, and in-situ observations) can lead to innovative on-fly model calibration procedures. It is expected that this process will further improve the accuracy of the hydrological and sediment modelling at a global scale as well as increase the relevance and applicability of the modelling results to local users.
Brando, V.E. Dekker, A.G. Park, Y.J. and Schroeder, T., 2012. Adaptive semianalytical inversion of ocean color radiometry in optically complex waters. Applied Optics, 51(15), pp.2808-2833.