PrimeWater explores the scientific frontiers of EO observation technology by exploiting the potential of multi- and hyper-spectral sensors in water quality monitoring and demonstrating their added value in hydro-ecological forecasting services.
PrimeWater has formulated the Earth Observations Science Virtual Lab which aims to investigate major scientific questions related to EO science and Water Resources Modelling, and contains 9 scientific experiments grouped in three scientific domains: A) remote sensing; B) process-based hydro-ecological modelling; and C) data driven water quality modelling. In (A) hyperspectral images from PRISMA and DESIS missions have been collected and processed using existing and new water quality algorithms for estimation of cyanobacterial concentrations. On the same time a series of in situ, airborne and UAV flight campaigns have been performed in Mulargia and Flumendosa reservoirs to explore the usage of concurrent measurements in aquatic ecosystems mapping. In (B) three-dimensional hydrodynamic and water quality models for lake Harsha and Mulargia reservoir have been setup and calibrated. Data assimilation techniques have been developed and used to benchmark the improvement of model performance using historical multispectral water quality products (e.g. chlorophyll-a, temperature, turbidity) from Sentinel 2 and Landsat 8. In lake Hume a one-dimensional process model coupled with a cyanobacteria growth model has been used to replicate the timing and magnitude of cyanobacteria blooms. In Umeälven River the Ensemble Kalman Filter method was used to assimilate EO-derived and in-situ data aiming to quantify their potential for seasonal hydrological forecasting. In (C) an experiment using a Random Forest algorithm was conducted in Mulargia reservoir and Lake Harsha to identify the most significant hydrometeorological drivers for forecasting of chlorophyll-a values. Consequently, two data-driven approaches, i.e. an ensemble learning method and a Gaussian Process Regression model, were trained for short-term forecasting horizons and compared against naïve predicting alternatives.
PrimeWater has also defined the functional and technical requirements of its operational platform that will offer a) EO-based water quality monitoring, b) operational hydrological (short-term and seasonal) and ecological (short-term) forecasting, c) Early warning functionality and d) a water blending optimization tool for managing interconnected reservoirs. A comprehensive consultation procedure has been applied for consolidating user requirements in the development of the proposed PrimeWater services, including the organization of the 1st (out of 3) Multi User Panel workshop, with representatives from various water sectors.
On the same time behavioral approaches have been used to investigate the determinants of adoption of innovative EO and forecasting services. A pilot-survey has been conducted, using a discrete choice experiment to collect information from participants on user preferences towards different monitoring and forecasting earth observation services.
A 1st prototype of the operational PrimeWater platform has been released online at M18, which will be communicated to the end-users to receive their feedback and initiate a round of further customization and improvements of the offered services.
