Publications

A data-driven approach to predict algal blooms using satellite-derived water quality and hydrometeorological drivers

Abstract The present work leverages simulated hydrometeorological factors and satellite-derived chlorophyll-a to predict phytoplankton dynamics for Mulargia reservoir (Sardinia, Italy). A Random Forest (RF) model was (a) calibrated to minimize out-of-bag errors of chlorophyll-a predictions for a 5-year-long period (2015-2019), and (b) benchmarked against a…

Limits of one-dimensional process models to forecast water quality

AboutParticipantsMethodologyData preparationAnalysisResultsReferencesAboutExperiment name: Limits of one-dimensional process models to forecast water quality Scientific question: Quantifying the relevance of the major sources of uncertainty and establishing the forecasting horizon of one-dimensional process models Experiment idea: 6 Partner 0 Publications 0 Datasets Participants EMVIS Kyriakos KANDRIS [email protected]...

Developing an error-correcting complementary modeling approach for surface water quality

AboutParticipantsMethodologyData preparationAnalysisResultsConclusionsAboutExperiment name: Developing an error-correcting complementary modeling approach for surface water quality Scientific question: Can we train data-driven algorithms to accurately detect systematic errors produced by process-based models? Experiment idea: This is a proof-of-concept for the development of hybrid modeling approaches in surface water...

Assimilation of Earth Observation products in water quality modelling

AboutParticipantsMethodologyData preparationAnalysisResultsConclusionsAboutExperiment name: Assimilation of Earth Observation products in water quality modelling Scientific question: Can we combine EOs and enKF/4dVAR data assimilation techniques to improve the predictive skill of hydro-ecological modelling in reservoirs? Experiment idea: This study focuses on improving the prediction accuracy of chlorophyll-a...

Develop and benchmark data-driven algorithms to forecast the short-term dynamics of phytoplankton in surface water reservoirs

AboutParticipantsMethodologyData preparationAnalysisResultsConclusionsAboutExperiment name: Develop and benchmark data-driven algorithms to forecast the short-term dynamics of phytoplankton in surface water reservoirs Scientific question: How much more accurate can data-driven algorithms be in predicting chlorophyll-a compared to naïve predicting alternatives? What are the limits of predictability in terms...

Assess the relevance of hydrometeorological variables in phytoplankton dynamics of surface waters using data-driven modeling

AboutParticipantsMethodologyData preparationAnalysisResultsConclusionsAboutExperiment name: Assess the relevance of hydrometeorological variables in phytoplankton dynamics of surface waters using data-driven modeling Scientific question: Can we use data-driven algorithms to gain insight into the drivers of phytoplankton dynamics in lake and reservoir ecosystems? Experiment idea: Data-driven models typically compromise...

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The project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 870497.

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