What is the impact of earth observation and in-situ data assimilation on seasonal hydrological predictions?
Abstract Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states…
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…
Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
Abstract Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration…
Hyperspectral PRISMA Products of Aquatic Systems
Abstract This study presents an assessment of PRISMA (PRecursore IperSpettrale della Missione Applicativa) for water applications, by including a general description of the mission and by focusing on the standard Level 1 (L1) and Level 2 (L2) products acquired on different aquatic systems. A preliminary…
Quantile-based modeling of phytoplankton dynamics using Random Forests
Limits of one-dimensional process models to forecast water quality
Developing an error-correcting complementary modeling approach for surface water quality
Assimilation of Earth Observation products in water quality modelling
Develop and benchmark data-driven algorithms to forecast the short-term dynamics of phytoplankton in surface water reservoirs
Assess the relevance of hydrometeorological variables in phytoplankton dynamics of surface waters using data-driven modeling
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