ALBERT

Description: Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

Description: Floodplain restoration measures are among the most well-known nature-based solutions for flood risk reduction but practitioners see their limitations in comparison to technical measures when considering both their effectiveness and profitability. The aim of this study is to show the co-benefits (besides flood risk reduction) of floodplain restoration and handle them in terms of monetized ecosystem services (ES). Our work focused on six ES groups for three study areas in the Danube catchment along the Krka, Morava, and Danube rivers. ES mapping through stakeholder engagement is also considered. We applied the methodologies suggested in the Toolkit for Ecosystem Service Site-Based Assessment (TESSA) complemented with alternative methodologies (e.g., questionnaires on social media). Results show annual combined benefits of floodplain restoration in a range from 237,000 USD2019 at Krka to 3.1 million USD2019 at Morava, suggesting the utility of ES assessment. The combination of stakeholder workshops and the TESSA guidelines, as well as the newly developed methods, were all central tools to provide decision-makers with arguments to use nature-based solutions for an integrated and holistic riparian land use management.