Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed
It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/3/607 |
| _version_ | 1797623074900672512 |
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| author | Erica Matta Mariano Bresciani Giulio Tellina Karin Schenk Philipp Bauer Fabian Von Trentini Nils Ruther Alena Bartosova |
| author_facet | Erica Matta Mariano Bresciani Giulio Tellina Karin Schenk Philipp Bauer Fabian Von Trentini Nils Ruther Alena Bartosova |
| author_sort | Erica Matta |
| collection | DOAJ |
| description | It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations. |
| first_indexed | 2024-03-11T09:20:29Z |
| format | Article |
| id | doaj.art-c79e2afe15644084aae2606bd95fc4d9 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T09:20:29Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-c79e2afe15644084aae2606bd95fc4d92023-11-16T18:25:04ZengMDPI AGWater2073-44412023-02-0115360710.3390/w15030607Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir WatershedErica Matta0Mariano Bresciani1Giulio Tellina2Karin Schenk3Philipp Bauer4Fabian Von Trentini5Nils Ruther6Alena Bartosova7CNR-IREA, National Research Council of Italy-Institute for the Electromagnetic Sensing of the Environment, Via Corti, 12, 20133 Milan, ItalyCNR-IREA, National Research Council of Italy-Institute for the Electromagnetic Sensing of the Environment, Via Corti, 12, 20133 Milan, ItalyCNR-IREA, National Research Council of Italy-Institute for the Electromagnetic Sensing of the Environment, Via Corti, 12, 20133 Milan, ItalyEOMAP, Earth Observation and Environmental Services, Schlosshof 4a, 82229 Seefeld, GermanyEOMAP, Earth Observation and Environmental Services, Schlosshof 4a, 82229 Seefeld, GermanyEOMAP, Earth Observation and Environmental Services, Schlosshof 4a, 82229 Seefeld, GermanyDepartment of Civil and Environmental Engineering, NTNU, Norwegian University of Science and Technology, Vassbygget, 405, Valgrinda, S. P. Andersens veg 5, 7034 Trondheim, NorwaySMHI, Swedish Meteorological and Hydrological Institute, SE-601 76 Norrköping, SwedenIt is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations.https://www.mdpi.com/2073-4441/15/3/607data integrationwater qualityhydropowerearth observation |
| spellingShingle | Erica Matta Mariano Bresciani Giulio Tellina Karin Schenk Philipp Bauer Fabian Von Trentini Nils Ruther Alena Bartosova Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed Water data integration water quality hydropower earth observation |
| title | Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed |
| title_full | Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed |
| title_fullStr | Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed |
| title_full_unstemmed | Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed |
| title_short | Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed |
| title_sort | data integration for investigating drivers of water quality variability in the banja reservoir watershed |
| topic | data integration water quality hydropower earth observation |
| url | https://www.mdpi.com/2073-4441/15/3/607 |
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