Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy)
Study region: The study focuses on the Dittaino River Basin, located in eastern Sicily, Italy. This Mediterranean watershed is characterized by water scarcity issues that challenge hydrological modeling and water resource management. Study focus: This study evaluates the use of ERA5-Land reanalysis...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825003945 |
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| author | Liviana Sciuto Daniela Vanella Giuseppe Luigi Cirelli Simona Consoli Feliciana Licciardello Giuseppe Longo-Minnolo |
| author_facet | Liviana Sciuto Daniela Vanella Giuseppe Luigi Cirelli Simona Consoli Feliciana Licciardello Giuseppe Longo-Minnolo |
| author_sort | Liviana Sciuto |
| collection | DOAJ |
| description | Study region: The study focuses on the Dittaino River Basin, located in eastern Sicily, Italy. This Mediterranean watershed is characterized by water scarcity issues that challenge hydrological modeling and water resource management. Study focus: This study evaluates the use of ERA5-Land reanalysis precipitation data and Sentinel-2/Landsat-8 satellite-derived land cover information to improve runoff simulations using the HEC-HMS model. The methodology was tested on 14 rainfall events (2015–2018) using different input configurations: standard data (SI), reanalysis precipitation data (CR), satellite-derived land cover data (SD), and a combination of both (CR&SD). Model performance was assessed through calibration and validation against observed streamflow data. New hydrological insights for the region: Results demonstrate that ERA5-Land precipitation considerably improves runoff simulations, with a Nash-Sutcliffe Efficiency (NSE) of 0.63 and a Percent Bias (PBIAS) of −16.02 %, confirming its validity as an alternative to ground-based rainfall observations. The CR dataset exhibited a stronger influence on model performance than SD, while the combined CR&SD dataset provided the most balanced results, reinforcing the value of data fusion. The study highlights the complementary impact of precipitation and land cover datasets in runoff modeling and underscores the importance of using multi-source data for improving hydrological simulations. These findings provide practical implications for flood risk mitigation, water resource planning, and irrigation management in Mediterranean semi-arid regions. |
| format | Article |
| id | doaj-art-18166c2e2cca4d7ea91776a2e5862735 |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-18166c2e2cca4d7ea91776a2e58627352025-08-20T03:08:21ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-08-016010256910.1016/j.ejrh.2025.102569Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy)Liviana Sciuto0Daniela Vanella1Giuseppe Luigi Cirelli2Simona Consoli3Feliciana Licciardello4Giuseppe Longo-Minnolo5Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyCorresponding author.; Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyDipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyDipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyDipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyDipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università degli Studi di Catania, Via S. Sofia, 100, Catania 95123, ItalyStudy region: The study focuses on the Dittaino River Basin, located in eastern Sicily, Italy. This Mediterranean watershed is characterized by water scarcity issues that challenge hydrological modeling and water resource management. Study focus: This study evaluates the use of ERA5-Land reanalysis precipitation data and Sentinel-2/Landsat-8 satellite-derived land cover information to improve runoff simulations using the HEC-HMS model. The methodology was tested on 14 rainfall events (2015–2018) using different input configurations: standard data (SI), reanalysis precipitation data (CR), satellite-derived land cover data (SD), and a combination of both (CR&SD). Model performance was assessed through calibration and validation against observed streamflow data. New hydrological insights for the region: Results demonstrate that ERA5-Land precipitation considerably improves runoff simulations, with a Nash-Sutcliffe Efficiency (NSE) of 0.63 and a Percent Bias (PBIAS) of −16.02 %, confirming its validity as an alternative to ground-based rainfall observations. The CR dataset exhibited a stronger influence on model performance than SD, while the combined CR&SD dataset provided the most balanced results, reinforcing the value of data fusion. The study highlights the complementary impact of precipitation and land cover datasets in runoff modeling and underscores the importance of using multi-source data for improving hydrological simulations. These findings provide practical implications for flood risk mitigation, water resource planning, and irrigation management in Mediterranean semi-arid regions.http://www.sciencedirect.com/science/article/pii/S2214581825003945Watershed modelingERA5-LandSentinel-2Landsat 8HEC-HMSStreamflow prediction |
| spellingShingle | Liviana Sciuto Daniela Vanella Giuseppe Luigi Cirelli Simona Consoli Feliciana Licciardello Giuseppe Longo-Minnolo Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) Journal of Hydrology: Regional Studies Watershed modeling ERA5-Land Sentinel-2 Landsat 8 HEC-HMS Streamflow prediction |
| title | Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) |
| title_full | Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) |
| title_fullStr | Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) |
| title_full_unstemmed | Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) |
| title_short | Improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the Dittaino River Basin (Eastern Sicily, Italy) |
| title_sort | improving runoff estimation in hydrological models using remote sensing and climate data reanalysis in the dittaino river basin eastern sicily italy |
| topic | Watershed modeling ERA5-Land Sentinel-2 Landsat 8 HEC-HMS Streamflow prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825003945 |
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