Study region Sicily, the largest island in the Mediterranean, exhibits complex hydro-climatic variability due to its diverse topography and proximity to contrasting maritime and continental influences. Accurate precipitation data are therefore essential for reliable hydrological modeling, disaster risk management, and climate-related assessments in this region. Study focus This study evaluates the performance of 11 widely used global daily precipitation datasets, satellite-based (GPM IMERG, TRMM, PERSIANN family), reanalysis (ERA5-Land, GLDAS-2), and blended products (MSWEP, HydroGFD), against ground-based observations across Sicily for the 2003–2023 period. A suite of statistical metrics (MAE, RMSE, KGE, Pearson’s r) was used to assess accuracy, precision, and bias. Additionally, the capability of MSWEP to represent extremes was analyzed using the Generalized Extreme Value (GEV) distribution and Peaks Over Threshold (POT) method. New hydrological insights for the region MSWEP consistently delivered the highest overall accuracy, followed by ERA5-Land and HydroGFD. All datasets effectively captured general precipitation patterns but struggled to reproduce high-intensity events, particularly in mountainous areas such as the northeastern slopes of Mount Etna. While MSWEP accurately represented moderate extremes, it underestimated the magnitude and frequency of severe events. These results emphasize the importance of local calibration and validation when applying global datasets in hydro-climatically complex regions. Although the 2003–2023 record limits long-term climatic inference, the findings provide practical guidance for short-term hydrological studies and dataset selection in Mediterranean settings, contributing to improved flood assessment and water resource planning.

Evaluating global precipitation datasets over Sicily: From daily estimates to extreme events

Yildiz M. B.;Di Nunno F.;de Marinis G.;Granata F.
2026-01-01

Abstract

Study region Sicily, the largest island in the Mediterranean, exhibits complex hydro-climatic variability due to its diverse topography and proximity to contrasting maritime and continental influences. Accurate precipitation data are therefore essential for reliable hydrological modeling, disaster risk management, and climate-related assessments in this region. Study focus This study evaluates the performance of 11 widely used global daily precipitation datasets, satellite-based (GPM IMERG, TRMM, PERSIANN family), reanalysis (ERA5-Land, GLDAS-2), and blended products (MSWEP, HydroGFD), against ground-based observations across Sicily for the 2003–2023 period. A suite of statistical metrics (MAE, RMSE, KGE, Pearson’s r) was used to assess accuracy, precision, and bias. Additionally, the capability of MSWEP to represent extremes was analyzed using the Generalized Extreme Value (GEV) distribution and Peaks Over Threshold (POT) method. New hydrological insights for the region MSWEP consistently delivered the highest overall accuracy, followed by ERA5-Land and HydroGFD. All datasets effectively captured general precipitation patterns but struggled to reproduce high-intensity events, particularly in mountainous areas such as the northeastern slopes of Mount Etna. While MSWEP accurately represented moderate extremes, it underestimated the magnitude and frequency of severe events. These results emphasize the importance of local calibration and validation when applying global datasets in hydro-climatically complex regions. Although the 2003–2023 record limits long-term climatic inference, the findings provide practical guidance for short-term hydrological studies and dataset selection in Mediterranean settings, contributing to improved flood assessment and water resource planning.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S2214581825008912-main.pdf

accesso aperto

Licenza: Creative commons
Dimensione 9.27 MB
Formato Adobe PDF
9.27 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/123244
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
social impact