Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy
Articolo
Data di Pubblicazione:
2022
Citazione:
Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy / D. Vanella, G. Longo-Minnolo, O. Rosario Belfiore, J. Miguel Ram??rez-Cuesta, S. Pappalardo, S. Consoli, G. D???urso, G. Battista Chirico, A. Coppola, A. Comegna, A. Toscano, R. Quarta, G. Provenzano, M. Ippolito, A. Castagna, C. Gandolfi. - 42:(2022 Aug), pp. 101182.1-101182.19. [10.1016/j.ejrh.2022.101182]
Abstract:
Study region: The study region is represented by seven irrigation districts distributed under different climate and topography conditions in Italy. Study focus: This study explores the reliability and consistency of the global ERA5 single levels and ERA5-Land reanalysis datasets in predicting the main agrometeorological estimates commonly used for crop water requirements calculation. In particular, the reanalysis data was compared, variable-by-variable (e.g., solar radiation, R-s; air temperature, T-air; relative humidity, RH; wind speed, u(10); reference evapotranspiration, ET0), with in situ agrometeorological obser-vations obtained from 66 automatic weather stations (2008-2020). In addition, the presence of a climate-dependency on their accuracy was assessed at the different irrigation districts. New hydrological insights for the region: A general good agreement was obtained between observed and reanalysis agrometeorological variables at both daily and seasonal scales. The best perfor-mance was obtained for T-air, followed by RH, R-s, and u(10) for both reanalysis datasets, especially under temperate climate conditions. These performances were translated into slightly higher accuracy of ET0 estimates by ERA5-Land product, confirming the potential of using reanalysis datasets as an alternative data source for retrieving the ET0 and overcoming the unavailability of observed agrometeorological data.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Weather ground -based observation; Reanalysis dataset; Data-processing; Modelling and simulation; Water management; Irrigation
Elenco autori:
D. Vanella, G. Longo-Minnolo, O. Rosario Belfiore, J. Miguel Ram??rez-Cuesta, S. Pappalardo, S. Consoli, G. D???urso, G. Battista Chirico, A. Coppola, A. Comegna, A. Toscano, R. Quarta, G. Provenzano, M. Ippolito, A. Castagna, C. Gandolfi
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