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, Rs; air temperature, Tair; relative humidity, RH; wind speed, u10; reference evapotranspiration, ET0), with in situ agrometeorological observations 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 performance was obtained for Tair, followed by RH, Rs, and u10 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.
Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy
	
	
	
		
		
		
		
		
	
	
	
	
	
	
	
	
		
		
		
		
		
			
			
			
		
		
		
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
						
							
							
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
		
		
		
	
Daniela VanellaPrimo
;Giuseppe Longo-Minnolo
;Juan Miguel Ramirez-Cuesta;Simona Consoli;
	
		
		
	
			2022-01-01
Abstract
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, Rs; air temperature, Tair; relative humidity, RH; wind speed, u10; reference evapotranspiration, ET0), with in situ agrometeorological observations 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 performance was obtained for Tair, followed by RH, Rs, and u10 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.| File | Dimensione | Formato | |
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