Abstract
Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.
Highlights
Fresh water scarcity is one of the main issues examined by climate-change adaptation policies [1,2].A common objective of climate change adaptation policies is to identify feasible water management strategies at regional and continental scales to secure the water required for food production, while preserving the ecosystems
From gridded data derived by geostatistical spatial interpolation, the spatial structures of the weather variables are the result of the integration of the physical laws embedded into the numerical models
The reanalysis data, but in the worst cases the increase of normalized BIAS (nBIAS) and normalized RMSE (nRMSE) is less than 15% for UERRA MESCAN-SURFEX (UMS) and computation of FAO‐56 daily reference evapotranspiration (ET0) requires dataset of multiple weather less than
Summary
Fresh water scarcity is one of the main issues examined by climate-change adaptation policies [1,2]. Even in highly developed countries, ground weather stations recording variables with consistent accuracy over time are generally available only in a few sites, as part of relatively sparse monitoring networks or very often far from rural areas [16] In some countries, such as Italy, data availability is constrained by the fact that the monitoring networks are managed by several regional and national services, which adopt different policies in data storage and distribution. Two aspects have been favoring the application of reanalysis data in water resources management studies [35]: (i) their consistency in space and time, covering several decades at global or regional scales; (ii) their free public availability by means of dedicated web platforms, which make these data ready to be used in standard formats, avoiding all those time-consuming procedures required for collecting and homogenizing weather station data from different service providers. The study was conducted for Campania Region (Southern Italy), in the irrigation seasons of years 2008–2018
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