Improved large-scale crop water requirement estimation through new high-resolution reanalysis dataset

Estimation of crop water needs is essential to understand the role of agriculture in the waterbalance modeling at various scales. In turn, this is relevant for water management purposes andfor the fulfilling of water-related environmental regulations. In this study, a comprehensiveassessment of crop water requirement at large scale is presented, both in terms of rainfall (greenwater) and irrigation (blue water).A water-balance model is built to provide estimates of actual evapotranspiration andaccompanying soil moisture by using high space-time resolution data. The new ERA5 reanalysisdataset, published by the ECMWF within the Copernicus monitoring system and obtained fromsatellite data and ground measurements, provides the precipitation and temperature inputvariables to the model. Data available at the hourly time scale are all aggregated on a daily scaleand used in the water balance model over a grid of cultivated areas from the MIRCA2000 dataset.Cultivated areas are available for 26 crops for year 2000 at a spatial resolution of 5 arcmin (about 9km at the Equator). Data from MIRCA2000 are separated between rainfed areas and areasequipped for irrigation and are characterized by specific monthly calendars of the crop growingseasons.The model performs the daily soil water balance throughout the whole year, considering all cropsat their growth stage and assuming as initial condition at each crop sowing date a monthlyaverage soil moisture. Results quantify the volumes of green and blue water necessary for cropgrowth and describe the spatial variability of the water requirements of each individual crop. Thehigh spatial and temporal resolution of Copernicus ERA5 data enables a great improvement in thecharacterization of hydro-climatic forcings with respect to previous assessments and a greateraccuracy in the crop water requirement estimates.Finally, the knowledge of water requirements is an important step to quantify the irrigationvolumes used in agriculture, on which there is a high uncertainty and little spatially distributedinformation. The model proposed enables the investigation of spatio-temporal variabilityassociated to varying meteorological forcings and of the effects of different irrigation techniques,enabling an improved management of water resources.