In order to improve the water use efficiency (WUE) of spring maize in northwest China, the irrigation strategy of adopting limited supplemental irrigation following a high quota pre-sowing irrigation was evaluated under field conditions in 2016 and 2018. There were three treatments (W1, W2 and W3) differing in designed wetting depth (Dh) where soil water was replenished. Dh in W1, W2 and W3 were 0–40, 0–50 and 0–60 cm, respectively. The limited supplemental irrigation was adopted to improve soil water content (SWC) within Dh to field capacity (θFC) when SWC within 0–40 cm layer decreased to 60%θFC following a high rate of pre-sowing irrigation. Results showed that the smaller Dh was beneficial for improving root length density and enhance the utilization of water in subsoil. In both seasons, different Dh led to similar grain yields, which were comparable to the typical regional yield (14.3 t ha−1). The highest WUE (2.79 kg m−3) was achieved in W1 and was 13% more than the typical regional level of 2.46 kg m−3, implying it was adequate for achieving high yield and WUE to maintain SWC in 0–40 cm above 60% θFC with not replenishing soil water in 40–100 cm during the growth season after pre-sowing irrigation.

This study aims to assess the accuracy of the reference evapotranspiration (ET0) estimated by CLDAS, ERA5 reanalysis products, and the quality of reanalysis weather variables required to calculate PM-ET0. For this purpose, the applicability of surface meteorological elements from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and observations, with a mean R2 of 0.96, 0.90 for the CLDAS maximum and minimum air temperatures, and 0.87, 0.84 for ERA5. For the reanalysis of solar radiation (Rs) and relative humidity (RH), an overestimation trend is shown for Rs and an underestimation trend is shown for RH. For reanalysis of wind speed, a relatively low accuracy is shown. The accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between the CLDAS estimates and site observations is higher, with a mean RMSE R2 of 0.91, 0.82 for CLDAS and 1.42, 0.70 for ERA5, respectively. Moreover, CLDAS reanalysis products are more effective in describing the boundary details of the study area.

This study aims to assess the accuracy of the crop reference evapotranspiration (ET0 CLDAS, ET0 ERA5) estimated by CLDAS, ERA5 reanalysis products, as well as the quality of reanalysis weather variables required to calculate PM-ET0, and to achieve the application of these reanalysis products to locations where weather data quality are low or (and) weather variables are missing. For this purpose, the applicability of surface meteorological elements such as daily maximum and minimum air temperatures, relative air humidity, 2m wind speed, and shortwave radiation from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistical statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and station observations, with a mean R2 of 0.96,0.90 for CLDAS reanalysis maximum and minimum air temperatures and 0.87,0.84 for ERA5. For the reanalysis of estimated solar radiation and relative humidity, an overestimation trend is shown for Rs, but to a lesser degree, an underestimation trend is shown for RH. Unlike the previous reanalysis variables, the reanalysis wind speed shows a lower accuracy, and average R2 = 0.25 (R2 = 0.18) for CLDAS reanalysis (ERA5 reanalysis) and site observations. In addition, the accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between CLDAS estimates and site observations is higher, with mean RMSE, R2 of 0.91,0.82 for ET0 CLDAS and 1.42, 0.70 for ET0 ERA5, respectively, and the performance of describing the boundary details of the study area is better since CLDAS reanalysis products integrate terrain adjustment, the elevation of target location, wind speed, and other factors are taken into account.