Assessing and comparing crop evapotranspiration in different climatic regions of China using reanalysis products

Abstract

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.

Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

“…The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift in the summer rain belt in eastern China [1][2][3][4]. The prediction of the summer WPSHRL has great application value, so it has been widely studied in the field of climatology [5][6][7][8][9][10][11][12].…”