1. Introduction
Evapotranspiration (ET) is an important meteorological element in the hydrological cycle, which is directly related to the energy and water balance of the Earth’s surface, determining the formation and evolution of the geographical environment [
1], and also an important basis and a key link in the evaluation of agricultural water use efficiency [
2,
3]. As the global competition for water resources intensifies and water resources become depleted, coping with water scarcity requires a more accurate knowledge of ET [
4].
Irrigation management in agricultural practices requires the accurate estimation of crop water consumption, which in turn requires an accurate estimation of crop evapotranspiration (ET
c), and its forecasting is significant in developing crop irrigation systems and real-time irrigation scheduling [
5,
6]. A commonly used ET
c estimation method at the field scale consists of using the Kc-ET
0 approach, as proposed in FAO56 [
7], where a crop coefficient (K
c) for the considered vegetation is multiplied by the crop reference evapotranspiration (ET
0) to estimate the ET
c, although the calculation of K
c is based on experience, and applications worldwide are successful [
8,
9,
10].
At present, ET
0 can also be estimated from pan evaporation, but such methods are expensive, technically complex, and not practical. Therefore, the Penman–Monteith model is usually used for the estimation ET
0 [
11,
12,
13,
14]. However, the model requires the input of several climate variables, including the maximum and minimum air temperature (T
max and T
min), relative humidity (RH), solar radiation (R
s), and wind speed at 2 m height (U
2). These variables are not observed, or the price of obtaining these meteorological data from the relevant meteorological services is prohibitive in many regions and locations in China. In addition, the common method of obtaining these meteorological data is station monitoring, which is time-consuming and makes it difficult to obtain long time series and high-quality meteorological variables.
Since the 1960s, the application of meteorological satellite data made up for the shortage of station observations, and the spatial and temporal continuity of meteorological data made great progress. After the 1990s, satellite data entered the data assimilation system, which further improved the accuracy of meteorological data [
15,
16]. Reanalysis information assimilates data obtained from remote sensing, ground-based observations and numerical simulations [
17,
18], which has the characteristics of high spatial and temporal resolutions and is an ideal driving data source for distributed models. The main advantage of reanalysis products is the free access to continuous meteorological data and its great advantage and potential to become an alternative to observed data.
Several global reanalysis datasets are available for ET
0 estimation, such as WorldClim, which provides the global historical and future climate data and elevation data [
19]; The World Meteorological Organization’s Climate Explorer weather data retrieval platform, with a wealth of global or regional climate data [
20]; the European Centre for Medium-range Weather Forecasts (ECMWF), which provides ERA5 reanalysis products [
21]; the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), known as NCEP/NCAR Reanalysis I [
22]; the National Aeronautics and Space Administration (NASA) Global Simulation; and the Assimilation Office for a Second Review of Modern Research and Applications (MERRA-2) [
23,
24], etc.
The main advantages of the reanalysis product are the high spatial and temporal resolutions, the ability to scale the product appropriately, and the stable improved model resolution and bias [
25]. Currently, many authors have used reanalysis products to represent the spatiotemporal variability of surface climate variables. ERA5_land reanalysis data were applied to the observations of Peruvian glaciers verified by Martin et al., and were found to be appropriate for characterizing 2 m air temperature and relative humidity, particularly in the wet outer tropics [
26]. Song et al. (2020) [
27] compared the applicability of various soil moisture data in Inner Mongolia, and found that the ERA5 simulation capability is optimal. Srivastava et al. (2016) [
28] compared the ET
0 estimation from ERA-interim and NCEP for all the seasons, and revealed a similar performance to the seasonal assessment, with a higher agreement for the ERA-interim. The only drawback of using a downscaling ERA-interim reanalysis dataset with the WRF is that it is time-consuming. Martins et al. (2016) [
29] used the ERA-interim and NOAA NCEP reanalysis datasets for the calculation of PM-ET
0 in the Iberian Peninsula and compared it with the ET
0 Observed at 130 meteorological stations in the Iberian Peninsula. The results supported the quality of the ERA-interim reanalysis data to calculate the ET
0 computations. The ECMWF ERA5 reanalysis products were selected for this study because they had previously performed well and showed great a potential for estimating ET
0. However, due to the bias reported for ERA products, the use of these data requires assessing the impact of bias on the performance of the estimations of reanalysis-based ET
0 when compared with ET
0 computed with observational data [
30,
31].
CLDAS-V2.0 is the latest version of the land surface assimilation system developed by the China National Meteorological Information Center, providing a variety of atmospheric driving field products (URL:
https://data.cma.cn/, accessed on 1 May 2021). At the same time, some scholars have evaluated the applicability of different land surface model data. Liu et al. (2021) [
32] compared CLDAS air temperature data with hourly air temperature data from 48,708 ground automatic meteorological stations in China for 2017–2018, and found that CLDAS air temperature data better reflects the interannual variability of air temperature in Chinese regions, with an average correlation coefficient of more than 0.991. Shi et al. (2018) [
33] analyzed the temporal variation characteristics of average soil moisture in China by comparing CLDAS soil moisture with hourly observations of automatic soil moisture observation stations, concluding that the simulated values were very close to the observed values, and the time-series variation of soil moisture was captured in each study area. Using GLDAS and CLDAS forcing data, Yang et al. (2017) [
34] evaluated precipitation and shortwave radiation from the ITPCAS, GLDAS, CLDAS and the gridded analysis based on CMA gauge observations (CN05.1) over mainland China during 2008–2014, and showed that CLDAS provides more realistic precipitation and radiation approximations than the other datasets. The CLDAS forcing dataset contains more in situ and remote sensing observations of China, so it is more accurate over mainland China. Liu et al. (2019) [
35] compared and evaluated the simulation results of monthly soil moisture and monthly evapotranspiration, and reported that CLDAS_Noah-MP significantly improved the simulation results for mainland China and eight river basins; the R-value increased from 0.451 to 0.534 and the RMSE reduced from 0.078 to 0.068, which confirmed the superiority of CLDAS, as previously reported by Cui et al. (2018) [
36] and Shi et al. (2018) [
37].
The most recent studies have focused on assessing one or several meteorological variables for the reanalysis of atmospheric field-driven products, but relatively few have conducted comprehensive assessments of the meteorological variables required to calculate ET
0 and reanalysis for ET
0 estimation, especially for small temporal scales. In addition, the numerical–physical approach, horizontal and vertical resolution, and temporal variability of the reanalysis model will bring uncertainties in the reanalysis simulation process [
38,
39]. The resolution and accuracy of these reanalysis products appear to vary with the location and timescale of the study [
40]. Although research indicates that the spatial resolution and reliability of the new generation of reanalysis data products have improved, there is still a need to evaluate the region-specific applicability of CLDASV2.0, ERA5 reanalysis weather variables and the estimation of ET
0 in different climate zones of China, and find the best method for researchers and users to carry out irrigation practices.
The main objective of this research is to apply the ET
0 calculated from reanalysis data to irrigation scheduling and management, for which it can be used in those places where the weather data quality is low or (and) weather variables are missing [
41,
42]. The correlations and bias of the five reanalyzed meteorological variables from CLDASV2.0 and ERA5 are compared based on the daily weather variable data obtained from 689 ground automatic meteorological stations in China from 2017–2019. Any biases in these products can be identified by comparison so that the bias correction method can be easily applied. Moreover, a comprehensive set of the statistical indicators is used to assess how calculated reanalysis PM-ET
0 compared with the corresponding station observations, which also offers a reference for the selection and application of CLDASV2.0 and ERA5 reanalysis meteorological data across different climatic regions of China.
5. Summary and Conclusions
It is difficult to obtain long timeseries meteorological variables required for PM-ET0 calculation from ground stations. The reanalysis datasets provide all the weather variables required for calculating ET0, but needs to be evaluated for accuracy. In this paper, meteorological factors such as Tmin and Tmax, Rs, RH and U2 derived from CLDAS and ERA5 are statistically analyzed. Moreover, based on the daily meteorological data of 689 surface meteorological stations covering seven different climatic regions in China, ET0 CLDAS and ET0 ERA5 at different timescales are compared and evaluated.
The quality of meteorological factors for calculating PM-ET0 obtained from CLDAS and ERA5 reanalysis products is acceptable, and the correlation between reanalysis temperature and station observation temperature is high, with R2 > 0.90 for Tmin CLDAS, Tmax CLDAS, and R2 > 0.84 for Tmin ERA5, Tmax ERA5; however, there is a tendency for an underestimation of Tmax, and conversely, a tendency for an overestimation of Tmin. Rs and RH can be accurately estimated from reanalysis datasets, but reanalysis Rs has a slight overestimation trend for site observation Rs and an underestimation trend for site observation RH. Compared with the ERA5 reanalysis products, Tmin, Tmax, Rs and RH derived from the CLDAS products are closer to the site observations, and the overall RMSE and MAE values are smaller. In contrast, the accuracy of both U2 CLDAS and U2 ERA5 is coarser, but the reanalysis of wind speed does not cause a large variability in ET0.
Two reanalysis products capture the seasonal cycle and monthly time evolution based on the observed ET0. During the year, 75–80% of ET0 is concentrated in April–October, but the monthly mean ET0 in different climate zones shows single-peak and double-peak type variations, with different peak occurrence times. In addition, the agreement between the reanalysis estimated ET0 and site-observed ET0 is high in arid and semi-arid areas, with the lowest value of ET0 CLDAS correlation coefficient being 0.90 and ET0 ERA5 0.85. The consistency is slightly worse in wet and semi-humid areas, with the R2 variation range of 0.69–0.88 for ET0 CLDAS and 0.54–0.79 for ET0 ERA5. CLDAS reanalyzed meteorological variables which are used to compute the PM-ET0, and estimated ET0 closer to the site observation ET0 Obs, comparing the corresponding values of ERA5.
These results suggest that reanalysis weather products (Rs, Tmax, Tmin, RH and U2) can be used to estimate ET0 when the observed weather data are unavailable. However, the causes of the impact of errors in each weather variable on the accuracy of the estimated ET0 are not clear, and further studies are required to clarify the quantitative sensitivity of the errors in the reanalysis weather variables on the estimated ET0 as a way to decrease the reanalysis product estimation errors obtained when using daily time steps. Moreover, studies are needed to combine long past series data with forecasts, to achieve the real-time operation of irrigation scheduling models.