**1. Introduction**

Reference Crop evapotranspiration (*ET*0) is a critical factor for calculating crop evapotranspiration, the accurate estimation of which plays a vital role in irrigation engineering design and planning, water resources management, and climate change research [1–3]. Due to its large population and rapid economic development, China is facing a severe water shortage problem. The country's per capita water resource is only one-fourth of the world average level [4]. Therefore, an accurate estimation of *ET*<sup>0</sup> in this region would provide a scientific basis for rationally allocating water resources and minimizing the imbalance between water supply and demand [5]. Currently, the standard estimating method of *ET*<sup>0</sup> is the Penman-Monteith equation (FAO56 PM) recommended by the Food and Agriculture Organization of the United Nations (FAO) [6,7]. This method combines energy balance and

**Citation:** Wu, L.-F.; Qian, L.; Huang, G.-M.; Liu, X.-G.; Wang, Y.-C.; Bai, H.; Wu, S.-F. Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China. *Water* **2022**, *14*, 1744. https://doi.org/10.3390/ w14111744

Academic Editor: Alban Kuriqi

Received: 9 April 2022 Accepted: 26 May 2022 Published: 29 May 2022

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the aerodynamic theory, which is strongly applicable under different climatic conditions. However, the main drawback of this method is that it requires a high quality of meteorological data, including air temperature, relative humidity or dew temperature, solar radiation, and wind speed [8,9]. In many regions of the world, there are not enough weather stations to monitor the meteorological factors. Additionally, high-quality, long-term observational data are lacking, especially in developing countries, which hinders the application of the PM method for *ET*<sup>0</sup> estimation on large spatial scales [10–12].

In recent years, reanalysis products have become one of the main grid data sources for water resource management research [13]. Reanalysis data are generated by running a numerical weather-predicting model that assimilates the observed atmospheric and surface data to reconstruct the past surface, ocean, and atmospheric state variables. Unlike geostatistical grid data derived from spatial interpolation, the spatial structure of weather variables (such as temperature and wind speed) synthesizes physical laws embedded in numerical models [8].

Nowadays, many reanalysis data sets have developed rapidly and are used in various fields. Baatz et al. (2020) [14] analyzed state-of-the-art methods, recent developments, and prospects of reanalysis for three subcomponents of the Earth system (atmosphere, ocean, and land), they points out the method's increasing computational capabilities, the growing availability of long-term satellite data with global coverage, and the advancements in model-data fusion methods such as variational and sequential data assimilation. In addition, the above paper discusses the increasing awareness of the drastic changes in the Earth system related to anthropogenic and climatic factors and the way they drive reanalysis development. Recently, networks of distributed in-situ sensors such as buoys and biogeochemical Argo floats [15], eddy covariance stations [16], surface water runoff observations [17], and meteorological station data [18] were used in the reanalysis of physical and biogeochemical Earth system processes. Munoz-Sabater et al. (2021) [19] presented the new global ERA5-Land reanalysis. The quality of ERA5-Land fields was evaluated by direct comparison to many in situ observations collected for the period 2001–2018, and for comparison to additional model or satellite-based global reference datasets. Overall, the water cycle was improved in ERA5-Land compared to ERA5 according to the different variables evaluated, whereas the energy cycle variables showed similar performances. Both ERA5 and ERA5-Land perform substantially better than ERA-Interim.

Reanalysis data have also been applied and compared to estimate evapotranspiration in different regions of the world. Boulard et al. (2016) [20] calculated *ET*<sup>0</sup> using the ERA-Interim reanalysis data and verified its accuracy in a water balance study in northeastern France. Srivastava et al. (2016) [21] found that ERA-Interim *ET*<sup>0</sup> was superior to NCEP/NCAR *ET*<sup>0</sup> in the UK. Pelosi et al. (2020) [22] also compared two reanalysis datasets for *ET*<sup>0</sup> estimation in southern Italy. Woldesenbet et al. (2021) [23] evaluated the *ET*<sup>0</sup> in the Omo-Gibetta watershed and achieved good prediction results. Song et al. (2015) [24] judged the spatiotemporal characteristics of *ET*<sup>0</sup> in the Shaanxi Province based on NCEP reanalysis data and made future predictions. Liu et al. (2019) [25] estimated the future *ET*<sup>0</sup> in the Poyang Lake basin based on the CMIP5 model. The results showed that the stepwise regression downscaling model established by the NCEP reanalysis data and the basin *ET*<sup>0</sup> had better simulation results. *ET*<sup>0</sup> was assessed in the Iberian Peninsula by Martins et al. (2016) [26]. The focus here is to use the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) hybrid reanalysis product and gridded dataset to calculate *ET*<sup>0</sup> with good simulation results. Raziei (2021) [27] used the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, combined with a gridded dataset, to calculate monthly *ET*<sup>0</sup> for 43 meteorological stations distributed across Iran. The results show that the *ET*<sup>0</sup> calculated by the mixed reanalysis had a better effect than the *ET*<sup>0</sup> calculated by the observations at most research stations. Milad and Mehdi (2022) [28] used reanalysis products to estimate *ET*<sup>0</sup> in areas with sparse data and showed that ERA5 provided more accurate estimates of daily and monthly *ET*0. Some scholars have also

compared satellite grid data with meteorological station values. Wang et al. (2019) [29] comprehensively evaluated and compared this newly released precipitation product (Integrated Multi-satellite Retrievals V05B) and its predecessor TRMM 3B42V7 based upon the ground-based observations under complex topographic and climatic conditions over the Hexi Region in the northwest arid region of China. Their results indicated that compared to ground-based observations, both IMERG and 3B42V7 showed good performance with slight overestimation. Prakash et al. (2016) [30] investigated the capabilities of the Tropical Rainfall Measuring Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. The results indicated that the multi-satellite product systematically overestimates its inter-annual variations. With continuous advances in numerical weather models, computing, information, and communication technology (ict) tools, and data assimilation techniques, along with continuous improvements in the quality of atmospheric and ground data obtained from satellites, the spatial and temporal resolution and reliability of reanalysis data have been gradually improved year after year.

The China Meteorological Administration Land Data Assimilation System (CLDAS) is the only real-time service system in land surface data assimilation systems in China. It uses a combined technology of integration and assimilation to fuse data from various sources, such as ground observation, satellite observation, and numerical model products [31]. The output of this system includes high spatial and temporal resolution land surface driving products such as temperature, air pressure, specific humidity, wind speed, precipitation, solar shortwave radiation, and soil moisture. These could be applied in agricultural drought monitoring, mountain flood geological disaster meteorological services, climate system model assessments, and spatial fine grid real data services. Although many studies have evaluated the quality of the CLDAS data, there are limited reports on the estimation of *ET*<sup>0</sup> by this dataset. In this paper, we used the meteorological reanalysis data of 689 surface meteorological stations in China from 2017 to 2020 and found four grid data points around each meteorological station through calculation and processing. We then calculated the value of the target station using the inverse distance weight method, compared it with the measured data of local meteorological stations and evaluated the accuracy of CLDAS data through statistical indicators. Therefore, this study aims to evaluate the accuracy of *ET*<sup>0</sup> simulation with CLDAS products for the first time by comparing meteorological data from 689 ground weather stations and to exploring a product that could provide accurate *ET*<sup>0</sup> for areas lacking meteorological data observation.
