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Article

Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China

1
School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
3
Faculty of Agriculture Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2022, 14(11), 1744; https://doi.org/10.3390/w14111744
Submission received: 9 April 2022 / Revised: 26 May 2022 / Accepted: 26 May 2022 / Published: 29 May 2022
(This article belongs to the Topic Water Management in the Era of Climatic Change)

Abstract

:
Reference Crop evapotranspiration (ET0) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET0 (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET0 is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET0, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

1. Introduction

Reference Crop evapotranspiration (ET0) 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 [1,2,3]. 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 ET0 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 ET0 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 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 ET0 estimation on large spatial scales [10,11,12].
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 ET0 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 ET0 was superior to NCEP/NCAR ET0 in the UK. Pelosi et al. (2020) [22] also compared two reanalysis datasets for ET0 estimation in southern Italy. Woldesenbet et al. (2021) [23] evaluated the ET0 in the Omo-Gibetta watershed and achieved good prediction results. Song et al. (2015) [24] judged the spatiotemporal characteristics of ET0 in the Shaanxi Province based on NCEP reanalysis data and made future predictions. Liu et al. (2019) [25] estimated the future ET0 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 ET0 had better simulation results. ET0 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 ET0 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 ET0 for 43 meteorological stations distributed across Iran. The results show that the ET0 calculated by the mixed reanalysis had a better effect than the ET0 calculated by the observations at most research stations. Milad and Mehdi (2022) [28] used reanalysis products to estimate ET0 in areas with sparse data and showed that ERA5 provided more accurate estimates of daily and monthly ET0. 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 ET0 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 ET0 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 ET0 for areas lacking meteorological data observation.

2. Materials and Methods

2.1. Introduction to CLDAS

CLDAS is an isolatitude and longitude mesh fusion analysis product covering the Asian region (0–65° N, 60–160° E) with a resolution of 0.0625° × 0.0625° and 1 h, and a spatial geometric distance of 9 km between the corresponding grid points, including six variables: 2 m air temperature, 2 m specific humidity, 10 m wind speed, surface pressure, precipitation, and shortwave radiation. This dataset is developed by using the Space and Time Mesoscale Analysis System (STMAS), optimal interpolation (OI), probability density function matching (CDF), physical inversion, terrain correction, and other techniques based on ground and satellite observations from a variety of sources. The dataset’s quality and spatio-temporal resolution in China is better and higher than in the international market. The scientific goal of CLDAS is to use data fusion and assimilation technology, on the ground observation, satellite observations, numerical model products, and other sources of data fusion to obtain high space-time resolution and high-quality temperature, pressure, humidity, wind speed, precipitation, and radiation elements such as lattice data to drive the land surface model, obtain soil temperature and humidity, etc. The research focuses on processing and acquiring land surface driving data, realizing the operation and integration of multiple land surface models, and improving the underlying surface data, vegetation parameters, and atmospheric driving data.

2.1.1. Data Sources for CLDAS

(1) Ground observation data: hourly temperature, air pressure, humidity, wind speed, precipitation, and other data observed by more than 2400 national automatic weather stations and nearly 40,000 regional automatic weather stations after quality control.
(2) ECMWF (European Center for Mediumrange Weather Forecasts) numerical analysis/forecast products: global 3 h, 0.125° resolution 2 m temperature, 2 m humidity, 10 mU/V wind speed, surface pressure, and other data products released by EC (European Center).
(3) GFS numerical analysis/prediction products: NCEP released global ozone, atmospheric precipitation, surface pressure, and other data products with 3 h and 0.5°.
(4) Satellite precipitation products: FY2 precipitation estimation products (nominal disk chart) of the National Satellite Meteorological Center; East Asia Multi-Satellite Integrated Precipitation Data Product (EMSIP) with a resolution of 1 h and 0.0625° for the Asian region operated by the National Meteorological Information Center.
(5) Fusion precipitation product: the fusion product of FY2/CMORPH precipitation and automatic ground station precipitation with 1 h and 0.1° resolution in China operated by the National Meteorological Information Center.
(6) FY2 satellite entire disk nominal map: multi-channel geostationary satellite observation data with 1 h and 5 km resolution (subsatellite point) of the Service of National Satellite Meteorological Center (nominal disk map).
(7) DEM data: a global 30m spatial resolution topographic data product jointly measured by NASA (National Aeronautics and Space Administration) and NIMA (National Bureau of Surveying and Mapping of the Ministry of Defense) was used to re-sample DEM topographic data with a spatial resolution of 0.0625° in the Asian region using the area weight method.

2.1.2. CLDAS Data Processing Methods

The 2 m temperature, 2 m specific humidity, 10 m wind speed, and surface pressure products take ECMWF numerical analysis/forecast products as the background field. Topographic adjustment and multi-grid variational technology (STMAS) are used to integrate the observation data of automatic ground stations in China. The background field outside China is formed by topographic adjustment, variable diagnosis, and interpolation to the analysis grid.
The DISORT radiative transfer model used ozone, atmospheric precipitation, and surface pressure in GFS numerical analysis products as the dynamic input parameters for the radiative transfer model. Additionally, FY2E/G satellite VIS channel complete disk nominal map data inversion was used to form the short-wave radiation product.
The above information comes from the China Meteorological Data Sharing Network (https://data.cma.cn/, accessed on 1 May 2021). The data used in this study included temperature, global solar radiation, relative humidity, and wind speed, of which the height of wind speed was 10 m and the height of other meteorological variables was 2 m. The data spanned from 2017 to 2020.

2.2. Reference Evapotranspiration

According to the FAO56 PM equation [32], reference evapotranspiration (ET0; mm d−1) can be calculated as:
E T 0 = 0.408 ( R n G ) + γ 900 T a + 273 U ( e s e a ) Δ + γ ( 1 + 0.34 U )
where R n is the net radiation at the crop surface, usually calculated by R s (Global solar radiation); G is the soil heat flux density; T a is the mean daily air temperature at 2 m height; U is the wind speed at 2 m height; e s and e a are the saturation and actual vapor pressure, respectively; Δ is the slope of vapor pressure curve, and γ is the air psychrometric constant. In daily time-step in this study G can be neglected [33,34].

2.3. Data Sources

To examine the performance of this dataset, meteorological data from 689 ground meteorological observation stations of the China Meteorological Administration (CMA) were collected, which included maximum and minimum temperatures at 2 m, global surface radiation or sunshine durations, relative humidity at 2 m, and wind speed at 10 m. If necessary, sunshine durations were converted into global radiation using a formula from a previous study [35]. The stations were divided into seven climate zones [36,37]. The specific distribution is shown in Figure 1, and the names are shown in Table 1.
To obtain the daily reanalysis variables for Equation (1) (identified by subscript CLD), the following steps were taken: (a) daily TmaxCLD and TminCLD were selected as the maximum and minimum of the 24 daily available 1-h values of the Tmax and Tmin sequences, respectively; (b) daily RHCLD was obtained by calculating the 24-h average value of 24 RH values per day; (c) calculating the 24-h cumulative value of the 12-h Rs as the daily RsCLD value; (d) wind speed at 10 m (U10CLD) was calculated as the 24-h average of 24 1-h values, which were then converted to a height of 2 m (UCLD) using Formula (2) as follows, respectively:
U = U z 4.87 ln ( 67.8 z 5.42 )
where z is the height of the wind speed observation instrument (in this paper, z is equal to 10) for each meteorological station. Grid data from four grid points around it were selected and interpolated to the station by the inverse distance weight (IDW) method. The formula is as follows:
V = i = 1 n v i D i 2 i = 1 n 1 D i 2
where V is the inverse value, v i is the value of the control point, and D i is the weight coefficient.

2.4. Statistics Indicators

Three common statistical indicators, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and percent bias (PBias) were chosen to evaluate the accuracy of the CLDAS meteorological variables and ET0 in this study. The corresponding formulas are:
MAE = 1 n i = 1 n | M i P i |
RMSE = 1 n i = 1 n ( M i P i ) 2
R 2 = [ i = 1 n ( M i M ¯ i ) ( P i P ¯ i ) ] 2 i = 1 n ( M i M ¯ i ) 2 i = 1 n ( P i P ¯ i ) 2
PB = i = 1 n ( P i M i ) i = 1 n M i
where M i is ET0 calculated by meteorological station data, P i is ET0 calculated by the CLDAS gridded data, M ¯ i is average ET0 calculated by meteorological station data, P ¯ i is average ET0 calculated by the CLDAS gridded data, and n is the number corresponding to ET0 data. Higher R2 values (closer to 1) or lower RMSE and MAE values indicate a better estimation performance of the CLDAS dataset. The closer PB is to 0, the better the estimation performance of the CLDAS dataset.

3. Results

3.1. Meteorological Factors

3.1.1. Air Temperature

Table 2 shows the statistical indicators of maximum and minimum temperatures in the CLDAS data for the seven climate zones in China. Results indicated that the accuracy for the maximum and minimum temperatures differed in different climatic regions. For the maximum temperature, CLDAS data showed a high correlation with data from ground stations in the four northern climate zones (i.e., climate zones 1–4), with R2 larger than 0.9. Climate zone 5 in the humid climate region also yielded a good correlation. In climate zones 6 and 7, the correlations between the two datasets were slightly worse when compared with other climate zones. However, climate zone 6 showed the smallest values in terms of statistical errors, with RMSE and MAE valued at 2.9 and 2.3 °C, respectively. This may be since the range of temperature changes in this region is not as large as that in other regions, and the area of this climate zone is significantly smaller than that in other climate zones, so the temperature change in this region is not as dramatic as that in other climate zones. The RMSE and MAE of the high-altitude climate zone (i.e., climate zone 7) were 6.55 °C and 5.83 °C, respectively. Figure 2, Figure 3 and Figure 4 show the spatial error distribution of the maximum temperature in CLDAS. Overall, the errors at most stations were within a small range. However, in climate zone 7 and the north-central area of climate zone 1, there was a big error in the regions, with RMSE and MAE of many stations more significant than 10 °C, while R2 was lower than 0.5. Such huge variations in model errors in these stations might be resulted from the regional climate model parameter variations and were unlikely caused by the overall overvalued or undervalued problem of models that may cause significant variation for ET0 estimation.
The minimum temperature behavior in the CLDAS data set was similar to the maximum temperature. However, compared with the maximum temperature, correlations between the minimum temperature of CLDAS and the station’s temperature were higher in all seven climate zones, with all R2 larger than 0.9. In climate zone 6, the minimum temperature error was the lowest among all climate zones, with RMSE and MAE valued at 1.87 °C and 1.5 °C, respectively. On the contrary, climate zone 7 had the highest error, with RMSE and MAE reaching 5.1 and 4.53 °C, respectively. In addition, the lowest temperature error in climate zone 1 was also relatively high, with RMSE and MAE valued at 4.03 °C and 3.45 °C, respectively, which might affect the accuracy of ET0 estimation. As can be seen from Figure 2, Figure 3 and Figure 4, the performance of Tmin was similar to that of Tmax. Therefore, the accuracy of most stations was within an acceptable range. However, some stations showed significant errors, which were mainly located in the middle of climate zone 1 and climate zone 7. These stations with high error in the minimum temperature had a high coincidence with corresponding high Tmax error stations, indicating severe problems in the temperature simulation of the stations.

3.1.2. Solar Radiation

Table 3 shows the statistical indicators of solar radiation (Rs) in the CLDAS data for the seven climate zones. Across climate zones, RMSE ranged from 5.18 to 6.21 MJ m−2 d−1, and MAE ranged from 3.83 to 4.54 MJ m−2 d−1. The differences in Rs errors among different climate regions were not as apparent as those in air temperature. However, the R2 of climate zone 7 was significantly lower than that of other climatic regions. These results were similar to the results reported by Liu et al. (2009) [38]. However, their values were generally higher than that of the radiation model based on temperature, where the median RMSE was 3.3 MJ m−2 d−1 in humid regions of China (Fan et al., 2019) [39]. The above phenomenon indicated that the radiation data in the CLDAS data set did not perform well.
Figure 2, Figure 3 and Figure 4 show the spatial distribution of Rs errors. Overall, the error of Rs in climate zones 1–3 was better than in other climate zones. The RMSE of most stations was more significant than 6 MJ m−2 d−1. This might be due to the severe air pollution in the above areas [40], which would pose particular challenges to accurate simulation.

3.1.3. Relative Humidity

Statistical indicators of CLDAS RH are shown in Table 3. Climate zone 7 had the most significant error among all regions, with RMSE and MAE reaching 31.29% and 27.62%, respectively, close to a random distribution. The consistency between CLDAS and site data in other climatic regions was also not high, with R2 ranging from 0.39 to 0.59. However, the values of RMSE and MAE indicated that they were still within acceptable limits. Compared climate zone 1 with climate zone 6, the consistency of climate zone 1 was higher, but the RMSE and MAE of climate zone 6 are lower. This result could be attributed to the fact that climate zone 6 is within the humid region with high annual average relative humidity, while climate zone 1 is in the arid area where the relative humidity changes more sharply. Figure 2, Figure 3 and Figure 4 showed that the overall error of this data set was larger in climate zone 7 than in other climate zones. In addition, compared with the northeast part of climate region 5, the error of RH in the western part of the same climate zone (i.e., areas bordering climate zone 7) was significantly larger. Although there was a relatively large error for RH in some regions, the estimation of ET0 would unlikely be affected, as previous studies have found that RH would have a low contribution to ET0 in most regions of China [41].

3.1.4. Wind Speed

Statistical indicators of CLDAS U are shown in Table 3. From the perspective of R2, the consistency between CLDAS near-surface wind speed and station data was poor in all climate zones, while from the perspective of RMSE and MAE, their accuracies were acceptable. In addition, the mean difference between climate zones was within 30%. However, according to the spatial distribution of the error (Figure 2, Figure 3 and Figure 4), the RMSE of some stations was more than 3 m s−1, of which the errors of most stations in climate zone 7 were significant. Compared with the inland stations, the R2 of the coastal stations was higher. However, RMSE and MAE were also higher, indicating a problem of overestimation or underestimation. The worldwide modeling for wind speed is challenging and often inaccurate. Similar results were obtained for ERA5 [22], NCEP/NCAR [27], and GLDAS [42]. This is mainly due to the complex terrain changes on the ground, and the wind speed is greatly affected by the roughness of the underlying surface. In addition, it is not easy to simulate the movement direction of winds accurately.

3.2. Reference Evapotranspiration

The statistical indicators of calculated ET0 based on the CLDAS dataset are shown in Table 4. Among all climate zones, climate zone 1 had the best consistency (R2 = 0.84) between CLDAS data and station data, while climate zone 3 showed the lowest errors (RMSE = 0.87 mm d−1 and MAE = 0.58 mm d−1). For climate zone 7, the values of RMSE (1.37 mm d−1) and MAE (1.19 mm d−1) were higher than the corresponding values in any of the other climate zones. Figure 5 shows the spatial distribution of statistical indicators. Across climate zones, R2 overall showed a decreasing trend from the north to the south, and the southernmost region (i.e., climate zone 6) had the lowest value of R2. However, the spatial distributing patterns of RMSE and MAE were different from R2. The stations with significant errors are mainly distributed west of climate zone 1, the coastal areas, and the boundary areas between climate zone 7 and other climate zones. This is mainly due to the more complex climate change between climate zones. In addition, the high wind speed error in the coastal areas often leads to a significant ET0 error.
To explore the differences in the CLDAS data in different climate regions, one station from each climate zone was randomly selected to fit the correlation between the calculated ET0 based on CLDAS and the FAO56-PM ET0 (Figure 6). Although there were a few outliers, the scatter points in climate zone 1 were more concentrated to the 1:1 line than those in other climate zones. Scatter points in climate region 2 were slightly more dispersed than in climate zone 1 and showed some obvious overestimations when ET0 was more significant than 6. In climate zone 3, the accuracy was excellent when the value of ET0 was low (<2 mm) but showed a decline as the following scatter points started to discretize. However, no overestimation or underestimation existed. In climate zone 4, the error was relatively large when ET0 ranged from 3 mm to 6 mm. When the ET0 of climate zone 5 was less than 2, the problem of underestimation appeared, and then the points were scattered in the 1:1 line for two measurements, but the distance from the 1:1 line was far. In climate zone 6, the error was significant when ET0 was greater than 3 mm, and some scatter points were obviously overestimated or underestimated. Although the points were not as discrete as those in climate zones 4 and 5, the ET0 of climate zone 7 showed a significant underestimation.
Figure 7 shows the ET0 box diagram of a station randomly selected from each climate zone. From the median value, there are differences in the performance of different climate regions. Among them, the ET0 prediction bias of climate region 2 and climate region 3 is slight. The bias of climate region 6 and 7 is large. In addition, from the extreme value, the bias of ET0 estimated in climate zone 4 and climate zone 6 is small, and other regions have overestimated or underestimated in varying degrees. From the quartile line, there are significant differences in ET0 estimation in climate regions 5, 6, and 7. The predicted ET0 performance of climate zones 1, 2, 3, and 4 is relatively good.

3.3. Seasonal Performance of Reference Evapotranspiration from CLDAS

Since the demand for water resources varies significantly between seasons, it is necessary to assess the performance of the CLDAS dataset in different seasons. Figure 8 shows the RMSE performance of CLDAS ET0 in the four seasons. In spring (March–May), stations with RMSE smaller than 1.5 mm d−1 accounted for more than 85% of all stations across China. The RMSE was lower in the south of climate zone 1 and the middle and north of climate zone 3, ranging from 0.5 to 1 mm d−1. For most stations of climate zones 2, 4, 5, and 6, RMSE values ranged from 1–1.5 mm d−1. Stations with errors greater than 1.5 mm d−1 are mainly located in climate zones 1 and 7.
In summer (June–August), the RMSE of CLDAS ET0 was generally higher than that of spring. More than 80% of the stations had RMSE ranging between 1 mm d−1 and 1.5 mm d−1. Stations with RMSE smaller than 1 mm d−1 were mainly concentrated in the southern part of China and near the boundary area between climate zones 5 and 6. Stations with RMSE greater than 1.5 mm d−1 were distributed in all climatic regions, of which climate zone 7 had the largest RMSE, followed by climate zone 1. Especially for the southwest area of climate zone 7, stations in this area were sparse, and the error was relatively large, with the value of RMSE larger than 2 mm d−1.
In autumn (September–November), RMSE was less than 1 mm d−1 in 80% of all stations, and stations with a significant error were still mainly concentrated in climate zone 7. It is worth mentioning that there were also many stations with RMSE greater than 1 mm d−1 in the coastal areas of climatic zone 6. This is mainly due to the relatively high temperature of this area in autumn, resulting in a relatively large RMSE.
In winter (December–February), RMSE in northern regions (i.e., climate zones 1–3) was lower than 0.5 mm d−1 due to the minimal ET0 value. RMSE of most stations in climate zones 4 and 5 was less than 1 mm d−1. However, the values of RMSE in the southern part of climate zone 7, the coastal part of climate zone 6, and the western part of climatic zone 5 were more outstanding than 1.5 mm d−1.
Among all seasons, summer had the most significant RMSE error, followed in order by spring, autumn, and winter. The CLDAS dataset performed well in climate zones 2, 3, 4, and 5, but performed poorly in all seasons in climate zone 7. In addition, the coastal areas of climate zone 6 also did not perform well in autumn and winter.
Because the demand for water resources varies significantly in different seasons, it is also necessary to evaluate the specific overestimation or underestimation of the CLDAS dataset in different seasons. This provides a more detailed reference for practical production and life applications. Figure 9 shows the PBias distribution of ET0 calculated by CLDAS in the four seasons. In spring, the sites with PBias between 0.2 and 0.2 accounted for about 70% of all sites in the country, and the overall forecast stability was good. The values of ET0CLD in the southern regions of climate zone 1, climate zone 2, the southern part of climate zone 3, most of climate zone 4, and the central and northern parts of climate zone 5 are within 10% of the local station data. In climate zone 7 (Underestimated), numerical biases are generally greater than 30%. The prediction of ET0CLD in the junction area of climate zone 7 and other climate zones is not very stable, and most of them are underestimated. In addition, the ET0CLD in coastal areas will have a relatively large bias.
In summer, the bias of ET0 calculated by CLDAS is generally smaller than that in spring, but some sites have large fluctuations (the bias is greater than 60%), and the PBias of more than 60% of the sites is between −10% and 10%. ET0CLD is in the climate zone 3. The southeastern coastal areas (overestimated), the southern part of climate zone 5 (overestimated), and the western coastal areas of climate zone 6 (overestimated) have large biases from the local weather station data, with a gap of about 10% to 30%. Numerical bias with zone 7 (underestimation) is generally greater than 30%. The prediction of ET0CLD for meteorological stations in the junction of climate zone 7 and other climate zones is not very stable, and most of them are underestimated.
In autumn, the bias of ET0 calculated by CLDAS is generally larger than that in spring and autumn, and only about 50% of the sites have PBias between 10% and 10%. The western region (underestimated), the central and western regions of climate zone 5 (underestimated), the southern coastal region of climate zone 6 (underestimated), and the climate zone 7 (underestimated) have large biases from the data of local meteorological stations, with a gap of more than 30%. In addition, the prediction accuracy of the ET0CLD of the meteorological stations at the junction of climate zone 3 and other climate zones decreased significantly. Most showed an overall underestimation.
In winter, the bias of ET0 calculated by CLDAS is generally the largest, among which the bias of ET0CLD from the local station data in the southern region of climate zone 1, the central region of climate zone 2, the central region of climate zone 3, and the central and eastern regions of climate zone 5 is 10%. Within %; ET0CLD in the northern region of climate zone 1 (overestimated), the southern coastal region of climate zone 3 (overestimated), most of climate zone 4 (underestimated), the central and western regions of climate zone 5 (underestimated), the southern part of climate zone 6 Coastal areas (underestimated), and climate zone 7 have large biases from local weather station data, with a gap of more than 30%. In addition, the prediction accuracy for the ET0CLD of the meteorological stations in the transition areas of different climatic zones will drop significantly, and both overestimation and underestimation exist.
Figure 10 shows a boxplot of the calculated PBias for the CLDAS dataset. From the median value of PBias, there are biass in the performance of different seasons. Among these, the estimated bias in spring and summer is smaller, the performance in autumn is second, and the performance in winter is the largest. From the quartile line (aside from winter), the estimated differences in the other three seasons were small. From the perspective of extreme values, the estimated maximum and minimum values in winter are not good. The performance in summer is the best, and the estimated bias is the smallest. In conclusion, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. The CLDAS dataset performs well in climate zones 2, 3, 4, and 5 but not in all seasons in climate zone 7. In addition, the coastal areas of climate zone 3 and climate zone 6 also performed poorly in autumn and winter, and the performance at the interface of different climate zones was also relatively poor.

3.4. Annual Performance of Reference Evapotranspiration from CLDAS

China is a country with frequent droughts and floods. Water demand also varies widely between years. Therefore, it is necessary to evaluate the difference in CLDAS ET0 error in different years. The RMSE of CLDAS ET0 in 2017–2020 is shown in Figure 11. Overall, RMSE in 2019 was lower than that in other years, with more than 85% of all stations having a value less than 1 mm d−1. In 2020, stations with RMSE less than 1 mm d−1 accounted for 60% of the total stations. Regarding the spatial distribution of errors, climate zones 3, 4, 5, and 6 overall performed better than other climate zones. Significant errors were in the southern part of climate zone 7 and the eastern part of climate zone 1, with RMSE generally more significant than 1.5 mm d−1. This may be related to the special geographical location of these stations, such as at the boundary of different climate zones. The above results further confirmed that the data set had good performance in some regions. At the same time, there were also significant uncertainties in other regions, which could bring certain risks to the application.
Figure 12 shows the PB distribution of ET0 calculated by CLDAS in 2019–2020. In 2017, the sites with PBias between 0.1 and 0.1 accounted for about 60% of all sites in the country, and the overall forecast stability was good. The values of ET0CLD in the southern region of climate zone 1, climate zone 2, the central and northern regions of climate zone 3, the central region of climate zone 4, the central and northern regions of climate zone 5, and the central region of climate zone 6 are compared with local weather station data. The bias is within 10%; the bias of ET0CLD from the local weather station data is larger in the northern area of climate zone 1, the southern coastal area of climate zone 3 (overestimated), and the southern coastal area of climate zone 6 (underestimated), with a gap of 10% to 30%. However, the numerical bias of climate zone 7 (underestimated) is generally greater than 30%. The prediction of ET0CLD in the junction area between climate zone 7 and other climate zones is not very stable, and most of them are underestimated.
In 2018, the bias of ET0 calculated by CLDAS was similar to that in 2017, with about 60% of sites having a PBias between 10% and 10%, and ET0CLD in the central region of climate zone 1 and a few in the southern part of climate zone 3. The coastal areas (overestimated), parts of the southern part of climate zone 5 (overestimated), and the southern coastal areas of climate zone 6 (overestimated) have large biases from local weather station data, with a gap of about 10% to 30%. The (underestimated) numerical bias is generally greater than 30%. Similarly, the prediction of ET0CLD for meteorological stations in the junction of climate zone 7 with other climate zones is not very stable, and most of them are underestimated.
In 2019, about 55% of the sites had PBias between −10% and 10%. The ET0CLD in the southern part of climate zone 1, most of climate zone 2, the middle part of climate zone 3, small parts of the central region of climate zone 4, the central and eastern regions of climate zone 5, and the central region of climate zone 6 within 10% of the data from local weather stations. ET0CLD is in the central region of climate zone 1, the southern coastal (overestimated) and northern regions (underestimated) of climate zone 3, the central and western regions of climate zone 4 (underestimated), and the southern part of climate zone 5 (overestimated). The bias between the southern coastal areas of climate zone 6 (overestimated) and the local weather station data is relatively large, about 10% to 30%. The numerical bias of climate zone 7 (underestimated) is generally greater than 30%.
In 2020, the bias of ET0 calculated by CLDAS was generally the smallest, and about 60% of the sites have PBias between 10% and 10%. ET0CLD is in the southern part of climate zone 1 and the southwest of climate zone 2. The central region of climate zone 3, the central and southern regions of climate zone 4, most of climate zone 5, and most of climate zone 6 were within 10% of data from local weather stations. ET0CLD is in the central region of climate zone 1, a few southern coastal (overestimated) and northern regions (underestimated) of climate zone 3, the central and western regions of climate zone 4 (underestimated), and a small number of regions in climate zone 5 (underestimated). The bias of local weather station data is large, with a gap of about 10% to 30%. The numerical bias of climate zone 7 (underestimated) was generally greater than 30%. Similarly, the prediction of ET0CLD for meteorological stations in the junction of climate zone 7 with other climate zones is not very stable, and most of them are underestimated.
Figure 13 shows a boxplot of annual PBias calculated for the CLDAS dataset. From the median value of PBias, there is a bias in the performance of different years. The estimated bias in 2020 and 2017 is smaller, and the performance of the other two years is relatively poor. From the quartile line, the estimated bias in 2020 is the smallest and more compact, and the estimated differences in other years have different degrees of fluctuation. From the perspective of extreme values, there was a clear overestimation in 2017 and a clear underestimation in 2018. Additionally, 2020 had the best performance, and the estimated bias was the smallest. In conclusion, from 2017 to 2020, bias in 2019 and 2020 was the smallest. The CLDAS dataset performs well in climate zones 2, 3, 4, and 5 but not in all seasons in climate zone 7. In addition, the coastal areas of climate zone 3 and climate zone 6 also performed poorly except in 2020. The performance of the boundary areas of different climate zones was also relatively poor.

3.5. Main Factors Affecting Reference Crop Evapotranspiration

The evapotranspiration process is affected by many factors [43], and its changes are mainly attributed to the changes in meteorological factors. The country’s climate is complex and diverse. From a geographical point of view, the eastern part is mostly a monsoon climate zone with a complex and changeable climate. The air above it is severely polluted, affecting solar radiation and surface wind speed. Therefore, the performance of estimated ET0 in coastal areas will decline. The northwest region is far from the sea, is a non-monsoon region, and belongs to a temperate continental climate. The ground topography in this region (the junction of climate zones 2, 4, and 5) is complex and changeable, and the wind speed is greatly affected by the roughness of the underlying surface. The direction of wind movement is accurately simulated, so the reduction in the accuracy of wind speed is likely to lead to a decline in the performance of estimating ET0 in some areas. The Qinghai-Tibet Plateau belongs to the plateau climate zone. Due to its complex and changeable terrain, the climate itself on the Qinghai-Tibet Plateau will fluctuate depending on the region, which greatly affects the estimation of ET0. To sum up, the closer are to inland areas (such as climate zones 1, 2, and 3), the higher the accuracy of ET0 estimation will be. The performance of ET0 estimation in coastal areas, the Qinghai-Tibet Plateau, and the junction of climatic zones will be negative effects [44,45,46]. From the seasonal point of view, the summer is affected by the warm and humid air from the ocean, with high temperature, humidity, and rain. The climate is oceanic, so the estimation error in summer is the largest. In winter, affected by the dry and cold airflow from the continent, the climate is cold, dry, and less rainy, and the climate is continental, and the estimation error will be relatively small [47,48]. In addition, specific regions need to be further analyzed according to the actual situation.

4. Discussion

The calculation of ET0 is affected by a variety of climatic factors. Ma et al. (2010) [49] studied the influence of main climatic factors on ET0 in mountainous plateau areas and found that the change of wind speed had the most significant impact on ET0 at each site. Luo et al. (2010) [50] conducted a sensitivity analysis on ET0 and main meteorological factors in the main agricultural areas of Tibet, and the results showed that ET0 in the whole region had a declining trend over the past 50 years. The meteorological element that had the most significant impact on ET0 was Rs. Similar results were obtained in our study, where the accuracy of ET0 was affected by the error of Rs. Xie et al. (2017) [51] analyzed the impact of changes in meteorological factors on ET0 in China’s main grain-producing areas from 1961 to 2013, in which ET0 showed a saw-tooth decline. The changing characteristics of main meteorological factors in the study area and the response of ET0 are similar to the results of our study, showing regional and seasonal variations. Overall, our study suggests that the errors of meteorological factors in the Qinghai-Tibet Plateau region and the boundary region of the climate zones are more significant than in other regions, with the highest errors observed in summer.
Due to the incomplete understanding of the physical mechanism of weather changes and limited observational data, there is still a specific error in the reanalysis data [52], and the magnitude of this error tends to vary with different climatic factors. Temperature is a meteorological variable with minor errors, usually less than 10% [53,54]. Similar results were found in our study, in which the R2 of Tmax and Tmin are generally greater than 0.9 in the seven climatic zones. Due to the influence of topography, the errors of wind speed and relative humidity are usually large [26], and similar results were obtained in our study.
It is worth mentioning that the weather stations in our study are affiliated with the China Meteorological Administration. The ground of the weather station is usually covered with short grass under adequate irrigation conditions. However, areas in the grid system do not necessarily have lush vegetation. Therefore, there might be some differences in the environmental factors between the two types of systems, especially for the radiant energy (i.e., R2 < 0.65 in the seven climate zones for the RsCLD estimation in our study). This may lead to the problem of overestimating or underestimating the reanalysis data, which indirectly explains why the estimated ET0 values in some areas fluctuate severely in our study. In addition, the variation of wind speed is greatly affected by the terrain and the type of underlying surface, and it is not easy to obtain the average wind speed in a specific area. Similar results were obtained in our study, where the overall UCLA accuracy is not satisfactory.
Finally, this study can provide an idea for economically underdeveloped countries and contribute to improving the reanalysis data set and the accuracy of ET0 estimation. Therefore, when other developing countries establish regional climate models, they can consider their own terrain and climate characteristics and establish a more local model.

5. Conclusions

ET0 data set based on reanalysis products can make up for the time discontinuity and spatial insufficiency of surface meteorological platform data, which is significant for water resources planning and irrigation system formulation. However, a rigorous evaluation of reanalysis products must be carried out to see if they have value in application. This study evaluated the ability of the CLDAS dataset officially published by the Chinese meteorological system for ET0 estimation. Results indicate that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the accuracy of the total radiation data is average, and the quality of relative humidity and wind speed data is poor. The overall accuracy of ET0 is acceptable except for QTP, but there are many stations with large errors. Among seasons, RMSE is the largest in summer and smallest in winter. There are also inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area, Northeast Taiwan, the Semi-Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are in the south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

Author Contributions

Conceptualization, L.-F.W. and L.Q.; methodology, L.-F.W., G.-M.H. and L.Q.; data curation, L.Q. and X.-G.L.; writing—original draft preparation, L.-F.W., Y.-C.W., G.-M.H. and L.Q.; writing—review and editing, L.-F.W., H.B., X.-G.L. and L.Q.; project administration, L.-F.W. and S.-F.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Project of Jiangxi Provincial Department of Education (GJJ180925), National Natural Science Foundation of China (51979133 and 51769010) and Natural Science Foundation of Jiangxi province in China (20181BBG78078 and 20212BDH80016).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Abbreviations

Meaning of main acronyms:
CLDAS: China Meteorological Administration Land Data Assimilation System; ET0: Reference Crop evapotranspiration; FAO: Food and Agriculture Organization of the United Nations; ECMWF: European Center for Medium Weather Forecasting; NCEP: National Centers for Environmental Prediction; Rs: Global solar radiation; U: wind speed at 2 m; RH: relative humidity; Tmax: maximum temperature; Tmin: minimum temperature. (When the subscript CLD exists in these meteorological data, it is the corresponding CLDAS meteorological data); RMSE: Root Mean Square Error; MAE: Mean Absolute Error; PBias: percent bias; R2: coefficient of determination.

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Figure 1. Climate zones of China and geographical distribution of 689 meteorological stations. (See Table 1 for the names of climatic zones 1–7).
Figure 1. Climate zones of China and geographical distribution of 689 meteorological stations. (See Table 1 for the names of climatic zones 1–7).
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Figure 2. RMSE values of the five meteorological factors of in all stations.
Figure 2. RMSE values of the five meteorological factors of in all stations.
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Figure 3. MAE values of the five meteorological factors of in all stations.
Figure 3. MAE values of the five meteorological factors of in all stations.
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Figure 4. R2 values of the five meteorological factors of in all stations.
Figure 4. R2 values of the five meteorological factors of in all stations.
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Figure 5. Spatial distribution of ET0 statistical indicators.
Figure 5. Spatial distribution of ET0 statistical indicators.
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Figure 6. Scatter plots of CLDAS and FAO56 PM values of ET0 in different climates.
Figure 6. Scatter plots of CLDAS and FAO56 PM values of ET0 in different climates.
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Figure 7. The box diagram of CLDAS and FAO56 PM values of ET0 in different climates.
Figure 7. The box diagram of CLDAS and FAO56 PM values of ET0 in different climates.
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Figure 8. Seasonal RMSE of ET0 calculated from CLDAS dataset.
Figure 8. Seasonal RMSE of ET0 calculated from CLDAS dataset.
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Figure 9. Seasonal PBias of ET0 calculated from CLDAS dataset.
Figure 9. Seasonal PBias of ET0 calculated from CLDAS dataset.
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Figure 10. The box diagram of Seasonal PBias of ET0 calculated from CLDAS dataset.
Figure 10. The box diagram of Seasonal PBias of ET0 calculated from CLDAS dataset.
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Figure 11. Annual RMSE of ET0 calculated from CLDAS dataset.
Figure 11. Annual RMSE of ET0 calculated from CLDAS dataset.
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Figure 12. ET0 annual PBias distribution calculated from CLDAS dataset.
Figure 12. ET0 annual PBias distribution calculated from CLDAS dataset.
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Figure 13. The box diagram of annual PBias of ET0 calculated from CLDAS dataset.
Figure 13. The box diagram of annual PBias of ET0 calculated from CLDAS dataset.
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Table 1. Names of the seven climate zones.
Table 1. Names of the seven climate zones.
ZoneArea Name
1Northwest desert zone
2Inner Mongolia grassland zone
3Northeast humid and semi humid temperate zone
4Humid and semi humid warm temperate zone
5Humid subtropical zone
6Humid tropical zone
7Qinghai Tibet Plateau zone
Table 2. Statistical indicators of maximum and minimum temperatures in different climate zones of China.
Table 2. Statistical indicators of maximum and minimum temperatures in different climate zones of China.
TmaxTmin
Climate ZoneRMSE °CMAE °CR2RMSE °CMAE °CR2
%%%%
14.994.320.954.033.450.96
23.822.940.932.501.850.97
33.752.880.943.022.270.97
43.833.050.922.652.110.97
53.512.750.882.241.790.96
62.902.300.821.871.500.94
76.555.830.815.104.530.92
Table 3. Statistical indicators of solar radiation, relative humidity, and wind speed in different climate zones of China.
Table 3. Statistical indicators of solar radiation, relative humidity, and wind speed in different climate zones of China.
RsRHU
Climate ZoneRMSE
MJ m−2 d−1
MAE
MJ m−2 d−1
R2RMSE %MAE %R2RMSE
m s−1
MAE
m s−1
R2
15.183.830.5514.9312.110.591.180.880.22
25.413.930.6512.609.790.551.321.010.29
35.333.930.5512.819.880.501.281.000.30
46.214.510.6013.6210.730.441.261.010.20
56.134.540.4811.909.720.421.050.850.21
65.544.190.599.197.430.391.381.130.22
75.804.430.2831.2927.620.331.030.810.14
Table 4. Statistical indicators of reference evapotranspiration in different climate zones of China.
Table 4. Statistical indicators of reference evapotranspiration in different climate zones of China.
ZoneRMSEMAER2
11.100.740.84
20.940.630.80
30.870.580.74
41.030.750.71
50.990.720.64
61.080.830.52
71.371.190.62
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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

AMA Style

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(11):1744. https://doi.org/10.3390/w14111744

Chicago/Turabian Style

Wu, Li-Feng, Long Qian, Guo-Min Huang, Xiao-Gang Liu, Yi-Cheng Wang, Hua Bai, and Shao-Fei Wu. 2022. "Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China" Water 14, no. 11: 1744. https://doi.org/10.3390/w14111744

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