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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. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) is one of the most important hydroclimatic variables for the hydrologic and crop models, as well as the actual evapotranspiration in the irrigation schedule. The ET0 prediction a few months in advance can be beneficial to the decision-making on the long-term planning in the water management and irrigation communities. In this research, the monthly predictions of ET0 were realized over the Huaihe River basin using the model data of Beijing Climate Center second-Generation Climate Prediction System (BCC_CPSv2), and the surface meteorological observations at 172 stations during 1991 to 2020. The bilinear interpolation and quantile mapping were also selected to downscale and then correct the mean air temperature, net radiation, relative humidity, and wind speed of BCC_CPSv2. The obtained four variables were used to calculate the ET0 predictions using the Penman-Monteith equation. The correlation coefficient (r), Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were employed to evaluate the ET0 prediction performances of the model, and the four climatic variables before and after correction. Results showed that the mean air temperature, net radiation, and relative humidity from the model were significantly smaller than those from the observations before correction in each month with the RMSE of 1.84℃, 1.70 MJ/(m2 ·d), and 15.79%, respectively. The wind speed was also smaller during March to June, but larger in other months, with the RMSE of 1.39 m/s. Errors in the climatic variables led to the lower ET0 during February to June, and the larger ET0 in January, as well as in July to December, compared with the calculations. The RMSE of ET0 before correction was ranged from 0.11 to 1.70 mm/d for each month, among which was the highest in summer. The larger RMSE exceeding 0.50 mm/d occurred in eastern coastal areas in January and October, while the southwestern mountainous areas presented the higher RMSE exceeding 0.70 mm/d in April and July. The model skills on predicting climatic variables and ET0 were improved effectively by the quantile mapping. The RMSE of air temperature, net radiation, relative humidity, and wind speed decreased to 1.32℃, 0.74 MJ/(m2 ·d), 7.38%, and 0.53 m/s, respectively. Especially, the MAPE of wind speed and net radiation was decreased by more than 19%. The RMSE of ET0 after correction was ranged between 0.05 and 1.22 mm/d for each month, indicating the decrease in 80% of the months. The performances of the model were improved significantly for the eastern coastal areas in January, and the southwest mountainous areas in April, July and October, where the RMSE decreased by more than 0.30 mm/d. Before and after the correction, the net radiation and relative humidity were the primary factors for the ET0 prediction errors in summer half year and winter half year, respectively, due to the larger errors of the model and the higher sensitivity of ET0 to them. A better performance was achieved in the monthly ET0 prediction after the model correction with the error about 10.7%. The finding can provide a strong reference for the water resources management, irrigation schedule planning, and agricultural water demand decision-making. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is an important variable to estimate all the forms of evaporation and transpiration from crops and vegetation. An accurate estimation of ET0 plays a critical role in crop water management. The Food and Agriculture Organization of the United Nations (FAO) has recommended the Penman Monteith (PM) model as the standard to estimate the ET0. However, the PM model requires a large number of input parameters, including the solar radiation (Rs) or sunshine hours (n), the maximum and minimum air temperatures (Tmax and Tmin), Relative Humidity (RH), and wind speed at 2m high (u2). In addition to temperature data, most datasets are often unavailable, incomplete, or of uncertain quality in practice. Thus, the ET0 estimation using temperature data has drawn much attention in recent years. In this study, the recent estimation models were compared to improve the high accuracy of ET0 using the temperature data. The traditional Hargreves-Samani (HS) model (HS1), the improved HS model (HS2), and the PM temperature approach (PMTPP) were recently proposed by Paredes and Pereira. Some latest models were also introduced, including the ea estimation model proposed by Qiu et al., and the temperature-based Rs model (Rs-K) proposed by Korachagaon to improve the PM temperature approach (PMTNew). All models were calibrated and tested using the dataset collected from 51 radiation stations in China during the period 1967-2016. The results showed that the Rs-K model performed a better accuracy for the ET0 estimation, compared with the Rs model proposed by Paredes and Pereira (Rs-HS). For instance, the values of modelling efficiency (EF) were much closer to 1.00, the correlation coefficient (r) increased from 0.92-0.99 to 0.94-0.99, the values of Root Mean Square Error (RMSE), and the Relative Root Mean Square Error (RRMSE) were reduced by 0.08-0.25 mm/d and 0.03-0.09, respectively, at different climate zones. Except for the arid and hyper-arid climate zones, the average values of RMSE and RRMSE in the HS2 model decreased by 0.05-0.09 mm/d and 0.02-0.03, respectively, and the average values of EF increased by 0.03-0.10, compared with the HS1 model. Although the PMTPP model produced a similar r, the mean values of RMSE and RRMSE from the dry sub-humid and humid climate zones were reduced by 0.01-0.15 mm/d and 0-0.05, respectively, while the EF values increased by 0.01-0.06, compared with the HS2 model. Therefore, the PMTPP model presented a better accuracy than the HS model in estimating the ET0 at various climate zones. Except for the arid and semi-arid climate zones, the PMTNew model further reduced the RMSE and RRMSE by 0.04-0.12 mm/d and 0-0.04, respectively, indicating the higher EF and r values, compared with the PMTPP. In addition, the HS2 model presented a higher Global Performance Index (GPI) value than the HS1 model, indicating a better GPI value in the humid zone. Except for the humid climate zones, the PMTPP model presented a higher GPI value at various climate zones, the mean value of GPI range -0.06-0.42, compared with the HS2 model. The PMTNew model also presented a better GPI of each station, 90% of which ranked the first. Therefore, it is strongly recommended to use the PMTNew model to estimate the ET0 of each station, when the limited data is available. [ABSTRACT FROM AUTHOR]

A reliable accuracy is essential for the application of land surface temperature (LST) products. Current satellite retrieved LSTs are mainly validated over a few homogeneous sites. However, most of the existing ground sites are located in inhomogeneous areas: thus, their spatial representativeness on satellite pixel scales is unknown. In this situation, how to evaluate the spatial representativeness of these inhomogeneous sites, quantify the influence introduced by the spatial representativeness and describe the variation of the site's spatial representativeness are critical questions for satellite LST validation. In an attempt to answer those questions, a so-called temporal variation method (TVM) is proposed for evaluating a ground site's spatial representativeness. The method defines a spatial representativeness indicator (SRI), which is the LST difference between a ground radiometer's field-of-view (FOV) and a satellite pixel, and describes a site's spatial representativeness. Based on the temporal variation of LST, the SRI time series consists of three temporal components: ∆ ATC , ∆ DTC F-P , and ∆ USC , which describe the annual, diurnal, and instantaneous variations of SRI , respectively. Associated with the Landsat TM/ETM+ data and weather parameters, the method is implemented and tested at 16 Chinese ground sites for the validation of MODIS and AATSR LST products. Results show that the temporally continuous SRI (SRI TPR) shows high correlations with the original SRI (SRI ORI). The variation of SRI TPR is mainly determined by changes in surface coverage (i.e. NDVI difference on the two scales) and affected by weather conditions (e.g. near-surface air temperature, accumulative downward solar radiation, and wind speed). Since the SRI is defined as the LST difference between the two scales, it can be used as a bridge to convert the in-situ LST to pixel scale to address the spatial scale mismatch in LST validation. With this idea, the in-situ LST at daytime was converted to pixel scale associated with the SRI TPR , and the corresponding MODIS and AATSR LST were validated at the 16 ground sites. Results for MODIS and AATSR LST show that the effect of spatial representativeness on the validation results over the sites is large, with mean biases between −1.95 K and 5.60 K and standard deviations between 0.07 K and 3.72 K. Since the TVM method does not rely on a specific satellite or land surface product, it is readily applied to other LST products (e.g. Sentinel-3 SLSTR LST, NOAA VIIRS LST) and surface parameters (e.g. surface longwave radiation). • A spatial representativeness evaluation method was proposed on T-based validation. • The method (TVM) relies on the physical mechanism of LST temporal variation. • TVM describes a ground site's daily spatial representativeness for satellite LST. • The method is tested over 16 Chinese measurement sites with MODIS/AATSR LST products. • The uncertainty caused by spatial representativeness in LST validation is quantified. [ABSTRACT FROM AUTHOR]