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

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.

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

“…These results are similar to the results reported by Liu et al (2009) [49] and Fan et al (2019) [50]. This may be due to the immature radiation simulation mechanism of the CLDAS and the severe air pollution in the areas mentioned above, which pose certain challenges in respect of obtaining accurate simulations [18]. Therefore, we propose using the corresponding CLDAS data instead of local meteorological data to predict ET 0 when the local meteorological RH or U 2 data are missing; when the Rs data are missing, we can use machine learning methods to predict ET 0 .…”

“…The digital elevation (DEM) data of CLDAS products are obtained from the global 30 m spatial resolution terrain data products that are jointly measured by NASA and NIMA of the Ministry of Defense. These products perform well in their respective fields [18,39].…”

“…The data not only contain various elements and have a large range, but also have a long extension period and a high resolution. Datasets from global atmospheric reanalysis can offer the necessary inputs for estimating ET 0 [18].…”

“…The CLDAS is a mesh fusion reanalysis product covering the entire Asian region (0-65 • N, 60-160 • E), with a spatial resolution of 0.0625 • × 0.0625 • (the spatial geometric distance between grid points is 9 km) and a minimum time resolution of 1 h. The meteorological variables included are 2 m air temperature, 2 m specific humidity, 10 m wind speed, surface pressure, precipitation and shortwave radiation [18].…”

Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands

“…Based on the findings from these research works, it is evident that ERA5 and ERA5-L can be favorable alternatives for modeling ET o in data-scarce regions of Iran. Additionally, several other research studies have been conducted in various regions worldwide to assess ET o models forced by reanalysis and remotely-sensed products [ 26 , [32] , [33] , [34] , [35] , [36] ]. Nevertheless, most of these studies have not been conducted at meteorological stations, which may have resulted in biased results in drylands, as the findings could be affected by cooling and coastline effects.…”