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

Abstract

The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Assessing and Comparing Reference Evapotranspiration across Different Climatic Regions of China Using Reanalysis Products

“…It was found that T min shows an overestimation trend and T max shows an underestimation trend, and the consistency of the reanalysis minimum temperature of the two datasets (average R 2 0.96 and 0.87) is higher than that of the maximum temperature (0.90 and 0.84), but the PBias of the former is larger. Similar conclusions were found by Wu et al (2022) [53] and Qian et al (2022) [54] when they used reanalysis data to assess air temperature in the Chinese region. In addition, Simmons et al (2010) [55] and Paredes et al [56] also reported a tendency for the overestimation of T min and underestimation of T max by the ERA-Interim in Continental Portugal [57].…”