Work

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

Year: 2022

Type: article

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.

Source: Sustainability

DOI: 10.3390/su142114577

Authors: Long Qian, Lifeng Wu, Xiaogang Liu, Yaokui Cui, Yongwen Wang

Institutions: Kunming University of Science and Technology, Jiangxi University of Water Resources and Electric Power, Nanchang Institute of Technology, Peking University

Language: English

Topic: Hydrology and Drought Analysis

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,094