Abstract
The Huanghuaihai River Basin (HRB) is one of the most prominent areas of water resource contradiction in China. It is of great significance to explore the relationship between water balance in this area for a deep understanding of the response of the water cycle to climate change. In this study, machine learning methods are used to prolong the actual evapotranspiration (ET) of the basin on the time scale and explore water balances calculated from various sources. The following conclusions are obtained: (1) it is found that the simulation accuracy of Global Land Evaporation Amsterdam Model (GLEAM) products in HRB is good. The annual average ET spatial distribution tends to increase from northwest to southeast; (2) three machine learning algorithms are used to construct the ET calculation model. The correlation coefficients of the three methods are all above 0.9 and the mean relative error values of random forest (RF) are all less than 30%. The RF has the best effect; (3) the relative errors of water balance in HRB from 1956–1979, 1980–2002 and 2003–2018 are less than ±5%, which indicates that the calculation of each element of the water cycle in the study area can well reflect the water balance relationship of the basin.
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