Abstract
The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (Epan) is an important indicator of climate change; however, current research on Epan has focused on its change in mean rather than its variability. The variability of monthly Epan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in Epan variance between space and time. The Epan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in Epan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of Epan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in Epan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring.
Significance Statement
The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.
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