Temporal and Spatial Changes in Evapotranspiration and Its Potential Driving Factors in Mongolia over the Past 20 Years

Abstract

Evapotranspiration (ET) plays an important role in the study of regional long-term water cycles. The water cycle in Mongolia has been seriously affected by global warming and the intensification of human activities. A significant relationship exists between climate factors and ET. In this paper, the temporal and spatial fluctuations and stability of ET in Mongolia from 2001 to 2020 were studied by using MOD16A2 ET, MOD13A2 NDVI and the climate data of ERA5-Land. ET trends were analysed by using the Breaks for Additive Season and Trend (BFAST) software package, Theil–Sen median trend analysis, Mann–Kendall method and Hurst index. The correlations between ET and temperature (Tem), precipitation (Pre), net solar radiation (Nsr), soil moisture (Swl) and human activities were determined by partial correlation analysis and a geographic detector. In the past 20 years, ET increased significantly in 49.4% of Mongolia, and NDVI also showed a significant increasing trend. BFAST detected two mutation years. ET decreased rapidly from 2006 to 2007 and increased rapidly from 2015 to 2016. In addition to winter, the meteorological factor that had a significant positive impact on ET in the east and west was Pre, whereas the impact of Tem was more obvious in central Mongolia. In winter, Tem had a great impact on ET. In the vegetation growing season, the joint action of NDVI and Pre greatly positively contributed to ET. The geographical detector showed that the influence of annual human factors on ET was weakened by changes in NDVI and Pre. In the growing season, Tem and Nsr increased nonlinearly to ET, and other natural and human factors showed bivariate enhancement. These results will help to understand the responses of ET changes to natural factors and human activities in Mongolia and provide data support for future research on ET and the water cycle.

Spatiotemporal Variation in Actual Evapotranspiration and the Influencing Factors in Ningxia from 2001 to 2020

“…The Theil-Sen median trend analysis is a robust nonparametric statistical method, which did not require the data to obey a certain distribution pattern and reduced the influence of noise [8]. This method has been widely used in the trend analysis of ET time-series data at the pixel scale [8,12,16,26,27]. The Theil-Sen estimator (S ET ) indicated the median of the slope of ET data combinations and was calculated as follows:…”

“…The Mann-Kendall test was insensitive to missing data, independent of data distribution and required fewer assumptions. This method is recommended by the World Meteorological Organization as a nonparametric statistical test and has been widely used in trend testing [8,27,[30][31][32][33][34][35][36].…”

“…Cheng et al investigated the spatiotemporal ET patterns in China [5]. The MOD16 ET product has also been used to analyze the trend of ET variation in northwestern Mexico [6], Bulgaria [7], Mongolia [8], China [5,[9][10][11], Henan Province [12], Guizhou Province [13], Shanxi Province [14], Xinjiang [15], Yinchuan Plain [16] and Sanjiang Plain [17].…”