Spatio-temporal variation of actual evapotranspiration in the Haihe River Basin of the past 50 years

Abstract

In this paper, the spatio-temporal variation and trend of daily actual evapotranspiration (ETa) are calculated for the Haihe River Basin from 1961 to 2010. The methodology is based on the complementary relationship approach, i.e. the advection–aridity (AA) model with parameter validation from 1961 to 2010, which allows the determination of ETa that cannot be instrumentally measured. Daily data on mean/maximum/minimum temperature, air pressure, actual water vapor pressure, sunshine hours, wind speed, sunshine duration and cloud cover from 31 meteorological stations from 1961 to 2010 are used in order to identify the main drivers of changes in evapotranspiration. The trend tests applied in this study are the linear regression method and the nonparametric Mann–Kendall test (MKtest). The results show: 1) the Haihe River Basin has an annual ETa at about 484 mm/yr. The highest ETa occurs in summer, followed by autumn. From 1961 to 2010, the annual ETa, the ETa in summer and the ETa in autumn show a significant negative trend in the Haihe River Basin. The ETa varies insignificantly in spring and winter; 2) the ETa shows distinct spatial variability in the Haihe River Basin. It closely follows the topography and increases with greater distance from the sea, but varies significantly during the seasons; 3) the central plain area of the Basin around the capital city of Beijing shows the highest occurrence of negative trends of ETa with a decrease of ETa of 40 mm for the time series. 4) negative trends of ETa in summer are related to multiple factors: decreasing relative humidity and decreasing reduced sunshine duration under increasing surface temperatures in summer. Increases in surface temperature include the mean, maximum and minimum daily temperature. The decline of ETa in autumn can be explained by a negative trend of relative humidity. The decreasing ETa suggests a reduction in water availability and cycle in seasons with highest water demand.