LAUSR

Hydrological processes are a key component of land surface models and link to the energy budget and carbon cycle. This study assessed the global hydrological processes of the Atmosphere–Vegetation Interaction Model (AVIM) using multiple datasets, including the Global Land Data Assimilation System (GLDAS), the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC), the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Evaporation Amsterdam Model (GLEAM), and the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) datasets. The comparisons showed that the AVIM gives a reasonable spatial pattern for surface soil moisture and surface runoff, but a less satisfactory spatial pattern for evapotranspiration. The AVIM clearly underestimates surface runoff worldwide and overestimates the surface soil moisture in the high latitudes of the Northern Hemisphere, while yielding moderately higher evapotranspiration in arid areas and lower evapotranspiration in low-latitude areas near the equator. The annual cycle of evapotranspiration in the AVIM shows good agreement with the GLEAM dataset, whereas the surface soil moisture in the AVIM has a poor annual cycle relative to the CCI dataset. The AVIM simulates a late start time for snowmelt, which leads to a two-month delay in the peak surface runoff. These results clearly point out the directions required for improvements in the AVIM, which will support future investigations of water–carbon–atmosphere interactions. In addition, the evapotranspiration in the MERRA-2 dataset had an overall good performance comparable with that of the GLEAM dataset, but its surface soil moisture did not perform well when validated against the CCI dataset.