Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China

The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the capability of LSM. In this study, three offline LSM simulations were conducted over China using the Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely China Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), and bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded and in situ reference data, including evapotranspiration (ET), soil moisture (SM), and runoff, were employed to evaluate the performance levels of three CLM5-based simulations across China and its ten basins. In general, all simulations realistically replicate the magnitudes, spatial patterns, and seasonal cycles of ET over China when compared with remote-sensing-based ET observations. Among ten basins, Yellow River Basin (YRB) is the basin where simulations are the best, supported by the higher KGE value of 0.79. However, substantial biases occur in Northwest Rivers Basin (NWRB) with significant overestimation for CMFD and WFDE5 and underestimation for GSWP3. In addition, both grid-based or site-based evaluations of SM indicate that systematic wet biases exist in all three CLM5 simulations for shallower soil layer over nine basins of China. Comparatively, the performance levels in simulating SM for deeper soil layer are slightly better. Moreover, all three types of CLM5 simulate reasonable runoff spatial patterns, among which CMFD can capture more detailed information, but GSWP3 presents more comparable change trends of runoff when compared to the reference data. In summary, this study explored the capacity of CLM5 driven by different meteorological forcing data, and the assessment results may provide important insights for the future developments and applications of LSM.