A Long-term Simulation of Land Surface Conditions at High-Resolution Over Continental China


With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979-2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling-Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales.