Evaluation and Uncertainty Analysis of the Land Surface Hydrology in LS3MIP Models Over China

The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU-NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high-quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi-model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%-85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900-2012 (1948-2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models. Land surface models (LSMs) have served as essential tools for simulating the response of land surface processes under changing climate. This study focuses on the performance and uncertainty of hydrological variables from historical (1900-2012) simulations in the Land Model Intercomparison Project (LMIP), which is a part of the Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP). Using various reference data sets over Chinese mainland, we evaluated precipitation, evapotranspiration, soil moisture, total runoff and snow cover fraction products from six LSMs driven by four meteorological forcing data sets. Our findings reveal that, on average, all hydrological variables are underestimated, but they exhibit a high spatial consistency of trend signs with reference data sets. Among all simulations, CLM5 stands out for its superior performance and GSWP3 forcing demonstrates the highest quality. Additionally, the Analysis of Variance (ANOVA) method is adopted to separate the simulation uncertainties into three sources from the model, the meteorological forcing data set and their interactions. It is indicated that the total uncertainty has substantially decreased in recent decades, and the model uncertainty is the dominant factor for these hydrological variables. This study may serve as some valuable references in selecting LSMs and forcing data sets in the future. The precipitation, evapotranspiration, soil moisture, total runoff, and snow cover fraction in LS3MIP are extensively evaluated in China For LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall