{'fraction_non_alphanumeric': 0.03218390804597701, 'fraction_numerical': 0.013793103448275862, 'mean_word_length': 5.066202090592334, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

The Global Land Data Assimilation System (GLDAS) project estimates long‐term runoff based on land surface models (LSMs) and provides a potential way to solve the issue of nonexistent streamflow data in gauge‐sparse regions such as the Tibetan Plateau (TP). However, the reliability of GLDAS runoff data must be validated before being practically applied. In this study, the streamflows simulated by four LSMs (CLM, Noah, VIC, and Mosaic) in GLDAS coupled with a river routing model are evaluated against observed streamflows in five river basins on the TP. The evaluation criteria include four aspects: monthly streamflow value, seasonal cycle of streamflow, annual streamflow trend, and streamflow component partitioning. The four LSMs display varying degrees of biases in monthly streamflow simulations: systematic overestimations are found in the Noah (1.74 ≤ bias ≤ 2.75) and CLM (1.22 ≤ bias ≤ 2.53) models, whereas systematic underestimations are observed in the VIC (0.36 ≤ bias ≤ 0.85) and Mosaic (0.34 ≤ bias ≤ 0.66) models. The Noah model shows the best performance in capturing the temporal variation in monthly streamflow and the seasonal cycle of streamflow, while the VIC model performs the best in terms of bias statistics. The Mosaic model provides the best performance in modeling annual runoff trends and runoff component partitioning. The possible reasons for the different performances of the LSMs are discussed in detail. In order to achieve more accurate streamflow simulations from the LSMs in GLDAS, suggestions are made to further improve the accuracy of the forcing data and parameterization schemes in all models.