The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Shuai Sun, Chunxiang Shi (), Xiao Liang, Shuai Zhang, Junxia Gu, Shuai Han, Hui Jiang, Bin Xu, Qingbo Yu, Yujing Liang and Shuai Deng
Additional contact information
Shuai Sun: Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China
Chunxiang Shi: National Meteorological Information Center, Beijing 100081, China
Xiao Liang: National Meteorological Information Center, Beijing 100081, China
Shuai Zhang: Institute of Urban Meteorology of Beijing, Beijing 100089, China
Junxia Gu: National Meteorological Information Center, Beijing 100081, China
Shuai Han: National Meteorological Information Center, Beijing 100081, China
Hui Jiang: National Meteorological Information Center, Beijing 100081, China
Bin Xu: National Meteorological Information Center, Beijing 100081, China
Qingbo Yu: Meteorological Information and Network Center of Jilin Province, Changchun 130062, China
Yujing Liang: National Meteorological Information Center, Beijing 100081, China
Shuai Deng: National Meteorological Information Center, Beijing 100081, China

Sustainability, 2023, vol. 15, issue 14, 1-17

Abstract: Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Keywords: snow depth; CMA Land Data Assimilation System; CLM3.5; Noah; Noah-MP; assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
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