The Performance of Multiple Model-Simulated Soil Moisture Datasets Relative to ECV Satellite Data in China
| Autor: | Yihan Tang, Xiaoyan Bai, Wenkui Bai, Shenlin Li, Xihui Gu, Xiling Gu, Yanhu He |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
China
lcsh:Hydraulic engineering 010504 meteorology & atmospheric sciences 0208 environmental biotechnology Geography Planning and Development reanalysis 02 engineering and technology Aquatic Science 01 natural sciences Biochemistry Normalized Difference Vegetation Index remote sensing lcsh:Water supply for domestic and industrial purposes Data assimilation lcsh:TC1-978 model simulation Precipitation Water content 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Coupled model intercomparison project Moisture Vegetation 020801 environmental engineering Climatology Soil water Environmental science soil moisture season |
| Zdroj: | Water Volume 10 Issue 10 Water, Vol 10, Iss 10, p 1384 (2018) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w10101384 |
| Popis: | Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|