Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4414
• Main Authors: Zhu, Chenxia, Li, Shijie, Hagan, Daniel Fiifi Tawia, Wei, Xikun, Feng, Donghan, Lu, Jiao, Ullah, Waheed, Wang, Guojie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Air temperature; Atmosphere; Climate change; Correlation coefficient; Correlation coefficients; Data collection; Global temperature changes; Heat; Hydrologic cycle; Machine learning; Moisture effects; Performance evaluation; Precipitation; Product information; Remote sensing; Sensors; Soil analysis; Soil moisture; Soil surfaces; Soil temperature; Spatial distribution; Tibetan Plateau; Topography; Trends; Vegetation; Weather forecasting; Wind; China; Tibetan Plateau; Tibet; Australia; Asia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15184414

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.