Drying–Wetting Changes of Surface Soil Moisture and the Influencing Factors in Permafrost Regions of the Qinghai-Tibet Plateau, China

Abstract

Soil moisture (SM), an important variable in water conversion between the atmosphere and terrestrial ecosystems, plays a crucial role in ecological processes and the evolution of terrestrial ecosystems. Analyzing and exploring SM’s processes and influencing factors in different permafrost regions of the Qinghai-Tibet Plateau (QTP) can better serve the regional ecological security, disaster warning, water management, etc. However, the changes and future trends of SM on the QTP in recent decades are uncertain, and the main factors affecting SM are not fully understood. The study used SM observations, the Global Land Evapotranspiration Amsterdam Model (GLEAM) SM products, meteorological and vegetation data, Mann–Kendall test, Theil–Sen estimation, Ensemble Empirical Mode Decomposition (EEMD), and correlation methods to analyze and explore the characteristics and influencing factors of SM change in different permafrost regions of the QTP. The results show that: (1) At the pixel scale, GLEAM SM products can better reflect SM changes in the QTP in the warm season. The seasonal permafrost region is closer to the real SM than the permanent region, with a median correlation coefficient (R) of 0.738, median bias of 0.043 m3 m−3, and median unbiased root mean square errors (ubRMSE) of 0.031 m3 m−3. (2) The average SM in the QTP warm season increased at a rate of 0.573 × 10−3 m3 m−3 yr−1 over the recent 40 years, and the trend accelerated from 2005–2020. In 64.31% of the region, the soil was significantly wetted, mainly distributed in the permafrost region, which showed that the wetting rate in the dry region was faster than in the wet region. However, the wetting trend does not have a long-term continuity and has a pattern of “wetting–drying-wetting” on interannual and decadal levels, especially in the seasonal permafrost region. (3) More than 65% of the SM wetting trend on the QTP is caused by temperature, precipitation, and vegetation. However, there is apparent spatial heterogeneity in the different permafrost regions and vegetation cover conditions, and the three factors have a more substantial explanatory power for SM changes in the seasonal permafrost region. With the global climate change, the synergistic SM–Climate–Vegetation effect on the QTP tends to be more evident in the seasonal permafrost region.

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

“…Soil moisture data were derived from the Global Land Evaporation Amsterdam Model (GLEAM) daily root‐zone soil moisture dataset v3.5a with a 0.25° × 0.25° grid, which showed good agreement with ground measurements (Martens et al, 2017). For example, the root‐zone soil moisture data were generally consistent with four stations during 2015–2016 on the TP, with mean correlation coefficients and bias errors of nearly 0.74 and 0.043 m 3 m −3 , respectively, and the accuracy was higher than that of the well‐known ESA CCI soil moisture product (Li et al, 2022).…”

Influence of Different Meteorological Factors on the Accuracy of Back Propagation Neural Network Simulation of Soil Moisture in China

“…Over the past 40 years, the total explanatory power of PR, temperature, and vegetation in the permafrost region of the Qinghai-Tibet Plateau was 51%, and the contribution rate of PR was the highest, accounting for 47.2%. Concurrently, the contribution rate of PR was over 41%, and the combined explanatory power of the three components in the seasonal permafrost area was 69.1% [14]. Surface SM in northeastern and central China and eastern Inner Mongolia exhibited a consistent trend of dryness from 1979 to 2010, while soils on the Qinghai-Tibet Plateau presented a trend of wetness.…”

“…It is a storage component of precipitation (PR) and radiation anomalies [5], influencing soil surface albedo and soil heat capacity [11], as well as climate change through latent and sensible heat transfer. Therefore, the study of SM is essential for water resources management, ecological security evaluation, the judgment of climate change trends, and meteorological disaster prediction in the context of global climate change [12][13][14]. Understanding the multiple impacts of SM and climate and its relevance to climate change projections is critical.…”

Environmental Factors Controls on Soil Water-Heat in the Qilian Mountains, China: A Quantitative Analysis

“…In the past, researchers have focused on stability [9], spatial and temporal characteristics [10], and relationship with environmental factors [11] of SWC. Currently, the development of remote sensing technology has led to more research on spatial and temporal variation [12], influencing factors [13], predictive mapping [14][15] and the interaction with environmental factors [16][17] of SWC. Some studies have already pointed out that SWC is influenced by environmental factors such as vegetation, climate, topography, and soil properties.…”