The spatio-temporal patterns of the topsoil organic carbon density and its influencing factors based on different estimation models in the grassland of Qinghai-Tibet Plateau

Abstract

The grassland soils of the Qinghai-Tibet Plateau (QTP) store a large amount of organic carbon because of the cold, humid climate, and topsoil organic carbon is quite sensitive to global climate changes. However, the spatio-temporal dynamics and factors that influence the soil organic carbon (SOC) on the QTP’s grassland are not understood well. Moreover, there are few comparative analyses of different approaches to estimate the QTP’ SOC. In this study, we estimated the storage and patterns of SOC density (SOCD) using several methods, including MODIS (moderate-resolution imaging spectroradiometer) retrieval, field data and previous empirical models (Models1-4, and soil organic matter (SOM)). And their relations with aboveground biomass, soil moisture, temperature, elevation, and soil conductivity were further explored. The results showed that SOC showed a similar variation trend in the different models, in which it decreased with increasing bulk density (BD) in the topsoil at 30 cm. For meadow and steppe grasslands, Models 1, 2, and 4 showed similar estimated values of SOCD, while Model3 had a lower value than them. SOC storage in the BD 3 and SOM methods had abnormal values, while the MODIS-NDVI, BD 1, 2, and 4 methods had similar SOC stock values for meadow and steppe grassland. Moreover, meadow grassland had a higher SOC storage than did steppe grassland, with means values of 397.9×1010 kg and 242.2×1010 kg, respectively. SOCD’s spatial distribution using MODIS-NDVI method differed clearly from the empirical models, with a significant tendency for spatial variation that increased from the northwestern to southeastern regions on the QTP. Therefore, based on the values estimated and spatial variation features, the MODIS-NDVI method may be a more feasible and valid model to estimate SOC. Moreover, the mean annual SOCD values during 2000–2015 showed an increasing trend, with a higher mean value in meadow and a lower mean value in steppe. Further, SOCD was correlated significantly and positively with aboveground biomass and soil moisture, and negatively correlated with elevation and soil conductivity. Increasing temperature had negative effects on SOCD, which was consistent with the global trend. These results indicated that topsoil moisture plays a key role in SOCD spatial patterns. Our results provide valuable support for the long-term estimation of SOCD in future research on the QTP.

Understanding drivers of the spatial variability of soil organic carbon in China's terrestrial ecosystems

“…The study's findings demonstrated that the SOC in grassland ecosystem was mostly influenced by MAT and BD. These findings are in agreement with past regional and national studies of grasslands conducted in China, including those conducted in the Hexi Corridor (Wang et al, 2014) and the Qinghai-Tibet Plateau (Liu et al, 2019), and found that soil TN, MAP, soil type, MAT provided the most reliable predictors of total SOC density in Chinese shrublands. The results were generally consistent with ours (Figure 3).…”

Increased Ecosystem Carbon Storage between 2001 and 2019 in the Northeastern Margin of the Qinghai-Tibet Plateau

“…Meanwhile, a significant portion of it is underlaid by permafrost, and with climate warming, permafrost thawing is likely to occur. These changes have induced changes in the ecosystem carbon processes, such as carbon gains attributed to stimulated vegetation productivity and carbon losses from thawing permafrost; the balance of these fluxes depends on the feedback of permafrost to warming [30,61]. In fact, the increase in carbon inputs from vegetation caused by climate change was greater than the loss of carbon, resulting in an obvious increase in ecosystem carbon storage during the study period.…”

“…Reviewing previous studies, we found that four main methods are used to estimate carbon density: literature reviews, field measurements, empirical modeling, and remote sensing. Due to differences in the estimation methods, the results may vary greatly [30]. Some studies used MODIS-NDVI products to establish the relationship between biomass and SOCD and demonstrated the scientific adaptability of this approach [30,[49][50][51].…”

“…Due to differences in the estimation methods, the results may vary greatly [30]. Some studies used MODIS-NDVI products to establish the relationship between biomass and SOCD and demonstrated the scientific adaptability of this approach [30,[49][50][51]. In this study, we used the spatial analysis tool of ArcGIS to acquire various values (including NDVI, biomass carbon, and SOCD) for each LULC type based on the dataset of 2000.…”

“…The soil has been reported to have accumulated plentiful soil organic matter, and its carbon density is obviously higher than that at similar latitudes, mainly due to its relatively low temperature and very high altitude, and thus the QTP has been regarded as a huge carbon pool [22,28,29]. Some studies of the QTP have evaluated carbon storage by using the InVEST model, but the spatial-temporal pattern of ecosystem carbon storage is still uncertain due to studies with low statistical power and insufficient sample sizes [30]. In addition, due to its unique geographic and ecological conditions, the alpine ecosystem of the QTP is very fragile and sensitive to climate change [31].…”