Defu Zou

Modeling ground surface temperature by means of remote sensing data in high-altitude areas: test in the central Tibetan Plateau with application of moderate-resolution imaging spectroradiometer Terra/Aqua land surface temperature and ground-based infrared radiometer

Abstract Ground surface temperature (GST) is a crucial parameter of surface energy budgets and controls the thermal state of the active layer and permafrost in permafrost regions. However, with limited observed datasets available for the Tibetan Plateau, a greater bias existed for GST products from remote sensing data. Model validation (the whole year 2012 data) showed that all three models performed well, with a determination ( R 2 ), mean error, mean absolute error, and root mean squared error of 0.86 to 0.93, − 0.61 to 1°C, 2.28 to 3.06°C, and 2.96 to 3.83°C, respectively. The model established by observations of Terra and Aqua satellites during the daytime and nighttime showed the highest correlation, with R 2 values ranging from 0.91 to 0.93, as well as the lowest MAE and RMSE of 2.28 to 2.42 and 2.96 to 3.05°C, respectively. However, the application of this model substantially reduced the available pixels. Models established with the automatic weather station observations at the satellite overpass times performed better than those using the moderate-resolution imaging spectroradiometer land surface temperature observations. The results might be useful to produce a more reliable dataset for monitoring and modeling permafrost changes.

Spatiotemporal changes of freezing/thawing indices and their response to recent climate change on the Qinghai–Tibet Plateau from 1980 to 2013

The spatial and temporal changes of the ground surface freezing indices (GFIs), ground surface thawing indices (GTIs), air freezing indices (AFIs), and air thawing indices (ATIs) in permafrost and seasonally frozen ground regions of the Qinghai–Tibet Plateau (QTP) were analyzed based on the daily ground surface and air temperatures from 69 meteorological stations using the Mann–Kendall test and Sen’s slope estimate. The spatial patterns of the freezing indices (FIs) and thawing indices (TIs) are nearly negatively correlated. On the annual scale, the GFI and GTI are greater than the AFI and ATI in both permafrost and seasonally frozen ground regions. The marked upward and downward trends have been observed for the time series of TI and FI, respectively, since 1998 on the QTP. Moreover, GFI and AFI decrease more significantly in permafrost regions than in seasonally frozen ground regions; the increasing rate of GTI and ATI in the seasonally frozen ground regions is greater than that in the permafrost regions. In permafrost regions, the downward trend of FI is greater than the upward trend of TI. However, the upward trend of TI shows a more drastic change than the FI in the seasonally frozen ground regions. The results indicate that the warming in the permafrost regions is more pronounced in winter than in the other seasons. The summer warming is more pronounced than the other seasons in the seasonally frozen ground regions. The decreasing rate of AFI and GFI increases as the altitude rises, while they decrease with increasing ATI. The average decreasing rate of GFI is greater than that of the AFI in different altitudinal zones. The greatest decrease of FI occurs in permafrost regions in the hinterland of the QTP, which indicates the dominant winter warming in this region. The downward trend of FI and upward trend of TI are responsible for the reported permafrost degradation on the QTP.

Soil moisture and texture primarily control the soil nutrient stoichiometry across the Tibetan grassland

Soil nutrient stoichiometry and its environmental controllers play vital roles in understanding soil-plant interaction and nutrient cycling under a changing environment, while they remain poorly understood in alpine grassland due to lack of systematic field investigations. We examined the patterns and controls of soil nutrients stoichiometry for the top 10cm soils across the Tibetan ecosystems. Soil nutrient stoichiometry varied substantially among vegetation types. Alpine swamp meadow had larger topsoil C:N, C:P, N:P, and C:K ratios compared to the alpine meadow, alpine steppe, and alpine desert. In addition, the presence or absence of permafrost did not significantly impact soil nutrient stoichiometry in Tibetan grassland. Moreover, clay and silt contents explained approximately 32.5% of the total variation in soil C:N ratio. Climate, topography, soil properties, and vegetation combined to explain 10.3-13.2% for the stoichiometry of soil C:P, N:P, and C:K. Furthermore, soil C and N were weakly related to P and K in alpine grassland. These results indicated that the nutrient limitation in alpine ecosystem might shifts from N-limited to P-limited or K-limited due to the increase of N deposition and decrease of soil P and K contents under the changing climate conditions and weathering stages. Finally, we suggested that soil moisture and mud content could be good predictors of topsoil nutrient stoichiometry in Tibetan grassland.

Vegetation Changes in the Permafrost Regions of the Qinghai-Tibetan Plateau from 1982-2012: Different Responses Related to Geographical Locations and Vegetation Types in High-Altitude Areas

The Qinghai-Tibetan Plateau (QTP) contains the largest permafrost area in a high-altitude region in the world, and the unique hydrothermal environments of the active layers in this region have an important impact on vegetation growth. Geographical locations present different climatic conditions, and in combination with the permafrost environments, these conditions comprehensively affect the local vegetation activity. Therefore, the responses of vegetation to climate change in the permafrost region of the QTP may be varied differently by geographical location and vegetation condition. In this study, using the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) product based on turning points (TPs), which were calculated using a piecewise linear model, 9 areas within the permafrost region of the QTP were selected to investigate the effect of geographical location and vegetation type on vegetation growth from 1982 to 2012. The following 4 vegetation types were observed in the 9 selected study areas: alpine swamp meadow, alpine meadow, alpine steppe and alpine desert. The research results show that, in these study areas, TPs mainly appeared in 2000 and 2001, and almost 55.1% and 35.0% of the TPs were located in 2000 and 2001. The global standardized precipitation evapotranspiration index (SPEI) and 7 meteorological variables were selected to analyze their correlations with NDVI. We found that the main correlative variables to vegetation productivity in study areas from 1982 to 2012 were precipitation, surface downward long-wave radiation and temperature. Furthermore, NDVI changes exhibited by different vegetation types within the same study area followed similar trends. The results show that regional effects rather than vegetation type had a larger impact on changes in vegetation growth in the permafrost regions of the QTP, indicating that climatic factors had a larger impact in the permafrost regions than the environmental factors (including permafrost) related to the underlying surface conditions.

Spatial distribution and changes of permafrost on the Qinghai-Tibet Plateau revealed by statistical models during the period of 1980 to 2010

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) and multi-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 × 106 km2 and 1.20 × 106 km2, respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 × 106 km2 decade-1 over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models.