Numerical Mapping and Modeling Permafrost Thermal Dynamics across the Qinghai-Tibet Engineering Corridor, China Integrated with Remote Sensing

Abstract

Permafrost thermal conditions across the Qinghai–Tibet Engineering Corridor (QTEC) is of growing interest due to infrastructure development. Most modeling of the permafrost thermal regime has been conducted at coarser spatial resolution, which is not suitable for engineering construction in a warming climate. Here we model the spatial permafrost thermal dynamics across the QTEC from the 2010 to the 2060 using the ground thermal model. Soil properties are defined based on field measurements and ecosystem types. The climate forcing datasets are synthesized from MODIS-LST products and the reanalysis product of near-surface air temperature. The climate projections are based on long-term observations of air temperature across the QTEC. The comparison of model results to field measurements demonstrates a satisfactory agreement for the purpose of permafrost thermal modeling. The results indicate a discontinuous permafrost distribution in the QTEC. Mean annual ground temperatures (MAGT) are lowest (<−2.0 °C) for the high mountains. In most upland plains, MAGTs range from −2.0 °C to 0 °C. For high mountains, the average active-layer thickness (ALT) is less than 2.0 m, while the river valley features ALT of more than 4.0 m. For upland plains, the modeled ALTs generally range from 3.0 m to 4.0 m. The simulated results for the future 50 years suggest that 12.0%~20.2% of the permafrost region will be involved in degradation, with an MAGT increase of 0.4 °C~2.3 °C, and the ALT increasing by 0.4 m~7.3 m. The results of this study are useful for the infrastructure development, although there are still several improvements in detailed forcing datasets and a locally realistic model.

Permafrost Deformation Monitoring Along the Qinghai-Tibet Plateau Engineering Corridor Using InSAR Observations with Multi-Sensor SAR Datasets from 1997–2018

“…For the Beiluhe basin areas, the relatively warm MAGTs ranged from −2.0 to 0 °C. The modeled MAGTs are consistent with the latest researches on MAGTs in QTP [58,59]. Comparing Figure 7 and Figure 14, we found that the subsiding regions are consistent with the ground with high MAGT value; the Tuotuohe and Beiluhe regions have experienced undergone serious ground deformation in recent years.…”

Ice loss in the Northeastern Tibetan Plateau permafrost as seen by 16 yr of ESA SAR missions

“…The impacts that permafrost has on engineering constructions, infrastructure stability (e.g. along the Qinghai-Tibet Plateau Engineering Corridor [e.g., 14,15,16,17,18,19]) and on gravitational processes that can pose a hazard to communities [e.g., 20,21,12] makes the understanding of how its thermal regime responds to temperature increase very relevant. Moreover, little is known about the effects of permafrost degradation on hydrological processes, moisture and related ecosystem or carbon dynamics [e.g., 22,23,24].…”

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

“…By combining the WOFOST crop yield model and the WEP-L hydrological model, Jia et al [85] were able to estimate wheat yield 30 years into the future. To model future permafrost distribution and characteristics, Yin et al [169] and Luo et al [170] employed the specialized GIPL2 model, whereas Pastick et al [168] trained a DRT model with future climate data for a similar task. In general, however, next to OLS, machine learning methods are used much less frequently in this forecast model category.…”

“…In general, however, next to OLS, machine learning methods are used much less frequently in this forecast model category. Of the 22 projection-based forecasting studies identified in this review, 15 were based on climate projections [78,85,130,131,134,142,[154][155][156][168][169][170]175,177,178], six on LULC projections [65,143,144,157,158,167], and one study on both [118].…”

“…Pastick et al [168] used a recent LULC mosaic and projected climate parameters in a regression-tree approach to map future permafrost distribution in Alaska. Yin et al [169] and Luo et al [170], in contrast, used a specialized model driven by MODIS land surface temperature time series data, in-situ, and projected temperature measurements as well as meteorological data to model future permafrost distribution and parameters such as active layer thickness and mean annual ground temperature in the Qinghai-Tibet Plateau. Giles [171] modeled the dynamics of the Mertz Glacier Tongue in Antarctica using historic photographs and satellite data and estimates a future break-off event based on the observed cyclic behavior of the glacier.…”