It is essential to monitor the ground temperature over large areas to understand and predict the effects of climate change on permafrost due to its rapid warming on the Qinghai-Tibet Plateau (QTP). Land surface temperature (LST) is an important parameter for the energy budget of permafrost environments. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are especially valuable for detecting permafrost thermal dynamics across the QTP. This study presents a comparison of MODIS-LST values with in situ near-surface air temperature (T-a), and ground surface temperature (GST) obtained from 2014 to 2016 at five sites in Beiluhe basin, a representative permafrost region on the QTP. Furthermore, the performance of the thermal permafrost model forced by MODIS-LSTs was studied. Averaged LSTs are found to strongly correlated with T-a and GST with R-2 values being around 0.9. There is a significant warm bias (4.43-4.67 degrees C) between averaged LST and T-a, and a slight warm bias (0.67-2.66 degrees C) between averaged LST and GST. This study indicates that averaged MODIS-LST is supposed to be a useful data source for permafrost monitoring. The modeled ground temperatures and active-layer thickness have a good agreement with the measurements, with a difference of less than 1.0 degrees C and 0.4 m, respectively.

Assessing possible permafrost degradation related to engineering projects, climate change and land use change is of critical importance for protecting the environment and in developing sustainable designs for vital infrastructure in cold regions. A major challenge in modelling the future degradation of permafrost is finding ways to constrain changes in the upper thermal boundary condition over time and space at appropriate scales. Here, we report on an approach designed to predict time series of air, ground surface and shallow ground temperatures at a spatial scale on the order of 102?m2 for engineering design of a railway or highway project. The approach uses a regional-scale atmospheric model to downscale global climate model output, and then stepwise multiple regression to develop an equation that provides a best-fit prediction of site-specific observational data using bilinearly interpolated output from the atmospheric model. This approach bridges the scale difference between atmospheric climate models and permafrost thermal models, and allows for a wider range of factors to be used in predicting the thermal boundary condition. For a research site located in Beiluhe, China, close to the Qinghai-Tibet Railway, a comparison of model predictions with observational data not used in the construction of the model shows that this method can be used with a high degree of accuracy to determine the upper boundary condition for a permafrost thermal model. Once a model is constructed, it can be used to predict future changes in boundary condition parameters under different greenhouse emission scenarios for climate change. Copyright (c) 2012 John Wiley & Sons, Ltd.