While climate models project strong future warming, continuous changes in permafrost will have major impact on the Earth system, affecting climate system, water cycle and carbon cycle. There is an urgent need to understand the nature of the change of permafrost dynamics in response to climate change. It is therefore timely for a session to bring together studies that address recent advances in understanding, diagnosis and prediction of past and future changes in permafrost regions in Asia as well as improvements in numerical permafrost modelling. 28 Jun to 4 Jul 2020, Sono Belle Vivaldi Park, Hongcheon.
Wu X, Nan Z*, Zhao S, Zhao L, Cheng G. Spatial modelling of permafrost distribution and properties on the Qinghai-Tibet Plateau. Permafrost and Periglacial Processes. 2018,(1-14). DOI:10.1002/ppp.1971.
Zhao S, Nan Z*, Huang Y, Zhao L. The application and evaluation of simple permafrost distribution models on the Qinghai-Tibet Plateau. Permafrost and Periglacial Processes. 2017, 28(2): 391-404. DOI:10.1002/ppp.1939.
The performance of simple permafrost distribution models widely used on the Qinghai–Tibet Plateau (QTP) has not been fully evaluated. In this study, two empirical models (the elevation model and mean annual ground temperature model) and three semi-physical models (the surface frost number model, the temperature at the top of permafrost model and the Kudryavtsev model) were investigated. The simulation results from the models were compared to each other and validated against existing permafrost maps of the entire QTP and in three representative areas investigated in the field. The models generally overestimated permafrost distribution in the investigated areas, but they captured the broad characteristics of permafrost distribution on the entire QTP, and performed best in areas with colder, continuous permafrost. Large variations in performance occurred at elevations of 3800–4500 m asl and in areas with thermally unstable permafrost. The two empirical models performed best in areas where permafrost is strongly controlled by elevation, such as eastern QTP. In contrast, the three semi-physical models were better in southern island permafrost areas with relatively flat terrain, where local factors considerably impact the distribution of permafrost. Model performance could be enhanced by explicitly considering the effects of elevation zones and regional conditions.