Modeling permafrost changes on the Qinghai–Tibetan plateau from 1966 to 2100: A case study from two boreholes along the Qinghai–Tibet engineering corridor

Abstract

Warming permafrost on a global scale is projected to have significant impacts on engineering, hydrology and environmental quality. Greater warming trends are predicted on the Qinghai–Tibetan Plateau (QTP), but most models for mountain permafrost have not considered the effects of water phase change and the state of deep permafrost due to a lack of detailed information. To better understand historical and future permafrost change based on in situ monitoring and field investigations, a numerical heat conduction permafrost model was introduced which differentiated the frozen and thawed state of soil, and considered unfrozen water content in frozen soil, distribution of ground ice and geothermal heat flow. Simulations were conducted at two sites with validation by long‐term monitoring of ground temperature data. After forcing with reconstructed historical ground surface temperature series starting from 1966, the model predicted permafrost changes until 2100 under different RCP scenarios. The results indicate a slow thermal response of permafrost to climate warming at the two investigated sites. Even under the most radical warming scenario (RCP8.5), deepening of the permafrost table is not obvious before 2040. At both sites, the model indicates that shallow permafrost may disappear but deep permafrost may persist by 2100. Moreover, the simulation shows that the degradation modes may differ between zones of discontinuous and continuous permafrost. The main degradation mode of the site in the discontinuous zone appears to be upward thawing from the permafrost base, while that of the site in the continuous zone is downward thawing at the permafrost table with little change at the permafrost base.

Insights in a remote cryosphere: A multi method approach to assess permafrost occurrence at the Qugaqie basin, western Nyainqêntanglha Range, Tibetan Plateau

“…This warming temperature trend is reconstructed by δ 18O records in four spatially well-distributed ice cores back to the beginning of the last century (Yao et al, 2006). Sun et al (2020) confirm the relationship between the temperature increase and permafrost degradation on the TP by a slow adaption until the year 2100 based on a numerical heat conduction permafrost model. New statistic and machine learning approaches determine that https://doi.org/10.5194/tc-2020-114 Preprint.…”

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

“…Earlier permafrost models like GIPL 2.0 (Qin et al, 2017) do not simulate surface energy balance, and usually directly set land/ ground surface temperature as the upper boundary condition for soil heat transfer. These models are able to simulate soil temperature for deep layers which could be used to identify the presence of permafrost, while other processes like surface energy balance, snow melting are generally not included (Qin et al, 2017;Sun et al, 2019). These models have relatively lower requirements on climatic forcing data and usually use land/ ground surface temperature and external soil moisture data as inputs.…”

“…It is generally difficult to directly use satellite data (such as land surface temperature) to drive these land models, which imposes additional difficulties for high-resolution (∼≤1 km) simulations as high-resolution surface meteorology datasets are not readily available in the Tibetan Plateau. Moreover, most land surface models are originally developed for surficial soil layers with a focus on the energy and mass exchange between the surface and the atmosphere, and therefore generally have simplified representation of the soil properties and thermal processes in deep soil layers (Sun et al, 2019).…”

Insights into a remote cryosphere: a multi-method approach to assess permafrost occurrence at the Qugaqie basin, western Nyainqêntanglha Range, Tibetan Plateau

“…Table 3 sums up our measured resistivity values and classifies the values in terms of material characteristics. Different studies show resistivity values of till in a range from 1 to 10 k m (Reynolds, 2011), from 5 to 10 k m (Thompson et al, 2017) and from 50 to 100 k m (Vanhala et al, 2009). The diversity of resistivity ranges and the resulting non-uniqueness can be overcome by using additional methods to support the final conclusions.…”