RTSEvo: A retrogressive thaw slump evolution model/热融滑塌演化模型

Existing retrogressive thaw slump (RTS) modeling studies are largely confined to static susceptibility mapping, lacking the capacity to predict their spatiotemporal evolution. To bridge this gap, we developed a new dynamic RTS evolution model (RTSEvo) that consists of three core, interlinked modules.

  1. RTS areal demand forecasting module: This module projects the total RTS area for the target year, establishing a top-down, macro-scale constraint on overall RTS expansion.
  2. Base occurrence probability mapping module: This module uses machine learning to calculate the baseline probability of RTS initiation for each individual pixel based on its unique environmental characteristics (e.g., topography, climate, geology).
  3. Constrained spatial allocation module: This module serves as the dynamic engine of the model. It iteratively allocates new RTS cells on the landscape by combining the base occurrence probability with spatial interaction rules (neighborhood and retrogressive erosion effects) until the estimate total area demand is met.

The primary contribution of this work is the successful development of a framework capable of moving beyond static susceptibility mapping to the dynamic, regional-scale simulation of RTS evolution. This dynamic modeling framework provides a more robust scientific basis for RTS-related risk mitigation strategies for critical infrastructure and for quantifying the cascading impacts of permafrost degradation.

RTSEvo模型框架

For more technical details, refer to our GMD paper.

代码：https://github.com/nanzt/rtsevo

关联数据：https://doi.org/10.6084/m9.figshare.30317599

Citation: 

Xu J, Zhao S*, Nan Z*, Niu F, Zhang Y. A Retrogressive Thaw Slump Evolution Model. Geoscientific Model Development[preprint], https://doi.org/10.5194/egusphere-2025-5005, 2025.