RTSEvo v1.0: a retrogressive thaw slump evolution model

Abstract

Widespread thermal degradation in permafrost regions is accelerating the development of retrogressive thaw slumps (RTS), severely threatening ecological stability and critical infrastructure. Current RTS modeling efforts, however, are largely confined to static susceptibility mapping, lacking the capacity to predict their spatiotemporal evolution. To bridge this gap, we developed RTSEvo, a novel dynamic RTS evolution model that couples three modules: (1) a time-series forecast of regional RTS area demand, (2) a machine-learning module for pixel-level probability mapping, and (3) a physically constrained spatial allocation module that simulates RTS expansion by integrating neighborhood effects, stochasticity, and a novel retrogressive erosion factor. Validated using manually interpreted RTS maps for 2021 and 2022 in the Beiluhe Basin on the Qinghai-Tibet Plateau, the model successfully simulated dynamic RTS growth, with the Logistic Regression-based model showing superior stability and accuracy. Furthermore, cross-regional validation confirmed the framework's structural generalizability. An interesting finding is that predictive skill is significantly enhanced by integrating process-based rules with statistical probability. Specifically, the inclusion of the retrogressive erosion factor, which mechanistically simulates upslope headwall retreat, proved critical, improving model performance by up to 29.3 % as measured by the Figure of Merit. The primary innovation of this study is the successful realization of a regional-scale dynamic simulation and prediction of RTS expansion. RTSEvo offers a highly robust scientific tool for informing proactive RTS-related risk mitigation strategies.