the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Stochastic Modelling of Thermokarst Lakes: Size Distributions and Dynamic Regimes
Abstract. Thermokarst lakes are among the most common and dynamic landscape features in ice-rich permafrost lowland regions. They influence carbon, water and energy fluxes between atmosphere and land surface and are an important component of Arctic lowland hydrology. Despite their significant role in the climate system, thermokarst lakes are only rudimentarily or not at all represented in Earth system models (ESMs). Attempts at stand-alone modelling of their dynamics have mostly been limited to the scale of individual lakes. Because lake formation, expansion, and drainage depend on small-scale surface and subsurface heterogeneities that are difficult to measure, a deterministic modelling-approach would be a challenge at the regional or pan-Arctic scale. We therefore treat these processes as probabilistic across a landscape and create a model of thermokarst lake dynamics using stochastic approaches. With the inclusion of stochasticity and volatility, our method allows us to account for the diversity of individual lake behaviour that results from the small-scale differences in environmental conditions. We present idealized simulations and, additionally, test novel high-resolution remote sensing data products that capture annual lake areas for model initialization and the calibration of inherent or climate-induced lake dynamics. Our model is able to capture three plausible regimes by incorporating the main processes behind thermokarst lake dynamics and represents a new step towards stochastic representation of permafrost landscapes in ESMs. Furthermore, our findings emphasize the importance of continued remote sensing data retrieval and additional data products containing information on past thermokarst lake behaviour for model parameterization.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
(6335 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 14 Sep 2025)
-
CC1: 'Comment on egusphere-2025-1817', Elchin Jafarov, 17 Jul 2025
reply
The study presents a new stochastic model to simulate the formation, expansion, and drainage of thermokarst lakes in Arctic permafrost regions. These lake processes are significant for carbon and energy fluxes, and are often underrepresented in Earth System Models (ESMs). The authors employ a probabilistic framework, where lake formation and abrupt drainage are modeled as Poisson processes, while size variations follow Geometric Brownian Motion. The model is calibrated using high-resolution satellite data and tested through both idealized simulations and observation-based scenarios from Siberia's Yana-Indigirka Lowland. Three dynamic regimes are demonstrated: complete drainage, oscillation, and stabilization of lake areas. The model’s simplicity and flexibility make it potentially integrable into ESMs, which could lead to more accurate projections of permafrost landscape changes and associated climate feedbacks. The study underscores the importance of remote sensing for parameterization and future model refinement. Overall, the study is well-conceived and clearly written. I have a few comments that could help clarify the model’s potential applications and provide more detail on how the authors envision its integration into Earth System Models (ESMs).
The authors briefly mention the challenges associated with incorporating phase change processes into their model. Expanding on this point would improve the manuscript, particularly by discussing how this stochastic model could be integrated into ESMs. For example, assuming a grid cell resolution of 0.5° or coarser, would the model estimate the ratio of land to water and apply distinct terrestrial and aquatic parameterizations based on that ratio? Providing more technical insight into how the model could be coupled with ESMs, specifically regarding the integration of thermal and hydrological processes, would be valuable.
Additionally, towards the end of the introduction, the authors refer to the abrupt thaw model introduced by Nitzbon et al. (2020). If the goal of this study is to model abrupt thaw processes, it would be helpful to clarify what new insights this model offers beyond those provided by Nitzbon et al. (2020). What additional understanding or capabilities does this approach contribute to the study of abrupt thaw? Furthermore, elaborating on how this model connects to the broader issue of permafrost carbon feedback, particularly in terms of coupling with lake carbon emissions, would strengthen the study’s relevance to permafrost climate feedback.
Citation: https://doi.org/10.5194/egusphere-2025-1817-CC1
Data sets
Surface water data: Supplementary Dataset used in: Reinken et al.: Stochastic Modelling of Thermokarst Lakes: Size Distributions and Dynamic Regimes Ingmar Nitze, Todd Nicholson https://doi.org/10.5281/zenodo.15011121
Model code and software
Thermokarst Lake Model (TLM) Constanze Reinken https://github.com/cowniflow/TLM
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
398 | 53 | 12 | 463 | 11 | 21 |
- HTML: 398
- PDF: 53
- XML: 12
- Total: 463
- BibTeX: 11
- EndNote: 21
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1