Stochastic modelling of thermokarst lakes: size distributions and dynamic regimes

Thermokarst lakes are among the most common and dynamic landscape features in ice-rich lowland permafrost 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 sub-surface 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 continuing the retrieval of remote sensing data for model parameterization and acquiring additional data products containing information on past thermokarst lake behaviour.

Authors

Publication Details

Journal
MPG.PuRe (Max Planck Society)
Published
2026-04-10
Primary Topic
Climate change and permafrost
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stochastic modelling of thermokarst lakes: size distributions and dynamic regimes

H. Bergstedt, P. de Vrese, G. Grosse, V. ; https://orcid.org/0000-0001-6420-3198 Brovkin et al.
MPG.PuRe (Max Planck Society)
Climate change and permafrost
article

Stochastic modelling of thermokarst lakes: size distributions and dynamic regimes

H. Bergstedt, P. de Vrese, G. Grosse, V. ; https://orcid.org/0000-0001-6420-3198 Brovkin, (. ; https://orcid.org/0000-0002-1568-923X Constanze Reinken (Climate-Biosphere Interaction, I. Nitze
article en

Abstract

Thermokarst lakes are among the most common and dynamic landscape features in ice-rich lowland permafrost 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 sub-surface 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 continuing the retrieval of remote sensing data for model parameterization and acquiring additional data products containing information on past thermokarst lake behaviour.

MPG.PuRe (Max Planck Society)
Climate action
Openalex Percentile: Top 5%
Climate change and permafrost
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.