Climate change has already made significant alterations to various elements of the hydrologic cycle. One relatively less attended hydrologic impact of climate change is on the landscape Freeze-Thaw (FT), which largely affects surface and sub-surface hydrology, phenology, and land-atmospheric interactions, particularly in cold regions. Understanding the impacts of climate change on FT patterns, however, is not trivial due to sparse networks of in-situ measurements as well as limitations in current physically-based modeling schemes, aiming at continuous simulations of FT states. Here, we propose shifting the focus of FT modeling from continuous simulations in time and space, and move toward statistical representations of FT characteristics at larger temporal and spatial scales. We accordingly suggest using copulas to formally describe the impacts of temperature alterations on FT characteristics using conditional probabilities. To showcase the application of this framework in practice, we pair datasets of satellite-based FT with gridded temperature over Quebec, Canada. The results show strong and rather unique dependencies between temperature and FT characteristics across different regions and/ or timeframes. Our findings demonstrate copulas as effective tools to capture such dependencies and to reconstruct marginal FT characteristics. Through a set of impact assessments, it is shown that a similar change in temperature conditions can result in different regional responses in landscape FT. These responses are often nonsymmetric, meaning that the magnitude of change in FT conditions can be different under warming and cooling conditions. In addition, we highlight intensifications in FT responses to a similar magnitude of change in temperature under more recent years, which is linked to alterations in dependencies between temperature and FT. This study provides another line of evidence for complex responses of landscape FT to climate change.

来源平台：JOURNAL OF HYDROLOGY