Challenges in hydrologic-land surface modelling of permafrost signatures
- Impacts of parameterization on model fidelity under the uncertainty of
Permafrost plays an important role in the hydrology of arctic/subarctic
regions. However, permafrost thaw/degradation has been observed over
recent decades in the Northern Hemisphere and is projected to
accelerate. Hence, understanding the evolution of permafrost areas is
urgently needed. Land surface models (LSMs) are well-suited for
predicting permafrost dynamics due to their physical basis and
large-scale applicability. However, LSM application is challenging
because of the large number of model parameters and the complex memory
of state variables. Significant interactions among the underlying
processes and the paucity of observations of thermal/hydraulic regimes
add further difficulty. This study addresses the challenges of LSM
application by evaluating the uncertainty due to meteorological forcing,
assessing the sensitivity of simulated permafrost dynamics to LSM
parameters, and highlighting issues of parameter identifiability.
Modelling experiments are implemented using the MESH-CLASS framework.
The VARS sensitivity analysis and traditional threshold-based
identifiability analysis are used to assess various aspects of
permafrost dynamics for three regions within the Mackenzie River Basin.
The study shows that the modeller may face significant trade-offs when
choosing a forcing dataset as some datasets enable the representation of
some aspects of permafrost dynamics, while being inadequate for others.
The results also emphasize the high sensitivity of various aspects of
permafrost simulation to parameters controlling surface insulation and
soil texture; a detailed list of influential parameters is presented.
Identifiability analysis reveals that many of the most influential
parameters for permafrost simulation are unidentifiable. These
conclusions will hopefully inform future efforts in data collection and