Challenges in Hydrologic‐Land Surface Modeling of Permafrost Signatures—A Canadian Perspective

• Published in: Journal of advances in modeling earth systems Vol. 15; no. 3
• Main Authors: Abdelhamed, Mohamed S., Elshamy, Mohamed E., Razavi, Saman, Wheater, Howard S.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01-03-2023
• Subjects: Analysis; Atmospheric forcing; Boundary conditions; Carbon; Climate change; Cold; Data collection; Datasets; Dynamics; Future climates; Global warming; Hydrologic models; Hydrology; identifiability; Insulating materials; Insulation; Land surface models; Mackenzie River Basin; Modelling; Parameter estimation; Parameterization; Parameters; Permafrost; River basins; Runoff; sensitivity; Sensitivity analysis; uncertainty
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2022MS003013

Permafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well‐suited for this due to their physical basis and large‐scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH‐CLASS modeling framework (Modélisation Environmenntale communautaire—Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold‐based identifiability analysis. The study shows that the modeler may face complex trade‐offs when choosing a forcing data set; for current and future scenarios, forcing data require multi‐variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.
Plain Language Summary
Permafrost (frozen ground for at least 2 years) is one of several elements that could affect the rate and magnitude of current global warming. Permafrost plays a critical role in the dynamics of water, heat, and carbon over vast areas globally. For more credible climate/hydrology modeling, it is necessary to assess the ability of available models to reliably reproduce observed permafrost characteristics before using them to evaluate future scenarios. Using a land surface model (LSM) for different permafrost regions in Canada, this study examined three challenges: (a) quantifying the impact of uncertainty in climate forcing data on permafrost simulation, (b) identifying the key parameters that control the quality of permafrost simulation, and (c) assessing the appropriateness of current model structures to reproduce observed permafrost characteristics in the context of parameter uncertainty. In selecting a forcing data set, permafrost characteristics exhibited significant trade‐offs. Those parameters with a large influence on permafrost simulation were identified for the different study areas, but due to model complexity, finding unique values for them was difficult. Several findings were presented to guide further LSM development, and hence reduce errors in weather/climate modeling.
Key Points
There are significant uncertainties in climate forcing data sets that affect the fidelity of permafrost simulations using land surface models
The quality of simulated permafrost signatures is primarily controlled by heat insulation and runoff generation parameters
Various highly influential model parameters are non‐identifiable, leading to significant uncertainty in simulated permafrost characteristics