Rising global temperatures are a threat to the current state of the Arctic. In particular, permafrost degradation has been impacting the terrestrial cryosphere in many ways, including effects on carbon cycling and the global climate, regional hydrological connectivity and ecosystem dynamics, as well as human health and infrastructure. However, the ability to simulate permafrost dynamics under future climate projections is limited, and model outputs are often associated with large uncertainties. A model structured on a Bayesian Network is presented to address existing limitations in the representation of physically complex processes and the limited availability of observational data. A strength of Bayesian methods over more traditional modeling methods is the ability to integrate various types of evidence (i.e., observations, model outputs, expert assessments) into a single model by mapping the evidence into probability distributions. Here, we outline PermaBN, a new modeling framework, to simulate permafrost thaw in the continuous permafrost region of the Arctic. Pre-validation and expert assessment validation results show that the model produces estimations of permafrost thaw depth that are consistent with current research, i.e., thaw depth increases during the snow-free season under initial conditions favoring warming temperatures, lowered soil moisture conditions, and low active layer ice content. Using a case study from northwestern Canada to evaluate PermaBN, we show that model performance is enhanced when certainty about the system components increases for known scenarios described by observations directly integrated into the model; in this case, insulation properties from vegetation were integrated to the model. Overall, PermaBN could provide informative predictions about permafrost dynamics without high computational cost and with the ability to integrate multiple types of evidence that traditional physics-based models sometimes do not account for, allowing PermaBN to be applied to carbon modeling studies, infrastructure hazard assessments, and policy decisions aimed at mitigation of, and adaptation to, permafrost degradation.

We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root-mean-square errors = 0.13-0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai-Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.

Little is known about the ecological impacts of permafrost degradation on water fluxes in boreal ecosystems, such as those in Interior Alaska. Low plant water stress suggests a reliance on a diversity of water sources. In addition to rainfall, we hypothesize that deep soil water derived from thawing seasonal ground ice (TSGI) supports plants during dry periods. We analyzed water stable isotopes from soils, plants, ice, and rain collected from stable and unstable permafrost sites. We found that TSGI provides a background water source for plants during wet years (at least 10-20%) and a stable source during dry years (at least 30-50%) and early in the growing season (60-80% in wet and dry years). Plant water uptake patterns track the soil thawing front, using deep and shallow layers in wet years and deep layers during dry years. This plasticity allows boreal plants to cope with seasonal drought and exploit available water sources. The availability of TGSI depends on the amount of rainfall the prior year and on permafrost stability. Thawing permafrost may reduce the buffering capacity of TGSI due to less seasonal ice from greater drainage and/or a deeper active layer. This study demonstrates the importance of two buffering mechanisms for plants to cope with rainfall variability within boreal forest underlain by permafrost-availability of TSGI and plasticity in water uptake patterns. We suggest that plant utilization of stored water may be why evapotranspiration in northern latitudes can exceed growing season precipitation.