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The Tibetan Plateau (TP) is experiencing extensive permafrost degradation due to climate change, which seriously threatens sustainable water and ecosystem management in the TP and its downstream areas. Understanding the evolution of permafrost is critical for studying changes in the water cycle, carbon flux, and ecology of the TP. In this study, we mapped the spatial distribution of permafrost and active layer thickness (ALT) at 1 km resolution for each decade using empirical models and machine learning methods validated with borehole data. A comprehensive comparison of model results and validation accuracy shows that the machine learning method is more advantageous in simulating the permafrost distribution, while the ALT simulated by the empirical model (i.e., Stefan model) better reflects the actual ALT distribution. We further evaluated the dynamics of permafrost distribution and ALT from 1980 to 2020 based on the results of the better-performing models, and analyzed the patterns and influencing factors of the changes in permafrost distribution and ALT. The results show that the permafrost area on the TP has decreased by 15.5 %, and the regionally average ALT has increased by 18.94 cm in the 2010s compared to the 1980s. The average decreasing rate of permafrost area is 6.33 x 104 km2 decade-1, and the average increasing rate of ALT is 6.31 cm decade-1. Permafrost degradation includes the decreasing permafrost area and the thickening active layer mainly related to the warming of the TP. Spatially, permafrost area decrease is more susceptible to occur at lower latitudes and lower altitudes, while ALT increases more dramatically at lower latitudes and higher altitudes. In addition, permafrost is more likely to degrade to seasonally frozen ground in areas with deeper ALT.
Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981-2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981-2010) and verified (1975-1980 and 2011-2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981-2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of -0.38 cm/decade or a net change of -28.14 cm from 1950-2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.
For remote communities in the discontinuous permafrost zone, access to permafrost distribution maps for hazard assessment is limited and more general products are often inadequate for use in local-scale planning. In this study we apply established analytical methods to illustrate a time- and cost-efficient method for conducting community-scale permafrost mapping in the community of Whati, Northwest Territories, Canada. We ran a binary logistic regression (BLR) using a combination of field data, digital surface model-derived variables, and remotely sensed products. Independent variables included vegetation, topographic position index, and elevation bands. The dependent variable was sourced from 139 physical checks of near-surface permafrost presence/absence sampled across the variable boreal-wetland environment. Vegetation is the strongest predictor of near-surface permafrost in the regression. The regression predicts that 50.0% (minimum confidence: 36%) of the vegetated area is underlain by near-surface permafrost with a spatial accuracy of 72.8%. Analysis of data recorded across various burnt and not-burnt environments indicated that recent burn scenarios have significantly influenced the distribution of near-surface permafrost in the community. A spatial burn analysis predicted up to an 18.3% reduction in near-surface permafrost coverage, in a maximum burn scenario without factoring in the influence of climate change. The study highlights the potential that in an ecosystem with virtually homogeneous air temperature, ecosystem structure and disturbance history drive short-term changes in permafrost distribution and evolution. Thus, at the community level these factors should be considered as seriously as changes to air temperature as climate changes.
The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990-2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km(2)) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE -46 and -29 g C m(-2) yr(-1), respectively) compared to tundra (average annual NEE +10 and -2 g C m(-2) yr(-1)). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990-2015, although uncertainty remains high.