Discontinuous Permafrost Regions Research Articles

Higher Stability of Soil Organic Matter near the Permafrost Table in a Peatland of Northeast China

Understanding the stability of soil organic matter (SOM) is essential for making accurate predictions regarding carbon release rates. However, there is limited information on the role of chemical composition of dissolved organic matter (DOM) in SOM stability. To address this gap, the peatland soil profile in the discontinuous frozen soil region of Northeast China was selected as the focus of this research, and a comprehensive analysis was conducted on the differences between the molecular composition of DOM and the stability of SOM. The results indicate a significant carbon accumulation phenomenon near the permafrost table. Through analyses using TG-50, δ13C, and δ15N, it was determined that SOM near the permafrost table exhibits high stability, whereas SOM within the permafrost layer demonstrates poor stability. Investigations utilizing UV-vis, 3D-EEM, FT-IR, and 1H-NMR technologies revealed that DOM near the permafrost table is of high quality and highly aromatic. Furthermore, compared to near the permafrost table, humic acid materials in the permafrost layer decreased by 17%, while protein materials increased by 17%. These findings offer a novel perspective on the understanding of SOM stability in peatland soil profiles within discontinuous permafrost regions.

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.

Assessment of a hydrologic-land surface model to simulate thermo-hydrologic evolution of permafrost regions

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

Discontinuous permafrost detection from neural network-ensemble learning based electrical resistivity tomography

Electrical resistivity tomography (ERT) is an effective method for detecting the distribution of permafrost. However, the general inversion method of ERT cannot satisfy the engineering designation demand, resulting in the foundation of thaw settlement in discontinuous permafrost regions. In this study, we proposed a neural network-ensemble learning inversion method to improve the detection accuracy of discontinuous permafrost. First, a series of different resistivity distributions was evaluated to establish forward models for the training of a backpropagation neural network (BPNN). The resistivity distributions of the forward models varied with the temperature gradient, similar to the resistivity distribution of real discontinuous permafrost. The bagging algorithm of ensemble learning was then used to optimize the BPNN inversion models. Finally, three discontinuous permafrost resistivity models and two field data examples are considered to demonstrate the feasibility of the proposed inversion model. The inversion results of synthetic and field examples show that the neural network-ensemble learning model achieved a greater inversion effect with better accuracy and less noisy points than a single BPNN model or the Res2Dinv method. The trained ensemble learning inversion method has good application in field permafrost exploration.

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

Abstract In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

AbstractPermafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter‐scale maps of near‐surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near‐surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k‐fold cross validation, and the modeled extents of near‐surface permafrost were compared to the observed extents at each site. At‐a‐site near‐surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at‐a‐site prediction (70%–83%), yet they had greater accuracy when transferred to the non‐training site (62%–78%). These models demonstrate the potential for integrating high‐resolution spatial data and machine learning models to develop maps of near‐surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

Development of safe operation technology of crude oil pipeline in permafrost regions

China Mohe‒Daqing Pipeline is the world first large diameter and positive temperature buried pipeline that crosses the unstable and discontinuous permafrost regions. The permafrost along the pipeline is characterized by its high temperature, large ice content, high sensitivity to external disturbance and extremely poor thermal stability. Thus, it is a great global engineering challenge to lay and operate a stable positive temperature pipeline under the harsh conditions of “permafrost land, frigid environment and fragile ecology” while, at the same time, protecting its fragile ecological environment. Herein, the characteristics of permafrost along the pipeline were systematically analyzed, and the current scientific and technical problems in the phases of pipeline designing, constructing and operating were put forward. This thesis made significant innovative breakthroughs in the coupling mechanism of permafrost and pipelines, the design and control of hydro-thermal stable environment for permafrost and pipeline, and the monitoring, prevention and control of permafrost and pipeline disasters, thus creating a complete set of key technical systems for pipeline construction and operation in frozen soil regions. Through analyzing the performance of this system in recent ten years, it is verified that the technical achievements served to the high-quality construction and stable operation of the pipeline.

Arctic-boreal lakes of interior Alaska dominated by contemporary carbon

Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.

Concentrations and Yields of Mercury, Methylmercury, and Dissolved Organic Carbon From Contrasting Catchments in the Discontinuous Permafrost Region, Western Canada

AbstractClimate change and permafrost thaw may impact the mobilization of terrestrial dissolved organic carbon (DOC), mercury (Hg), and neurotoxic methylmercury (MeHg) into aquatic ecosystems; thus, understanding processes that control analyte export in northern catchments is needed. We monitored water chemistry for 3 years (2019–2021) at a peatland catchment (Scotty Creek) and a mixed catchment (Smith Creek) in the Dehcho (Northwest Territories), within the discontinuous permafrost zone of boreal western Canada. The peatland catchment had higher DOC and dissolved MeHg, but lower total Hg concentrations (mean ± standard deviation; 19 ± 2.6 mg DOC L−1; 0.08 ± 0.04 ng DMeHg L−1; 1.1 ± 0.3 ng THg L−1) than the mixed catchment (12 ± 4.4 mg DOC L−1; 0.05 ± 0.01 ng DMeHg L−1; 3.1 ± 2.2 ng THg L−1). Analyte concentrations increased with discharge at the mixed catchment, suggesting transport limitation and the flushing of near‐surface, organic‐rich flow paths during wet periods. In contrast, analyte concentrations in the peatland catchment were not primarily associated with discharge. MeHg concentrations, MeHg:THg, and MeHg:DOC increased with water temperature, suggesting enhanced Hg methylation during warmer periods. Mean open water season DOC and total MeHg yields were greater and more variable from the peatland than the mixed catchment (1.1–6.6 vs. 1.4–2.4 g DOC m−2; 5.2–36 vs. 6.1–10 ng MeHg m−2). Crucial storage thresholds controlling runoff generation likely drove greater inter‐annual variability in analyte yields from the peatland catchment. Our results suggest climate change may influence the production and transport of MeHg from boreal‐Arctic catchments as temperatures increase, peatlands thaw, and runoff generation is altered.

Characteristics of Microbial Abundance in Rhizosphere and Non-Rhizosphere Soils of Permafrost Peatland, Northeast China

The rhizosphere microenvironment is crucial to plant–soil physiological processes. The differences among microbial communities in the rhizosphere and non-rhizosphere peatland topsoil (0–15 cm) and subsoil (15–30 cm) in five plant communities dominated by Carex schmidtii, Chamaedaphne calyculata, Ledum palustre, Betula fruticosa, and Vaccinium uliginosum, as well as non-rhizosphere soil in discontinuous and continuous permafrost regions, were studied. We found that the bacteria and nifH gene abundances in the C. calyculata rhizosphere soil in the discontinuous permafrost region were higher than those in continuous permafrost region, while the nirK and nifH gene abundances in the non-rhizosphere soil of the discontinuous permafrost region were lower than those in the continuous permafrost region. The ratio of bacteria to fungi decreased and that of nirK to nirS increased significantly from the discontinuous to the continuous permafrost region, indicating that permafrost degradation can change soil microbial community composition. Fungal abundance was higher in the rhizosphere than the non-rhizosphere soils, suggesting that plant roots provide a more suitable environment for fungi. Moreover, the abundances of the topsoil bacteria; the fungi; and the nirK, nirS, and nifH genes were higher than those in the subsoil because of the organic matter from plant litter as a source of nutrients. The microbial abundance in the subsoil was also more affected by nutrient availability. To sum up, the microbial abundance varied among the different types of rhizosphere and non-rhizosphere soils, and the carbon and nitrogen cycling processes mediated by soil microorganisms may be greatly altered due to permafrost degradation under climate warming.

Evaluation of ground surface deformation in discontinuous permafrost regions along the China-Russia Crude Oil Pipelines in Northeast China using InSAR and ground surveys

Adequately and timely measuring and evaluating the ground surface deformation (GSD) are keys to the prudent mitigation of frost hazards of foundation soils and thus to the safe and cost-effective operation of oil pipelines in permafrost regions. Thus, it is crucial to accurately evaluate the GSD along the route of China-Russia Crude Oil Pipelines (CRCOPs) for a better understanding of frequent and substantial (differential) frost heave and thaw settlement of pipeline foundation soils in permafrost regions. In this study, GSDs along the CRCOPs from Xiufeng town to Jinsong town, northern Heilongjiang Province were monitored in discontinuous permafrost regions in the northern Da Xing'anling Mountains, Northeast China. The Sentinel-1B images from 2017 to 2021 and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology were used in combination with the Geographical Detector (GeoDetector) for identifying the drivers for GSDs and with ground survey data for validation analysis of GSD at chosen sites (e.g., MDX304 and others). InSAR results show that most of the annual GSD rates along the CRCOPs range from −10 to 0 mm/a, with larger deformation rates in Ta’he, Cuigang, and Jinsong. GeoDetector analysis shows that latitude and soil types are the most important contributors to GSDs along the CRCOPs, and; GSDs are closely related to elevation, ground temperature at the depth of 1 m (GT1m), normalized difference vegetation index (NDVI), annual precipitation (AP), and slope aspects. Analyses of ground and oil-flow temperatures and survey results of electrical resistivity tomography (ERT) show that permafrost along CRCOPs has been degrading and thawing, resulting in thaw settlement, consolidation, and deformation of pipeline foundation soils and underlying/lateral permafrost layers and subsequently, the ground surface subsidence. This study provides key deformation data for the assessment and mitigation of frost hazards along the CRCOPs.

Spatial distribution of permafrost degradation and its impact on vegetation phenology from 2000 to 2020

As global temperatures rise, permafrost is degraded. Permafrost degradation alters vegetation phenology and community composition, thereby affecting local and regional ecosystems. The Xing'an Mountains, located on the southern edge of the Eurasian permafrost region, are very sensitive to the impact of permafrost degradation on ecosystems. Climate change has direct effects on permafrost and vegetation growth, and analysis of the indirect effects of permafrost degradation on vegetation phenology based on the normalized difference vegetation index (NDVI) can explain the internal impact mechanisms of ecosystem components. Based on the temperature at the top of permafrost (TTOP) model, which was used to simulate the spatial distribution of permafrost areas in the Xing'an Mountains from 2000 to 2020, the areas of the three permafrost types showed a decreasing trend. The mean annual surface temperature (MAST) increased significantly at a rate of 0.008 °C year−1 from 2000 to 2020, and the southern boundary of the permafrost region moved north by 0.1–1 degrees. The average NDVI value of the permafrost region increased significantly in 8.34 % of the region. The significant correlations between NDVI and permafrost degradation, temperature and precipitation in the permafrost degradation region were 92.06 % (80.19 % positive, 11.87 % negative), 50.37 % (42.72 % positive, 7.65 % negative), and 81.59 % (36.25 % positive, 45.34 % negative), and were mainly distributed along the southern boundary of the permafrost region. A significance test of phenology in the Xing'an Mountains showed that the end of the growing season (EOS) and the length of the growing season (GLS) were significantly delayed and prolonged in the southern sparse island permafrost region. Sensitivity analysis showed that permafrost degradation was the main factor that affected the start of the growing season (SOS) and GLS. When the effects of temperature, precipitation, and sunshine duration were excluded, the regions with a significant positive correlation between permafrost degradation and SOS (20.96 %) and GLS (28.55 %) were located in both continuous and discontinuous permafrost regions. The regions with a significant negative correlation between permafrost degradation and SOS (21.11 %) and GLS (8.98 %) were mainly distributed on the southern edge of the island permafrost region. In summary, the NDVI changed significantly in the southern boundary of the permafrost region, and this change was mainly attributed to permafrost degradation.

Performance and changes of high‐resolution (1 km) surface air temperature in Northern Hemisphere permafrost regions

AbstractSurface air temperatures are significant indicators of environmental and climatic change that affect a diverse set of physical systems including permafrost. Most temperature products, such as gridded or reanalysis data, are still at a relatively low spatial resolution, limiting the ability to simulate heterogeneous permafrost changes and leading to large uncertainties. Here we apply a downscaling method based on elevation to obtain high‐resolution surface air temperatures from the sixth Coupled Model Intercomparison Project in Northern Hemisphere permafrost regions. Root‐mean‐square errors and mean absolute errors after downscaling are reduced by 34 and 37%, respectively, relative to meteorological site data and gridded observations from the Climatic Research Unit. Compared to the downscaled surface air temperature data, nondownscaled model projections overestimate by 0.12–0.39°C in the discontinuous, isolated, and sporadic permafrost regions and underestimate up to 0.18°C in the continuous permafrost region under different emission scenarios. The warming rates in Northern Hemisphere permafrost regions were 0.093°C/10 year during the historical (1850–2014) period and are projected to be 0.22°C/10 year for SSP1‐2.6, 0.48°C/10 year for SSP2‐4.5, 0.75°C/10 year for SSP3‐7.0, and 0.95°C/10 year for SSP5‐8.5 during 2015–2100, which is 1.4–1.6 times the warming of nonpermafrost regions. Warming rates in high latitudes are 1.2–1.7 times higher than those in high‐elevation regions. Continuous permafrost regions' warming will be 1.2–1.4 times higher than in other permafrost regions. For permafrost with high ground ice content, warming will be 1.1 times greater than in permafrost regions with medium or low ground ice content.

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Simulating thaw-induced land cover change in discontinuous permafrost landscapes

Permafrost thaw is causing a rapid evolution of the lowland discontinuous permafrost regions of the Taiga Plains in Northern Canada and elsewhere in the Northern Hemisphere. Notably, this thaw is changing the spatial distribution of the dominant hydrologic land cover types (permafrost plateaus, fens, and isolated bogs) in parts of the Northwest Territories (NWT), Canada. Here, we develop a multinomial time series land cover model (TSLCM) to simulate historical land cover transitions, model spatial patterns of transition, and predict the long term evolution of land cover in the areas surrounding the Scotty Creek Research Station (SCRS), NWT, and similar discontinuous permafrost landscapes. The machine learning-based TSLCM is informed by a set of observed spatio-temporal variables. The independent variables represent driving factors of change, and include the estimated summertime land surface temperature anomaly (LST), the distance and a custom cost distance to land cover interfaces, time increment between initial and final states, and time-accumulated temperature; the dependent variable is classified land cover maps from 1970 to 2008. First, we applied both random forest (RF) and Multinomial Log-Linear Regression (MLR) methods to train a synthetic data model; the model which improves the performance of the TSLCM in extrapolating time series change by adding new data instances to the initial data set. We boosted the initial data by combining the predicted land cover change maps from synthetic data model and the real data set. Then, we evaluated a MLR, RF, and an extreme gradient boosting (XGBoost) model in their ability to simulate land cover change.The final results of this study show that the Ensemble Learning (EL) based approaches are capable of effectively representing historical land cover change and can produce physically consistent and plausible future land cover scenarios. Deterministic predictions from the TSLCM indicate that the permafrost plateaus’ coverage will continue to decrease, with corresponding decreases in isolated bogs’ coverage and their secondary runoff contributing areas.

Subarctic soil carbon losses after deforestation for agriculture depend on permafrost abundance

The northern circumpolar permafrost region is experiencing considerable warming due to climate change, which is allowing agricultural production to expand into regions of discontinuous and continuous permafrost. The conversion of forests to arable land might further enhance permafrost thaw and affect soil organic carbon (SOC) that had previously been protected by frozen ground. The interactive effect of permafrost abundance and deforestation on SOC stocks has hardly been studied. In this study, soils were sampled on 18 farms across the Yukon on permafrost and non-permafrost soils to quantify the impact of land-use change from forest to cropland and grassland on SOC stocks. Furthermore, the soils were physically and chemically fractionated to assess the impact of land-use change on different functional pools of SOC. On average, permafrost-affected forest soils lost 15.6 ± 21.3% of SOC when converted to cropland and 23.0 ± 13.0% when converted to grassland. No permafrost was detected in the deforested soils, indicating that land-use change strongly enhanced warming and subsequent thawing. In contrast, the change in SOC at sites without permafrost was not significant but had a slight tendency to be positive. SOC stocks were generally lower at sites without permafrost under forest. Furthermore, land-use change increased mineral-associated SOC, while the fate of particulate organic matter (POM) after land-use change depended on permafrost occurrence. Permafrost soils showed significant POM losses after land-use change, while grassland sites without permafrost gained POM in the topsoil. The results showed that the fate of SOC after land-use change greatly depended on the abundance of permafrost in the pristine forest, which was driven by climatic conditions more than by soil properties. It can be concluded that in regions of discontinuous permafrost in particular, initial conditions in forest soils should be considered before deforestation to minimize its climate impact.

WIDESPREAD CAPACITY FOR DENITRIFICATION ACROSS A BOREAL FOREST LANDSCAPE.

A warming climate combined with frequent and severe fires cause permafrost to thaw, especially in the region of discontinuous permafrost, where soil temperatures may only be a few degrees below 0 °C. Soil thaw releases carbon (C) and nitrogen (N) into the actively cycling pools, and whereas C emissions following permafrost thaw are well documented, the fates of N remain unclear. Denitrification could release N from ecosystems as nitrous oxide (N2O) or nitrogen gas (N2), but the contributions of these processes to the high-latitude N cycle remain uncertain. We quantified microbial capacity for denitrification and N2O production in boreal soils, lakes, and streams using anoxic C- and N-amended assays, and assessed correlates of denitrifying enzyme activity (DEA) in Interior Alaska. Riparian soils and stream sediments supported the highest potential rates of denitrification, upland soils were intermediate, and lakes supported lower rates, whereas deep permafrost soils supported little denitrification. Time since fire had no effect on denitrification potential in upland soils. Across all landscape positions, DEA was negatively correlated with ammonium pools. Within each landscape position, potential rate of denitrification increased with soil or sediment organic matter content. Widespread N loss to denitrification in boreal forests could constrain the capacity for N-limited primary producers to maintain C stocks in soils following permafrost thaw.

Evaluation of Ground Surface Deformation in Discontinuous Permafrost Regions Along the China-Russia Crude Oil Pipelines, Northeast China in the Northern Da Xing’Anling Mountains Using Insar and Ground Surveys

Evaluation of Ground Surface Deformation in Discontinuous Permafrost Regions Along the China-Russia Crude Oil Pipelines, Northeast China in the Northern Da Xing’Anling Mountains Using Insar and Ground Surveys

Hydrogeochemical Response of a Variably Saturated Sulfide‐Bearing Mine Waste‐Rock Pile to Precipitation: A Field‐Scale Study in the Discontinuous Permafrost Region of Northern Canada

AbstractGlobally, drainage from sulfide‐bearing waste‐rock piles, containing high concentrations of toxic metal(loid)s, can severely degrade surrounding ecosystems. Waste‐rock piles are typically deposited as unsaturated porous media. The complex physical and chemical heterogeneity of waste rock poses significant challenges to the evaluation of internal hydrological, (bio)geochemical, and mineralogical controls on the magnitude and duration of environmental impacts. An operational‐scale waste‐rock pile located in the discontinuous permafrost region of Northern Canada was well‐instrumented and monitored to examine hydrological processes (including precipitation, evaporation, and infiltration), hydrological and thermal responses to freeze‐thaw and dry‐wet cycles, and concentration‐discharge (cQ) relationships in drainage‐water quantity and quality. The net infiltration (subtraction of evaporation from precipitation) into the waste‐rock pile occurred from May to November, resulting in rainfall‐dominated recharge to pore water, surface seepage, and groundwater. Pronounced variations in water content and temperature in response to freeze‐thaw and dry‐wet cycles and variations in surface topography were observed in regions of the waste‐rock pile impacted by preferential flow pathways. In contrast, distinct variations in water content and temperature were not observed in regions of matrix‐dominated flow pathways. The cQ relationships show chemodynamically controlled dilution behavior for most dissolved constituents (e.g., SO4, Fe, Pb, Zn, Cu, Ca, Mn) in the drainage. Therefore, hydrological processes and (bio)geochemical and mineralogical reactions both play prominent roles in determining drainage‐water chemistry. This work presents an integrated approach to site‐specific monitoring and characterization to evaluate remediation and reclamation options for operational‐scale waste‐rock piles for long‐term ecosystem preservation.

Long-term Circumpolar Active Layer Monitoring (CALM) program observations in Northern Alaskan tundra

ABSTRACT The Circumpolar Active Layer Monitoring (CALM) network is an ongoing international effort to collect and disseminate standardized measurements of active-layer dynamics to monitor the response of near-surface permafrost parameters to climate change. This work presents a distillation of 25 years (1995–2019) of observations from three north–south transects of CALM sites in tundra environments of Alaska. Transects examined in this work bisect tundra regions of discontinuous permafrost on the Seward Peninsula, and the continuous permafrost zone on the western and eastern sections of the Arctic Foothills and Arctic Coastal Plain. These transects represent regional climatic gradients, several physiographic provinces, and regionally characteristic landcover associations. Total active-layer thickening at observed sites ranged from 7 to 26 cm; more significant thaw occurred in the foothills despite less pronounced warming air temperature trends. This summary highlights several regional active layer responses to climate warming, complicated by distinct thermal landscape sensitivities, landscape variability, and documented thaw subsidence. Data summarized in this report are publicly available and represent an important validation resource for earth-system models that include regions in the continuous and discontinuous permafrost zones of northern and western Alaska.