Wildfire strongly influences permafrost environment and soil organic carbon (SOC) pool. In this study, we reviewed the effects of fire severity, time after a fire, and frequency on SOC in boreal permafrost regions. This review highlighted several key points: the effect of wildfires on SOC increased with an increase of fire severity, and the amount of vegetation returned and surface organic matter replenished was less in a short term, which resulted in a significantly lower SOC content compared to that of before the fire. Within a short period after fire, the SOC in near-surface permafrost and the active layer decreased significantly due to the loss of above ground biomass, permafrost thaw, and increased microbial decomposition; as the years pass after a fire, the SOC gradually accumulates due to the contributions of litter layer accumulation and rooting systems from different stages of succession. The increase in fire frequency accelerated permafrost thawing and the formation of thermokarst, resulting in the rapid release of a large amount of soil carbon and reduced SOC storage. Therefore, the study on the effects of wildfires on SOC in the boreal permafrost region is of great significance to understanding and quantifying the carbon balance of the ecosystem.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought-induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi-day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short-term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability. This research explores how climate change could impact the water stress experienced by black spruce and tamarack trees in the western boreal forest of Canada. We focused on a key measure called tree water deficit to understand if the trees were under stress due to insufficient water. We examined how tree water deficit relates to environmental factors such as temperature, sunlight, and soil moisture. The findings revealed that, on a daily basis, factors like sunlight and temperature cause trees to release more water into the air. However, over longer periods (days to weeks), the amount of water in the soil becomes crucial, suggesting that trees might face water stress during dry spells. So, while trees could grow more on hotter, sunnier days, they could also experience water stress and reduced growth if the soil becomes too dry for an extended period. This study helps us grasp how various factors interact to influence tree water stress in the boreal forest, providing insights important for managing these ecosystems in a changing climate. A novel approach to determine environmental controls of tree water deficit across time scales with wavelet analysis and Granger causality Soil moisture emerges as a significant control of tree water deficit in boreal trees at longer scales (multi-days) Daily productivity gains with warming will be mitigated by decreased soil water availability in longer periods of tree water deficit

Boreal ecosystems account for 29% of the world's total forested area and contain more carbon than any other terrestrial biome. Over the past 60 years, Alaska has warmed twice as rapidly as the contiguous U.S. and wildfire activity has increased, including the number of fires, area burned, and frequency of large wildfire seasons. These recent and rapid changes in climate and wildfire have implications for future vegetation composition, structure, and biomass in interior Alaska, given that the vegetation is highly dependent on active layer thickness, soil moisture, organic layer depth, and plant-available nutrients. Here we developed a new succession extension (DGS) of the LANDIS-II forest landscape model which integrates a vegetation dynamics model (NECN) with a soil carbon model (DAMM-McNiP), a hydrologic model (SHAW), and a deep soil profile permafrost model (GIPL) in a spatially-explicit framework. DGS Succession uses the algorithms in the NECN Succession extension of LANDIS-II to simulate growth, mortality and reproduction of vegetation but has three major improvements. First, the simple bucket model in NECN was replaced with a physically-based model (SHAW) that simulates energy and water fluxes (e.g. snow depth, evapotranspiration, soil moisture) at multiple levels in the canopy and soil. Second, the active, slow, and passive soil pools in NECN were replaced by seven soil pools that are measurable in the field, with carbon and nitrogen dynamics dictated by DAMM-McNiP. Finally, soil temperature and soil moisture are simulated only at one depth in NECN, but in DGS, soil temperature (and hence permafrost dynamics) are simulated at as many as 50 user-defined depths down to 4 m with SHAW and 75 m with GIPL. During the initial calibration phase, DGS was applied at three inventory sites at the Bonanza Creek Long Term Ecological Research area in Interior Alaska where climate forcings, species biomass, soil temperature, and/or soil moisture were available. For the landscape-scale simulations, DGS was run with the SCRPPLE fire extension of LANDIS-II under two scenarios of climate using a similar to 400,000 ha landscape that included the inventory sites. Across all three sites, DGS generally captured the variation in soil moisture and temperature across depths, seasons, and years reasonably well, though there were some discrepancies at each site. DGS had better agreement with field measurements of soil moisture and temperature than its predecessor NECN which produced unrealistically low soil moisture and unrealistically high seasonal fluctuations in soil temperature. At the landscape scale, ignitions, area burned, and soil temperature increased under climate change, as expected, while soil moisture was relatively unchanged across climate scenarios. Biomass tended to decline under climate change, which differs from other modeling studies in this region but is consistent with the browning trends observed from remote sensing data. Simulating climate, vegetation succession, hydrology, permafrost, carbon and nutrient cycling, and wildfire in an integrated, spatially-explicit framework like LANDIS-II will allow us to disentangle the drivers and ecosystem responses in this rapidly changing ecosystem, as well as other forested systems with complex hydrologic, biochemical, cryospheric, and vegetation feedbacks.

Transpiration is a globally important component of evapotranspiration. Careful upscaling of transpiration from point measurements is thus crucial for quantifying water and energy fluxes. In spatially heterogeneous landscapes common across the boreal biome, upscaled transpiration estimates are difficult to determine due to variation in local environmental conditions (e.g., basal area, soil moisture, permafrost). Here, we sought to determine stand-level attributes that influence transpiration scalars for a forested boreal peatland complex consisting of sparsely treed wetlands and densely treed permafrost plateaus as land cover types. The objectives were to quantify spatial and temporal variability in stand-level transpiration, and to identify sources of uncertainty when scaling point measurements to the stand-level. Using heat ratio method sap flow sensors, we determined sap velocity for black spruce and tamarack for 2-week periods during peak growing season in 2013, 2017 and 2018. We found greater basal area, drier soils, and the presence of permafrost increased daily sap velocity in individual trees, suggesting that local environmental conditions are important in dictating sap velocity. When sap velocity was scaled to stand-level transpiration using gridded 20 x 20 m resolution data across the similar to 10 ha Scotty Creek ForestGEO plot, we observed significant differences in daily plot transpiration among years (0.17-0.30 mm), and across land cover types. Daily transpiration was lowest in grid-cells with sparsely treed wetlands compared to grid-cells with well-drained and densely treed permafrost plateaus, where daily transpiration reached 0.80 mm, or 30% of the daily evapotranspiration. When transpiration scalars (i.e., sap velocity) were not specific to the different land cover types (i.e., permafrost plateaus and wetlands), scaled stand-level transpiration was overestimated by 42%. To quantify the relative contribution of tree transpiration to ecosystem evapotranspiration, we recommend that sampling designs stratify across local environmental conditions to accurately represent variation associated with land cover types, especially with different hydrological functioning as encountered in rapidly thawing boreal peatland complexes.

Permafrost, an important source of soil disturbance, is particularly vulnerable to climate change in Alaska where 85% of the land is underlained with discontinuous permafrost. Boreal forests, home to plants integral to subsistence diets of many Alaska Native communities, are not immune to the effects of climate change. Soil disturbance events, such as permafrost thaw, wildfires, and land use change can influence abiotic conditions, which can then affect active layer soil microbial communities. In a previous study, we found negative effects on boreal plants inoculated with microbes impacted by soil disturbance compared to plants inoculated with microbes from undisturbed soils. Here, we identify key shifts in microbial communities altered by soil disturbance using 16S rRNA gene sequencing and make connections between microbial community changes and previously observed plant growth. Additionally, we identify further community shifts in potential functional mechanisms using long read metagenomics. Across a soil disturbance gradient, microbial communities differ significantly based on the level of soil disturbance. Consistent with the earlier study, the family Acidobacteriaceae, which consists of known plant growth promoters, was abundant in undisturbed soil, but practically absent in most disturbed soil. In contrast, Comamonadaceae, a family with known agricultural pathogens, was overrepresented in most disturbed soil communities compared to undisturbed. Within our metagenomic data, we found that soil disturbance level is associated with differences in microbial community function, including mechanisms potentially involved in plant pathogenicity. These results indicate that a decrease in plant growth can be linked to changes in the microbial community and functional composition driven by soil disturbance and climate change. Together, these results build a genomic understanding of how shifting soil microbiomes may affect plant productivity and ecosystem health as the Arctic warms.

Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing'an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0-10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.

Boreal forests cover over half of the global permafrost area and protect underlying permafrost. Boreal forest development, therefore, has an impact on permafrost evolution, especially under a warming climate. Forest disturbances and changing climate conditions cause vegetation shifts and potentially destabilize the carbon stored within the vegetation and permafrost. Disturbed permafrost-forest ecosystems can develop into a dry or swampy bush- or grasslands, shift toward broadleaf- or evergreen needleleaf-dominated forests, or recover to the pre-disturbance state. An increase in the number and intensity of fires, as well as intensified logging activities, could lead to a partial or complete ecosystem and permafrost degradation. We study the impact of forest disturbances (logging, surface, and canopy fires) on the thermal and hydrological permafrost conditions and ecosystem resilience. We use a dynamic multilayer canopy-permafrost model to simulate different scenarios at a study site in eastern Siberia. We implement expected mortality, defoliation, and ground surface changes and analyze the interplay between forest recovery and permafrost. We find that forest loss induces soil drying of up to 44%, leading to lower active layer thicknesses and abrupt or steady decline of a larch forest, depending on disturbance intensity. Only after surface fires, the most common disturbances, inducing low mortality rates, forests can recover and overpass pre-disturbance leaf area index values. We find that the trajectory of larch forests after surface fires is dependent on the precipitation conditions in the years after the disturbance. Dryer years can drastically change the direction of the larch forest development within the studied period.

Boreal forests efficiently insulate underlying permafrost. The magnitude of this insulation effect is dependent on forest density and composition. A change therein modifies the energy and water fluxes within and below the canopy. The direct influence of climatic change on forests and the indirect effect through a change in permafrost dynamics lead to extensive ecosystem shifts such as a change in composition or density, which will, in turn, affect permafrost persistence. We derive future scenarios of forest density and plant functional type composition by analyzing future projections provided by the dynamic global vegetation model (LPJ-GUESS) under global warming scenarios. We apply a detailed permafrost-multilayer canopy model to study the spatial impact-variability of simulated future scenarios of forest densities and compositions for study sites throughout eastern Siberia. Our results show that a change in forest density has a clear effect on the ground surface temperatures (GST) and the maximum active layer thickness (ALT) at all sites, but the direction depends on local climate conditions. At two sites, higher forest density leads to a significant decrease in GSTs in the snow-free period, while leading to an increase at the warmest site. Complete forest loss leads to a deepening of the ALT up to 0.33 m and higher GSTs of over 8 C-circle independently of local climatic conditions. Forest loss can induce both, active layer wetting up to four times or drying by 50%, depending on precipitation and soil type. Deciduous-dominated canopies reveal lower GSTs compared to evergreen stands, which will play an important factor in the spreading of evergreen taxa and permafrost persistence under warming conditions. Our study highlights that changing density and composition will significantly modify the thermal and hydrological state of the underlying permafrost. The induced soil changes will likely affect key forest functions such as the carbon pools and related feedback mechanisms such as swamping, droughts, fires, or forest loss.

The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information. Plain Language Summary The boreal forest is one of the most sensitive regions on the planet to climate change, yet its sensitivity remains poorly understood. In particular, the timing and drivers of the spring transition, as the forest changes from a winter adapted state to a summer adapted state, carry significant uncertainties. Remote sensing metrics can be used to characterize the spring transition, but their interpretation is complicated by persistent greenness, frequent snow cover, and a high prevalence of forests containing both deciduous and evergreen species. We collected tower-based remotely sensed metrics, stem radius, and carbon uptake measurements and show that the spring transition occurs in two distinct phases. The first phase is a reactivation of photosynthesis in evergreens and is triggered by thawed stems, warm air temperature, and moist soil. The second phase is a change in evergreen photoprotective pigment levels and the leaf-out of deciduous species. It is triggered by soil thaw. Both phases were detected with different remote sensing metrics that depended on species type. Our results illustrate how satellite measurements could be improved to capture the spring transition over diverse landscapes and what environmental factors control the spring transition.