Methane is a potent greenhouse gas, which traps significantly more heat than carbon dioxide over short timescales, but remains underrepresented in models used for projecting future climate scenarios, particularly in permafrost landscapes. Understanding methane dynamics in northern wetlands is critical for improving projections of high-latitude carbon feedbacks to the global climate system. To address this, we integrated ecological processes driving methane production, oxidation, and transport pathways into the DVM-DOS-TEM terrestrial biosphere model. Using long-term site observations from a thermokarst bog in boreal Alaska, we calibrated and benchmarked the model against observations of methane and carbon dioxide fluxes, carbon and nitrogen stocks, and soil temperature and moisture. Parameter sensitivity analyses identified opposing correlations for methanogenesis and heterotrophic respiration, revealing a dependence on carbon availability. We also found sensitivity to the distribution of carbon relative to the water table position. Investigation of the dominance of different methane transport pathways demonstrated the need for observations of methane flux partitioning, and the utility in comparing simulated to observed seasonality. The cumulative methane efflux projected to the year 2100 had a range of 211-3470 g C m-2 between low and high warming scenarios. Methane emissions dominated by diffusion showed the greatest variability across projections. Increased soil temperature and carbon availability from permafrost thaw resulted in increasing methane emissions between 2030 and 2060. But methane emission was then limited by a deepening of the water table. Nevertheless, we estimated a positive radiative forcing (i.e., warming) from these mid-century methane emissions that persisted until 2100. An ebullition-dominant parameterization led to lower variability but a net negative radiative forcing (i.e., cooling) on average. Our study highlights the importance of representing methane emission pathways and the uncertainty associated with partitioning them on predicting the carbon budget and radiative forcing of wetland ecosystems in high latitudes.

Rangelands play a crucial role in providing various ecosystem services and have significant potential for carbon sequestration. However, monitoring soil organic carbon (SOC) stocks in rangelands is challenging due to the large size of ranches and the high spatial variability influenced by climate and management factors. To address these challenges, we have developed the Rangeland Carbon Tracking and Management (RCTM) system, which integrates remote sensing inputs, survey data sources, and both empirical and process-based SOC models. In this work, we will introduce the structure of RCTM v1.0, its data input requirements, data processing pipelines, and the resulting data outputs. Additionally, we will discuss the high-resolution soil moisture data layers, baseline SOC maps, and the targeted field sampling plan generated through an empirical digital soil mapping approach. The Bayesian calibration and validation scheme for obtaining grassland plant functional type (PFT)-specific parameters using flux tower network data will also be explained. After calibration, the RCTM system generated estimates of rangeland carbon fluxes across PFTs (R2 between 0.6 and 0.7) and surface depth SOC stocks (R2 = 0.6) with moderate accuracy at the regional scale. The visualization of modeling results associated with long-term rangeland C dynamics at different scales will be demonstrated using the Google Earth Engine platform to inform management decisions and policymaking.

The permafrost region contains a significant portion of the world's soil organic carbon, and its thawing, driven by accelerated Arctic warming, could lead to substantial release of greenhouse gases, potentially disrupting the global climate system. Accurate predictions of carbon cycling in permafrost ecosystems hinge on the robust calibration of model parameters. However, manually calibrating numerous parameters in complex process-based models is labor-intensive and is complicated further by equifinality - the presence of multiple parameter sets that can equally fit the observed data. Incorrect calibration can lead to unrealistic ecological predictions. In this study, we employed the Model Analysis and Decision Support (MADS) software package to automate and enhance the accuracy of parameter calibration for carbon dynamics within the coupled Dynamic Vegetation Model, Dynamic Organic Soil Model, and Terrestrial Ecosystem Model (DVM-DOS-TEM), a process-based ecosystem model designed for high-latitude regions. The calibration process involved adjusting rate-limiting parameters to accurately replicate observed carbon and nitrogen fluxes and stocks in both soil and vegetation. Gross primary productivity, net primary productivity, vegetation carbon, vegetation nitrogen, and soil carbon and nitrogen pools served as synthetic observations for a black spruce boreal forest ecosystem. To validate the efficiency of this new calibration method, we utilized model-generated synthetic and actual observations. When matching model outputs to observed data, we encountered difficulties in maintaining mineral soil carbon stocks. Additionally, due to strong interdependencies between parameters and target values, the model consistently overestimated carbon and nitrogen allocation to the stems of evergreen trees. This study demonstrates the calibration workflow, offers an in-depth analysis of the relationships between parameters and observations (synthetic and actual), and evaluates the accuracy of the calibrated parameter values.

Fluxes of carbon dioxide (CO 2 ) and methane (CH 4 ) from open water bodies are critical components of carbon‐climate feedbacks in high latitudes. Processes governing the spatial and temporal variability of these aquatic greenhouse gas (GHG) fluxes are still highly uncertain due to limited observational data sets and lack of modeling studies incorporating comprehensive thermal and biochemical processes. This research investigates how slight variations in climate propagate through the biogeochemical cycles of ponds and resulting impacts on GHG emissions. We examine the thermal and biogeochemical dynamics of two ponds in the Yukon–Kuskokwim Delta, Alaska, under varying climatic conditions to study the impacts on CO 2 , CH 4 , and oxygen (O 2 ) concentrations and fluxes. We performed multiple numerical experiments, using the LAKE process‐based model and field measurements, to analyze how these ponds respond to variations in air temperature, shortwave radiation, and snow cover. Our study demonstrates that ice cover duration and water temperature are primary climatic drivers of GHG fluxes. Climate experiments led to reductions in ice cover duration and increased water temperatures, which subsequently enhanced CH 4 and CO 2 gas emissions from two study ponds. On average, cumulative CH 4 and CO 2 emissions were 5% and 10% higher, respectively, under increases in air temperature and shortwave radiation. Additionally, we uncovered a need to incorporate groundwater influxes of dissolved gases and nutrients in order to fully represent processes governing aquatic biochemical activity. Our work highlights the importance of understanding local‐scale processes in predicting future Arctic contributions to GHG emissions.

Abstract Accelerated Arctic warming is thawing permafrost and changing the distribution of lakes. Understanding the evolution of Arctic‐Boreal lakes is critical to predicting climate feedbacks and monitoring ecosystems; however, previous research has found diverging lake area responses to permafrost thaw. Here, we demonstrate that permafrost controls on lake distribution are substantially modified by glacial history and geologic substrate texture. We present the new Alaska Lake and Pond Occurrence Data set (ALPOD), which shows a positive association between lake area and permafrost extent in unglaciated regions with fine substrate, but a negative relationship in postglacial areas. These spatial patterns indicate that thaw‐driven lake area declines are concentrated in unglaciated regions, which we corroborate by showing that most documented decadal‐scale lake area decreases occurred in unglaciated study sites. Overall, these findings provide potential mechanisms to explain previous disagreements in lake area trends and help constrain predictions of surface water change under further warming.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.

Abstract. The permafrost region contains a significant portion of the world's soil organic carbon, and its thawing, driven by accelerated Arctic warming, could lead to the substantial release of greenhouse gases, potentially disrupting the global climate system. Accurate predictions of carbon cycling in permafrost ecosystems hinge on the robust calibration of model parameters. However, manually calibrating numerous parameters in complex process-based models is labor-intensive and further complicated by equifinality – the presence of multiple parameter sets that can equally fit the observed data. Incorrect calibration can lead to unrealistic ecological predictions. In this study, we employed the Model Analysis and Decision Support (MADS) software package to automate and enhance the accuracy of parameter calibration for carbon dynamics within the coupled Dynamic Vegetation Model, Dynamic Organic Soil Model, and Terrestrial Ecosystem Model (DVM-DOS-TEM), a process-based ecosystem model designed for high-latitude regions. The calibration process involved adjusting rate-limiting parameters to accurately replicate observed carbon and nitrogen fluxes and stocks in both soil and vegetation. Gross primary production, net primary production, vegetation carbon, vegetation nitrogen, and soil carbon and nitrogen pools served as synthetic observations for a black spruce boreal forest ecosystem. To validate the efficiency of this new calibration method, we utilized model-generated synthetic observations. This study demonstrates the calibration workflow, offers an in-depth analysis of the relationships between parameters and synthetic observations, and evaluates the accuracy of the calibrated parameter values.

Retrogressive thaw slumps (RTS) are a form of abrupt permafrost thaw that can rapidly mobilize ancient frozen soil carbon, magnifying the permafrost carbon feedback. However, the magnitude of this effect is uncertain, largely due to limited information about the distribution and extent of RTS across the circumpolar region. Although deep learning methods such as Convolutional Neural Networks (CNN) have shown the ability to map RTS from high-resolution satellite imagery (≤10 m), challenges remain in deploying these models across large areas. Imagery selection and procurement remain one of the largest challenges to upscaling RTS mapping projects, as the user must balance cost with resolution and sensor quality. In this study, we compared the performance of three satellite imagery sources that differed in terms of sensor quality and cost in predicting RTS using a Unet3+ CNN model and identified RTS characteristics that impact detectability. Maxar WorldView imagery was the most expensive option, with a ground sample distance of 1.85 m in the multispectral bands (downloaded at 4 m resolution). Planet Labs PlanetScope imagery was a less expensive option with a ground sample distance of approximately 3.0–4.2 m (downloaded at 3 m resolution). Although PlanetScope imagery was downloaded at a higher resolution than WorldView, the radiometric footprint is around 10–12 m, resulting in less crisp imagery. Finally, Sentinel-2 imagery is freely available and has a 10 m resolution. We used 756 RTS polygons from seven sites across Arctic Canada and Siberia in model training and 63 RTS polygons in model testing. The mean IoU of the validation and testing data sets were 0.69 and 0.75 for the WorldView model, 0.70 and 0.71 for the PlanetScope model, and 0.66 and 0.68 for the Sentinel-2 model, respectively. The IoU of the RTS class was nonlinearly related to the RTS Area, showing a strong positive correlation that attenuated as the RTS Area increased. The models were better able to predict RTS that appeared bright on a dark background and were less able to predict RTS that had higher plant cover, indicating that bare ground was a primary way the models detected RTS. Additionally, the models performed less well in wet areas or areas with patchy ground cover. These results indicate that all imagery sources tested here were able to predict larger RTS, but higher-quality imagery allows more accurate detection of smaller RTS.

&lt;p&gt;Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain and can cause dynamic changes to the landscape. Their occurrence in the Arctic has become increasingly frequent. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial distribution of RTS is critical to understanding and modelling global warming factors from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 as the vegetation feature and 2-m ArcticDEM as the elevation feature. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. Our experiments shed light on the impact of within-class and between-class variances of RTS in different regions on the model performance and provided critical implications for our follow-up study. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.&lt;/p&gt;

Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain, and their occurrence in the warming Arctic is increasingly frequent and has caused dynamic changes to the landscape. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial and temporal distribution of RTS is a critical step to understanding and modelling greenhouse gas emissions from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We further tested the impact of negative data on the model performance. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 and 2-m elevation data from the ArcticDEM as model inputs and applied image augmentation techniques to enhance training. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. The predicted RTS matched very well with the ground truth labels visually. We also tested how model performance varied across different regional contexts. The result shows an overall positive impact on the model performance when data from different regions were incorporated into the training. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.

Abstract Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts.

Snow albedo is highly variable over multiple temporal and spatial scales. This variability is more pronounced in areas that experience seasonal snowpack. Satellite retrievals, physically based models and parameterizations for snow albedo all require ground-based measurements for calibration, initialization, and validation. Ground measurements are generally made using upward and downward-facing pyranometers at opportunistically located weather stations that are sparsely distributed, particularly in mountainous regions. These station-based measurements cannot capture the spatial variability of albedo across the land surface. Uncrewed Aerial Vehicles (UAVs) equipped with upward and downward-facing pyranometers provide near-surface measurements of broadband albedo that are spatially distributed across landscapes, offering improvements over in-situ sensors. At the hillslope to watershed scale albedo measurements from UAVs taken over heterogeneous terrain are a function of the spatial variability in albedo and topography within the downward-facing sensor’s field-of-view (FOV). In this research we propose methods for topographic correction of UAV snow albedo measurements and comparison to gridded satellite albedo products. These methods account for the variability of surface topography and albedo within the sensor FOV, sensor tilt, and the angular response of pyranometers. We applied the proposed methodologies to UAV snow albedo measurements collected over an alpine meadow in southwest Montana, United States (45.23°, −111.28°). Sensitivity analyses were conducted to determine the effect of altering the processing FOV (PFOV) for both topographic corrections and comparison to coincident Landsat 8-derived albedo measurements. Validation from ground-based albedo measurements showed the topographic correction to reduce albedo measurement error considerably over mildly sloping terrain. Our sensitivity analyses demonstrated that outcomes from the topographic correction and satellite comparison are highly dependent on the specified PFOV. Based on field observations and analyses of UAV albedo measurements made at different altitudes, we provide guidelines for strategizing future UAV albedo surveys. This research presents considerable advances in the standardization of UAV-based albedo measurement. We establish the foundation for future research to utilize this platform to collect near-surface validation measurements over heterogeneous terrain with high accuracy and consistency.

We present technical advances and methods to measure effective broadband physical albedo in snowy mountain headwaters using a prototype dual-sensor pyranometer mounted on an Autonomous Aerial Vehicle (an AAV). Our test flights over snowy meadows and forested areas performed well during both clear sky and snowy/windy conditions at an elevation of ~2650 m above mean sea level (MSL). Our AAV-pyranometer platform provided high spatial (m) and temporal resolution (sec) measurements of effective broadband (310–2700 nm) surface albedo. The AAV-based measurements reveal spatially explicit changes in landscape albedo that are not present in concurrent satellite measurements from Landsat and MODIS due to a higher spatial resolution. This AAV capability is needed for validation of satellite snow albedo products, especially over variable montane landscapes at spatial scales of critical importance to hydrological applications. Effectively measuring albedo is important, as annually the seasonal accumulation and melt of mountain snowpack represent a dramatic transformation of Earth’s albedo, which directly affects headwaters’ water and energy cycles.