Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (+/- 25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001-2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m(3)/m(3)) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m(3)/m(3)), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001-2020 with the southwest and northeast showing a dry gets wetter scheme and the southeast presenting a wet gets drier trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5-9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11-12 cm and bias of 2.7-6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

Soil moisture (SM), as a crucial variable in the soil-vegetation-atmosphere continuum, plays an important role in the terrestrial water cycle. Analyzing SM's variation and driver factors is crucial to maintaining ecosystem diversity on the Tibetan Plateau (TP) and ensuring food security as well as water supply balance in developing countries. Gradual wetting of the soil has been detected and attributed to precipitation in this area. However, there is still a gap in understanding the potential mechanisms. It is unclear whether the greening, glacier melting, and different vegetation degradation caused by asymmetrical climate change and intensified human activities have significantly affected the balance of SM. Here, to test the hypothesis that heterogeneous SM caused by precipitation was subject to temperatures and anthropogenic constraints, GLDAS-2.1 (Global Land Data Assimilation System-2.1) SM products combined with the statistical downscaling and Geographic detectors were applied. The results revealed that: (1) Seasonal SM gradually increased (p < 0.05), while SM deficit frequently appeared with exposure to extreme climates, such as in the summer of 2010 and 2013, and changed into a pattern of precipitation transport to western dry lands in autumn. (2) There was a synergistic reaction between greening and local moisture in autumn. SM was dominated by low temperature (TMN) in winter, warming indirectly regulated SM by exacerbating the thawing of glaciers and permafrost. The spatial coupling between the faster rising rate of TMN and the frozen soil might further aggravate the imbalance of SM. (3) The land cover's mutual transformation principally affected SM in spring and autumn, and degradation accelerated the loss of SM replenished by precipitation. (4) Land cover responses were different; SM in grassland was less affected by external disturbance, while degraded woodland and shrub performed adaptive feedback under dry environments, SM increased by 0.05 and 0.04 m(3)/(m(3) 10a), respectively. Our research provides a scientific basis for improving hydrological models and developing vegetation restoration strategies for long-term adaptation to TP-changing environments.

Climate-driven permafrost thaw alters the strongly coupled carbon and nitrogen cycles within the Arctic tundra, influencing the availability of limiting nutrients including nitrate (NO3-). Researchers have identified two primary mechanisms that increase nitrogen and NO3- availability within permafrost soils: (1) the 'frozen feast', where previously frozen organic material becomes available as it thaws, and (2) 'shrubification', where expansion of nitrogen-fixing shrubs promotes increased soil nitrogen. Through the synthesis of original and previously published observational data, and the application of multiple geospatial approaches, this study investigates and highlights a third mechanism that increases NO3- availability: the hydrogeomorphic evolution of polygonal permafrost landscapes. Permafrost thaw drives changes in microtopography, increasing the drainage of topographic highs, thus increasing oxic conditions that promote NO3- production and accumulation. We extrapolate relationships between NO3- and soil moisture in elevated topographic features within our study area and the broader Alaskan Coastal Plain and investigate potential changes in NO3- availability in response to possible hydrogeomorphic evolution scenarios of permafrost landscapes. These approximations indicate that such changes could increase Arctic tundra NO3- availability by similar to 250-1000%. Thus, hydrogeomorphic changes that accompany continued permafrost degradation in polygonal permafrost landscapes will substantially increase soil pore water NO3- availability and boost future fertilization and productivity in the Arctic.

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5-7 cm relative to validation samples, and RMSEs of approximately 10-12 cm relative to in situ ALT measurements.

Taiga-tundra boundary ecosystems are affected by climate change. Methane (CH4) emissions in taiga-tundra boundary ecosystems have sparsely been evaluated from local to regional scales. We linked in situ CH4 fluxes (2009-2016) with vegetation cover, and scaled these findings to estimate CH4 emissions at a local scale (10x10km) using high-resolution satellite images in an ecosystem on permafrost (Indigirka lowland, north-eastern Siberia). We defined nine vegetation classes, containing 71 species, of which 16 were dominant. Distribution patterns were affected by microtopographic height, thaw depth and soil moisture. The Indigirka lowland was covered by willow-dominated dense shrubland and cotton-sedge-dominated wetlands with sparse larch forests. In situ CH4 emissions were high in wetlands. Lakes and rivers were CH4 sources, while forest floors were mostly neutral in terms of CH4 emission. Estimated local CH4 emissions (37mg m(-2) d(-1)) were higher than those reported in similar studies. Our results indicate that: (i) sedge and emergent wetland ecosystems act as hot spots for CH4 emissions, and (ii) sparse tree coverage does not regulate local CH4 emissions and balance. Thus, larch growth and distribution, which are expected to change with climate, do not contribute to decreasing local CH4 emissions.

In this study we assess the total storage, landscape distribution, and vertical partitioning of soil organic carbon (SOC) stocks on the Brogger Peninsula, Svalbard. This type of high Arctic area is underrepresented in SOC databases for the northern permafrost region. Physico-chemical, elemental, and radiocarbon (C-14) dating analyses were carried out on thirty-two soil profiles. Results were upscaled using both a land cover classification (LCC) and a landform classification (LFC). Both LCC and LFC approaches provide weighted mean SOC 0-100 cm estimates for the study area of 1.0 +/- 0.3 kg C m(-2) (95% confidence interval) and indicate that about 68 percent of the total SOC storage occurs in the upper 30 cm of the soil, and about 10 percent occurs in the surface organic layer. Furthermore, LCC and LFC upscaling approaches provide similar spatial SOC allocation estimates and emphasize the dominant role of vegetated area (4.2 +/- 1.6 kg C m(-2)) and solifluction slopes (6.7 +/- 3.6 kg C m(-2)) in SOC 0-100 cm storage. LCC and LFC approaches report different and complementary information on the dominant processes controlling the spatial and vertical distribution of SOC in the landscape. There is no evidence for any significant SOC storage in the permafrost layer. We hypothesize, therefore, that the Brogger Peninsula and similar areas of the high Arctic will become net carbon sinks, providing negative feedback on global warming in the future. The surface area that will have vegetation cover and incipient soil development will expand, whereas only small amounts of organic matter will experience increased decomposition due to active-layer deepening.

Rapid and extensive snowmelt occurred during 2 days in March 2013 at a low-Arctic study site in the ice-free part of southwest Greenland. Meteorology, snowmelt, and snow-property observations were used to identify the meteorological conditions associated with this episodic snowmelt event (ESE) occurring prior to the spring snowmelt season. In addition, outputs from the SnowModel snowpack-evolution tool were used to quantify the snow-related consequences of ESEs on ecosystem-relevant snow properties. We estimated a 50-80% meltwater loss of the pre-melt snowpack water content, a 40-100% loss of snow thermal resistance, and a 4-day earlier spring snowmelt snow-free date due to this March 2013 ESE. Furthermore, the accumulated meltwater loss from all ESEs in a hydrological year represented 25-52% of the annual precipitation and may potentially have advanced spring snowmelt by 6-12 days. Guided by the knowledge gained from the March 2013 ESE, we investigated the origin, past occurrences, frequency, and abundance of ESEs at spatial scales ranging from local (using 2008-2013 meteorological station data) to all of Greenland (using 1979-2013 atmospheric reanalysis data). The frequency of ESEs showed large interannual variation, and a maximum number of ESEs was found in southwest Greenland. The investigations suggested that ESEs are driven by foehn winds that are typical of coastal regions near the Greenland Ice Sheet margin. Therefore, ESEs are a common part of snow-cover dynamics in Greenland and, because of their substantial impact on ecosystem processes, they should be accounted for in snow-related ecosystem and climate-change studies.

We scale a model of net ecosystem CO2 exchange (NEE) for tundra ecosystems and assess model performance using eddy covariance measurements at three tundra sites. The model, initially developed using instantaneous (seconds-minutes) chamber flux (similar to m(2)) observations, independently represents ecosystem respiration (ER) and gross primary production (GPP), and requires only temperature (T), photosynthetic photon flux density (I-0), and leaf area index (L) as inputs. We used a synthetic data set to parameterize the model so that available in situ observations could be used to assess the model. The model was then scaled temporally to daily resolution and spatially to about 1 km(2) resolution, and predicted values of NEE, and associated input variables, were compared to observations obtained from eddy covariance measurements at three flux tower sites over several growing seasons. We compared observations to modeled NEE calculated using T and I-0 measured at the towers, and L derived from MODIS data. Cumulative NEE estimates were within 17 and 11% of instrumentation period and growing season observations, respectively. Predictions improved when one site-year experiencing anomalously dry conditions was excluded, indicating the potential importance of stomatal control on GPP and/or soil moisture on ER. Notable differences in model performance resulted from ER model formulations and differences in how L was estimated. Additional work is needed to gain better predictive ability in terms of ER and L. However, our results demonstrate the potential of this model to permit landscape scale estimates of NEE using relatively few and simple driving variables that are easily obtained via satellite remote sensing.