Resource recovery from RO concentrate using nanofiltration: Impact of active layer thickness on performance.

Modelling the removal of monovalent and divalent ions from seawater via nanofiltration is crucial for pre-treatment in seawater reverse osmosis systems. Effective separation of divalent ions through nanofiltration and allowing the permeate containing only monovalent ions to pass through the reverse osmosis system produces pure NaCl salt from the concentrate. However, the Donnan steric pore model and dielectric exclusion assume a uniformly distributed cylinder pore morphology, which is not representative of the actual membrane structure. This study analyzed the impact of membrane thickness on neutral solute removal and investigated the effect of two different methods for calculating the Peclet number on rejection rates of monovalent and divalent salts. Results show that membrane thickness has a significant effect on rejection rates, particularly for uncharged solutes in the range of 0.5-0.7 solute radius to membrane pore size ratio. Operating pressures above 10 bar favour the use of effective active layer thickness over the membrane pore size to calculate the Peclet number. At low pressures, using the effective active layer can lead to overestimation of monovalent salt rejection and underestimation of divalent salt rejection. This study highlights the importance of appropriate Peclet number calculation methods based on applied pressure when modelling membrane separation performance.

Vegetation green-up date is more sensitive to permafrost degradation than climate change in spring across the northern permafrost region.

Global climate change substantially influences vegetation spring phenology, that is, green-up date (GUD), in the northern permafrost region. Changes in GUD regulate ecosystem carbon uptake, further feeding back to local and regional climate systems. Extant studies mainly focused on the direct effects of climate factors, such as temperature, precipitation, and insolation; however, the responses of GUD to permafrost degradation caused by warming (i.e., indirect effects) remain elusive yet. In this study, we examined the impacts of permafrost degradation on GUD by analyzing the long-term trend of satellite-based GUD in relation to permafrost degradation measured by the start of thaw (SOT) and active layer thickness (ALT). We found significant trends of advancing GUD, SOT, and thickening ALT (p < 0.05), with a spatially averaged slope of -2.1 days decade-1 , -4.1 days decade-1 , and +1.1 cm decade-1 , respectively. Using partial correlation analyses, we found more than half of the regions with significantly negative correlations between spring temperature and GUD became nonsignificant after considering permafrost degradation. GUD exhibits dominant-positive (37.6% vs. 0.6%) and dominant-negative (1.8% vs. 35.1%) responses to SOT and ALT, respectively. Earlier SOT and thicker ALT would enhance soil water availability, thus alleviating water stress for vegetation green-up. Based on sensitivity analyses, permafrost degradation was the dominant factor controlling GUD variations in 41.7% of the regions, whereas only 19.6% of the regions were dominated by other climatic factors (i.e., temperature, precipitation, and insolation). Our results indicate that GUDs were more sensitive to permafrost degradation than direct climate change in spring among different vegetation types, especially in high latitudes. This study reveals the significant impacts of permafrost degradation on vegetation GUD and highlights the importance of permafrost status in better understanding spring phenological responses to future climate change.

The influence of vegetation and soil characteristics on active-layer thickness of permafrost soils in boreal forest.

Carbon release from thawing permafrost soils could significantly exacerbate global warming as the active-layer deepens, exposing more carbon to decay. Plant community and soil properties provide a major control on this by influencing the maximum depth of thaw each summer (active-layer thickness; ALT), but a quantitative understanding of the relative importance of plant and soil characteristics, and their interactions in determine ALTs, is currently lacking. To address this, we undertook an extensive survey of multiple vegetation and edaphic characteristics and ALTs across multiple plots in four field sites within boreal forest in the discontinuous permafrost zone (NWT, Canada). Our sites included mature black spruce, burned black spruce and paper birch, allowing us to determine vegetation and edaphic drivers that emerge as the most important and broadly applicable across these key vegetation and disturbance gradients, as well as providing insight into site-specific differences. Across sites, the most important vegetation characteristics limiting thaw (shallower ALTs) were tree leaf area index (LAI), moss layer thickness and understory LAI in that order. Thicker soil organic layers also reduced ALTs, though were less influential than moss thickness. Surface moisture (0-6 cm) promoted increased ALTs, whereas deeper soil moisture (11-16 cm) acted to modify the impact of the vegetation, in particular increasing the importance of understory or tree canopy shading in reducing thaw. These direct and indirect effects of moisture indicate that future changes in precipitation and evapotranspiration may have large influences on ALTs. Our work also suggests that forest fires cause greater ALTs by simultaneously decreasing multiple ecosystem characteristics which otherwise protect permafrost. Given that vegetation and edaphic characteristics have such clear and large influences on ALTs, our data provide a key benchmark against which to evaluate process models used to predict future impacts of climate warming on permafrost degradation and subsequent feedback to climate.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

The surface deformation of permafrost and active layer thickness in the upper reaches of the Black River basin, revealed by InSAR observations and independent component analysis.

The Heihe River Basin, located in the northeastern part of the Qinghai-Tibetan Plateau, is part of the perennial permafrost belt of the Qilian Mountains. Recent observations indicate ongoing permafrost degradation in this region. This study utilizes data from 255 observations provided by Sentinel-1 satellites, MODIS Land Surface Temperature, SMAP-L4 soil moisture data, GNSS measurements, and in situ measurement. We introduced Variational Bayesian independent Component Analysis (VB-ICA) in multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing to investigate the spatial-temporal characteristics of surface deformation and permafrost active layer thickness (ALT) variations. The analysis demonstrates strong agreement with borehole data and offers improvements over traditional methodologies. The maximum value of ALT in the basin is found to be 5.7 m. VB-ICA effectively delineates seasonal deformations related to the freeze-thaw cycles, with a peak seasonal deformation amplitude of 60 mm. Moreover, the seasonal permafrost's lower boundary reaches an elevation of 3700 m, revealing that permafrost is experiencing widespread degradation and associated soil erosion in the high elevation region of The Heihe River Basin. The paper also explores the efficacy of reference point selection and baseline network establishment for employing the InSAR method in monitoring freeze-thaw deformations. The study underscores the InSAR method's adaptability and its importance for interpreting permafrost deformation and related parameters.

How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiagvik, Alaska?

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physically-based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling.

Response of active layer thickening to wildfire in the pan-Arctic region: Permafrost type and vegetation type influences.

Northern high-latitude permafrost holds the largest soil carbon pool in the world. Understanding the responses of permafrost to wildfire is crucial for improving our ability to predict permafrost degradation and further carbon emissions. Recently, studies have demonstrated that wildfires in the pan-Arctic region induced the thickening of the active layer based on site or fire event observations. However, how this induced thickening is influenced by vegetation and permafrost types remains not fully understood due to the lack of wall-to-wall analysis. Therefore, this study employed remotely sensed fire data and modelled active layer thickness (ALT) to identify the fire-induced ALT change (ΔALT) for the pan-Arctic region, and the contributions of vegetation and permafrost were quantified using the random forest (RF) model. Our results showed that the average ΔALT and the sensitivity of ΔALT to burn severity both increased with decreasing ground ice content in permafrost. The largest values were detected in thick permafrost with low ground ice content. Regarding vegetation, the average and sensitivity of ΔALT in tundra were highest, followed by those in forest and shrub. When the individual environmental factors were all taken into account, the results showed that the contribution of vegetation types was much higher than that of permafrost types (20.2 % vs. 3.5 %). Our findings highlighted the importance of environmental factors in regulating the responses of permafrost to fire.

Changes in permafrost spatial distribution and active layer thickness from 1980 to 2020 on the Tibet Plateau.

The Tibetan Plateau (TP) is experiencing extensive permafrost degradation due to climate change, which seriously threatens sustainable water and ecosystem management in the TP and its downstream areas. Understanding the evolution of permafrost is critical for studying changes in the water cycle, carbon flux, and ecology of the TP. In this study, we mapped the spatial distribution of permafrost and active layer thickness (ALT) at 1 km resolution for each decade using empirical models and machine learning methods validated with borehole data. A comprehensive comparison of model results and validation accuracy shows that the machine learning method is more advantageous in simulating the permafrost distribution, while the ALT simulated by the empirical model (i.e., Stefan model) better reflects the actual ALT distribution. We further evaluated the dynamics of permafrost distribution and ALT from 1980 to 2020 based on the results of the better-performing models, and analyzed the patterns and influencing factors of the changes in permafrost distribution and ALT. The results show that the permafrost area on the TP has decreased by 15.5 %, and the regionally average ALT has increased by 18.94 cm in the 2010s compared to the 1980s. The average decreasing rate of permafrost area is 6.33 × 104 km2 decade-1, and the average increasing rate of ALT is 6.31 cm decade-1. Permafrost degradation includes the decreasing permafrost area and the thickening active layer mainly related to the warming of the TP. Spatially, permafrost area decrease is more susceptible to occur at lower latitudes and lower altitudes, while ALT increases more dramatically at lower latitudes and higher altitudes. In addition, permafrost is more likely to degrade to seasonally frozen ground in areas with deeper ALT.

Polymer Solar Cells with Active Layer Thickness Compatible with Scalable Fabrication Processes: A Meta-Analysis.

Organic photovoltaics (OPV) has been considered for a long time a promising emerging solar technology. Currently, however, market shares of OPV are practically non-existent. A detailed meta-analysis of the literature published until mid-2021 is presented, focusing on one of the remaining issues that need to be addressed to translate the recent remarkable progress, obtained in devices' performance at lab-scale level, into the requirements able to boost the manufacturing-scale production. Namely, the active layer's thickness is referred to, which, together with device efficiency and stability, represents one of the biggest challenges of this technological research field. Papers describing solar cells containing non-fullerene acceptor (NFA) binary and ternary blends, as well as NFA plus fullerene acceptor (FA) ternary blends are reviewed. The common ground of all analyzed devices is their high-thickness active layers, compatible with large-area deposition techniques. By defining a new figure of merit to discuss the OPV thickness (thickness tolerance, TT), it is found that this parameter is not affected by the chemical family's nature of the active blend components. On the other hand, the analysis suggests that there are promising strategies to improve the TT, which are discussed in the conclusion section.

Changes in permafrost extent and active layer thickness in the Northern Hemisphere from 1969 to 2018.

Understanding the evolutions of the permafrost extent and active layer thickness (ALT) in the Northern Hemisphere (NH) are critical for global carbon flux simulation, climate change prediction, and engineering risk assessment. The temporal change characteristics of the permafrost extent and ALT for the NH have not been studied. We used the Kudryavtsev method, integrating a 0.5° × 0.5° spatial resolution of air temperature, soil texture, snow depth, vegetation type, soil volume moisture content, and organic content to simulate the changes of permafrost extent and ALT in the NH from 1969 to 2018. The results indicated that permafrost extent decreased from 23.25 × 106 km2 (average from 1969 to 1973) to 21.64 × 106 km2 (average from 2014 to 2018), with a linear rate of -0.023 × 106 km2/a. Siberia had the highest degradation rate of 0.014 × 106 km2/a, followed by Alaska, Mongolian Plateau, Qinghai-Tibet Plateau, Northern Canada, and Greenland, with linear rates of -0.012 × 106, -0.005 × 106, -0.004 × 106, -0.0014 × 106, and - 0.0004× 106 km2/a, respectively. The average ALT in the NH increased at a linear rate of 0.0086 m/a. Alaska and Mongolian Plateau had the highest thickening rate of 0.024 m/a, followed by Qinghai-Tibet Plateau, Siberia, Northern Canada, and Greenland, which had linear rates of 0.009, 0.008, 0.0072, and 0.003 m/a, respectively. The uncertainty of the results could be attributed to the inaccurate forcing data and limitations of the Kudryavtsev model.

Interannual and seasonal variations of permafrost thaw depth on the Qinghai-Tibetan Plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest.

An accurate estimation of thaw depth is critical to understanding permafrost changes due to climate warming on the Qinghai-Tibetan Plateau (QTP). However, previous studies mainly focused on the interannual changes of active layer thickness (ALT) across the QTP, and little is known about the changes in the seasonal thaw depth. Machine learning (ML) is a critical tool to accurately estimate the ALT of permafrost, but a direct comparison of ML with deep learning (DL) in ALT projection regarding the model performance is still lacking. Here, ML, namely random forest (RF), and DL algorithms like convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks were compared to estimate the interannual changes of ALT and seasonal thaw depth on the QTP. Meteorological series, in-situ collected ALT observations, and geospatial information were used as predictors. The results show that both ML and DL methods are capable of estimating ALT and seasonal thaw depth in permafrost areas. The CNN and LSTM models developed using longer lagging times exhibit better performance in thaw depth prediction while the RF models are either mediocre or sometimes even worse as the lagging time increases. The results show that the ALT from 2003 to 2011 on the QTP exhibits an increasing trend, especially in the northern region. In addition, 68.8%, 88.7%, 52.5%, and 47.5% of the permafrost regions on the QTP have deepened seasonal thaw depth in spring, summer, autumn, and winter, respectively. The correlation between air temperature and permafrost thaw depth ranges from 0.65 to 1 with the time lag ranging from 1 to 32 days. This study shows that ML and DL can be effectively used in retrieving ALT and seasonal thaw depth of permafrost, and could present an efficient way to figure out the interannual and seasonal variations of permafrost conditions under climate warming.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross-comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.

Fine Root Growth of Black Spruce Trees and Understory Plants in a Permafrost Forest Along a North-Facing Slope in Interior Alaska.

Permafrost forests play an important role in the global carbon budget due to the huge amounts of carbon stored below ground in these ecosystems. Although fine roots are considered to be a major pathway of belowground carbon flux, separate contributions of overstory trees and understory shrubs to fine root dynamics in these forests have not been specifically characterized in relation to permafrost conditions, such as active layer thickness. In this study, we investigated fine root growth and morphology of trees and understory shrubs using ingrowth cores with two types of moss substrates (feather- and Sphagnum mosses) in permafrost black spruce (Picea mariana) stands along a north-facing slope in Interior Alaska, where active layer thickness varied substantially. Aboveground biomass, litterfall production rate, and fine root mass were also examined. Results showed that aboveground biomass, fine root mass, and fine root growth of black spruce trees tended to decrease downslope, whereas those of understory Ericaceae shrubs increased. Belowground allocation (e.g., ratio of fine root growth/leaf litter production) increased downslope in both of black spruce and understory plants. These results suggested that, at a lower slope, belowground resource availability was lower than at upper slope, but higher light availability under open canopy seemed to benefit the growth of the understory shrubs. On the other hand, understory shrubs were more responsive to the moss substrates than black spruce, in which Sphagnum moss substrates increased fine root growth of the shrubs as compared with feather moss substrates, whereas the effect was unclear for black spruce. This is probably due to higher moisture contents in Sphagnum moss substrates, which benefited the growth of small diameter (high specific root length) fine roots of understory shrubs. Hence, the contribution of understory shrubs to fine root growth was greater at lower slope than at upper slope, or in Sphagnum than in feather-moss substrates in our study site. Taken together, our data show that fine roots of Ericaceae shrubs are a key component in belowground carbon flux at permafrost black spruce forests with shallow active layer and/or with Sphagnum dominated forest floor.

Spatial and temporal change patterns of near-surface CO2 and CH4 concentrations in different permafrost regions on the Mongolian Plateau from 2010 to 2017.

Greenhouse gases (GHGs) released from permafrost regions may have a positive feedback to climate change, but there is much uncertainty about additional warming from the permafrost carbon cycle. One of the main reasons for this uncertainty is that the observation data of large-scale GHG concentrations are sparse, especially for areas with rapid permafrost degradation. We selected the Mongolian Plateau as the study area. We first analyzed the active layer thickness and ground temperature changes using borehole observations. Based on ground observation data, we assessed the applicability of Greenhouse Gases Observing Satellite (GOSAT) carbon dioxide (CO2) and methane (CH4) datasets. Finally, we analyzed the temporal and spatial changes in near-surface CO2 and CH4 concentrations from 2010 to 2017 and their patterns in different permafrost regions. The results showed that the Mongolian permafrost has been experiencing rapid degradation. The annual average near-surface CO2 concentration increased gradually between 2.19 ppmv/yr and 2.38 ppmv/yr, whereas the near-surface CH4 concentration increased significantly from 7.76 ppbv/yr to 8.49 ppbv/yr. There were significant seasonal variations in near-surface CO2 and CH4 concentrations for continuous, discontinuous, sporadic, and isolated permafrost zones. The continuous and discontinuous permafrost zones had lower near-surface CO2 and CH4 concentrations in summer and autumn, whereas sporadic and isolated permafrost zones had higher near-surface CO2 and CH4 concentrations in winter and spring. Our results indicated that climate warming led to rapid permafrost degradation, and carbon-based GHG concentrations also increased rapidly in Mongolia. Although, GHG concentrations increased at rates similar to the global average and many factors can account for their changes, GHG concentration in the permafrost regions merits more attention in the future because the spatiotemporal distribution has indicated a different driving force for regional warming.

Single-year thermal regime and inferred permafrost occurrence in the upper Ganglass catchment of the cold-arid Himalaya, Ladakh, India.

Cold-arid regions of the trans-Himalaya in the Indian Himalayan Region (IHR) is suspected to have a significant area of permafrost. However, information on the ground thermal regime of these permafrost areas is so far not available. This study bridge this knowledge gap by analysing the sub-surface thermal regime of selected sites in the Ganglass catchment, Ladakh range. Near surface ground temperature data recorded during September 2016 to August 2017 using 24-miniature temperature data loggers distributed across 12 plots and covering an elevation range of 4700-5612 m a.s.l. are used in this study. Permafrost characteristics including plausible ranges of thermal offset, active-layer thickness and mean annual ground temperature at 10 m depth were estimated by driving a one-dimensional heat conduction model. Two statistical models were used to map first order estimates of permafrost area in this 15.4 km2 catchment. Study suggest permafrost occurrence at all sites above 4900 m a.s.l. with active-layer thickness ranging from 0.1 to 4.2 m and the mean annual ground surface temperature ranging from between -10.0 and -0.85 °C for these sites. MAAT at these sites range from -4.1 to -8.9 °C and the surface offsets vary from -1.1 to 3.9 °C. Estimated thermal offset range from -0.9 to 0 °C. Both statistical models show comparable results and suggest 95% mean permafrost cover in the catchment above 4727 m a.s.l. These results strongly indicate existence of significant permafrost areas across the high elevations of the cold-arid regions of IHR. So far, permafrost processes are not considered for assessing present and future estimates of water and regional climate and as a causative factor for disasters like debris flows and landslides in the region. This study highlight the need for greater research efforts on Himalayan permafrost to have a comprehensive understanding of Himalayan cryosphere.

Permafrost Degradation Leads to Biomass and Species Richness Decreases on the Northeastern Qinghai-Tibet Plateau.

Degradation of permafrost with a thin overlying active layer can greatly affect vegetation via changes in the soil water and nutrient regimes within the active layer, while little is known about the presence or absence of such effects in areas with a deep active layer. Here, we selected the northeastern Qinghai-Tibet Plateau as the study area. We examined the vegetation communities and biomass along an active layer thickness (ALT) gradient from 0.6 to 3.5 m. Our results showed that plant cover, below-ground biomass, species richness, and relative sedge cover declined with the deepening active layer, while the evenness, and relative forb cover showed a contrary trend. The vegetation indices and the dissimilarity of vegetation composition exhibited significant changes when the ALT was greater than 2.0 m. The vegetation indices (plant cover, below-ground biomass, evenness index, relative forb cover and relative sedge cover) were closely associated with soil water content, soil pH, texture and nutrient content. Soil water content played a key role in the ALT-vegetation relationship, especially at depths of 30-40 cm. Our results suggest that when the ALT is greater than 2.0 m, the presence of underlying permafrost still benefits vegetation growth via maintaining adequate soil water contents at 30-40 cm depth. Furthermore, the degradation of permafrost may lead to declines of vegetation cover and below-ground biomass with a shift in vegetation species.

Drain Current Stress-Induced Instability in Amorphous InGaZnO Thin-Film Transistors with Different Active Layer Thicknesses.

In this study, the initial electrical properties, positive gate bias stress (PBS), and drain current stress (DCS)-induced instabilities of amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) with various active layer thicknesses (TIGZO) are investigated. As the TIGZO increased, the turn-on voltage (Von) decreased, while the subthreshold swing slightly increased. Furthermore, the mobility of over 13 cm²·V−1·s−1 and the negligible hysteresis of ~0.5 V are obtained in all of the a-IGZO TFTs, regardless of the TIGZO. The PBS results exhibit that the Von shift is aggravated as the TIGZO decreases. In addition, the DCS-induced instability in the a-IGZO TFTs with various TIGZO values is revealed using current–voltage and capacitance–voltage (C–V) measurements. An anomalous hump phenomenon is only observed in the off state of the gate-to-source (Cgs) curve for all of the a-IGZO TFTs. This is due to the impact ionization that occurs near the drain side of the channel and the generated holes that flow towards the source side along the back-channel interface under the lateral electric field, which cause a lowered potential barrier near the source side. As the TIGZO value increased, the hump in the off state of the Cgs curve was gradually weakened.

Exploring the photoleakage current and photoinduced negative bias instability in amorphous InGaZnO thin-film transistors with various active layer thicknesses.

The photoleakage current and the negative bias and illumination stress (NBIS)-induced instability in amorphous InGaZnO thin-film transistors (a-IGZO TFTs) with various active layer thicknesses (T IGZO) were investigated. The photoleakage current was found to gradually increase in a-IGZO TFTs irrespective of the T IGZO when the photon energy of visible light irradiation exceeded ≈2.7 eV. Furthermore, the influence of the T IGZO on NBIS-induced instability in a-IGZO TFTs was explored by the combination of current-voltage measurements in double-sweeping V GS mode and capacitance-voltage measurements. The NBIS-induced hysteresis was quantitatively analyzed using a positive gate pulse mode. When the T IGZO was close to the Debye length, the trapped electrons at the etch-stopper/IGZO interface, the trapped holes at the IGZO/gate insulator interface, and the generation of donor-like states in an a-IGZO layer were especially prominent during NBIS.

Active layer monitoring at CALM-S site near J.G.Mendel Station, James Ross Island, eastern Antarctic Peninsula.

The Circumpolar Active Layer Monitoring - South (CALM-S) site was established in February 2014 on James Ross Island as the first CALM-S site in the eastern Antarctic Peninsula region. The site, located near Johann Gregor Mendel Station, is labelled CALM-S JGM. The grid area is gently sloped (<3°) and has an elevation of between 8 and 11ma.s.l. The lithology of the site consists of the muddy sediments of Holocene marine terrace and clayey-sandy Cretaceous sedimentary rocks, which significantly affect the texture, moisture content, and physical parameters of the ground within the grid. Our objective was to study seasonal and interannual variability of the active layer depth and thermal regime at the CALM-S site, and at two ground temperature measurement profiles, AWS-JGM and AWS-CALM, located in the grid. The mean air temperature in the period March 2013 to February 2016 reached -7.2°C. The mean ground temperature decreased with depth from -5.3°C to -5.4°C at 5cm, to -5.5°C to -5.9°C at 200cm. Active layer thickness was significantly higher at AWS-CALM and ranged between 86cm (2014/15) and 87cm (2015/16), while at AWS-JGM it reached only 51cm (2013/14) to 65cm (2015/16). The mean probed active layer depth increased from 66.4cm in 2013/14 to 78.0cm in 2014/15. Large differences were observed when comparing the minimum (51cm to 59cm) and maximum (100cm to 113cm) probed depths. The distribution of the active layer depth and differences in the thermal regime of the uppermost layer of permafrost at CALM-S JGM clearly show the effect of different lithological properties on the two lithologically distinct parts of the grid.

Interactive effects between plant functional types and soil factors on tundra species diversity and community composition.

Plant communities are coupled with abiotic factors, as species diversity and community composition both respond to and influence climate and soil characteristics. Interactions between vegetation and abiotic factors depend on plant functional types (PFT) as different growth forms will have differential responses to and effects on site characteristics. However, despite the importance of different PFT for community assembly and ecosystem functioning, research has mainly focused on vascular plants. Here, we established a set of observational plots in two contrasting habitats in northeastern Siberia in order to assess the relationship between species diversity and community composition with soil variables, as well as the relationship between vegetation cover and species diversity for two PFT (nonvascular and vascular). We found that nonvascular species diversity decreased with soil acidity and moisture and, to a lesser extent, with soil temperature and active layer thickness. In contrast, no such correlation was found for vascular species diversity. Differences in community composition were found mainly along soil acidity and moisture gradients. However, the proportion of variation in composition explained by the measured soil variables was much lower for nonvascular than for vascular species when considering the PFT separately. We also found different relationships between vegetation cover and species diversity according the PFT and habitat. In support of niche differentiation theory, species diversity and community composition were related to edaphic factors. The distinct relationships found for nonvascular and vascular species suggest the importance of considering multiple PFT when assessing species diversity and composition and their interaction with edaphic factors. Synthesis: Identifying vegetation responses to edaphic factors is a first step toward a better understanding of vegetation-soil feedbacks under climate change. Our results suggest that incorporating differential responses of PFT is important for predicting vegetation shifts, primary productivity, and in turn, ecosystem functioning in a changing climate.