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Redox-driven biogeochemical cycling of iron plays an integral role in the complex process network of ecosystems, such as carbon cycling, the fate of nutrients and greenhouse gas emissions. We investigate Fe-(hydr)oxide (trans)formation pathways from rhyolitic tephra in acidic topsoils of South Patagonian Andosols to evaluate the ecological relevance of terrestrial iron cycling for this sensitive fjord ecosystem. Using bulk geochemical analyses combined with micrometer-scale-measurements on individual soil aggregates and tephra pumice, we document biotic and abiotic pathways of Fe released from the glassy tephra matrix and titanomagnetite phenocrysts. During successive redox cycles that are controlled by frequent hydrological perturbations under hyper-humid climate, (trans)formations of ferrihydrite-organic matter coprecipitates, maghemite and hematite are closely linked to tephra weathering and organic matter turnover. These Fe-(hydr)oxides nucleate after glass dissolution and complexation with organic ligands, through maghemitization or dissolution-(re)crystallization processes from metastable precursors. Ultimately, hematite represents the most thermodynamically stable Fe-(hydr)oxide formed under these conditions and physically accumulates at redox interfaces, whereas the ferrihydrite coprecipitates represent a so far underappreciated terrestrial source of bio-available iron for fjord bioproductivity. The insights into Fe-(hydr)oxide (trans)formation in Andosols have implications for a better understanding of biogeochemical cycling of iron in this unique Patagonian fjord ecosystem.
We use a novel sea-ice lead climatology for the winters of 2002/03 to 2020/21 based on satellite observations with 1 km2 spatial resolution to identify predominant patterns in Arctic wintertime sea-ice leads. The causes for the observed spatial and temporal variabilities are investigated using ocean surface current velocities and eddy kinetic energies from an ocean model (Finite Element Sea Ice–Ice-Shelf–Ocean Model, FESOM) and winds from a regional climate model (CCLM) and ERA5 reanalysis, respectively. The presented investigation provides evidence for an influence of ocean bathymetry and associated currents on the mechanic weakening of sea ice and the accompanying occurrence of sea-ice leads with their characteristic spatial patterns. While the driving mechanisms for this observation are not yet understood in detail, the presented results can contribute to opening new hypotheses on ocean–sea-ice interactions. The individual contribution of ocean and atmosphere to regional lead dynamics is complex, and a deeper insight requires detailed mechanistic investigations in combination with considerations of coastal geometries. While the ocean influence on lead dynamics seems to act on a rather long-term scale (seasonal to interannual), the influence of wind appears to trigger sea-ice lead dynamics on shorter timescales of weeks to months and is largely controlled by individual events causing increased divergence. No significant pan-Arctic trends in wintertime leads can be observed.
Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.
The microbial enzyme alkaline phosphatase contributes to the removal of organic phosphorus compounds from wastewaters. To cope with regulatory threshold values for permitted maximum phosphor concentrations in treated wastewaters, a high activity of this enzyme in the biological treatment stage, e.g., the activated sludge process, is required. To investigate the reaction dynamics of this enzyme, to analyze substrate selectivities, and to identify potential inhibitors, the determination of enzyme kinetics is necessary. A method based on the application of the synthetic fluorogenic substrate 4-methylumbelliferyl phosphate is proven for soils, but not for activated sludges. Here, we adapt this procedure to the latter. The adapted method offers the additional benefit to determine inhibition kinetics. In contrast to conventional photometric assays, no particle removal, e.g., of sludge pellets, is required enabling the analysis of the whole sludge suspension as well as of specific sludge fractions. The high sensitivity of fluorescence detection allows the selection of a wide substrate concentration range for sound modeling of kinetic functions.
- Fluorescence array technique for fast and sensitive analysis of high sample numbers
- No need for particle separation – analysis of the whole (diluted) sludge suspension
- Simultaneous determination of standard and inhibition kinetics
Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory “Ice Base Cape Baranova” (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of Polar Prediction) project “Boundary layer measurements in the high Arctic” (CATS_BL) within the scope of a joint German–Russian project. In addition to SODAR-derived vertical profiles of wind speed and direction, a suite of complementary measurements at the observatory was available. ABL measurements were used for verification of the regional climate model COSMO-CLM (CCLM) with a 5 km resolution for 2017–2020. The CCLM was run with nesting in ERA5 data in a forecast mode for the measurement period. SODAR measurements were mostly limited to wind speeds <12 m/s since the signal was often lost for higher winds. The SODAR data showed a topographical channeling effect for the wind field in the lowest 100 m and some low-level jets (LLJs). The verification of the CCLM with near-surface data of the observatory showed good agreement for the wind and a negative bias for the 2 m temperature. The comparison with SODAR data showed a positive bias for the wind speed of about 1 m/s below 100 m, which increased to 1.5 m/s for higher levels. In contrast to the SODAR data, the CCLM data showed the frequent presence of LLJs associated with the topographic channeling in Shokalsky Strait. Although SODAR wind profiles are limited in range and have a lot of gaps, they represent a valuable data set for model verification. However, a full picture of the ABL structure and the climatology of channeling events could be obtained only with the model data. The climatological evaluation showed that the wind field at Cape Baranova was not only influenced by direct topographic channeling under conditions of southerly winds through the Shokalsky Strait but also by channeling through a mountain gap for westerly winds. LLJs were detected in 37% of all profiles and most LLJs were associated with channeling, particularly LLJs with a jet speed ≥ 15 m/s (which were 29% of all LLJs). The analysis of the simulated 10 m wind field showed that the 99%-tile of the wind speed reached 18 m/s and clearly showed a dipole structure of channeled wind at both exits of Shokalsky Strait. The climatology of channeling events showed that this dipole structure was caused by the frequent occurrence of channeling at both exits. Channeling events lasting at least 12 h occurred on about 62 days per year at both exits of Shokalsky Strait.
Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.
Influence of Ozone and Drought on Tree Growth under Field Conditions in a 22 Year Time Series
(2022)
Studying the effect of surface ozone (O3) and water stress on tree growth is important for planning sustainable forest management and forest ecology. In the present study, a 22-year long time series (1998–2019) on basal area increment (BAI) and fructification severity of European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) H.Karst.) at five forest sites in Western Germany (Rhineland Palatinate) was investigated to evaluate how it correlates with drought and stomatal O3 fluxes (PODY) with an hourly threshold of uptake (Y) to represent the detoxification capacity of trees (POD1, with Y = 1 nmol O3 m−2 s−1). Between 1998 and 2019, POD1 declined over time by on average 0.31 mmol m−2 year−1. The BAI showed no significant trend at all sites, except in Leisel where a slight decline was observed over time (−0.37 cm2 per year, p < 0.05). A random forest analysis showed that the soil water content and daytime O3 mean concentration were the best predictors of BAI at all sites. The highest mean score of fructification was observed during the dry years, while low level or no fructification was observed in most humid years. Combined effects of drought and O3 pollution mostly influence tree growth decline for European beech and Norway spruce.
The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.
Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2
(2022)
Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.
A model-based temperature adjustment scheme for wintertime sea-ice production retrievals from MODIS
(2022)
Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.
The larval stage of the European fire salamander (Salamandra salamandra) inhabits both lentic and lotic habitats. In the latter, they are constantly exposed to unidirectional water flow, which has been shown to cause downstream drift in a variety of taxa. In this study, a closed artificial creek, which allowed us to keep the water flow constant over time and, at the same time, to simulates with predefined water quantities and durations, was used to examine the individual movement patterns of marked larval fire salamanders exposed to unidirectional flow. Movements were tracked by marking the larvae with VIAlpha tags individually and by using downstream and upstream traps. Most individuals showed stationarity, while downstream drift dominated the overall movement pattern. Upstream movements were rare and occurred only on small distances of about 30 cm; downstream drift distances exceeded 10 m (until next downstream trap). The simulated flood events increased drift rates significantly, even several days after the flood simulation experiments. Drift probability increased with decreasing body size and decreasing nutritional status. Our results support the production hypothesis as an explanation for the movements of European fire salamander larvae within creeks.
Measurements of dust emissions and the modeling of dissipation dynamics and total values are related to great uncertainties. Agricultural activity, especially soil cultivation, may be an essential component to calculate and model local and regional dust dynamics and even connect to the global dust cycle. To budget total dust and to assess the impact of tillage, measurement of mobilized and transported dust is an essential but rare basis. In this study, a simple measurement concept with Modified Wilson and Cook samplers was applied for dust measurements on a small temporal and spatial scale on steep-slope vineyards in the Moselle area. Without mechanical impact, a mean horizontal flux of 0.01 g m2 min−1 was measured, while row tillage produced a mean horizontal flux of 5.92 g m2 min−1 of mobilized material and 4.18 g m2 min−1 emitted dust from site (=soil loss). Compared on this singular-event basis, emissions during tillage operations generated 99.89% of total emitted dust from the site under low mean wind velocities. The results also indicate a differing impact of specific cultivation operations, mulching, and tillage tools as well as the additional influence of environmental conditions, with highest emissions on dry soil and with additional wind impact. The dust source function is strongly associated with cultivation operations, implying highly dynamic but also regular and thus predictable and projectable emission peaks of total suspended particles. Detailed knowledge of the effects of mechanical impulses and reliable quantification of the local dust emission inventory are a basis for analysis of risk potential and choice of adequate management options.
Introduction:In patients with common variable immunodeficiency (CVID),immunological response is compromised. Knowledge about COVID‐19 in CVIDpatients is sparse. We, here, synthesize current research addressing the level ofthreat COVID‐19posestoCVIDpatientsandthebest‐known treatments.
Method:Review of 14 publications.
Results:The number of CVID patients with moderate to severe (~29%) andcritical infection courses (~10%), and the number of fatal cases (~13%), areincreased compared to the general picture of COVID‐19 infection. However,this might be an overestimate. Systematic cohort‐wide studies are lacking, andasymptomatic or mild cases among CVID patients occur that can easily remainunnoticed. Regular immunoglobulin replacement therapy was administered inalmost all patients, potentially explaining why the numbers of critical and fatalcases were not higher. In addition, the application of convalescent plasma wasdemonstrated to have positive effects.
Conclusions:COVID‐19 poses an elevated threat to CVID patients. However,only systematic studies can provide robust information on the extent of thisthreat. Regular immunoglobulin replacement therapy is beneficial to combatCOVID‐19 in CVID patients, and best treatment after infection includes theuse of convalescent plasma in addition to common medication.
Amphibian diversity in the Amazonian floating meadows: a Hanski core-satellite species system
(2021)
The Amazon catchment is the largest river basin on earth, and up to 30% of its waters flow across floodplains. In its open waters, floating plants known as floating meadows abound. They can act as vectors of dispersal for their associated fauna and, therefore, can be important for the spatial structure of communities. Here, we focus on amphibian diversity in the Amazonian floating meadows over large spatial scales. We recorded 50 amphibian species over 57 sites, covering around 7000 km along river courses. Using multi-site generalised dissimilarity modelling of zeta diversity, we tested Hanski's core-satellite hypothesis and identified the existence of two functional groups of species operating under different ecological processes in the floating meadows. ‘Core' species are associated with floating meadows, while ‘satellite' species are associated with adjacent environments, being only occasional or accidental occupants of the floating vegetation. At large scales, amphibian diversity in floating meadows is mostly determined by stochastic (i.e. random/neutral) processes, whereas at regional scales, climate and deterministic (i.e. niche-based) processes are central drivers. Compared with the turnover of ‘core' species, the turnover of ‘satellite' species increases much faster with distances and is also controlled by a wider range of climatic features. Distance is not a limiting factor for ‘core' species, suggesting that they have a stronger dispersal ability even over large distances. This is probably related to the existence of passive long-distance dispersal of individuals along rivers via vegetation rafts. In this sense, Amazonian rivers can facilitate dispersal, and this effect should be stronger for species associated with riverine habitats such as floating meadows.
Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.
Background
The morphology of anuran larvae is suggested to differ between species with tadpoles living in standing (lentic) and running (lotic) waters. To explore which character combinations within the general tadpole morphospace are associated with these habitats, we studied categorical and metric larval data of 123 (one third of which from lotic environments) Madagascan anurans.
Results
Using univariate and multivariate statistics, we found that certain combinations of fin height, body musculature and eye size prevail either in larvae from lentic or lotic environments.
Conclusion
Evidence for adaptation to lotic conditions in larvae of Madagascan anurans is presented. While lentic tadpoles typically show narrow to moderate oral discs, small to medium sized eyes, convex or moderately low fins and non-robust tail muscles, tadpoles from lotic environments typically show moderate to broad oral discs, medium to big sized eyes, low fins and a robust tail muscle.
The state-of-the-art finite element software Plaxis 3D was applied in a real-world study site of the Turaida castle mound to investigate the slope stability of the mound and understand the mechanisms triggering landslides there. During the simulation, the stability of the castle mound was analysed and the most landslide-susceptible zones of hillslopes were determined. The 3D finite-element stability analysis has significant advantages over conventional 2D limit-equilibrium methods where locations of 2D stability sections are arbitrarily selected. Two modelling scenarios of the slope stability were elaborated considering deep-seated slides in bedrock and shallow landslides in the colluvial material of slopes. The model shows that shallow slides in colluvium are more probable. In the finite-element model, slope failure occurs along the weakest zone in colluvium, similarly to the situation observed in previous landslides in the study site. The physical basis of the model allows results to be obtained very close to natural conditions and delivers valuable insight in triggering mechanisms of landslides.
Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
Low-level jets (LLJs) are climatological features in polar regions. It is well known that katabatic winds over the slopes of the Antarctic ice sheet are associated with strong LLJs. Barrier winds occurring, e.g., along the Antarctic Peninsula may also show LLJ structures. A few observational studies show that LLJs occur over sea ice regions. We present a model-based climatology of the wind field, of low-level inversions and of LLJs in the Weddell Sea region of the Antarctic for the period 2002–2016. The sensitivity of the LLJ detection on the selection of the wind speed maximum is investigated. The common criterion of an anomaly of at least 2 m/s is extended to a relative criterion of wind speed decrease above and below the LLJ. The frequencies of LLJs are sensitive to the choice of the relative criterion, i.e., if the value for the relative decrease exceeds 15%. The LLJs are evaluated with respect to the frequency distributions of height, speed, directional shear and stability for different regions. LLJs are most frequent in the katabatic wind regime over the ice sheet and in barrier wind regions. During winter, katabatic LLJs occur with frequencies of more than 70% in many areas. Katabatic LLJs show a narrow range of heights (mostly below 200 m) and speeds (typically 10–20 m/s), while LLJs over the sea ice cover a broad range of speeds and heights. LLJs are associated with surface inversions or low-level lifted inversions. LLJs in the katabatic wind and barrier wind regions can last several days during winter. The duration of LLJs is sensitive to the LLJ definition criteria. We propose to use only the absolute criterion for model studies.
With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.
Intense, southward low-level winds are common in Nares Strait, between Ellesmere Island and northern Greenland. The steep topography along Nares Strait leads to channelling effects, resulting in an along-strait flow. This research study presents a 30-year climatology of the flow regime from simulations of the COSMO-CLM climate model. The simulations are available for the winter periods (November–April) 1987/88 to 2016/17, and thus, cover a period long enough to give robust long-term characteristics of Nares Strait. The horizontal resolution of 15 km is high enough to represent the complex terrain and the meteorological conditions realistically. The 30-year climatology shows that LLJs associated with gap flows are a climatological feature of Nares Strait. The maximum of the mean 10-m wind speed is around 12 m s-1 and is located at the southern exit of Smith Sound. The wind speed is strongly related to the pressure gradient. Single events reach wind speeds of 40 m s-1 in the daily mean. The LLJs are associated with gap flows within the narrowest parts of the strait under stably stratified conditions, with the main LLJ occurring at 100–250 m height. With increasing mountain Froude number, the LLJ wind speed and height increase. The frequency of strong wind events (>20 m s-1 in the daily mean) for the 10 m wind shows a strong interannual variability with an average of 15 events per winter. Channelled winds have a strong impact on the formation of the North Water polynya.
The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.
Digital technologies have become central to social interaction and accessing goods and services. Development strategies and approaches to governance have increasingly deployed self-labelled ‘smart’ technologies and systems at various spatial scales, often promoted as rectifying social and geographic inequalities and increasing economic and environmental efficiencies. These have also been accompanied with similarly digitalized commercial and non-profit offers, particularly within the sharing economy. Concern has grown, however, over possible inequalities linked to their introduction. In this paper we critically analyse the role of sharing economies’ contribution to more inclusive, socially equitable
and spatially just transitions. Conceptually, this paper brings together literature on sharing economies, smart urbanism
and just transitions. Drawing on an explorative database of sharing initiatives within the cross-border region of Luxembourg and Germany, we discuss aspects of sustainability as they relate to distributive justice through spatial accessibility, intended benefits, and their operationalization. The regional analysis shows the diversity of sharing models, how they are appropriated in different ways and how intent and operationalization matter in terms of potential benefits.
Results emphasize the need for more fine-grained, qualitative research revealing who is, and is not, participating and
benefitting from sharing economies.
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation.
The study analyzes the long-term trends (1998–2019) of concentrations of the air pollutants ozone (O3) and nitrogen oxides (NOx) as well as meteorological conditions at forest sites in German midrange mountains to evaluate changes in O3 uptake conditions for trees over time at a plot scale. O3 concentrations did not show significant trends over the course of 22 years, unlike NO2 and NO, whose concentrations decreased significantly since the end of the 1990s. Temporal analyses of meteorological parameters found increasing global radiation at all sites and decreasing precipitation, vapor pressure deficit (VPD), and wind speed at most sites (temperature did not show any trend). A principal component analysis revealed strong correlations between O3 concentrations and global radiation, VPD, and temperature. Examination of the atmospheric water balance, a key parameter for O3 uptake, identified some unusually hot and dry years (2003, 2011, 2018, and 2019). With the help of a soil water model, periods of plant water stress were detected. These periods were often in synchrony with periods of elevated daytime O3 concentrations and usually occurred in mid and late summer, but occasionally also in spring and early summer. This suggests that drought protects forests against O3 uptake and that, in humid years with moderate O3 concentrations, the O3 flux was higher than in dry years with higher O3 concentrations.
Roof and wall slates are fine-grained rocks with slaty cleavage, and it is often difficult to determine their mineral composition. A new norm mineral calculation called slatecalculation allows the determination of a virtual mineral composition based on full chemical analysis, including the amounts of carbon dioxide (CO2), carbon (C), and sulfur (S). Derived norm minerals include feldspars, carbonates, micas, hydro-micas, chlorites, ore-minerals, and quartz. The mineral components of the slate are assessed with superior accuracy compared to the petrographic analysis based on the European Standard EN 12326. The inevitable methodical inaccuracies in the calculations are limited and transparent. In the present paper, slates, shales, and phyllites from worldwide occurrences were examined. This also gives an overview of the rocks used for discontinuous roofing and external cladding.
The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice leads contribute to the sea ice production and are sources for the formation of dense water which affects the ocean circulation. Atmospheric and ocean models strongly rely on observational data to describe the respective state of the sea ice since numerical models are not able to produce sea ice leads explicitly. For the Arctic, some lead datasets are available, but for the Antarctic, no such data yet exist. Our study presents a new algorithm with which leads are automatically identified in satellite thermal infrared images. A variety of lead metrics is used to distinguish between true leads and detection artefacts with the use of fuzzy logic. We evaluate the outputs and provide pixel-wise uncertainties. Our data yield daily sea ice lead maps at a resolution of 1 km2 for the winter months November– April 2002/03–2018/19 (Arctic) and April–September 2003–2019 (Antarctic), respectively. The long-term average of the lead frequency distributions show distinct features related to bathymetric structures in both hemispheres.
Soil degradation due to erosion is a significant worldwide problem at different spatial (from pedon to watershed) and temporal scales. All stages and factors in the erosion process must be detected and evaluated to reduce this environmental issue and protect existing fertile soils and natural ecosystems. Laboratory studies using rainfall simulators allow single factors and interactive effects to be investigated under controlled conditions during extreme rainfall events. In this study, three main factors (rainfall intensity, inclination, and rainfall duration) were assessed to obtain empirical data for modeling water erosion during single rainfall events. Each factor was divided into three levels (− 1, 0, + 1), which were applied in different combinations using a rainfall simulator on beds (6 × 1 m) filled with soil from a study plot located in the arid Sistan region, Iran. The rainfall duration levels tested were 3, 5, and 7 min, the rainfall intensity levels were 30, 60, and 90 mm/h, and the inclination levels were 5, 15, and 25%. The results showed that the highest rainfall intensity tested (90 mm/h) for the longest duration (7 min) caused the highest runoff (62 mm3/s) and soil loss (1580 g/m2/h). Based on the empirical results, a quadratic function was the best mathematical model (R2 = 0.90) for predicting runoff (Q) and soil loss. Single-factor analysis revealed that rainfall intensity was more influential for runoff production than changes in time and inclination, while rainfall duration was the most influential single factor for soil loss. Modeling and three-dimensional depictions of the data revealed that sediment production was high and runoff production lower at the beginning of the experiment, but this trend was reversed over time as the soil became saturated. These results indicate that avoiding the initial stage of erosion is critical, so all soil protection measures should be taken to reduce the impact at this stage. The final stages of erosion appeared too complicated to be modeled, because different factors showed differing effects on erosion.
Laboratory landslide experiments enable the observation of specific properties of these natural hazards. However, these observations are limited by traditional techniques: frequently used high-speed video analysis and wired sensors (e.g. displacement). These techniques lead to the drawback that either only the surface and 2D profiles can be observed or wires confine the motion behaviour. In contrast, an unconfined observation of the total spatiotemporal dynamics of landslides is needed for an adequate understanding of these natural hazards.
The present study introduces an autonomous and wireless probe to characterize motion features of single clasts within laboratory-scale landslides. The Smartstone probe is based on an inertial measurement unit (IMU) and records acceleration and rotation at a sampling rate of 100 Hz. The recording ranges are ±16 g (accelerometer) and ±2000∘ s−1 (gyroscope). The plastic tube housing is 55 mm long with a diameter of 10 mm. The probe is controlled, and data are read out via active radio frequency identification (active RFID) technology. Due to this technique, the probe works under low-power conditions, enabling the use of small button cell batteries and minimizing its size.
Using the Smartstone probe, the motion of single clasts (gravel size, median particle diameter d50 of 42 mm) within approx. 520 kg of a uniformly graded pebble material was observed in a laboratory experiment. Single pebbles were equipped with probes and placed embedded and superficially in or on the material. In a first analysis step, the data of one pebble are interpreted qualitatively, allowing for the determination of different transport modes, such as translation, rotation and saltation. In a second step, the motion is quantified by means of derived movement characteristics: the analysed pebble moves mainly in the vertical direction during the first motion phase with a maximal vertical velocity of approx. 1.7 m s−1. A strong acceleration peak of approx. 36 m s−2 is interpreted as a pronounced hit and leads to a complex rotational-motion pattern. In a third step, displacement is derived and amounts to approx. 1.0 m in the vertical direction. The deviation compared to laser distance measurements was approx. −10 %. Furthermore, a full 3D spatiotemporal trajectory of the pebble is reconstructed and visualized supporting the interpretations. Finally, it is demonstrated that multiple pebbles can be analysed simultaneously within one experiment. Compared to other observation methods Smartstone probes allow for the quantification of internal movement characteristics and, consequently, a motion sampling in landslide experiments.
Phylogeographic analyses point to long-term survival on the spot in micro-endemic Lycian salamanders
(2020)
Lycian salamanders (genus Lyciasalamandra) constitute an exceptional case of microendemism of an amphibian species on the Asian Minor mainland. These viviparous salamanders are confined to karstic limestone formations along the southern Anatolian coast and some islands. We here study the genetic differentiation within and among 118 populations of all seven Lyciasalamandra species across the entire genus’ distribution. Based on circa 900 base pairs of fragments of the mitochondrial 16SrDNA and ATPase genes, we analysed the spatial haplotype distribution as well as the genetic structure and demographic history of populations. We used 253 geo-referenced populations and CHELSA climate data to infer species distribution models which we projected on climatic conditions of the Last Glacial Maximum (LGM). Within all but one species, distinct phyloclades were identified, which only in parts matched current taxonomy. Most haplotypes (78%) were private to single populations. Sometimes population genetic parameters showed contradicting results, although in several cases they indicated recent population expansion of phyloclades. Climatic suitability of localities currently inhabited by salamanders was significantly lower during the LGM compared to recent climate. All data indicated a strong degree of isolation among Lyciasalamandra populations, even within phyloclades. Given the sometimes high degree of haplotype differentiation between adjacent populations, they must have survived periods of deteriorated climates during the Quaternary on the spot. However, the alternative explanation of male biased dispersal combined with a pronounced female philopatry can only be excluded if independent nuclear data confirm this result.
Climate change is expected to cause mountain species to shift their ranges to higher elevations. Due to the decreasing amounts of habitats with increasing elevation, such shifts are likely to increase their extinction risk. Heterogeneous mountain topography, however, may reduce this risk by providing microclimatic conditions that can buffer macroclimatic warming or provide nearby refugia. As aspect strongly influences the local microclimate, we here assess whether shifts from warm south-exposed aspects to cool north-exposed aspects in response to climate change can compensate for an upward shift into cooler elevations.
Although gravitropism forces trees to grow vertically, stems have shown to prefer specific orientations. Apart from wind deforming the tree shape, lateral light can result in prevailing inclination directions. In recent years a species dependent interaction between gravitropism and phototropism, resulting in trunks leaning down-slope, has been confirmed, but a terrestrial investigation of such factors is limited to small scale surveys. ALS offers the opportunity to investigate trees remotely. This study shall clarify whether ALS detected tree trunks can be used to identify prevailing trunk inclinations. In particular, the effect of topography, wind, soil properties and scan direction are investigated empirically using linear regression models. 299.000 significantly inclined stems were investigated. Species-specific prevailing trunk orientations could be observed. About 58% of the inclination and 19% of the orientation could be explained by the linear models, while the tree species, tree height, aspect and slope could be identified as significant factors. The models indicate that deciduous trees tend to lean down-slope, while conifers tend to lean leeward. This study has shown that ALS is suitable to investigate the trunk orientation on larger scales. It provides empirical evidence for the effect of phototropism and wind on the trunk orientation.
The parameterization of the boundary layer is a challenge for regional climate models of the Arctic. In particular, the stable boundary layer (SBL) over Greenland, being the main driver for substantial katabatic winds over the slopes, is simulated differently by different regional climate models or using different parameterizations of the same model. However, verification data sets with high-resolution profiles of the katabatic wind are rare. In the present paper, detailed aircraft measurements of profiles in the katabatic wind and automatic weather station data during the experiment KABEG (Katabatic wind and boundary-layer front experiment around Greenland) in April and May 1997 are used for the verification of the regional climate model COSMO-CLM (CCLM) nested in ERA-Interim reanalyses. CCLM is used in a forecast mode for the whole Arctic with 15 km resolution and is run in the standard configuration of SBL parameterization and with modified SBL parameterization. In the modified version, turbulent kinetic energy (TKE) production and the transfer coefficients for turbulent fluxes in the SBL are reduced, leading to higher stability of the SBL. This leads to a more realistic representation of the daily temperature cycle and of the SBL structure in terms of temperature and wind profiles for the lowest 200 m.
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016.
Harvesting of silage maize in late autumn on waterlogged soils may result in several ecological problems such as soil compaction and may subsequently be a major threat to soil fertility in Europe. It was hypothesized that perennial energy crops might reduce the vulnerability for soil compaction through earlier harvest dates and improved soil stability. However, the performance of such crops to be grown on soil that are periodically waterlogged and implications for soil chemical and microbial properties are currently an open issue. Within the framework of a two-year pot experiment we investigated the potential of the cup plant (Silphium perfoliatum L.), Jerusalem artichoke (Helianthus tuberosus), giant knotweed (Fallopia japonicum X bohemica), tall wheatgrass (Agropyron elongatum), and reed canary grass (Phalaris arundinacea) for cultivation under periodically waterlogged soil conditions during the winter half year and implications for soil chemical and biological properties. Examined perennial energy crops coped with periodical waterlogging and showed yields 50% to 150% higher than in the control which was never faced with waterlogging. Root formation was similar in waterlogged and non-waterlogged soil layers. Soil chemical and microbial properties clearly responded to different soil moisture treatments. For example, dehydrogenase activity was two to four times higher in the periodically waterlogged treatment compared to the control. Despite waterlogging, aerobic microbial activity was significantly elevated indicating morphological and metabolic adaptation of the perennial crops to withstand waterlogged conditions. Thus, our results reveal first evidence of a site-adapted biomass production on periodical waterlogged soils through the cultivation of perennial energy crops and for intense plant microbe interactions.
Abstract: Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.
Background
In light of the current biodiversity crisis, DNA barcoding is developing into an essential tool to quantify state shifts in global ecosystems. Current barcoding protocols often rely on short amplicon sequences, which yield accurate identification of biological entities in a community but provide limited phylogenetic resolution across broad taxonomic scales. However, the phylogenetic structure of communities is an essential component of biodiversity. Consequently, a barcoding approach is required that unites robust taxonomic assignment power and high phylogenetic utility. A possible solution is offered by sequencing long ribosomal DNA (rDNA) amplicons on the MinION platform (Oxford Nanopore Technologies).
Findings
Using a dataset of various animal and plant species, with a focus on arthropods, we assemble a pipeline for long rDNA barcode analysis and introduce a new software (MiniBar) to demultiplex dual indexed Nanopore reads. We find excellent phylogenetic and taxonomic resolution offered by long rDNA sequences across broad taxonomic scales. We highlight the simplicity of our approach by field barcoding with a miniaturized, mobile laboratory in a remote rainforest. We also test the utility of long rDNA amplicons for analysis of community diversity through metabarcoding and find that they recover highly skewed diversity estimates.
Conclusions
Sequencing dual indexed, long rDNA amplicons on the MinION platform is a straightforward, cost-effective, portable, and universal approach for eukaryote DNA barcoding. Although bulk community analyses using long-amplicon approaches may introduce biases, the long rDNA amplicons approach signifies a powerful tool for enabling the accurate recovery of taxonomic and phylogenetic diversity across biological communities.
For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI)orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality.
A satellite-based climatology of wind-induced surface temperature anomalies for the Antarctic
(2019)
It is well-known that katabatic winds can be detected as warm signatures in the surface temperature over the slopes of the Antarctic ice sheets. For appropriate synoptic forcing and/or topographic channeling, katabatic surges occur, which result in warm signatures also over adjacent ice shelves. Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used to detect warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for the years of 2002 to 2016. We present a case study and a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological signal with a mean warm anomaly of more than 5K on more than 120 days per winter for the Byrd Glacier and the Nimrod Glacier on the Ross Ice Shelf. The mean anomaly for the Brunt Ice Shelf is weaker, and exceeds 5K on about 70 days per winter. Model simulations of the IST are compared to the MODIS IST, and show a very good agreement. The model data show that the near-surface stability is a better measure for the response to the wind than the IST itself.
Background: The growing production and use of engineered AgNP in industry and private households make increasing concentrations of AgNP in the environment unavoidable. Although we already know the harmful effects of AgNP on pivotal bacterial driven soil functions, information about the impact of silver nanoparticles (AgNP) on the soil bacterial community structure is rare. Hence, the aim of this study was to reveal the long-term effects of AgNP on major soil bacterial phyla in a loamy soil. The study was conducted as a laboratory incubation experiment over a period of 1 year using a loamy soil and AgNP concentrations ranging from 0.01 to 1 mg AgNP/kg soil. Effects were quantified using the taxon-specific 16S rRNA qPCR.
Results: The short-term exposure of AgNP at environmentally relevant concentration of 0.01 mg AgNP/kg caused significant positive effects on Acidobacteria (44.0%), Actinobacteria (21.1%) and Bacteroidetes (14.6%), whereas beta-Proteobacteria population was minimized by 14.2% relative to the control (p ≤ 0.05). After 1 year of exposure to 0.01 mg AgNP/kg diminished Acidobacteria (p = 0.007), Bacteroidetes (p = 0.005) and beta-Proteobacteria (p = 0.000) by 14.5, 10.1 and 13.9%, respectively. Actino- and alpha-Proteobacteria were statistically unaffected by AgNP treatments after 1-year exposure. Furthermore, a statistically significant regression and correlation analysis between silver toxicity and exposure time confirmed loamy soils as a sink for silver nanoparticles and their concomitant silver ions.
Conclusions: Even very low concentrations of AgNP may cause disadvantages for the autotrophic ammonia oxidation (nitrification), the organic carbon transformation and the chitin degradation in soils by exerting harmful effects on the liable bacterial phyla.
In the context of accelerated global socio-environmental change, the Water-Energy-Food Nexus has received increasing attention within science and international politics by promoting integrated resource governance. This study explores the scientific nexus debates from a discourse analytical perspective to reveal knowledge and power relations as well as geographical settings of nexus research. We also investigate approaches to socio-nature relations that influence nexus research and subsequent political implications. Our findings suggest that the leading nexus discourse is dominated by natural scientific perspectives and a neo-Malthusian framing of environmental challenges. Accordingly, the promoted cross-sectoral nexus approach to resource governance emphasizes efficiency, security, future sustainability, and poverty reduction. Water, energy, and food are conceived as global trade goods that require close monitoring, management and control, to be achieved via quantitative assessments and technological interventions. Within the less visible discourse, social scientific perspectives engage with the social, political, and normative elements of the Water-Energy-Food Nexus. These perspectives criticize the dominant nexus representation for itsmanagerial, neoliberal, and utilitarian approach to resource governance. The managerial framing is critiqued for masking power relations and social inequalities, while alternative framings acknowledge the political nature of resource governance and socio-nature relations. The spatial dimensions of the nexus debate are also discussed. Notably, the nexus is largely shaped by western knowledge, yet applied mainly in specific regions of the Global South. In order for the nexus to achieve integrative solutions for sustainability, the debate needs to overcome its current discursive and spatial separations. To this end, we need to engage more closely with alternative nexus discourses, embrace epistemic pluralism and encourage multi-perspective debates about the socio-nature relations we actually intend to promote.
The availability of data on the feeding habits of species of conservation value may be of great importance to develop analyses for both scientific and management purposes. Stomach flushing is a harmless technique that allowed us to collect extensive data on the feeding habits of six Hydromantes species. Here, we present two datasets originating from a three-year study performed in multiple seasons (spring and autumn) on 19 different populations of cave salamanders. The first dataset contains data of the stomach content of 1,250 salamanders, where 6,010 items were recognized; the second one reports the size of the intact prey items found in the stomachs. These datasets integrate considerably data already available on the diet of the European plethodontid salamanders, being also of potential use for large scale meta-analyses on amphibian diet.
The trophic niche is a life trait that identifies the consumer’s position in a local food web. Several factors, such as ontogeny, competitive ability and resource availability contribute in shaping species trophic niches. To date, information on the diet of European Hydromantes salamanders are only available for a limited number of species, no dietary studies have involved more than one species of the genus at a time, and there are limited evidences on how multiple factors interact in determining diet variation. In this study we examined the diet of multiple populations of six out of the eight European cave salamanders, providing the first data on the diet for five of them. In addition, we assessed whether these closely related generalist species show similar diet and, for each species, we tested whether season, age class or sex influence the number and the type of prey consumed. Stomach condition (empty/full) and the number of prey consumed were strongly related to seasonality and to the activity level of individuals. Empty stomachs were more frequent in autumn, in individuals far from cave entrance and in juveniles. Diet composition was significantly different among species. Hydromantes imperialis and H. supramontis were the most generalist species; H. flavus and H. sarrabusensis fed mostly on Hymenoptera and Coleoptera Staphylinidae, while H. genei and H. ambrosii mostly consumed Arachnida and Endopterygota larvae. Furthermore, we detected seasonal shifts of diet in the majority of the species examined. Conversely, within each species, we did not find diet differences between females, males and juveniles. Although being assumed to have very similar dietary habits, here Hydromantes species were shown to be characterized by a high divergence in diet composition and in the stomach condition of individuals.
Leeches can parasitize many vertebrate taxa. In amphibians, leech parasitism often has potential detrimental effects including population decline. Most of studies on the host-parasite interactions involving leeches and amphibians focus on freshwater environments, while they are very scarce for terrestrial amphibians. In this work, we studied the relationship between the leech Batracobdella algira and the European terrestrial salamanders of the genus Hydromantes, identifying environmental features related to the presence of the leeches and their possible effects on the hosts. We performed observation throughout Sardinia (Italy), covering the distribution area of all Hydromantes species endemic to this island. From September 2015 to May 2017, we conducted >150 surveys in 26 underground environments, collecting data on 2629 salamanders and 131 leeches. Water hardness was the only environmental feature correlated with the presence of B. algira, linking this leech to active karstic systems. Leeches were more frequently parasitizing salamanders with large body size. Body Condition Index was not significantly different between parasitized and non-parasitized salamanders. Our study shows the importance of abiotic environmental features for host-parasite interactions, and poses new questions on complex interspecific interactions between this ectoparasite and amphibians.
This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1"5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2"12.5 -µm (instrument NEDT 0.05 K"0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0"10.25 -µm and 10.25"12.5 -µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1"3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.
Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes.rnWe compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources.
Avoiding aerial microfibre contamination of environmental samples is essential for reliable analyses when it comes to the detection of ubiquitous microplastics. Almost all laboratories have contamination problems which are largely unavoidable without investments in clean-air devices. Therefore, our study supplies an approach to assess background microfibre contamination of samples in the laboratory under particle-free air conditions. We tested aerial contamination of samples indoor, in a mobile laboratory, within a laboratory fume hood and on a clean bench with particles filtration during the examining process of a fish. The used clean bench reduced aerial microfibre contamination in our laboratory by 96.5%. This highlights the value of suitable clean-air devices for valid microplastic pollution data. Our results indicate, that pollution levels by microfibres have been overestimated and actual pollution levels may be many times lower. Accordingly, such clean-air devices are recommended for microplastic laboratory applications in future research work to significantly lower error rates.
Global human population growth is associated with many problems, such asrnfood and water provision, political conflicts, spread of diseases, and environmental destruction. The mitigation of these problems is mirrored in several global conventions and programs, some of which, however, are conflicting. Here, we discuss the conflicts between biodiversity conservation and disease eradication. Numerous health programs aim at eradicating pathogens, and many focus on the eradication of vectors, such as mosquitos or other parasites. As a case study, we focus on the "Pan African Tsetse and Trypanosomiasis Eradication Campaign," which aims at eradicating a pathogen (Trypanosoma) as well as its vector, the entire group of tsetse flies (Glossinidae). As the distribution of tsetse flies largely overlaps with the African hotspots of freshwater biodiversity, we argue for a strong consideration of environmental issues when applying vector control measures, especially the aerial applications of insecticides.rnFurthermore, we want to stimulate discussions on the value of speciesrnand whether full eradication of a pathogen or vector is justified at all. Finally, we call for a stronger harmonization of international conventions. Proper environmental impact assessments need to be conducted before control or eradication programs are carried out to minimize negative effects on biodiversity.
Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011"2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.
It is generally assumed that the temperature increase associated with global climate change will lead to increased thunderstorm intensity and associated heavy precipitation events. In the present study it is investigated whether the frequency of thunderstorm occurrences will in- or decrease and how the spatial distribution will change for the A1B scenario. The region of interest is Central Europe with a special focus on the Saar-Lor-Lux region (Saarland, Lorraine, Luxembourg) and Rhineland-Palatinate.Daily model data of the COSMO-CLM with a horizontal resolution of 4.5 km is used. The simulations were carried out for two different time slices: 1971"2000 (C20), and 2071"2100 (A1B). Thunderstorm indices are applied to detect thunderstorm-prone conditions and differences in their frequency of occurrence in the two thirty years timespans. The indices used are CAPE (Convective Available Potential Energy), SLI (Surface Lifted Index), and TSP (Thunderstorm Severity Potential).The investigation of the present and future thunderstorm conducive conditions show a significant increase of non-thunderstorm conditions. The regional averaged thunderstorm frequencies will decrease in general, but only in the Alps a potential increase in thunderstorm occurrences and intensity is found. The comparison between time slices of 10 and 30 years length show that the number of gridpoints with significant signals increases only slightly. In order to get a robust signal for severe thunderstorm, an extension to more than 75 years would be necessary.