High Local Variability Research Articles

Geographic variability of carbon and nitrogen isotope ratios of nonvolant terrestrial small mammals (Rodentia) across 3 Brazilian biomes

Abstract In this study, we investigated landscape variability of the carbon and nitrogen isotopic composition of nonvolant small mammals in the 3 main Brazilian biomes (Amazon, Atlantic Forest, and Cerrado) while also considering the differential spatial distribution of C4 plants in these biomes. We compiled a subset of data on stable carbon and nitrogen isotope ratios from nonvolant small mammals of the order Rodentia and compared the aggregated isotopic variability at the biome level using classical δ13C–δ15N biplot. The concept of isotopic niche width was used to test whether different foraging attributes drive their isotopic composition, while a Bayesian isotopic mixing model was used to estimate the proportion of 3 main food categories available to these small mammals. We also developed isoscapes in the Brazilian biomes, interpolating animals δ13C and δ15N based on the geographic coordinates of each sample and “sourcescapes” considering geographic variation of the dietary sources. The findings of this study advance our understanding of the foraging ecology of small mammals in biodiversity-rich regions of the Neotropics. Classical methodologies such as stomach content confirmed dietary choices revealed by the stable isotopic composition and also highlighted the importance of C3 and C4 plants in the diet of this group of animals, especially in biomes such as the Atlantic Forest and Cerrado, where there is a predominance of highly altered landscapes. These results confirm that replacing the original vegetation with C4 crops has altered the feeding patterns of small mammals, which could lead to critical ecological changes in the trophic structure of these areas. Vertical stratification of the dietary niche and the interaction between biome and foraging lifestyle were also observed. However, in each biome, there was significant intraspecific and interspecific variation caused by preferences for ingestion of plant and animal material, leading to different degrees of omnivory. Finally, the high local variability between individuals and species requires a larger sampling design that may also contribute to improved spatial resolution of the isoscapes.

Hydrological dynamics, wetland morphology and vegetation structure determine riparian arthropod communities in constructed wetlands

Wetland hydrological dynamics often dictate the composition of biological communities found in or near wetlands, either directly or through changes in vegetation composition. However, much remains unknown, particularly regarding how riparian arthropods respond to such dynamics. In this study, we used high-resolution hydrological data, along with presence of grazing livestock and shoreline vegetation height from 41 constructed wetlands in south-western Sweden to explore flood zone areas, flood frequencies, vegetation and grazing as drivers of the resident arthropod communities. The collected material consisted of 26,817 arthropods, where the dominant groups were Diptera (13,258 specimens), spiders (6,207) and Coleoptera (2,858), which were collected using SLAM (Sea Land and Air Malaise) trapping, along with pitfall trapping and vacuum sampling of riparian arthropods. We found group-specific responses to inundation frequencies, where wetlands with higher frequencies had lower abundances of some beetles and tipulids, and where wetlands with longer low-water table periods contained less trichopterans and heteropterans. In contrast, the size of flood zone areas only affected some wolf spider groups, that were more abundant in wetlands with intermediately sized flood zones. Shoreline vegetation height affected multiple groups, spiders, beetles and dipterans, but in different directions, whereas presence of grazing livestock had limited impact on abundances and community compositions. Given the variable responses to wetland hydrological and structural drivers, it seems that wetland arthropod communities would benefit from a high local wetland habitat variability, or wetlandscapes where individual wetlands have differing hydrological dynamics, morphology and vegetation.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.

Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain.

Mountain soils have received significant attention due to their profound influence on ecological processes and environmental factors. However, mapping these soils in digital soil mapping technique encounters several challenges, including high local variability, non-linear relationships between environmental covariates and soil properties, limited accessibility in complex topographical settings, and the absence of universally applicable covariates for soil formation. To address these issues, this study integrates soil-forming factors of the scorpan model to map soil organic carbon (SOC) and soil texture in the mid-Himalayas. By considering over 100 environmental covariates, with a focus on terrain parameters relevant to mountainous environments, the study aims to enhance the accuracy of ML regression models through augmentation techniques that overcome data insufficiency. Using augmented soil observations and covariates, a non-parametric random forest regression model is trained and applied to predict soil variables across the study area, generating a continuous fine-resolution map. The model's performance, evaluated against an unknown dataset, was significant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively. Furthermore, a sensitivity analysis of the environmental covariates and their impact on the model revealed that all the soil-forming factors make a significant contribution to the model's effectiveness. The insights gained from this research contribute to a better understanding of mountain soils and facilitate the development of effective conservation and sustainable management strategies for mountainous regions.

Where are cities under pressure? – An indicator for measuring the impact of building changes on urban density

In the course of urbanisation, cities are becoming increasingly dense with high local variability. Worldwide, there is a high demand for living space, especially in larger cities, which leads to settlement pressure. It is a declared goal to further densify cities in order to avoid the consumption of ecologically valuable land. But cities that are too densely built also bring negative consequences. Therefore, it is important to understand and describe the densification processes of cities. To better describe settlement densification processes, we have developed a novel indicator that can be used to determine changes in the development density, which is of interest from a planning perspective. The indicator takes into account both the extent of the building change and the existing development. If an area is already intensively developed, a newly constructed building will result in a higher density than if that building were built in a previously undeveloped area. The indicator can also be used to measure deconstruction effects symmetrically, so that densification and de-densification processes can be analysed in parallel. In addition, various spatial entities such as cadastral parcels, grid cells or city districts can be used as reference areas for indicator calculation. In addition to an area-based analysis, we have also taken into account the number of storeys of each building, which allows us for more precise calculations. We tested the indicator in this study using the City of Hamburg as an example. Freely accessible building floor plans and parcels from 2015 and 2020, taken from the Authoritative Real Estate Cadastral Information System (ALKIS) of the City of Hamburg, served as the data basis. Furthermore, we used land use data to examine how various land use classes differ in terms of density development. The results show that extensive densification is often observed on previously built-up land, especially in the city centre, which indicates particularly high settlement pressure. Newly developed residential areas are also densely built up, but comparatively do not contribute to such high building density. On the one hand, this indicates an efficient and thus sparing use of the finite resource of land in the city. On the other hand, it points to increasingly dense development in the city centre.

Dynamic disequilibrium: Recent widespread increases in vegetation cover on subarctic floodplains of Interior Alaska

AbstractBoreal forest and tundra ecosystems are undergoing rapid climatic and environmental changes with consequences for ecosystem structure, function, and services. Although riparian zones occupy a small footprint within subarctic landscapes, they have disproportionately high value as foci of hydrological processes, biogeochemical cycling, ecological disturbance, biodiversity, and wildlife activity. Recent observations of increased winter discharge, reduced peak flows, and increased connectivity between catchments, streams, and groundwater in subarctic riparian zones have prompted predictions of altered riverine disturbance regimes, increased channelization, and a decline in the extent of active floodplains. However, few observational data exist concerning the spatiotemporal dynamics of subarctic floodplain vegetation, which can serve as a bioindicator to corroborate such predictions. We analyzed the distribution and extent of riparian ecotypes across a network of streams in 12 Interior Alaska watersheds using high‐resolution image pairs from circa 1981–2010. All stream reaches encompassed pronounced elevational gradients and included elevational forest–tundra ecotones. We classified riparian ecotypes using an image‐based point‐intercept sampling approach, calculated the probability of ecotype transitions, and evaluated relationships between ecotype transitions and environmental covariates. Our results reveal widespread increases in the stature, density, and extent of riparian vegetation spanning gradients of elevation, floodplain morphology, and climate. Ecotype transitions occurred at >20% of sample points, and there was a strong imbalance toward forward successional transitions (16.5%) versus backward transitions (4.0%). That is, we observed a strong tendency toward increasing cover, stature, and density of vegetation communities across our extensive sampling domain across our approximately 30‐year sample period. This relatively rapid riparian “greening” signal tended to be most pronounced in our glaciated watersheds. Although the streams we studied displayed high local variability in ecotype transitions, our results support hypotheses of increasing channelization and reduced extent of unvegetated surfaces on subarctic floodplains. They also likely reflect a trend toward more rapid and extensive plant recruitment and growth due to processes associated with conspicuous warming in northern ecosystems, consistent with greening documented in other subarctic landscape segments along gradients of elevation and latitude.

A NOVEL DIGITAL METHOD FOR EVALUATION OF MUCOSA THICKNESS CHANGES IN TIME: CHANGES ASSOCIATED WITH A REMOVABLE PARTIAL DENTURE

The aim of the study was to propose a method for three-dimensional evaluation and visualisation of mucosa thickness and mucosa thickness changes and to validate it in four different software implementations. Cone-beam computed tomography (CBCT) and digitised mandibular impression, i.e., optical scan, of five patients treated with a mandibular distal extension removable partial denture were acquired at baseline (T0) and 1-year follow-up (T1). CBCT images were automatically segmented and then superimposed with a corresponding optical scan and within a patient. To obtain mucosa thickness changes in the T0-T1 interval, firstly, the distances between T0 and T1 models were computed for mucosa surface, representing surface changes (dSurface) and bone surface, representing bone changes (dBone). The distances were saved as scalar field values in the mesh model. Finally, the mucosa thickness changes (dMucosa) were calculated by subtracting the corresponding dBone from dSurface values. Distance computation algorithms in four different software were tested and compared. No differences were found between all four tested software (p0.001). Mean (and standard deviation) of median dSurface, dBone, and dMucosa of right and left residual ridge (n=10) was -0.47mm (0.43), -0.44mm (0.62), and 0.00mm (0.35), respectively. High local variability of dBone and dMucosa was found on the colour-coded maps. A novel method facilitates precise three-dimensional evaluation and visualisation of mucosa thickness and thickness changes, regardless of the software used.

Mercury and stable isotopes portray colony-specific foraging grounds in southern rockhopper penguins over the Patagonian Shelf.

Mercury pollution is a serious global environmental issue and the characterization of its distribution and its driving forces should be urgently included in research agendas. We report unusually high mercury (Hg) concentrations (>5μg/g) along with stable isotopes values in feathers of southern rockhopper penguins (Eudyptes chrysocome) from colonies in the Southwest Atlantic Ocean. We found a highly heterogenous prevalence of Hg throughout the study area and over a three-fold higher mean Hg concentration in southernmost colonies. Variation in Hg concentrations among colonies is primarily explained by site, rather than by trophic position. We provide further support to the existence of a Hg hotspot in the food web of the Patagonian Shelf and spatially restrict it to the southern tip of South America. Our findings highlight the need for regional and colony-based seabird conservation management when high local variability and plasticity in foraging habits is evident.

Combining Traditional Taxonomy and Metabarcoding: Assemblage Structure of Nematodes in the Shelf Sediments of the Eastern Antarctic Peninsula

This study provides a snapshot of the largely understudied meiobenthic and nematode communities in the Prince Gustav Channel (PGC) and Duse Bay (DB). We compared five stations sampled at different water depths along the shelf and investigated their meiobenthic community structure. We approached nematode biodiversity combining traditional taxonomic identification and high throughput sequencing (HTS), with the use of Amplicon Sequence Variants (ASVs). Additionally, we characterized the environment by primary production proxies, grain size and seasonal ice conditions. Our results suggest that the availability of organic matter and its freshness are responsible for the high densities found at all depths. However, potential factors influencing the high local and regional variability of meiofauna density and biodiversity are less clear. A bathymetric transect consisting of three stations in DB (200, 500, and 1,000 m depth) showed increasing pigment concentrations in the first centimeters of the sediment vertical profile with increasing water depth, whereas the meiofauna densities showed the opposite trend. The deepest station of DB seems to function as a sink for fine material as supported by the higher silt fraction and higher organic matter concentrations. When comparing the two basins in the PGC (1,000 and 1,250 m) and the one in DB (1,000 m), differences in terms of environmental variables, meiofaunal densities, and composition were observed. The deepest basin in PGC is located further South (closer to the highly unstable Larsen area), and marked differences with the other basins suggest that it might be experiencing different conditions as a result of its presence near the summer ice margin and its more elongated topography. Both, the shallowest and the deepest stations showed the highest number of unique sequences, suggesting a more biodiverse nematode assemblage. The morphological identification did not show significant differences in the biodiversity of all stations, differently from the ASVs approach. However, the lack of reference sequences in online databases and the thickness of nematode’s cuticule are still important issues to consider as they potentially lead to underestimations of biodiversity and functional traits.

Decision tree-based detection of blowing snow events in the European Alps

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Deterministic and geostatistical models for predicting soil organic carbon in a 60 ha farm on Inceptisol in Varanasi, India

Soil organic carbon (SOC) is the regulatory soil property for soil fertility. The SOC status and its variability pattern can be studied through spatial interpolation techniques. In the current study we compared deterministic (Inverse Distance Weightage, IDW), geostatistical (spherical and exponential kriging (OK) and Empirical Bayesian Kriging, EBK) and Machine Learning (Random Forest, RF, Support Vector Machine, SVM) method for samples collected at four grid spacings (20, 40, 60 and 80 m) to find out the combination of best interpolation method and sample spacing to produce a variability map for SOC. Geostatistical models with corresponding highest R2 (31.9%), Lin CCC (0.49) and Pearson Correlation Coefficient (0.57) performed better than deterministic and machine learning models. A more precise prediction technique of geostatistical methods than deterministic interpolation may be due to differences in weightage calculation technique and a higher biasness in terms of Mean Error associated with IDW as compared to EBK. The presence of higher local uncertainty and lack of auxiliary supporting parameters e.g., crop and nutrient management) made ML (R2 19.4%, 21.7% in case of RF and SVM respectively) to be a poor predictor than EBK. Among the six interpolation methods, EBK was found to be the best interpolation method in each sample grid spacing [31.9% (p < 0.001), 10.5% (p < 0.01), 13.5% (p < 0.01), 10.5% (p < 0.05)]. Better simulation technique of variogram generation through EBK makes it the best fit model among the three geostatistical techniques. A 20 m grid spacing was found to be the best minimum spacing for studying SOC at a small scale as a higher density sampling could better capture the variability pattern of a heterogenous field having moderate autocorrelation. High local variability and lack of covariates have resulted in poor performance of ML as compared to kriging. However, the prediction performance of models can be improved by using covariate data which has a high correlation with soil properties combined with intense sampling in regions of high variability.

Detecting multi‐scale riverine topographic variability and its influence on Chinook salmon habitat selection

AbstractQuantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

The Development and Use of Isoscapes to Determine the Geographical Origin of Quercus spp. in the United States

The stable isotope ratios of oxygen, hydrogen, carbon and sulfur from extracted wood of 87 samples of oaks from the United States were analysed. Relationships with climate variables and the stable isotope ratios of the 69 training dataset samples were investigated to a monthly resolution using long-term monthly mean climate data from NASA and the University of East Anglia’s Climate Research Unit, in conjunction with forecast data for hydrogen and oxygen isotope ratios in precipitation. These relationships were used to construct model isoscapes for oxygen, hydrogen, carbon and sulfur for US oak with the aim of using them to forecast isotopic patterns in areas that were not sampled and predict values in samples not used to construct the models. The leading predictors for isoscape generation were oxygen isotope ratios in January precipitation for oak oxygen isotope ratios, hydrogen isotope ratios in July precipitation for oak hydrogen isotope ratios, water vapour in April for carbon isotope ratios, and reflected shortwave radiation in March in combination with sulfate concentration in May for oak sulfur isotopes. The generated isoscapes can be used to show regions an unknown sample may have originated from with a resolution dependent on the rarity of the stable isotope signature within the United States. The models were assessed using the data of 18 samples of georeferenced oak. The assessment found that 100% of oxygen, 94% of hydrogen, 78% of carbon, and 94% of sulfur isotope ratios in the 18 test dataset samples fell within two standard deviations of the isoscape models. Using the results of the isoscapes in combination found that there were 4/18 test samples which did not fall within two standard deviations of the four models, this is largely attributed to the lower predictive power of the carbon isoscape model in conjunction with high local variability in carbon isotope ratios in both the test and training data. The method by which this geographic origin method has been developed will be useful to combat illegal logging and to validate legal supply chains for the purpose of good practice due diligence.

A well‐observed polar low analysed with a regional and a global weather‐prediction model

AbstractThe capability of a regional (AROME‐Arctic) and a global (ECMWF HRES) weather‐prediction model are compared for simulating a well‐observed polar low (PL). This PL developed on 3–4 March 2008 and was measured by dropsondes released from three flights during the IPY‐THORPEX campaign. Validation against these measurements reveals that both models simulate the PL reasonably well. AROME‐Arctic appears to represent the cloud structures and the high local variability more realistically. The high local variability causes standard error statistics to be similar for AROME‐Arctic and ECMWF HRES. A spatial verification technique reveals that AROME‐Arctic has improved skills at small scales for extreme values. However, the error growth of the forecast, especially in the location of the PL, is faster in AROME‐Arctic than in ECMWF HRES. This is likely associated with larger convection‐induced perturbations in the former than the latter model. Additionally, the PL development is analysed. This PL has two stages, an initial baroclinic and a convective mature stage. Sensible heat flux and condensational heat release both contribute to strengthen the initial baroclinic environment. In the mature stage, latent heat release appears to maintain the system. At least two conditions must be met for this stage to develop: (a) the sensible heat flux sufficiently destabilises the local environment around the PL, and (b) sufficient moisture is available for condensational heat release. More than half of the condensed moisture within the system originates from the surroundings. The propagation of the PL is “pulled” towards the area of strongest condensational heating. Finally, the sensitivity of the PL to the sea‐surface temperature is analysed. The maximum near‐surface wind speed connected to the system increases by 1–2 m·s−1 per K of surface warming and a second centre develops in cases of highly increased temperature.

Modelling of bauxite seam attributes and quantifying in-situ ore volume uncertainty in the presence of geophysical information

ABSTRACTThe precise prediction of the footwall variability of a lateritic bauxite seam is of critical importance for the quantification of ferricrete dilution and ore loss that is likely to occur during mining activity. However, the majority of bauxite deposits have economic drillhole intercepts that are too widely spaced to reflect the accurate contact variability, resulting in uncertainties in the in-situ ore volume and the characteristics of the ore being sent to the refinery. In a case study, the seam attributes were modelled using drillhole data and geophysical information through univariate and bivariate geostatistical approaches. The uncertainties in the volumes of ore, dilution and loss were assessed through conditional simulation. The results indicated that the in-situ ore volume was predicted more accurately when the secondary information was incorporated. The realisations suggested a high local variability in the footwall contact, which is the source of dilution and loss considering the selectivity and operating constraints.

Estimation of Groundwater Temperatures in Paris, France

Subsurface temperature data is usually only accessible as point information with a very limited number of observations. To spatialize these isolated insights underground, we usually rely on interpolation methods. Unfortunately, these conventional tools are in many cases not suitable to be applied to areas with high local variability, like densely populated areas, and in addition are very vulnerable to uneven distributions of wells. Since thermal conditions of the surface and shallow subsurface are coupled, we can utilize this relationship to estimate shallow groundwater temperatures from satellite-derived land surface temperatures. Here, we propose an estimation approach that provides spatial groundwater temperature data and can be applied to natural, urban, and mixed environments. To achieve this, we combine land surface temperatures with anthropogenic and natural processes, such as downward heat transfer from buildings, insulation through snow coverage, and latent heat flux in the form of evapotranspiration. This is demonstrated for the city of Paris, where measurements from as early as 1977 reveal the existence of a substantial subsurface urban heat island (SUHI) with a maximum groundwater temperature anomaly of around 7 K. It is demonstrated that groundwater temperatures in Paris can be well predicted with a root mean squared error of below 1 K by means of satellite-derived land surface images. This combined approach is shown to improve existing estimation procedures that are focused either on rural or on urban conditions. While they do not detect local hotspots caused by small-scaled heat sources located underground (e.g., sewage systems and tunnels), the findings for the city of Paris for the estimation of large-scale thermal anomalies in the subsurface are promising. Thus, the new estimation procedure may also be suitable for other cities to obtain a more reliable insight into the spatial distribution of urban ground and groundwater temperatures.

Arthropod communities in fungal fruitbodies are weakly structured by climate and biogeography across European beech forests

AbstractAimThe tinder fungusFomes fomentariusis a pivotal wood decomposer in European beechFagus sylvaticaforests. The fungus, however, has regionally declined due to centuries of logging. To unravel biogeographical drivers of arthropod communities associated with this fungus, we investigated how space, climate and habitat amount structure alpha and beta diversity of arthropod communities in fruitbodies ofF. fomentarius.LocationTemperate zone of Europe.TaxonArthropods.MethodsWe reared arthropods from fruitbodies sampled from 61 sites throughout the range of European beech and identified 13 orders taxonomically or by metabarcoding. We estimated the total number of species occurring in fruitbodies ofF. fomentariusin European beech forests using the Chao2 estimator and determined the relative importance of space, climate and habitat amount by hierarchical partitioning for alpha diversity and generalized dissimilarity models for beta diversity. A subset of fungi samples was sequenced for identification of the fungus’ genetic structure.ResultsThe total number of arthropod species occurring in fruitbodies ofF. fomentariusacross European beech forests was estimated to be 600. Alpha diversity increased with increasing fruitbody biomass; it decreased with increasing longitude, temperature and latitude. Beta diversity was mainly composed by turnover. Patterns of beta diversity were only weakly linked to space and the overall explanatory power was low. We could distinguish two genotypes ofF. fomentarius,which showed no spatial structuring.Main conclusionFomes fomentariushosts a large number of arthropods in European beech forests. The low biogeographical and climatic structure of the communities suggests that fruitbodies represent a habitat that offers similar conditions across large gradients of climate and space, but are characterized by high local variability in community composition and colonized by species with high dispersal ability. For European beech forests, retention of trees withF. fomentariusand promoting its recolonization where it had declined seems a promising conservation strategy.

Characterizing and quantifying the discontinuous bank erosion of a small low energy river using Structure-from-Motion Photogrammetry and erosion pins

River bank erosion is controlled by a complex combination of factors and processes that play a main role in fluvial dynamics. However, the role played by lateral erosion in small low energy rivers functioning still needs to be better understood. This study deals with the characterization and the quantification of the bank erosion of a small low energy river of the Seine Basin (France) in periurban catchment where urbanization had led to an incision and widening of the bed in the past decades, using adapted monitoring surveys: erosion pins and Structure-from-Motion Photogrammetry. The paper provides an opportunity to compare reach-scale flux estimates using both techniques. Spatial and temporal variations in river bank rates were quantified over 16 months and the causes and controls of riverbank erosion were identified. The estimated morphological activity rate ranges from 117 cm/yr to 1 cm/yr, illustrating the very heterogeneous spatio-temporal pattern of bank erosion observed on the Merantaise River. Whereas a number of studies consider subaerial processes as the dominant processes, fluvial erosion was found to be the most significant causal process, and in particular the mean discharge of floods. However, the high local variability of the bank erosion of the studied river appears to be controlled by very local factors such as the bank angle, the presence of a berm at the bank-toe (providing protection against fluvial erosion), lateral supply (outlet of rainwater pipes) and deflected flow in the direction of the bank.

Using Time‐Lapse Lidar to Quantify River Bend Evolution on the Meandering Coastal Trinity River, Texas, USA

AbstractTime‐lapse airborne lidar on the coastal Trinity River in Texas, USA, shows profound downstream variability in point bar growth and cut bank erosion resulting from a historically large flood that occurred in 2015. The difference map generated from two surveys covers 55 river bends and captures the transition from quasi‐uniform flow into backwater flow. In the upstream portion of the survey, reach‐averaged deposition is greater than erosion, with high local variability between the two due to the occurrence of bend cutoffs. Point bars shift laterally up to 3 times as much as cut bank retreat in this area. In the backwater zone, cut bank erosion consistently outpaces deposition on point bars and cut bank retreat exceeds the lateral shift of the point bar. Edge‐to‐edge channel width, measured from active scroll bar to outer bank, increases for all bends. This widening is often partially due to point bar widening and movement of the scroll bar away from the channel. The combination of net erosion and limited lateral movement of point bars within the backwater zone implies that during large sustained floods, bank pull instigates channel migration in this area. Farther upstream, net deposition and the high ratio of point bar to cut bank lateral motion imply bar push is more important. Patterns of deposition and erosion within a single bend suggest that channel migration was dominated by downstream bend translation with little bend deformation. Results from this study help to deepen the understanding of coastal river system morphodynamics by connecting changes in the geomorphology to changes in river bend kinematics, hydraulics, and sediment transport.