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    • Geosciences, Vol. 9, Pages 142: Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data

      High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes.

    • Geosciences, Vol. 9, Pages 141: Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf

      In 2017, the New South Wales (NSW) Office of Environment and Heritage (OEH) initiated a state-wide mapping program, SeaBed NSW, which systematically acquires high-resolution (2–5 m cell size) multibeam echosounder (MBES) and marine LiDAR data along more than 2000 km of the subtropical-to-temperate southeast Australian continental shelf. This program considerably expands upon existing efforts by OEH to date, which have mapped approximately 15% of NSW waters with these technologies. The delivery of high volumes of new data, together with the vast repository of existing data, highlights the need for a standardised, automated approach to classify seabed data. Here we present a methodological approach with new procedures to semi-automate the classification of high-resolution bathymetry and intensity (backscatter and reflectivity) data into a suite of data products including classifications of seabed morphology (landforms) and composition (substrates, habitats, geomorphology). These methodologies are applied to two case study areas representing newer (Wollongong, NSW) and older (South Solitary Islands, NSW) MBES datasets to assess the transferability of classification techniques across input data of varied quality. The suite of seabed classifications produced by this study provide fundamental baseline data on seabed shape, complexity, and composition which will inform regional risk assessments and provide insights into biodiversity and geodiversity.

    • Geosciences, Vol. 9, Pages 140: A Numerical Investigation on Tidally Induced Sediment Transport and Morphological Changes with Changing Sea Level in South-East England

      The impact of tide-induced morphological changes and water level variations on the sediment transport in a tidally dominated system has been investigated using the numerical model Delft3D and South-East England as a test case. The goal of this manuscript is to explore the long-term changes in morphology due to sea level rise and the large-scale morphodynamic equilibrium of the South-East England. Our results suggest that the long term (century scale) tidally-induced morphological evolution of the seabed slows down in time and promotes a vanishing net transport across the large scale system. Century-scale morphologically updated simulations show that both morphological changes and net transport values tend to decrease in time as the system attains a dynamic equilibrium configuration. Results further suggest that the presence of a gradual increase in mean sea level accelerates the initial morphological evolution of the system whose morphological rate of change gradually attains, however, same plateau values as in the absence of sea level rise. Given the same base morphology, increasing water levels enhance residual currents and the net transport near the coastline; and vice-versa, decreasing sea levels minimize both residuals and net transport near the coastline. The areas that are more affected by, water level and morphological changes, are the ones where the net transport is the highest. This manuscript explores and allows extending the idea of morphodynamic equilibrium at a regional scale, larger than the one for which this concept has been generally explored i.e., estuarine scale.

    • Geosciences, Vol. 9, Pages 139: Subsampling of Regional-Scale Database for improving Multivariate Analysis Interpretation of Groundwater Chemical Evolution and Ion Sources

      Multivariate statistics are widely and routinely used in the field of hydrogeochemistry. Trace elements, for which numerous samples show concentrations below the detection limit (censored data from a truncated dataset), are removed from the dataset in the multivariate treatment. This study now proposes an approach that consists of avoiding the truncation of the dataset of some critical elements, such as those recognized as sensitive elements regarding human health (fluoride, iron, and manganese). The method aims to reduce the dataset to increase the statistical representativeness of critical elements. This method allows a robust statistical comparison between a regional comprehensive dataset and a subset of this regional database. The results from hierarchical Cluster analysis (HCA) and principal component analysis (PCA) were generated and compared with results from the whole dataset. The proposed approach allowed for improvement in the understanding of the chemical evolution pathways of groundwater. Samples from the subset belong to the same flow line from a statistical point of view, and other samples from the database can then be compared with the samples of the subset and discussed according to their stage of evolution. The results obtained after the introduction of fluoride in the multivariate treatment suggest that dissolved fluoride can be gained either from the interaction of groundwater with marine clays or from the interaction of groundwater with Precambrian bedrock aquifers. The results partly explain why the groundwater chemical background of the region is relatively high in fluoride contents, resulting in frequent excess in regards to drinking water standards.

    • Geosciences, Vol. 9, Pages 138: Bench-Scale Experiments on Effects of Pipe Flow and Entrapped Air in Soil Layer on Hillslope Landslides

      Soil pipes are commonly found in landslide scarps, and it has been suggested that build-up of pore water pressure due to clogged soil pipes influences landslide initiation. Several researchers have also suggested that entrapped air in the soil layer increases the pore water pressure. We carried out bench-scale model experiments to investigate the influence of soil pipes and entrapped air on the build-up of pore water pressure. We installed a water supply system consisting of an artificial rainfall simulator, and used a water supply tank to supply water to the model slope and artificial pipe. We used two types of artificial pipe: A straight pipe, and a confluence of three pipes. Furthermore, we placed a layer of silica sand on top of the model slope to investigate the effect of entrapped air in the soil layer on the build-up of pore water pressure. Silica sand is finer than the sand that we used for the bulk of the model slope. Our results indicate that, although artificial pipes decrease the pore water pressure when the amount of water supplied was smaller than the pipe drainage capacity, the pore water pressure increased when the water supply was too large for the artificial pipe to drain. In particular, the confluence of pipes increased the pore water pressure because the water supply exceeded the drainage capacity. The results also indicate that entrapped air increases the pore water pressure in the area with relatively low drainage capacity, too. Based on these results, we found that although soil pipes can drain a certain amount of water from a soil layer, they can also increase the pore water pressure, and destabilize slopes. Furthermore, entrapped air enhances the trend that the pore water pressure can increase in the area with relatively low drainage capacity, as pore water pressure increases when too much water is supplied, and the artificial pipe cannot drain all of it.

    • Multi-source data integration for soil mapping using deep learning

      Multi-source data integration for soil mapping using deep learning Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny SOIL, 5, 107-119, https://doi.org/10.5194/soil-5-107-2019, 2019 With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual digital soil mapping (DSM) using uncertain measurements of the soil property. The deep learning model, called the convolutional neural network (CNN), has the advantage that it uses as input a local representation of environmental covariates to leverage the spatial information contained in the vicinity of a location. Spatial non-linear relationships between measured soil properties and neighbouring covariate pixel values are found by optimizing an objective function, which can be weighted with respect to a measurement error of soil observations. In addition, a single model can be trained to predict a soil property at different soil depths. This method is tested in mapping top- and subsoil organic carbon using laboratory-analysed and spectroscopically inferred measurements. Results show that the CNN significantly increased prediction accuracy as indicated by the coefficient of determination and concordance correlation coefficient, when compared to a conventional DSM technique. Deeper soil layer prediction error decreased, while preserving the interrelation between soil property and depths. The tests conducted suggest that the CNN benefits from using local contextual information up to 260 to 360 m. We conclude that the CNN is a flexible, effective and promising model to predict soil properties at multiple depths while accounting for contextual covariate information and measurement error.

    • On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil

      On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil Tino Colombi, Florian Walder, Lucie Büchi, Marlies Sommer, Kexing Liu, Johan Six, Marcel G. A. van der Heijden, Raphaël Charles, and Thomas Keller SOIL, 5, 91-105, https://doi.org/10.5194/soil-5-91-2019, 2019 The role of soil aeration in carbon sequestration in arable soils has only been explored little, especially at the farm level. The current study, which was conducted on 30 fields that belong to individual farms, reveals a positive relationship between soil gas transport capability and soil organic carbon content. We therefore conclude that soil aeration needs to be accounted for when developing strategies for carbon sequestration in arable soil.

    • Arable soil formation and erosion: a hillslope-based cosmogenic-nuclide study in the United Kingdom

      Arable soil formation and erosion: a hillslope-based cosmogenic-nuclide study in the United Kingdom Daniel L. Evans, John N. Quinton, Andrew M. Tye, Ángel Rodés, Jessica A. C. Davies, and Simon M. Mudd SOIL Discuss., https//doi.org/10.5194/soil-2019-8,2019 Manuscript under review for SOIL (discussion: open, 0 comments) Policy to conserve thinning arable soils relies on a balance between the rates of soil erosion and soil formation. Our knowledge of the latter is meagre. Here, we present soil formation rates for an arable hillslope, the first of their kind globally, and a woodland hillslope, the first of their kind in Europe. Rates range between 23–64 mm kyr. On the arable site, erosion rates are two orders of magnitude greater and in a worst-case scenario, bedrock exposure could occur in 209 years.

    • Using deep learning for digital soil mapping

      Using deep learning for digital soil mapping José Padarian, Budiman Minasny, and Alex B. McBratney SOIL, 5, 79-89, https://doi.org/10.5194/soil-5-79-2019, 2019 Digital soil mapping has been widely used as a cost-effective method for generating soil maps. DSM models are usually calibrated using point observations and rarely incorporate contextual information of the landscape. Here, we use convolutional neural networks to incorporate spatial context. We used as input a 3-D stack of covariate images to simultaneously predict organic carbon content at multiple depths. In this study, our model reduced the error by 30 % compared with conventional techniques.

    • Catchment export of base cations: Improved mineral dissolution kinetics influence the role of water transit time

      Catchment export of base cations: Improved mineral dissolution kinetics influence the role of water transit time Martin Erlandsson Lampa, Harald U. Sverdrup, Kevin H. Bishop, Salim Belyazid, Ali Ameli, and Stephan J. Köhler SOIL Discuss., https//doi.org/10.5194/soil-2019-3,2019 Manuscript under review for SOIL (discussion: open, 2 comments) In this study, we demonstrate how new equations describing base cation release from mineral weathering can reproduce patterns in observations from stream and soil water. This is a major step towards modelling base cation cycling on a catchment scale, which would be valuable for defining the highest sustainable rates of forest harvesting and levels of acidifying deposition.