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    • Geosciences, Vol. 8, Pages 491: The Application of Freely-Available Satellite Imagery for Informing and Complementing Archaeological Fieldwork in the “Black Desert” of North-Eastern Jordan

      Recent developments in the availability of very high-resolution satellite imagery through platforms like GoogleEarth (Google, Santa Clara County, CA, USA) and Bing Maps (Microsoft, Redmond, WA, USA) have greatly opened up the possibilities of their use by researchers. This paper focusses on the exclusive use of free remote sensing data by the Western Harra Survey (WHS), an archaeological project investigating the arid “Black Desert” of north-eastern Jordan, a largely impenetrable landscape densely strewn with basalt blocks. The systematic analysis of such data by conducting a holistic satellite survey prior to the commencement of fieldwork allowed for the precise planning of ground surveys, with advanced knowledge of which sites were vehicle-accessible and how to efficiently visit a stratified sample of different site types. By subsequently correlating the obtained ground data with this analysis, it was possible to create a typological seriation of the site forms known as “wheels”, determine that at least two-thirds of sites are within 500 m of valleys or mudflats (highlighting these features’ roles as access routes and resource clusters) and identify numerous anthropogenic paths cleared through the basalt for site access and long-distance travel. These results offer new insights into this underrepresented region and allow for supra-regional comparisons with better investigated areas by a method that is rapid and cost-effective.

    • Geosciences, Vol. 8, Pages 490: Time-Space Characterization of Wellbore-Cement Alteration by CO2-Rich Brine

      The risk of CO2 leakage from damaged wellbore is identified as a critical issue for the feasibility and environmental acceptance of CO2 underground storage. For instance, Portland cement can be altered if flow of CO2-rich water occurs in hydraulic discontinuities such as cement-tubing or cement-caprock interfaces. In this case, the raw cement matrix is altered by diffusion of the solutes. This fact leads to the formation of distinctive alteration fronts indicating the dissolution of portlandite, the formation of a carbonate-rich layer and the decalcification of the calcium silicate hydrate, controlled by the interplay between the reaction kinetics, the diffusion-controlled renewing of the reactants and products, and the changes in the diffusion properties caused by the changes in porosity induced by the dissolution and precipitation mechanisms. In principle, these mass transfers can be easily simulated using diffusion-reaction numerical models. However, the large uncertainties of the parameters characterizing the reaction rates (mainly the kinetic and thermodynamic coefficients and the evolving reactive surface area) and of the porosity-dependent diffusion properties prevent making reliable predictions required for risk assessment. In this paper, we present the results of a set of experiments consisting in the alteration of a holed disk of class-G cement in contact with a CO2-rich brine at reservoir conditions (P = 12 MPa and T = 60 °C) for various durations. This new experimental protocol allows producing time-resolved data for both the spatially distributed mass transfers inside the cement body and the total mass transfers inferred from the boundary conditions mass balance. The experimental results are used to study the effect of the fluid salinity and the pCO2 on the overall reaction efficiency. Experiments at high salinity triggers more portlandite dissolution, thinner carbonate layers, and larger alteration areas than those at low salinity. These features are accompanied with different spatial distribution of the alteration layers resulting from a complex interplay between salinity-controlled dissolution and precipitation mechanisms. Conversely, the effect of the pCO2 is more intuitive: Increasing pCO2 results in increasing the overall alteration rate without modifying the relative distribution of the reaction fronts.

    • Geosciences, Vol. 8, Pages 489: Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

      The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

    • Geosciences, Vol. 8, Pages 488: Alongshore Variability in the Response of a Mixed Sand and Gravel Beach to Bimodal Wave Direction

      Characterising spatial and temporal variations in coastal behaviour is essential for the management of beach systems. Recent studies have shown that beach response is more complex in coasts subjected to bimodal wave directions. Despite being pervasive at higher latitudes, relatively little is known about the spatial variability in the response of mixed sand and gravel beaches. This work presents evidence that the response of mixed sand and gravel beaches to bimodal wave directions can be highly variable (both in magnitude and direction of change) even within short shoreline stretches. The analyses focused on beach topography data collected between 2009 and 2018 along five cross-shore transects within a 2-km-long shoreline in Suffolk (East England) and offshore wave data recorded at the West Gabbard Smart buoy. The dominant offshore wave direction oscillates between the southwest and the northeast from year to year, and the bimodal beach sediment has modes at 0.35 mm and 16 mm. Analyses were undertaken considering two timeframes: Biannual surveys from January 2009 to February 2018, and more intensive surveying (from seasonal to pre- and post-storm) from July 2016 to March 2018. Results highlighted large differences in beach response even between transects 350 m apart and no clear seasonal pattern of change. Instead, response seemed to depend on a complex interaction between wave power, dominant wave direction, and local settings. Although correlations were identified between indicators of beach change and wave conditions, these varied across transects. Divergence of longshore transport may occur locally, likely influencing the high alongshore variability.

    • Geosciences, Vol. 8, Pages 487: A Weighted Overlay Method for Liquefaction-Related Urban Damage Detection: A Case Study of the 6 September 2018 Hokkaido Eastern Iburi Earthquake, Japan

      We performed interferometric synthetic aperture radar (InSAR) analyses to observe ground displacements and assess damage after the M 6.6 Hokkaido Eastern Iburi earthquake in northern Japan on 6 September 2018. A multitemporal SAR coherence map is extracted from 3-m resolution ascending (track 116) and descending (track 18) ALOS-2 Stripmap datasets to cover the entire affected area. To distinguish damaged buildings associated with liquefaction, three influential parameters from the space-based InSAR results, ground-based LiquickMap (from seismic intensities in Japanese networks) and topographic slope of the study area are considered together in a weighted overlay (WO) analysis, according to prior knowledge of the study area. The WO analysis results in liquefaction potential values that agree with our field survey results. To investigate further, we conducted microtremor measurements at 14 points in Hobetsu, in which the predominant frequency showed a negative correlation with the WO values, especially when drastic coherence decay occurred.

    • Distribution of phosphorus fractions of different plant availability in German forest soils and their relationship to common soil properties and foliar P concentrations

      Distribution of phosphorus fractions of different plant availability in German forest soils and their relationship to common soil properties and foliar P concentrations Jörg Niederberger, Martin Kohler, and Jürgen Bauhus SOIL Discuss., https//doi.org/10.5194/soil-2018-40,2018 Manuscript under review for SOIL (discussion: open, 0 comments) At German forest sites, many trees showed a deficiency in P nutrition. Half of soil P is contained in moderately-labile fractions, whereas stable and labile fractions contribute to ca. one quarter of total P. Soil properties such as pH, SOC and soil texture may be used to predict certain P pools in large forest soil inventories. Models using soil properties and soil P pools of different plant availability are not yet adequate to explain the P nutrition status in tree foliage.

    • Assessment and quantification of marginal lands for biomass production in Europe using soil-quality indicators

      Assessment and quantification of marginal lands for biomass production in Europe using soil-quality indicators Werner Gerwin, Frank Repmann, Spyridon Galatsidas, Despoina Vlachaki, Nikos Gounaris, Wibke Baumgarten, Christiane Volkmann, Dimitrios Keramitzis, Fotis Kiourtsis, and Dirk Freese SOIL, 4, 267-290, https://doi.org/10.5194/soil-4-267-2018, 2018 The need for biomass for energetic or material use is increasing parallel to the need to extend the production of food for a growing world population. This results in conflicts between both land use strategies. Use of marginal lands could solve this conflict, however, the understanding of marginal lands and the knowledge of their potentials are still not fully developed. We present an approach to assess land marginality based on soil quality and an estimation of land potentials all over Europe.

    • 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 Discuss., https//doi.org/10.5194/soil-2018-39,2018 Manuscript under review for SOIL (discussion: open, 0 comments) 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 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 covariate pixel values and measured soil properties 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 analyzed and spectroscopically inferred measurements. Results show that CNNs 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 using different window size of input covariates matrix to predict organic carbon suggest that CNN benefits from using local contextual information up to 260 to 360 metres. We conclude that CNN is a flexible, effective and promising model to predict soil properties at multiple depths while accounting for contextual covariates information and measurement error.

    • Spatial assessments of soil organic carbon for stakeholder decision-making – a case study from Kenya

      Spatial assessments of soil organic carbon for stakeholder decision-making – a case study from Kenya Tor-Gunnar Vågen, Leigh Ann Winowiecki, Constance Neely, Sabrina Chesterman, and Mieke Bourne SOIL, 4, 259-266, https://doi.org/10.5194/soil-4-259-2018, 2018 Land degradation impacts the health and livelihoods of about 1.5 billion people worldwide. The state of the environment and food security are strongly interlinked in tropical landscapes. This paper demonstrates the integration of soil organic carbon (SOC) and land health maps with socioeconomic datasets into an online, open-access platform called the Resilience Diagnostic and Decision Support Tool for Turkana County in Kenya.

    • Effect of deforestation and subsequent land use management on soil carbon stocks in the South American Chaco

      Effect of deforestation and subsequent land use management on soil carbon stocks in the South American Chaco Natalia Andrea Osinaga, Carina Rosa Álvarez, and Miguel Angel Taboada SOIL, 4, 251-257, https://doi.org/10.5194/soil-4-251-2018, 2018 The sub-humid Argentine Chaco, originally covered by forest, has been subjected to clearing since the end of the 1970s and replacement of the forest by no-till farming. The organic carbon stock content up to 1 m depth varied as follows: forest > pasture > continuous cropping, with no impact of the number of years under cropping. The incorporation of pastures of warm-season grasses was able to mitigate the decrease of C stocks caused by cropping and so could be considered sustainable management.