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  • 1
    Language: English
    In: Computational Geosciences, 2013, Vol.17(1), pp.139-149
    Description: Carbon capture and storage is a recently discussed new technology, aimed at allowing an ongoing use of fossil fuels while preventing the produced CO 2 to be released to the atmosphere. CCS can be modeled with two components (water and CO 2 ) in two phases (liquid and CO 2 ). To simulate the process, a multiphase flow equation with equilibrium phase exchange is used. One of the big problems arising in two-phase two-component flow simulations is the disappearance of the nonwetting phase, which leads to a degeneration of the equations satisfied by the saturation. A standard choice of primary variables, which is the pressure of one phase and the saturation of the other phase, cannot be applied here. We developed a new approach using the pressure of the nonwetting phase and the capillary pressure as primary variables. One important advantage of this approach is the fact that we have only one set of primary variables that can be used for the biphasic as well as the monophasic case. We implemented this new choice of primary variables in the DUNE simulation framework and present numerical results for some test cases.
    Keywords: Two-phase flow ; Multicomponent flow ; Porous medium ; CO storage
    ISSN: 1420-0597
    E-ISSN: 1573-1499
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  • 2
    Language: English
    In: Advances in Water Resources, April 2017, Vol.102, pp.161-177
    Description: In the geostatistical inverse problem of subsurface hydrology, continuous hydraulic parameter fields, in most cases hydraulic conductivity, are estimated from measurements of dependent variables, such as hydraulic heads, under the assumption that the parameter fields are autocorrelated random space functions. Upon discretization, the continuous fields become large parameter vectors with elements. While cokriging-like inversion methods have been shown to be efficient for highly resolved parameter fields when the number of measurements is small, they require the calculation of the sensitivity of each measurement with respect to all parameters, which may become prohibitive with large sets of measured data such as those arising from transient groundwater flow. We present a Preconditioned Conjugate Gradient method for the geostatistical inverse problem, in which a single adjoint equation needs to be solved to obtain the gradient of the objective function. Using the autocovariance matrix of the parameters as preconditioning matrix, expensive multiplications with its inverse can be avoided, and the number of iterations is significantly reduced. We use a randomized spectral decomposition of the posterior covariance matrix of the parameters to perform a linearized uncertainty quantification of the parameter estimate. The feasibility of the method is tested by virtual examples of head observations in steady-state and transient groundwater flow. These synthetic tests demonstrate that transient data can reduce both parameter uncertainty and time spent conducting experiments, while the presented methods are able to handle the resulting large number of measurements.
    Keywords: Inverse Modeling ; Geostatistical Inversion ; Uncertainty Quantification ; Nonlinear Conjugate Gradients ; Preconditioning ; Engineering
    ISSN: 0309-1708
    E-ISSN: 1872-9657
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  • 3
    Language: English
    In: Computers and Geosciences, July, 2012, Vol.44, p.78(8)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cageo.2012.03.014 Byline: Ronnie L. Schwede (a), Adrian Ngo (b), Peter Bastian (b), Olaf Ippisch (b), Wei Li (a), Olaf A. Cirpka (a) Abstract: Hydraulic conductivity is a key parameter for the simulation of groundwater flow and transport. Typically, it is highly variable in space and difficult to determine by direct methods. The most common approach is to infer hydraulic-conductivity values from measurements of dependent quantities, such as hydraulic head and concentration. In geostatistical inversion, the parameters are estimated as continuous, spatially auto-correlated fields, the most likely values of which are obtained by conditioning on the indirect data. In order to identify small-scaled features, a fine three-dimensional discretization of the domain is needed. This leads to high computational demands in the solution of the forward problem and the calculation of sensitivities. In realistic three-dimensional settings with many measurements parallel computing becomes mandatory. In the present study, we investigate how parallelization of the quasi-linear geostatistical approach of inversion can be made most efficient. We suggest a two-level approach of parallelization, in which the computational domain is subdivided and the evaluation of sensitivities is also parallelized. We analyze how these two levels of parallelization should be balanced to optimally exploit a given number of computing nodes. Author Affiliation: (a) University of Tubingen, Center for Applied Geoscience, Holderlinstr. 12, 72074 Tubingen, Germany (b) University of Heidelberg, Interdisciplinary Center of Scientific Computing, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany Article History: Received 20 December 2011; Revised 24 February 2012; Accepted 19 March 2012
    Keywords: Hydrogeology -- Analysis ; Groundwater Flow -- Analysis ; Hydraulic Flow -- Analysis ; Groundwater -- Analysis ; Geostatistics -- Analysis
    ISSN: 0098-3004
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: Frontiers in Environmental Science, 01 April 2018, Vol.6
    Description: Soil-borne nitrous oxide (N2O) emissions have a high spatial and temporal variability which is commonly attributed to the occurrence of hotspots and hot moments for microbial activity in aggregated soil. Yet there is only limited information about the biophysical processes that regulate the...
    Keywords: Greenhouse Gas Emissions ; Denitrification Kinetics ; Microbial Hotspots ; Microsites ; Anoxic Aggregate Centers ; Agrobacterium Tumefaciens ; Environmental Sciences
    E-ISSN: 2296-665X
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  • 5
    Language: English
    In: Geomorphology, 2008, Vol.95(1), pp.61-73
    Description: Mud boils, a form of non-sorted circles, cover the ground surface in many periglacial landscapes. The vegetation-covered trough acts as an effective buffer to the downward movement of water and chemicals, while the bare center experiences larger fluxes of heat and mass. Since dissolved ions affect the electric conductivity of the soil solution, measurements of the bulk soil electric conductivity offer potential for estimating solute concentration. Since 1998, bulk soil electric conductivity has been measured automatically and hourly using 32 time domain reflectometry probes over an approximately 1 m diameter mud boil close to Ny Ålesund, Spitsbergen. Soil water electric conductivity was calculated from bulk soil electric conductivity using volumetric soil water content and a calibration parameter. The seasonal and spatial behaviour of water, temperature and solute concentration within two profiles of this mud boil were analyzed. Concentrations of estimated soil water electric conductivity were highest during the summer period when the active layer was thawed. Thermodynamic equilibrium modelling of the soil solution during freezing suggests that precipitation of dissolved species leads to the observed decrease in electric conductivity. There is a pronounced vertical solute concentration gradient in both profiles, while there is little evidence for horizontal solute concentration gradients beneath the mudboil.
    Keywords: Permafrost ; Freezing ; Geophysical Methods ; Electric Conductivity ; Solute Dynamics ; Patterned Ground ; Geography ; Geology
    ISSN: 0169-555X
    E-ISSN: 1872-695X
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  • 6
    Language: English
    In: Earth System Science Data, March 5, 2018, Vol.10(1), p.355
    Description: Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate global warming. This positive feedback functions via changing land-atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva site at Ny-#xC3;#x85;lesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20#xC2;#xA0;years. Additional data include a high-resolution digital elevation model (DEM) that can be used together with the snow physical information for snowpack modeling and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves as a baseline for future studies. The mean permafrost temperature is minus;2.8 #xB0;C, with a zero-amplitude depth at 5.5 m (2009--2017). Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in earth system models.The presented data are available in the Supplement for this paper (time series) and through the PANGAEA and Zenodo data portals: time series (https://doi.org/10.1594/PANGAEA.880120, https://zenodo.org/record/1139714) and HRSC-AX data products (https://doi.org/10.1594/PANGAEA.884730, https://zenodo.org/record/1145373).
    Keywords: Permafrost – Natural History
    ISSN: 1866-3516
    Source: Cengage Learning, Inc.
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