Articles | Volume 8, issue 11
Model description paper 12 Nov 2015
Model description paper | 12 Nov 2015
GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models
T. Fischer et al.
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007,
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007,
W. He, C. Beyer, J. H. Fleckenstein, E. Jang, O. Kolditz, D. Naumov, and T. Kalbacher
Geosci. Model Dev., 8, 3333–3348,Short summary
This technical paper presents a new tool to simulate reactive transport processes in subsurface systems and which couples the open-source software packages OpenGeoSys and IPhreeqc. A flexible parallelization scheme was developed and implemented to enable an optimized allocation of computer resources. The performance tests of the coupling interface and parallelization scheme illustrate the promising efficiency of this generally valid approach to simulate reactive transport problems.
Related subject area
Earth and Space Science InformaticsClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weatherA spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and timeA new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolutionUsing SHAP to interpret XGBoost predictions of grassland degradation in Xilingol, ChinaComparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast modelCurrent status on the need for improved accessibility to climate change modelsVISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigationTopological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasetsGlobal hydro-climatic biomes identified via multitask learningA run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applicationsAn improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mappingHigh-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programmingA high-fidelity multiresolution digital elevation model for Earth systemsCPMIP: measurements of real computational performance of Earth system models in CMIP6Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modelingCommunity Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observationsAsynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)An open and extensible framework for spatially explicit land use change modelling: the lulcc R packagePlant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change InitiativeNon-singular spherical harmonic expressions of geomagnetic vector and gradient tensor fields in the local north-oriented reference frameAn approach to enhance pnetCDF performance in environmental modeling applicationsA strategy for GIS-based 3-D slope stability modelling over large areasAn approach to computing direction relations between separated object groupsImproving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimationCoupling technologies for Earth System ModellingQuality assessment concept of the World Data Center for Climate and its application to CMIP5 dataA web service based tool to plan atmospheric research flightsAutomated continuous verification for numerical simulation
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124,Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164,Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581,Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886,Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
XGBoost can provide alternative insights that conventional land-use models are unable to generate. SHAP can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957,Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274,Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Juan Antonio Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all the development teams and research centres to make public the models that they use to produce scientific results.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480,Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents. The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628,Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153,Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895,Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539,Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427,Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646,Short summary
We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545,Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253,Short summary
This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34,Short summary
Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991,Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110,Short summary
In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892,Short summary
The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229,Short summary
The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328,Short summary
Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990,
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046,
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982,Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599,
V. Yadav and A. M. Michalak
Geosci. Model Dev., 6, 583–590,
S. Valcke, V. Balaji, A. Craig, C. DeLuca, R. Dunlap, R. W. Ford, R. Jacob, J. Larson, R. O'Kuinghttons, G. D. Riley, and M. Vertenstein
Geosci. Model Dev., 5, 1589–1596,
M. Stockhause, H. Höck, F. Toussaint, and M. Lautenschlager
Geosci. Model Dev., 5, 1023–1032,
M. Rautenhaus, G. Bauer, and A. Dörnbrack
Geosci. Model Dev., 5, 55–71,
P. E. Farrell, M. D. Piggott, G. J. Gorman, D. A. Ham, C. R. Wilson, and T. M. Bond
Geosci. Model Dev., 4, 435–449,
Ayachit, U.: The ParaView Guide Community Edition, United States of America: Kitware Inc., available at: http://www.paraview.org/paraview-guide/, last access: 10 August 2015.
BFN: Bundesamt fuer Naturschutz, Kartendienst Flussauen in Deutschland, available at: http://www.bfn.de/, last access: 5 February 2015.
Bilke, L., Fischer, T., Helbig, C., Krawczyk, C., Nagel, T., Naumov, D., Paulick, S., Rink, K., Sachse, A., Schelenz, S., Walther, M., Watanabe, N., Zehner, B., Ziesch, J., and Kolditz, O.: TESSIN VISLab-laboratory for scientific visualization, Environ. Earth Sci., 72, 3881–3899, https://doi.org/10.1007/s12665-014-3785-5, 2014.
Bourke, P.: GOCAD developer kit: ASCII file data format, available at: http://paulbourke.net/dataformats/gocad/gocad.pdf (last access: 12 October 2015), 2008.
de Hoyos, A., Viennot, P., Ledoux, E., Matray, J. M., Rocher, M., and Certes, C.: Influence of thermohaline effects on groundwater modelling – application to the Paris sedimentary Basin, J. Hydrol., 464–465, 12–26, https://doi.org/10.1016/j.jhydrol.2012.06.014, 2012.
De Lucia, M., Kempka, T., and Kühn, M.: A coupling alternative to reactive transport simulations for long-term prediction of chemical reactions in heterogeneous CO2 storage systems, Geosci. Model Dev., 8, 279–294, https://doi.org/10.5194/gmd-8-279-2015, 2015.
Fletcher, C.: Computational Techniques for Fluid Dynamics 1, Computational Techniques for Fluid Dynamics, Springer, Berlin Heidelberg, Germany, 1991.
Gallagher, M. and Doherty, J.: Parameter estimation and uncertainty analysis for a watershed model, Environ. Modell. Softw., 22, 1000–1020, https://doi.org/10.1016/j.envsoft.2006.06.007, 2007.
Geuzaine, C. and Remacle, J.-F.: Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities, Int. J. Numer. Meth. Eng., 79, 1309–1331, https://doi.org/10.1002/nme.2579, 2009.
Gichamo, T. Z., Popescu, I., Jonoski, A., and Solomatine, D.: River cross-section extraction from the ASTER global DEM for flood modeling, Environ. Modell. Softw., 31, 37–46, https://doi.org/10.1016/j.envsoft.2011.12.003, 2012.
Goode, D. J.: Direct simulation of groundwater age, Water Resour. Res., 32, 289–296, https://doi.org/10.1029/95WR03401, 1996.
Gräbe, A., Rödiger, T., Rink, K., Fischer, T., Sun, F., Wang, W., Siebert, C., and Kolditz, O.: Numerical analysis of the groundwater regime in the western Dead Sea escarpment, Israel + West Bank, Environ. Earth Sci., 69, 571–585, https://doi.org/10.1007/s12665-012-1795-8, 2013.
Graf, T. and Degener, L.: Grid convergence of variable-density flow simulations in discretely-fractured porous media, Adv. Water Resour., 34, 760–769, https://doi.org/10.1016/j.advwatres.2011.04.002, 2011.
Hardebol, N. J. and Bertottia, G.: DigiFract: A software and data model implementation for flexible acquisition and processing of fracture data from outcrops. Comput. Geosci., 54, 326–336, https://doi.org/\hrefhttp://dx.doi.org/10.1016/j.cageo.2012.10.021, 2013.
Helbig, C., Bauer, H.-S., Rink, K., Wulfmeyer, V., Frank, M., and Kolditz, O.: Concept and workflow for 3D visualization of atmospheric data in a virtual reality environment for analytical approaches, Environ. Earth Sci., 72, 3767–3780, https://doi.org/10.1007/s12665-014-3136-6, 2014.
Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database, available at: http://srtm.csi.cgiar.org (last access: 7 May 2015), 2008.
Johannsen, K.: Numerical aspects of density driven flow in porous media, in: Proceedings of the CMWR XVI, vol. 1, Kopenhagen, Denmark, 19–22 June, 2006, 1–8, https://doi.org/10.4122/1.1000000245, 2006.
Johannsen, K., Oswald, S., Held, R., and Kinzelbach, W.: Numerical simulation of three-dimensional saltwater-freshwater fingering instabilities observed in a porous medium, Adv. Water Resour., 29, 1690–1704, https://doi.org/10.1016/j.advwatres.2005.12.008, 2006.
Knupp, P. M.: Remarks on mesh quality, in: 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, USA, 8–11 January 2007, 2007.
Kolditz, O., Bauer, S., Bilke, L., Böttcher, N., Delfs, J.-O. O., Fischer, T., Görke, U. J., Kalbacher, T., Kosakowski, G., McDermott, C. I., Park, C. H., Radu, F., Rink, K., Shao, H. B., Sun, F., Sun, Y. Y., Singh, A. K., Taron, J., Walther, M., Wang, W., Watanabe, N., Wu, Y., Xie, M., Xu, W., and Zehner, B.: OpenGeoSys: an open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media, Environ. Earth Sci., 67, 589–599, https://doi.org/10.1007/s12665-012-1546-x, 2012a.
Kolditz, O., Görke, U.-J., Shao, H., and Wang, W.: Thermo–Hydro–Mechanical–Chemical Processes in Porous Media: Benchmarks and Examples, Lecture Notes in Computational Science and Engineering, Vol. 1, Springer Berlin Heidelberg, Germnay, 2012b.
Kolditz, O., Görke, U.-J., Shao, H., Wang, W., and Bauer, S.: Thermo–Hydro–Mechanical–Chemical Processes in Fractured Porous Media: Modelling and Benchmarking, Vol. 2, Springer Berlin Heidelberg, Germany, 2015.
Kunkel, C., Attinger, S., and Gaupp, R.: 3D-small-scale facies models of Buntsandstein formations as foundation for fluid pathway reconstructions in the Thuringian Syncline, in: Sedimentary Basins Jena – Research Modelling Exploration, Friedrich-Schiller-Universität Jena, Germany, 76, 2013.
Laniak, G. F., Olchin, G., Goodall, J., Voinov, A., Hill, M., Glynn, P., Whelan, G., Geller, G., Quinn, N., Blind, M., Peckham, S., Reaney, S., Gaber, N., Kennedy, R., and Hughes, A.: Integrated environmental modeling: a vision and roadmap for the future, Environ. Modell. Softw., 39, 3–23, https://doi.org/10.1016/j.envsoft.2012.09.006, 2013.
Li, H., Brunner, P., Kinzelbach, W., Li, W., and Dong, X.: Calibration of a groundwater model using pattern information from remote sensing data, J. Hydrol., 377, 120–130, https://doi.org/10.1016/j.jhydrol.2009.08.012, 2009.
Luo, J., Monninkhoff, B., Schätzl, P., Becker, J. K., Gmünder, C., and Jordan, P.: Elaboration of numerical models for the simulation of groundwater flow in Northern Switzerland, tech. rep., in: FEFLOW User Conference 2012, Berlin, Germany, 3–7 September 2012, 381–389, 2012.
Maier, U., Becht, A., Kostic, B., Bürger, C., Bayer, P., Teutsch, G., and Dietrich, P.: Characterization of quaternary gravel aquifers and their implementation in hydrogeological models, in: GQ2004 International Conference of Groundwater Quality: Bringing Groundwater Quality Research to the Watershed Scale, Waterloo, Canada, 19–22 July 2004, IAHS Publ. 297, 159–168, 2004.
Matter, J. M., Waber, H. N., Loew, S., and Matter, A.: Recharge areas and geochemical evolution of groundwater in an alluvial aquifer system in the Sultanate of Oman, Hydrogeol. J., 14, 203–224, https://doi.org/10.1007/s10040-004-0425-2, 2006.
Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A., and Sulis, M.: Surface-subsurface model intercomparison: a first set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 50, 1–52, https://doi.org/10.1002/2013WR013725, 2013.
McKenna, S. A., Walker, D. D., and Arnold, B.: Modeling dispersion in three-dimensional heterogeneous fractured media at Yucca Mountain, J. Contam. Hydrol., 62–63, 577–594, https://doi.org/10.1016/S0169-7722(02)00189-4, 2003.
Nagel, T., Shao, H., Singh, A. K., Watanabe, N., Roßkopf, C., Linder, M., Wörner, A., and Kolditz, O.: Non-equilibrium thermochemical heat storage in porous media: Part 1 – Conceptual model, Energy, 60, 254–270, https://doi.org/10.1016/j.energy.2013.06.025, 2013.
Nguyen, T. and de Kok, J.: Systematic testing of an integrated systems model for coastal zone management using sensitivity and uncertainty analyses, Environ. Modell. Softw., 22, 1572–1587, https://doi.org/10.1016/j.envsoft.2006.08.008, 2007.
Ni, X. D. and Chen, K.: Study on the conversion of GOCAD models to FLAC3D models, Appl. Mech. Mater., 501–504, 2527–2531, 2014.
Park, C.-H., Shinn, Y., Park, Y.-C., Huh, D.-G., and Lee, S.: PET2OGS: algorithms to link the static model of Petrel with the dynamic model of OpenGeoSys, Comput. Geosci., 62, 95–102, 2014.
Pozdniakov, S. P., Bakshevskaya, V. A., Krohicheva, I. V., Danilov, V. V., and Zubkov, A. A.: The influence of conceptual model of sedimentary formation hydraulic heterogeneity on contaminant transport simulation, Moscow University Geology Bulletin, 67, 43–51, https://doi.org/10.3103/S0145875212010097, 2012.
Qu, D., Røe, P., and Tveranger, J.: A method for generating volumetric fault zone grids for pillar gridded reservoir models, Comput. Geosci., 81, 28–37, https://doi.org/10.1016/j.cageo.2015.04.009, 2015.
Ragettli, S., Pellicciotti, F., Immerzeel, W. W., Miles, E. S., Petersen, L., Heynen, M., Shea, J. M., Stumm, D., Joshi, S., and Shrestha, A.: Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model, Adv. Water Resour., 78, 94–111, https://doi.org/10.1016/j.advwatres.2015.01.013 2015.
Refsgaard, J. C., Christensen, S., Sonnenborg, T. O., Seifert, D., Hojberg, A. L., and Troldborg, L.: Review of strategies for handling geological uncertainty in groundwater flow and transport modeling, Adv. Water Resour., 36, 36–50, https://doi.org/10.1016/j.advwatres.2011.04.006, 2012.
Rink, K., Fischer, T., Selle, B., and Kolditz, O.: A data exploration framework for validation and setup of hydrological models, Environ. Earth Sci., 69, 469–477, https://doi.org/10.1007/s12665-012-2030-3, 2013.
Rink, K., Bilke, L., and Kolditz, O.: Visualisation strategies for environmental modelling data, Environ. Earth Sci., 72, 3857–3868, https://doi.org/10.1007/s12665-013-2970-2, 2014.
Ritzema, H., Froebrich, J., Raju, R., Sreenivas, C., and Kselik, R.: Using participatory modelling to compensate for data scarcity in environmental planning: a case study from India, Environ. Modell. Softw., 25, 1450–1458, https://doi.org/10.1016/j.envsoft.2010.03.010, 2010.
Rödiger, T.: Charakterisierung und Modellierung des Buntsandsteinfließsystems im Osten des Thüringer Beckens, PhD thesis, Faculty of Chemistry and Earth Sciences, Friedrich Schiller University Jena, Germany, 2005.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Schmelzbach, C., Tronicke, J., and Dietrich, P.: Three-dimensional hydrostratigraphic models from ground-penetrating radar and direct-push data, J. Hydrol., 398, 235–245, https://doi.org/10.1016/j.jhydrol.2010.12.023, 2011.
Schroeder, W., Martin, K., and Lorensen, B.: The Visualization Toolkit: An Object-oriented Approach to 3D Graphics, Kitware, Upper Saddle River, NJ, Prentice Hall PTR, 2006.
Seidel, H. G. (Ed.): Geologie von Thüringen, Schweizerbart'sche Verlagsbuchhandlung, Stuttgart, Germany, 2003.
Sharpe, D. R., Hinton, M. J., Russell, H. J., and Desbarats, A. J.: The need for basin analysis in regional hydrogeological studies: Oak Ridges Moraine, southern Ontario, Geosci. Can., 29, 3–20, 2002.
Shen, C., Niu, J., and Fang, K.: Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model, Environ. Modell. Softw., 59, 146–161, https://doi.org/10.1016/j.envsoft.2014.05.0062014.
Si, H.: TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator, ACM Trans. Math. Softw., 41, 11:1–11:36, https://doi.org/10.1145/2629697, 2015.
Sun, F., Shao, H., Kalbacher, T., Wang, W., Yang, Z., Huang, Z., and Kolditz, O.: Groundwater drawdown at Nankou site of Beijing Plain: model development and calibration, Environ. Earth Sci., 64, 1323–1333, https://doi.org/10.1007/s12665-011-0957-4, 2011.
Sutanudjaja, E. H., van Beek, L. P. H., de Jong, S. M., van Geer, F. C., and Bierkens, M. F. P.: Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin, Hydrol. Earth Syst. Sci., 15, 2913–2935, https://doi.org/10.5194/hess-15-2913-2011, 2011.
Thorleifson, H., Berg, R. C., and Russell, H. J.: Geological mapping goes 3-D in response to societal needs, Geol. Soc. Am. Bull., 20, 27–29, https://doi.org/10.1130/GSATG86GW.1, 2010.
Tian, Y., Zheng, Y., Wu, B., Wu, X., Liu, J., and Zheng, C.: Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture, Environ. Modell. Softw., 63, 170–184, https://doi.org/10.1016/j.envsoft.2014.10.011, 2015.
TLUG: Abschlussbericht des Zentralprojektes Koordination und Datenmanagement innerhalb des PROSIN-Projektes INFLUINS, Tech. rep., Thüringer Landesanstalt für Umwelt und Geologie, in preparation, 2015.
Van Dam, R. L.: Landform characterization using geophysics – recent advances, applications, and emerging tools, Geomorphology, 137, 57–73, https://doi.org/10.1016/j.geomorph.2010.09.005, 2012.
Walther, M., Böttcher, N., and Liedl, R.: A 3D interpolation algorithm for layered tilted geological formations using an adapted inverse distance weighting approach, in: ModelCare2011, Models – Repositories of Knowledge, IAHS Publ., Leipzig, Geramny, 119–126, 355 (2012) ISBN 978-1-907161-34-6, 374, 2012a.
Walther, M., Delfs, J.-O., Grundmann, J., Kolditz, O., and Liedl, R.: Saltwater intrusion modeling: verification and application to an agricultural coastal arid region in Oman, J. Comput. Appl. Math., 236, 4798–4809, https://doi.org/10.1016/j.cam.2012.02.008, 2012b.
Walther, M., Solpuker, U., Böttcher, N., Kolditz, O., Liedl, R., and Schwartz, F. W.: Description and verification of a novel flow and transport model for silicate-gel emplacement, J. Contam. Hydrol., 157, 1–10, https://doi.org/10.1016/j.jconhyd.2013.10.007, 2013.
Walther, M., Bilke, L., Delfs, J.-O., Graf, T., Grundmann, J., Kolditz, O., and Liedl, R.: Assessing the saltwater remediation potential of a three-dimensional, heterogeneous, coastal aquifer system, Environ. Earth Sci., 72, 3827–3837, https://doi.org/10.1007/s12665-014-3253-2, 2014.
Wang, W., Fischer, T., Zehner, B., Böttcher, N., Görke, U.-J., and Kolditz, O.: A parallel finite element method for two-phase flow processes in porous media: OpenGeoSys with PETSc, Environ. Earth Sci., 73, 2269–2285, https://doi.org/10.1007/s12665-014-3576-z, 2014.
Wojda, P. and Brouyère, S.: An object-oriented hydrogeological data model for groundwater projects, Environ. Modell. Softw., 43, 109–123, https://doi.org/10.1016/j.envsoft.2013.01.015, 2013.
Wu, Q., Xu, H., and Zou, X.: An effective method for 3D geological modeling with multi-source data integration, Comput. Geosci., 31, 35–43, https://doi.org/10.1016/j.cageo.2004.09.005, 2005.
Wycisk, P., Hubert, T., Gossel, W., and Neumann, C.: High-resolution 3D spatial modelling of complex geological structures for an environmental risk assessment of abundant mining and industrial megasites, Comput. Geosci., 35, 165–182, https://doi.org/10.1016/j.cageo.2007.09.001, 2009.
Zanchi, A., Francesca, S., Stefano, Z., Simone, S., and Graziano, G.: 3D reconstruction of complex geological bodies: Examples from the Alps, Comput. Geosci., 35, 49–69, https://doi.org/10.1016/j.cageo.2007.09.003, 2009.
Zech, A.: Impact of Aquifer Heterogeneity on Subsurface Flow and Salt Transport at Different Scales, PhD thesis, Faculty of Chemistry and Earth Sciences, Friedrich Schiller University Jena, Germany, 2013.
Zehner, B.: Constructing geometric models of the subsurface for finite element simulation, Proceedings IAMG 2011 conference, 5–9 September 2011, Salzburg, Austria, 695–708, https://doi.org/10.5242/iamg.2011.0069, 2011.
Zehner, B., Börner, J. H., Görz, I., and Spitzer, K.: Workflows for generating tetrahedral meshes for finite element simulations on complex geological structures, Comput. Geosci., 79, 105–117, https://doi.org/10.1016/j.cageo.2015.02.009, 2015.
Zienkiewicz, O. C., Taylor, R. L., and Taylor, R. L.: The Finite Element Method: the Basis, Butterworth-Heinemann, Burlington, UK, 2000.
We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
We present a workflow to convert geological models into the open-source VTU format for usage in...