Articles | Volume 8, issue 11
https://doi.org/10.5194/gmd-8-3681-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3681-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
Helmholtz-Centre for Environmental Research – UFZ Leipzig, Department Environmental Informatics, Permoserstr. 15, 04318 Leipzig, Germany
D. Naumov
Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig, Germany
S. Sattler
LBEG (State Authority for Mining, Energy and Geology), Hannover, Germany
formerly at: Thüringer Landesanstalt für Umwelt und Geologie, Jena, Germany
O. Kolditz
Helmholtz-Centre for Environmental Research – UFZ Leipzig, Department Environmental Informatics, Permoserstr. 15, 04318 Leipzig, Germany
Technische Universität Dresden, Applied Environmental Systems Analysis, Dresden, Germany
M. Walther
Helmholtz-Centre for Environmental Research – UFZ Leipzig, Department Environmental Informatics, Permoserstr. 15, 04318 Leipzig, Germany
Technische Universität Dresden, Contaminant Hydrology, Dresden, Germany
Related authors
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-144, https://doi.org/10.5194/gmd-2024-144, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study presents FINAM ("FINAM Is Not A Model"), a new coupling framework written in Python to dynamically link independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
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, https://doi.org/10.5194/gmd-11-1989-2018, https://doi.org/10.5194/gmd-11-1989-2018, 2018
Olaf Kolditz, Christopher McDermott, Jeoung Seok Yoon, Jörg Renner, Li Zhuang, Andrew Fraser-Harris, Michael Chandler, Samuel Graham, Ju Wang, and Mostafa Mollaali
Saf. Nucl. Waste Disposal, 3, 15–31, https://doi.org/10.5194/sand-3-15-2025, https://doi.org/10.5194/sand-3-15-2025, 2025
Short summary
Short summary
The DECOVALEX Task SAFENET is dedicated to advancing the understanding of fracture nucleation and evolution processes in crystalline rocks, with applications in nuclear waste management and geothermal reservoir engineering.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-144, https://doi.org/10.5194/gmd-2024-144, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study presents FINAM ("FINAM Is Not A Model"), a new coupling framework written in Python to dynamically link independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Thomas Kohl, Ingo Sass, Olaf Kolditz, Christoph Schüth, Wolfram Rühaak, Jürgen Schamp, Judith Bremer, Bastian Rudolph, Katharina Schätzler, and Eva Schill
Saf. Nucl. Waste Disposal, 2, 135–136, https://doi.org/10.5194/sand-2-135-2023, https://doi.org/10.5194/sand-2-135-2023, 2023
Short summary
Short summary
Crystalline rocks are being considered as potential host rocks in the ongoing search for a suitable site for a nuclear waste repository in Germany, where there is no existing experience in terms of excavating a repository in crystalline rocks. The planned underground laboratory GeoLaB addressing crystalline geothermal reservoirs offers unique opportunities for synergies with nuclear waste disposal research and development, especially in the exploration and building phases.
René Kahnt, Heinz Konietzky, Thomas Nagel, Olaf Kolditz, Andreas Jockel, Christian B. Silbermann, Friederike Tiedtke, Tobias Meisel, Florian Zill, Anton Carl, Aron D. Gabriel, and Marcel Schlegel
Saf. Nucl. Waste Disposal, 2, 117–118, https://doi.org/10.5194/sand-2-117-2023, https://doi.org/10.5194/sand-2-117-2023, 2023
Short summary
Short summary
In the AREHS project, the effect of the alternation of cold and warm periods over 1 million years on the hydrogeological system in the vicinity of a repository was simulated. This was done with thermal–hydraulic–mechanical (–chemical) simulations. The simulations were implemented for generic 3D models for all three host rock formations: clay rock, salt rock and crystalline rock. In addition to the results for the generic sites, a workflow was developed that can be applied to concrete sites.
Michael Kühn, Dirk Bosbach, Horst Geckeis, Vinzenz Brendler, and Olaf Kolditz
Saf. Nucl. Waste Disposal, 2, 195–195, https://doi.org/10.5194/sand-2-195-2023, https://doi.org/10.5194/sand-2-195-2023, 2023
Short summary
Short summary
The Repository Site Selection Act explicitly emphasises that targeting the disposal of high-level radioactive waste is a so-called learning process. We are of the opinion that the procedure and the available data should be combined with geoscientific knowledge to support the identification of siting regions. We propose this workshop and invite all experts who have dealt with the search for a repository site from a geoscientific perspective.
Chaofan Chen, Tao Yuan, Renchao Lu, Cornelius Fischer, Olaf Kolditz, and Haibing Shao
Adv. Geosci., 58, 77–85, https://doi.org/10.5194/adgeo-58-77-2022, https://doi.org/10.5194/adgeo-58-77-2022, 2022
Short summary
Short summary
The moving distance of the diffusion front is farther away the canister center, along the direction with the neighboring layer having lower diffusion coefficient.
When the bedding angle increases, the diffusion front moves farther in z+ direction, reflecting the increase in effective diffusivity and higher impact of parallel-to-bedding diffusion.
The neighboring layers can slightly reshape the diffusion front line of the radionuclide.
Dirk Bosbach, Horst Geckeis, Frank Heberling, Olaf Kolditz, Michael Kühn, Katharina Müller, Thorsten Stumpf, and the iCROSS team
Saf. Nucl. Waste Disposal, 1, 85–87, https://doi.org/10.5194/sand-1-85-2021, https://doi.org/10.5194/sand-1-85-2021, 2021
Short summary
Short summary
The present contribution provides an overview on the collaborative project iCROSS and reports selected results. The impact of considering complex coupled processes in repository subsystems for the assessment of the integrity of a given (generic) repository arrangement will be discussed. The interdisciplinary team combines experimental work in the lab, in the underground rock laboratory and environmental simulations in order to achieve process understanding across variable scales.
René Kahnt, Heinz Konietzky, Thomas Nagel, Olaf Kolditz, Andreas Jockel, Christian B. Silbermann, Friederike Tiedke, Tobias Meisel, Karsten Rink, Wenqing Wang, Florian Zill, Antje Carl, Aron D. Gabriel, Marcel Schlegel, and Torsten Abraham
Saf. Nucl. Waste Disposal, 1, 175–177, https://doi.org/10.5194/sand-1-175-2021, https://doi.org/10.5194/sand-1-175-2021, 2021
Short summary
Short summary
In the framework of the Site Selection Act – StandAG, the geoscientific and planning requirements and criteria for the site selection for a repository for high-active nuclear waste are specified. This includes, among others, the modelling of hydrogeological scenarios such as how future cold and warm periods and associated glaciation events can change the (petro-)physical properties as well as the natural hydrogeological properties of the overall system which is the focus of the AREHS project.
Gesa Ziefle, Tuanny Cajuhi, Sebastian Condamin, Stephan Costabel, Oliver Czaikowski, Antoine Fourriére, Larissa Friedenberg, Markus Furche, Nico Graebling, Bastian Graupner, Jürgen Hesser, David Jaeggi, Kyra Jantschik, Tilo Kneuker, Olaf Kolditz, Franz Königer, Herbert Kunz, Ben Laurich, Jobst Maßmann, Christian Ostertag-Henning, Dorothee Rebscher, Karsten Rink, Wolfram Rühaak, Senecio Schefer, Rainer Schuhmann, Marc Wengler, and Klaus Wieczorek
Saf. Nucl. Waste Disposal, 1, 79–81, https://doi.org/10.5194/sand-1-79-2021, https://doi.org/10.5194/sand-1-79-2021, 2021
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, https://doi.org/10.5194/hess-23-171-2019, 2019
Short summary
Short summary
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
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, https://doi.org/10.5194/gmd-11-1989-2018, https://doi.org/10.5194/gmd-11-1989-2018, 2018
W. He, C. Beyer, J. H. Fleckenstein, E. Jang, O. Kolditz, D. Naumov, and T. Kalbacher
Geosci. Model Dev., 8, 3333–3348, https://doi.org/10.5194/gmd-8-3333-2015, https://doi.org/10.5194/gmd-8-3333-2015, 2015
Short summary
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 informatics
A GPU parallelization of the neXtSIM-DG dynamical core (v0.3.1)
The Earth System Grid Federation (ESGF) Virtual Aggregation (CMIP6 v20240125)
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
DNS (v1.0): An open source ray-tracing tool for space geodetic techniques
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?
FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites
Checking the consistency of 3D geological models
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
A Time-Dependent Three-Dimensional Magnetopause Model Based on Quasi-elastodynamic Theory
RiverBedDynamics v1.0: A Landlab component for computing two-dimensional sediment transport and river bed evolution
Skillful neural network predictions of Saharan dust
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
OpenMindat v1.0.0 R package: A machine interface to Mindat open data to facilitate data-intensive geoscience discoveries
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
A close look at using national ground stations for the statistical modeling of NO2
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
Robert Jendersie, Christian Lessig, and Thomas Richter
Geosci. Model Dev., 18, 3017–3040, https://doi.org/10.5194/gmd-18-3017-2025, https://doi.org/10.5194/gmd-18-3017-2025, 2025
Short summary
Short summary
Accurate computer simulations are critical to understanding how climate change will affect local communities. An important part of such simulations is sea ice, which affects even distant areas in the long term. In our work, we explore how GPUs (graphics processing units), computer chips originally designed for gaming allow for faster simulation of sea ice with a new software, the neXtSIM-DG dynamical core. We discuss multiple options and demonstrate that using GPUs makes more accurate simulations feasible.
Ezequiel Cimadevilla, Bryan N. Lawrence, and Antonio S. Cofiño
Geosci. Model Dev., 18, 2461–2478, https://doi.org/10.5194/gmd-18-2461-2025, https://doi.org/10.5194/gmd-18-2461-2025, 2025
Short summary
Short summary
The Earth System Grid Federation (ESGF) stores an enormous amount of climate data spread across millions of files in data centres all over the world. Accessing and working with this scientific information is quite complex. This work presents ESGF Virtual Aggregation, an approach that combines data from different sources into a format that is ready for analysis straightaway.
Elena Tomasi, Gabriele Franch, and Marco Cristoforetti
Geosci. Model Dev., 18, 2051–2078, https://doi.org/10.5194/gmd-18-2051-2025, https://doi.org/10.5194/gmd-18-2051-2025, 2025
Short summary
Short summary
High-resolution weather data are crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using latent diffusion models (LDMs) to mimic this process, increasing weather data resolution over Italy from 25 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
Florian Zus, Kyriakos Balidakis, Ali Hasan Dogan, Rohith Thundathil, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-237, https://doi.org/10.5194/gmd-2024-237, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Atmospheric signal propagation effects are one of the largest error sources in the analysis of space geodetic techniques. Inaccuracies in the modelling map into errors in positioning, navigation and timing. We describe the open source ray tracing tool DNS and show the two outstanding features of this tool compared to previous model developments: it can handle both the troposphere and the ionosphere and it does so efficiently. This makes the tool perfectly suited for geoscientific applications.
Ryan J. O'Loughlin, Dan Li, Richard Neale, and Travis A. O'Brien
Geosci. Model Dev., 18, 787–802, https://doi.org/10.5194/gmd-18-787-2025, https://doi.org/10.5194/gmd-18-787-2025, 2025
Short summary
Short summary
We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
Nikola Besic, Nicolas Picard, Cédric Vega, Jean-Daniel Bontemps, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Martin Schwartz, Agnès Pellissier-Tanon, Gabriel Destouet, Frédéric Mortier, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev., 18, 337–359, https://doi.org/10.5194/gmd-18-337-2025, https://doi.org/10.5194/gmd-18-337-2025, 2025
Short summary
Short summary
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive analysis of these remote-sensing-based products.
Jie Lai, Yuan Zhang, Anzhi Wang, Wenli Fei, Yiwei Diao, Rongping Li, and Jiabin Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-169, https://doi.org/10.5194/gmd-2024-169, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
In this study, a new model called FLAML-LUE was created by combining the FLAML model with LUE-based models, the latter provides the key variables of vegetation growth for modeling. These models utilize the Fast Lightweight Automated Machine Learning (FLAML) framework, using variables of LUE models, to investigate the potential of estimating site-scale GPP.
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
Geosci. Model Dev., 18, 71–100, https://doi.org/10.5194/gmd-18-71-2025, https://doi.org/10.5194/gmd-18-71-2025, 2025
Short summary
Short summary
This is a proof-of-concept paper outlining a general approach to how 3D geological models would be checked to be geologically
reasonable. We do this with a consistency-checking tool that looks at geological feature pairs and their spatial, temporal, and internal polarity characteristics. The idea is to assess if geological relationships from a specific 3D geological model match what is allowed in the real world from the perspective of geological principles.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev., 17, 8455–8468, https://doi.org/10.5194/gmd-17-8455-2024, https://doi.org/10.5194/gmd-17-8455-2024, 2024
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural-network-based spatiotemporal regression models and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Yaxin Gu, Yi Wang, Fengsi Wei, Xueshang Feng, Andrey Samsonov, Xiaojian Song, Boyi Wang, Pingbing Zuo, Chaowei Jiang, Yalan Chen, Xiaojun Xu, and Zhilu Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3012, https://doi.org/10.5194/egusphere-2024-3012, 2024
Short summary
Short summary
This study presents the POS model, the first time-dependent three-dimensional magnetopause model. The POS model captures the real-time movement and shape of the magnetopause with superior accuracy. Its concise formulation and fast computational speed make it suitable for future onboard satellite deployment, enhancing space weather forecasting capabilities and offering new methodologies for magnetopause modeling on other planets.
Angel D. Monsalve, Samuel R. Anderson, Nicole M. Gasparini, and Elowyn M. Yager
EGUsphere, https://doi.org/10.5194/egusphere-2024-3390, https://doi.org/10.5194/egusphere-2024-3390, 2024
Short summary
Short summary
Rivers shape landscapes by moving sediments and changing their beds, but existing computer models oversimplify these processes by only considering erosion. We developed new software that simulates how rivers transport sediments and change over time through both erosion and deposition. This helps scientists and engineers better predict river behavior for water management, flood prevention, and ecosystem protection.
Trish Ewa Nowak, Andy T. Augousti, Benno I. Simmons, and Stefan Siegert
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, 2024
Short summary
Short summary
Described here DustNet model uses advanced neural networks to accurately predict the Saharan dust transport in the atmosphere. It offers fast and precise forecasts with predictions achieved in just 2.1 seconds on a standard computer. This innovative approach outperforms traditional models, which take hours to produce a forecast and use high energy super-computers. By making high-quality dust monitoring accessible and efficient, DustNet can improve weather, climate and air quality forecasts.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
Short summary
With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Xiang Que, Jiyin Zhang, Weilin Chen, Jolyon Ralph, and Xiaogang Ma
EGUsphere, https://doi.org/10.5194/egusphere-2024-1141, https://doi.org/10.5194/egusphere-2024-1141, 2024
Short summary
Short summary
This paper describes an R package as the machine interface to the open data of Mindat.org, one of the world's most widely used databases of mineral species and their distribution. In the past decades many geoscientists have been using the Mindat data, but an open data service has never been fully established. The machine interface described in this paper will be an efficient way to meet the overwhelming data needs.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
Short summary
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
Short summary
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Foeke Boersma and Meng Lu
EGUsphere, https://doi.org/10.5194/egusphere-2023-1260, https://doi.org/10.5194/egusphere-2023-1260, 2023
Short summary
Short summary
Air pollution harms health and society. Understanding and predicting it is crucial. Various models are developed to model air pollution. However, the consistency exhibited by a model in different areas is commonly neglected. Our study accounts for this and shows lower accuracy near busy roads, but higher in less populated areas. Considering location characteristics in air pollution predictions is important in comparing statistical models and understanding the health-society-space relationship.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
Short summary
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
Short summary
We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
Short summary
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (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.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
Short 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 development teams and research centres to make public the models they use to produce scientific results.
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, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
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.
Cited articles
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.
Short summary
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...
