@article{MuellerSchuelerZechetal.2022, author = {M{\"u}ller, Sebastian and Sch{\"u}ler, Lennart and Zech, Alraune and Heße, Falk}, title = {GSTools v1.3: a toolbox for geostatistical modelling in Python}, series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, volume = {15}, journal = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, number = {7}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1991-959X}, doi = {10.5194/gmd-15-3161-2022}, pages = {3161 -- 3182}, year = {2022}, abstract = {Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.}, language = {en} } @article{SchweppeThoberMuelleretal.2022, author = {Schweppe, Robert and Thober, Stephan and M{\"u}ller, Sebastian and Kelbling, Matthias and Kumar, Rohini and Attinger, Sabine and Samaniego, Luis}, title = {MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models}, series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, volume = {15}, journal = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1991-959X}, doi = {10.5194/gmd-15-859-2022}, pages = {859 -- 882}, year = {2022}, abstract = {Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 \% in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.}, language = {en} } @article{MuellerZechHesse2021, author = {M{\"u}ller, Sebastian and Zech, Alraune and Hesse, Falk}, title = {ogs5py: APython-APIfor theOpenGeoSys5 Scientific Modeling Package}, series = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, volume = {59}, journal = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {0017-467X}, doi = {10.1111/gwat.13017}, pages = {117 -- 122}, year = {2021}, abstract = {High-performance numerical codes are an indispensable tool for hydrogeologists when modeling subsurface flow and transport systems. But as they are written in compiled languages, like C/C++ or Fortran, established software packages are rarely user-friendly, limiting a wider adoption of such tools. OpenGeoSys (OGS), an open-source, finite-element solver for thermo-hydro-mechanical-chemical processes in porous and fractured media, is no exception. Graphical user interfaces may increase usability, but do so at a dramatic reduction of flexibility and are difficult or impossible to integrate into a larger workflow. Python offers an optimal trade-off between these goals by providing a highly flexible, yet comparatively user-friendly environment for software applications. Hence, we introduceogs5py, a Python-API for the OpenGeoSys 5 scientific modeling package. It provides a fully Python-based representation of an OGS project, a large array of convenience functions for users to interact with OGS and connects OGS to the scientific and computational environment of Python.}, language = {en} } @article{FabianKunzKonnegenetal.2012, author = {Fabian, Benjamin and Kunz, Steffen and Konnegen, Marcel and M{\"u}ller, Sebastian and G{\"u}nther, Oliver}, title = {Access control for semantic data federations in industrial product-lifecycle management}, series = {Computers in industry : an international, application oriented research journal}, volume = {63}, journal = {Computers in industry : an international, application oriented research journal}, number = {9}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0166-3615}, doi = {10.1016/j.compind.2012.08.015}, pages = {930 -- 940}, year = {2012}, abstract = {Information integration across company borders becomes increasingly important for the success of product lifecycle management in industry and complex supply chains. Semantic technologies are about to play a crucial role in this integrative process. However, cross-company data exchange requires mechanisms to enable fine-grained access control definition and enforcement, preventing unauthorized leakage of confidential data across company borders. Currently available semantic repositories are not sufficiently equipped to satisfy this important requirement. This paper presents an infrastructure for controlled sharing of semantic data between cooperating business partners. First, we motivate the need for access control in semantic data federations by a case study in the industrial service sector. Furthermore, we present an architecture for controlling access to semantic repositories that is based on our newly developed SemForce security service. Finally, we show the practical feasibility of this architecture by an implementation and several performance experiments.}, language = {en} } @article{FabianKunzMuelleretal.2013, author = {Fabian, Benjamin and Kunz, Steffen and M{\"u}ller, Sebastian and G{\"u}nther, Oliver}, title = {Secure federation of semantic information services}, series = {Decision support systems : DSS ; the international journal}, volume = {55}, journal = {Decision support systems : DSS ; the international journal}, number = {1}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-9236}, doi = {10.1016/j.dss.2012.05.049}, pages = {385 -- 398}, year = {2013}, abstract = {fundamental challenge for product-lifecycle management in collaborative value networks is to utilize the vast amount of product information available from heterogeneous sources in order to improve business analytics, decision support, and processes. This becomes even more challenging if those sources are distributed across multiple organizations. Federations of semantic information services, combining service-orientation and semantic technologies, provide a promising solution for this problem. However, without proper measures to establish information security, companies will be reluctant to join an information federation, which could lead to serious adoption barriers. Following the design science paradigm, this paper presents general objectives and a process for designing a secure federation of semantic information services. Furthermore, new as well as established security measures are discussed. Here, our contributions include an access-control enforcement system for semantic information services and a process for modeling access-control policies across organizations. In addition, a comprehensive security architecture is presented. An implementation of the architecture in the context of an application scenario and several performance experiments demonstrate the practical viability of our approach.}, language = {en} }