Geochemical models are used to seek answers about composition and evolution of groundwater, spill remediation, viability of geothermal resources and other important geoscientific applications. To understand these processes, it is useful to evaluate geochemical model response to different input parameter combinations. Running the model with varying input parameters creates a large amount of output data. It is a challenge to screen this data from the model to identify the significant relationships between input parameters and output variables. For addressing this problem we developed a Visual Analytics approach in an ongoing collaboration between Geoinformatics and Hydrogeology sections of GFZ German Research Centre for Geosciences. We implement our approach as an interactive data exploration tool called the GCex. GCex is a Visual Analytics approach and prototype that supports interactive exploration of geochemical models. It encodes many-to-many input/output relationships by the simple yet effective approach called Stacked Parameter Relation (SPR). GCex assists in the setup of simulations, model runs, data collection and result exploration, greatly enhancing the user experience in tasks such uncertainty and sensitivity analysis, inverse modeling and risk assessment. While in principle model-agnostic, the prototype currently supports and is tied to the popular geochemical code PHREEQC. Modification to support other models would not be complicated. GCex prototype was originally written by Janis Jatnieks at GFZ-Potsdam. It relies on Rphree (R-PHREEQC geochemical simulation model interface) written by Marco De Lucia at GFZ-Potsdam. A compatible version of Rphee is bundled with this installation.,https://gitext.gfz-potsdam.de/sec15pub/GCex/tags/1.0,
The file is an XML Graph file, which can be used to process Sentinel-1 satellite images in the Sentinel Application Platform (SNAP). Using this file enables batch processing of Sentinel-1 (IW, GRDH) images. The preprocessing is optimized for land use classification.
The following tools are executed:
Apply Orbit File
Terrain Flattening (using DGM1)
Speckle Filter (Gamma Map 3x3)
Range-Doppler Terrain Correction using DGM1
Conversion to dB
Conversion of datatype
Environmental seismoloy is a scientific field that studies the seismic signals, emitted by Earth surface processes. This R package eseis provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history. eseis contains a growing set of function to handle the complete workflow of environmental seismology, i.e., the scientific field that studies the seismic signals that are emitted by Earth surface processes. The package supports reading the two most common seismic data formats, general functions for preparational and analytical signal processing aswell as specified functions for handling signals generated by Earth surface processes. Finally, graphical plot functions are provided, too.
The software package contains 51 functions and two example data sets (eseis-supplementary_material.zip). It makes use of a series of dependency packages described in the DESCRIPTION file of the package.
This publication contains tools for statistical evaluation and exploration of data published by Radosavljevic et al. (2016). These data contain bulk geochemistry data (total organic carbon, nitrogen, stable carbon isotope) and granulometry of nearshore samples in the vicinity of Herschel Island, Yukon, Canadian Beaufort Sea. In addition, the functions of the script herein provide a means for summaries and comparison with terrestrial (Couture, 2010; Tanski et al., 2017; Obu et al., 2016) and marine (a subset of Naidu et al., 2000) data. The tools are contained in a script written for the R software environment for statistical computing and graphics. The script (sediments_geochemistry_plots_and_summaries.r) is richly documented and explains the functionality. Each data file also contains a description of the data in a comma separated file (csv).The functions of the script are:myinteract() - interactive modemysum() - provides numerical summaries for WBP and TB, a box plot and runs a Two-sided Mann-Whitney-Wilcoxon testmyloc() - provides numerical summaries and comparisons among the current study, marine, and terrestrial samples, a box plot and runs a Two-sided Mann-Whitney-Wilcoxon testmyseds() - provides numerical summaries and comparisons of grain size data among the current studymycums() - plots cumulative frequency curves of grain size distributions by transectThe package contains (included in the zip folder):sediments_geochemistry_plots_and_summaries.r - script filegeochemistry_data_including_other_studies.csv - contains data by Radosavljevic et al. (2016) and other studies in the regionVolFrequenciesCoordsTransects.csv - contains volumetric grain size frequenciesgranulometry_stats.csv - contains summary statistics of grain size dataTransectSampleIndex.csv - provides an index of transectsTransectMap.png - an overview map of sample transects
LPJmL5 is a dynamical global vegetation model that simulates climate and land-use change impacts on the terrestrial biosphere, the water, carbon and nitrogen cycle and on agricultural production. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included into the model. A comprehensive description of the model is given by von Bloh et al. (2017,http://doi.org/10.5194/gmd-2017-228). We here present the LPJmL5 model code described and used by the publications in GMD: Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5) (von Bloh et al., 2017) The model code of LPJmL5 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL5 is given in the INSTALL file. Additionally to the publication a html documentation and manual pages are provided. The LPJmL5 version is based on LPJmL3.5 that is not publicly available. The LPJmL4 version without nitrogen cycle but with an updated phenology scheme can be found on github (https://github.com/PIK-LPJmL/LPJmL).
Contributors:Ziegenhagen T., Schucht T., Rudolf M., Nagel H., Ludwikowski V., Rosenau M., Oncken O.
The presented datasets and scripts have been obtained for testing the performance of a trigger algorithm for use in combination with a ringshear tester ‘RST-01.pc’. Glass beads (fused quartz microbeads, 300-400 µm diameter) and thai rice are sheared at varying velocity, stiffness and normal load. The data is provided as preprocessed mat-files ('*.mat') to be opened with Matlab R2015a and later. Several scripts are provided to reproduce the figures found in (Rudolf et al., submitted). A detailed list of files together with the respective software needed to view and execute them is available in 'List_of_Files_Rudolf-et-al-2018.pdf' (also available in MS Excel Format). More information on the datasets and a small documentation of the scripts is given in 'Explanations_Rudolf-et-al-2018.pdf'. The complete data publication, including all descriptions, datasets, and evaluation scripts is available as 'Dataset_Rudolf-et-al-2018.zip'.
PyRQA is a tool to conduct recurrence quantification analysis (RQA) and to create recurrence plots in a massively parallel manner using the OpenCL framework. It is designed to process very long time series consisting of hundreds of thousands of data points efficiently.
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
The task of downloading comprehensive datasets of event-based seismic waveforms has been made easier through the development of standardised web services, but is still highly non-trivial, as the likelihood of temporary network failures or even worse subtle data errors naturally increase when the amount of requested data is in the order of millions of relatively short segments. This is even more challenging as the typical workflow is not restricted to a single massive download but consists of fetching all possible available input data (e.g., with several repeated download executions) for a processing stage producing any desired user-defined output. Here, we present stream2segment, a highly customisable Python 2+3 package helping the user through the whole workflow of downloading, inspecting and processing event-based seismic data by means of a relational database management system as archiving storage, which has clear performance and usability advantages. Stream2segment provides an integrated processing implementation able to produce any kind of user-defined output based on a configuration file and a user-defined Python function. Stream2segment can also produce diagnostic maps or user-defined plots which, unlike existing tools, do not require external software dependencies and are not static images but interactive browser-based applications ideally suited for data inspection or annotation tasks.
Contributors:Baugh K., Kyba C.C.M., Elvidge C.D., Schernthanner H., Anderson S.J., Coesfeld J.
This set of Python-code is used to analyse the variation of VIIRS DNB nighttime imagery. The code is inline documented and the readme provides information on what is needed to run the code, and what order to run it in. These routines were used to produce the data and plots in the paper: Variation of Individual Location Radiance in VIIRS Day/Night Band Monthly Composite Images (Coesfeld et al. 2018).
Monthly VIIRS DNB data can be downloaded from NOAA: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html,Copyright  [Jacqueline Coesfeld, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences]Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.,