The data collection presented here is the data inventory of the VARved sediments DAtabase (VARDA) in version 1.3. VARDA is freely accessible and was created to assess outputs from climate models with high-resolution terrestrial palaeoclimatic proxies. All data were collected as raw data from freely available online sources, either from online data repositories (Pangaea, NOAA, and Neotoma) or data archives within the supplementary materials section of online publications. The current data collection consists of meta information and datasets from 95 lake archives. The data is stored in JSON and CSV format. All datasets are stored as individual files (JSON and CSV). Each dataset consists of samples for either i) chronologies; ii) radiocarbon data; iii) tephra layer; or iv) varve thickness data. Meta-information for each dataset is summarized in one csv and seven JSON files. Additional paleoclimate proxy data will be provided in forthcoming updates of VARDA.
The data collection of VARDA Version 1.3 is provided as an archive (.tar.gz) with the following files/folders. Overview lists with categories, cores, countries, datasets, lakes and publications included in VARDA. Each item in the lists is cross-referenced with the other files via its $ref property which includes the corresponding list index or the dataset's UUID (from the VARDA database). The data points themselves are provided in the "records" folder and named with each dataset's UUID respectively. For more information on the data structure please read the "index.html" file included in the archive and available on the DOI landing page.
VERSION HISTORY:
26 July 2020: release of Version 1.3:
1. Fix issues with chronologies in the export
2. Provide recalculated machine readable error estimates
3. Correct some metadata values (e.g. core labels)
5 March 2020: release of Version 1.1
1. Added fields:
"distributor" - Field containing name of data distributor
"url" - Field containing DOIs and URLs, which lead to the original data publications
2. Correction of publication DOIs in 9 cases
The version 1.0 is available in the "previous-versions" subfolder via the Data Download link. The index file is unchanged.
The main component of this data publication is a dataset of predicted daily nutrient concentrations for NO3-N and TP for 150 monitoring stations along 60 German rivers (main rivers). The aim of this dataset is to fill the data gap of daily nutrient concentrations for a better understanding of nutrient transport from the rivers to the seas. So far, nutrient concentrations are sampled on a fortnightly basis, which can be insufficient for nutrient retention models working on a daily basis. With this method and available datasets, river basin managers have the opportunity to look at nutrient concentrations or load patterns on a finer resolution to adapt their management to improve water quality.
The dataset was obtained by a random forest model (RF) based on measured NO3-N and TP concentrations between the years 2000 and 2019. The data was requested or where available downloaded from official websites of the Federal States or River Basins. Different variables for NO3-N and TP were finally considered in the models to produce the RF, like discharge, land use, day of the year.
The aim of this software is to assess the influence of water well production rates to the measured water level data dependent on the reservoir properties. Daily abstraction rates can be used for this production rate well test analysis. For the analysis, a modified deconvolution algorithm is implemented in the code. The algorithm derives the reservoir response function by solving a least square problem with the unique feature of imposing only implicit constraints on the solution space.
sandbox is an R-tool for probabilistic numerical modelling of sediment properties. A flexible framework for definition and application of time/depth- based rules for sets of parameters for single grains that can be used to create artificial sediment profiles. Such profiles can be used for virtual sample preparation and synthetic, for instance, luminescence measurements.