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# The External Drift Kriging -- EDK program

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<div align="center">
<img src="https://git.ufz.de/chs/logos/-/raw/master/EDK.png" alt="EDK-LOGO" width="251px" style="width:251px;"/>
</div>

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This repository contains the external drift kriging (EDK) Fortran program developed at the Dept. Computational Hydrosystems at the Helmholtz Centre for Environmental Research - UFZ.

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The EDK comes with a LICENSE agreement, this includes also the GNU Lesser General Public License.
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**Please note**: The GitLab repository grants read access to the code.
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If you like to contribute to the code, please contact stephan.thober@ufz.de or sebastian.mueller@ufz.de.

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## Installation

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Installation instructions can be found in [INSTALL](doc/INSTALL.md) for Windows, MacOS, and GNU/Linux distributions.
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The simplest way to compile EDK is to use a [conda](https://docs.conda.io/en/latest/) environment (on Linux (including Windows/WSL) or MacOS)
provided by [Miniforge](https://github.com/conda-forge/miniforge):
```bash
conda create -y --prefix ./fortran_env
conda activate ./fortran_env
conda install -y git cmake make fortran-compiler netcdf-fortran liblapack
source scripts/compile
```
This will give an executable `edk`.

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## Usage

To run the EDK program, you need a set of station data files, a look-up-table for these stations and a DEM file for the external drift.
All configuration is done with a namelist file, which is called `edk.nml` by default. See the example file for all input specifications.

Then you can just execute `./edk` next to this file.

You can also explicitly specify the namelist file and/or change the working directory by passing options the the `edk` command like:
```bash
./edk -n edk.nml check/case_01
```

To see the help text, execute:
```bash
./edk --help
```
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## Cite as

Please refer to the EDK algorithm by citing Samaniego et al. (2011). EDK aplications in Samaniego et al. (2013) or Zink et al. (2017).
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Rainfall network design and EDK cross-validation in Zacharias, S. et al. (2011).
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- Samaniego, L., R. Kumar, and C. Jackisch (2011), "Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data", Hydrology research, 42(5), 338–355, doi:10.2166/nh.2011.156.
- Samaniego, L. R. Kumar, M. Zink (2013), "Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany", J Hydrometeor, 2013 vol. 14 (1) pp. 47-68. http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-12-075.1
- Zink, M., R. Kumar, M. Cuntz, and L. Samaniego (2017), "A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty", Hydrol. Earth Syst. Sci., 21(3), 1769–1790, doi:10.5194/hess-21-1769-2017.
- Zacharias, S., H. Bogena, L. Samaniego et al. (2011), "A Network of Terrestrial Environmental Observatories in Germany", Vadose Zone Journal, 10(3), 955, doi:10.2136/vzj2010.0139.