Version of readme:        24-October-2019
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This dataset contains three directories:
- 1-data:         			contains the input data stored in csv.
- 2-scripts:             	contains the Python scripts.
- 3-pastas_model_objects: 	contains the Pastas model objects in .pas format (text files).

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directory 1-data
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The data directory contains subdirectories containing the various input datasets:

Subdirectory: 1a - SMAP
	Filename: 		SMAP_Twente_2015-2019.csv
	NoData value:  -9999
	Period: 		1 January 2015 -- 1 January 2019
	Description:    The data columns in the csv file represent the volumetric moisture content in [m^3 m^-3] 
					for each study area location.

	Contains the SMAP L3 Enhanced surface soil moisture data, obtained from:
	O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., & Bindlish, R. (2018). SMAP Enhanced l3 radiometer 
	global daily 9 km EASE-Grid soil moisture, version 2. NASA National Snow and Ice Data Center Distributed 
	Active Archive Center. doi: 10.5067/RFKIZ5QY5ABN

Subdirectory: 1b-precipitation
	Filename: 		prec_2016_2018.csv
	NoData value:  -9999
	Period: 		1 January 2016 -- 1 January 2019
	Description: 	The data columns in the csv file represent the daily precipitation sum in [mm] 
					for each study area location.

	Contains KNMI daily precpitation sum data, obtained from:
	KNMI. (2018, December 1 2018). Dataset: interpolated daily precipitation sum in the Netherlands. 
	Retrieved from: https://data.knmi.nl/datasets/rd1/5? 

Subdirectory: 1c-reference_ET
	Filename: 		knmi_Makkink_ET_2016_2018.csv
	NoData value:  -9999
	Period: 		1 January 2016 -- 1 January 2019
	Description: 	The data columns in the csv file represent the daily Makkink crop reference 	
					evapotranspiration amount in [mm] for each study area location.

	Contains KNMI daily interpolated Makkink crop reference evapotranspiration data, obtained from:
	KNMI. (2018, December 1 2018). Dataset: interpolated daily makkink evapotranspiration in the Netherlands.
	Retrieved from https://data.knmi.nl/datasets/ev24/2?
	
Subdirectory: 1d-soil_moisture
	The soil moisture data can be found at:
	 Velde, dr. ir. R van der (University of Twente) (2018): Validation of SMAP L2 passive-only soil 
	 moisture products using in situ measurements collected in Twente, The Netherlands. DANS. 
	 https://doi.org/10.17026/dans-x3c-5cvq 

Subdirectory: 1e-location
	Filename: 		Twente_stations_2016_RD.csv
	NoData value:   Not applicable
	Period: 		Not applicable
	Description:	The data columns in the csv file represent the station name [-], the horizontal (X) 
					coordinate [m], the vertical (Y) coordinate [m], and the station number [-].
	
	Contains the coordinates of the study area locations in Rijksdriehoekstels projection (EPSG: 28992):
	https://spatialreference.org/ref/epsg/amersfoort-rd-new/

Subdirectory: 1f-assessment
	Filename: 		rmse_full.csv
	NoData value:  	-9999
	Period: 		No applicable
	Description: 	The data columns in the csv file represent the RMSE values described in the manuscript 
					[m^3 m^-3].

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directory 2-scripts
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This directory contains four Python 3 scripts. These scripts have been used to setup and analyse the TFN
models.

Script: 2a-paper_smap_exponential_predict1year_3panels2016.py
	This script is the main script to setup and use the Pastas TFN models. The script uses the input data of 
	the 1-data directory and outputs the Pastas models found in the 3-pastas_model_objects directory.

Script: 2b-paper_smap_exponential_predict1year_sensitivity2016.py
	This script is an extension of the previous script. Various model input variables and parameters can be 
	varied to perform a sensitivity analysis.

Script: 2c-plot_rmse_figure.py
	This script contains the code to analyse the RMSE values found for each location.

Script: 2d-main_functions.py
	This script contains various supporting functions which are used in the other Python scripts.

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directory 3-pastas_model_objects
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This directory contains the Pastas model objects in .pas format.
These files contain the setup of the TFN models for all 20 locations.
The file has a plain text format. 

The open-source Python 3 library Pastas is needed to read the model objects.
More information on Pastas can be found at https://pastas.readthedocs.io/ .