TITLE:
A groundwater well database for Brazil (GWDBrazil)

ABSTRACT:
Sufficient spatiotemporal in-situ groundwater-level measurements are essential for sustainable water management. Despite their importance, lack of harmonized, quality-controlled datasets has hindered large-scale groundwater studies in Brazil. In collaboration with the Geological Survey of Brazil, we present the Groundwater Wells Database for Brazil (GWDBrazil), which consolidates and standardizes information from over 351,000 wells, with records dating from 1900 to 2024, including about 450 wells with continuous daily monitoring from 2010 to 2024. Cross-verification steps were applied to ensure data accuracy. GWDBrazil is available in both tabular form and vector points, comprising information such as location, well depth, and well purpose. The dataset also provides data to support integrated surface and groundwater management, such the distance from each well to the nearest river and aquifer information. This dataset is intended to serve as a valuable resource for researchers, decision-makers, and stakeholders, providing essential information to support comprehensive water management strategies in Brazil.

FOLDER STRUCTURE:
The dataset is organized into four main folders:
1. data - This folder contains the processed products derived from the study A Groundwater Well Database for Brazil (GWDBrazil).
	1.1 csv - Tabular Data.
		1.1.1 SIAGAS_data.csv - Final SIAGAS dataset.
		
		1.1.2 SIAGAS_data_flagged.csv - Final SIAGAS dataset with flagged data.
		
		1.1.3 Additional_data.csv - Supplementary data for surface water and groundwater interaction studies.
		
		1.1.4 RIMAS_data_flagged - Final RIMAS dataset with error and outlier flags.
			1.1.4.1 Rimas_IdWell.csv - Overview of the number of data available in the final RIMAS dataset.

			1.1.4.2 Rimas_IdWell.csv - Final RIMAS dataset where each CSV represents a single well. Note: Some RIMAS wells may contain data prior to 2010 as they were used in previous SGB projects.
	
	1.2 netCDF - Includes data from continuous groundwater level monitoring wells (2010 - 2024) in netCDF format.
		1.2.1 rimas_groundwater_levels.nc - NetCDF equivalent of the RIMAS_data_flagged folder, excluding data with potential errors. The file is not in a regular grid format.

		1.2.2 rimas_groundwater_levels.csv - CSV file with all data from the RIMAS_data_flagged folder, excluding data with potential errors.

		1.2.3 rimas_groundwater_atts.csv - File with the locations (latitude and longitude) of the data in the RIMAS_data_flagged folder.
	
	1.3 shapefile - Shapefile Data.
		1.3.1 SIAGAS_data.shp - Shapefile equivalent of SIAGAS_data.csv
		
		1.3.2 SIAGAS_data_flagged.shp - Shapefile equivalent of SIAGAS_data_flagged.csv
		
		1.3.3 Additional_data.shp - Shapefile equivalent of Additional_data.csv

2. raw_data - This folder contains the original datasets extracted from Geological Survey of Brazil projects.
	2.1 RIMAS - Data from the Integrated Groundwater Monitoring Network Project (RIMAS – in Portuguese: Rede Integrada de Monitoramento das Águas Subterrâneas; SGB, 2024a)
		2.1.1 groundwater_level_monitoring - Groundwater data timeseries.  
			2.1.1.1 RimasWeb_Exportacao_Dados_Nivel_Dagua_IdWell.csv - Each CSV represents a unique well.
		
		2.1.2 hydrochemical_monitoring - Hydrogeochemical data.
			2.1.2.1 RimasWeb_Exportacao_Dados_Analise_Quimica_IdWell.csv - Each CSV represents a unique well.
	
	2.2 SIAGAS - Data from the Groundwater Information System (SIAGAS – in Portuguese: Sistema de Informações de Águas Subterrâneas; SGB, 2024b).
		2.2.1 PT_amostra-fisico-quimica_EN_water_quality_data_physicochemical_analysis.csv - Water quality data.
		
		2.2.2 PT_aquifero_EN_aquifer_data.csv - Aquifer-related data.
		
		2.2.3 PT_dados_construtivos_EN_drilling_data.csv - Well construction data.
		
		2.2.4 PT_dados_gerais_EN_general_data.csv - General well information.
		
		2.2.5 PT_dados_hidraulicos_EN_pumping_data.csv - Pumping test data.
		
		2.2.6 PT_litologia_EN_lithological_data.csv - Lithological well data.

3. supplementary_tables.
	3.1 Table_S1-Definitions_of_attributes_in_SIAGAS_dataset - Definition of attributes from the SIAGAS project by the Geological Survey of Brazil.
	
	3.2 Table_S2-Translation_of_terms_used_by_SGB(in Portuguese)_into_internationally_used_terms.xlsx - Translation of SIAGAS terms from Portuguese to internationally recognized terms.
	
	3.3 Table_S3-Overlaid_Aquifers_and_Aquifer_Confinement.xlsx - Summary of aquifer layers per record and their confinement status based on raw data from the SIAGAS project.
	
	3.4 Table_S4-No_well_records_step_quality_control.xlsx - Summary of the 9,655 records classified as non-wells that were removed during the quality control step.
	
	3.5 Table_S5-Duplicate_records_step_quality_control.xlsx - Summary of the 4,711 records classified as duplicates that were removed during the quality control step.
	
	3.6 Table_S6-Records_whitout_any_data_step_quality_control.xlsx - Summary of the 5,814 records removed during the quality control step due to the absence of any data indicating when they were drilled.
	
	3.7 Table_S8-RIMAS_wells_step_quality_control.xlsx - Overview of the data from 453 RIMAS wells.

4. codes - This folder contains the main codes used for this study.
Note: Some steps in this workflow were performed manually with support from members of the Brazilian Geological Survey. Nonetheless, the methodology is reproducible using the procedures detailed in the accompanying paper.
	4.1 merge_and_standardization.R - Merges and standardizes the data.

	4.2 check_aquifer_data.R - Verifies aquifer data in the database.

	4.3 check_lithological_and_water_quality_data.R - Verifies lithological and water quality data in the database.

	4.4 check_gw_data_RIMAS - Verifies water level data in the RIMAS dataset. 

	4.5 check_hydrochemical_data_RIMAS - Verifies hydrochemical data in the RIMAS dataset. 

	4.6 figures_and_analysis - Generates the main analyses and figures in the paper.

	4.7 csv_to_NetCDF.ipynb - Jupyter Notebook (Python) to convert csv RIMAS data to NetCDF format. The output NetCDF file is not in a regular grid.

	4.8 test_NetCDFfile.R - Tests the NetCDF files generated in this study.

USAGE NOTE: 
The GWDBrazil dataset has wide-ranging applications. Users are strongly encouraged to read the accompanying paper A Groundwater Well Database for Brazil (GWDBrazil) before using the data. This will help understand the criteria used for data refinement and its limitations.
Users should critically evaluate the level of detail and accuracy required for their specific applications. While extensive quality control has been applied in collaboration with the Geological Survey of Brazil, additional regional and local validation may be necessary for specific studies.

CONTACT: 
For any questions or recommendations, please contact:
Lead Author: J.G.S.M.U (gescilam@usp.br)
Corresponding Author: P.T.S.O (paulotarsoms@gmail.com)

CITATION: 
If you use this dataset, please cite it as follows:
Uchôa, J.G.S.M., Oliveira, P.T.S., Ballarin, A.S., Gastmans, A., Anache, J.A.A., Scanlon, B.R.S., Camanho, C.R.C., Filho, V.J.F. & Wendland, E.C. A groundwater well database for Brazil (GWDBrazil) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15098047

LICENSE:
This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. You are free to share and adapt the material as long as appropriate credit is given.

We recommend checking this README regularly for updates.

###### Overview of the variables in SIAGAS_data.csv ######
Type			Header in the study	Definition							NA values	Data Type 
General data		ID_well			Unique well ID							0.00%		Integer
			Latitude		Well latitude							0.00%		Float
			Longitude		Well longitude 							0.00%		Float
			ID_city_IBGE		IBGE City ID 							0.00%		Integer
			City			Brazilian city where the well is located			0.00%		String
			State			Brazilian state where the well is located			0.00%		String
			Types_of_wells		Type of well							3.53%		String
			Well_status		Well status 							38.07%		String
			Well_water_use		Primary use of the groundwater					41.56%		String
			Surface_elevation	Surface elevation of the well [m.a.s.l]				83.81%		Float
			Year_reported		Year in which it was entered into SIAGAS system			2.58%		String
			Last_updated_reported	Last updated reported into SIAGAS system			81.66%		String
Drilling data		Drilling_reported	Drilling date							23.44%		String
			Well_depth		Well depth [m]							17.58%		Float
Pumping data		Pumping_test_date	Pumping test date						55.78%		String
			Static_water_level	Static water level [m]						55.48%		Float
			Dynamic_water_level	Dynamic water level [m]						63.85%		Float
			Well_capacity		Well capacity [m3/h]						60.28%		Float
Aquifer data		Aquifer_broad		General aquifer, or aquifer systems, that the well overlays	68.56%		String
			Aquifer_confinement	Aquifer confinement 						74.52%		String
Lithological data	Lithological_data	Lithological records (Yes, NA)					63.58%*		String
Water quality data	Water_quality_data	Water quality data records (Yes, NA)				65.26%*		String
*For lithological data and water quality data, NA values were assigned if the data was not provided.
##########################################################

###### Overview of the variables in SIAGAS_data_flagged.csv ######
Same variables in the SIAGAS_data.csv with flagging system to indicate data quality: -1 for inconsistent data, and 0 for raw data (i.e., data where no inconsistencies were identified, or data quality could not be assessed) in 14 variables: ‘Latitude’; ‘Longitude’; ‘ID_city_IBGE’; ‘City’; ‘State’; ‘Surface_elevation’; ‘Year_reported’; ‘Last_updated_reported’; ‘Drilling_reported’; ‘Well_depth’; ‘Pumping_test_date’; ‘Static_water_level’; ‘Dynamic_water_level’ and ‘Well_capacity’
##################################################################

###### Overview of the variables in Rimas_IdWell.csv ######
Header in the study	Data Type
Month			String
Day			String
Hour			String
Level (m)		Float
Flag_error		Integer (0 or -1)
Flag_outlier		Integer (0 or -1)
The error flag was set to -1 if the data met any of the following criteria: (i) duplicate values, (ii) absolute daily water level changes exceeding 10 meters, (iii) constant head values persisting for more than 30 days, or (iv) physically implausible readings (e.g., values exceeding well depth or negative values). 
The outlier flag was set to -1 for data identified as outliers by the HydroSight toolbox double exponential smoothing time-series model.
##################################################################


###### Overview of the variables in Additional_data.csv ######
Category	Header in the study	Definition						Data Type 	Source
Climate		Mean_pr_an		Mean annual precipitation (mm)				Float		(Xavier et al., 2022)
		Mean_eto_an		Mean annual potential evapotranspiration (mm)		Float		(Xavier et al., 2022)
		Aridity_index		Aridity index (-) 					Float		(Xavier et al., 2022)
Topography	Sur_ele_ANADEM		Surface Elevation from ANADEM (m.a.s.l)			Float		(Laipelt et al., 2024)
		Sur_ele_MERIT		Surface Elevation from MERIT (m.a.s.l)			Float		(Yamazaki et al., 2017)
LULC		Main_landuse		Main land use around the well				String		(MapBiomas, 2023)
Aquifer		Aq_permeability 	Log aquifer permeability (m2)				Float		(Huscroft et al., 2018)
		Aq_porosity		Aquifer porosity [%]					Float		(Gleeson et al., 2014)
		Overlaying_aq		Aquifer outcrop zone overlaid by the well		String		(ANA, 2016)
Surface waters	Dis_stream		Distance to the closest stream 2nd order or higher (m)	Float		(Linke et al., 2019)
#############################################################

REFERENCES: 
ANA - National Water and Sanitation Agency. (2016). Sistemas Aquíferos. https://metadados.snirh.gov.br/geonetwork/srv/api/records/3ec60e4f-85ea-4ba7-a90c-734b57594f90 Last acess: 10/30/2024.

Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and mapping global permeability of the unconsolidated and Consolidated Earth: Global Hydrogeology Maps 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017gl075860 

Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. (2014). A glimpse beneath Earth’s surface: Global Hydrogeology maps (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891–3898. https://doi.org/10.1002/2014gl059856

Laipelt, L., Comini de Andrade, B., Collischonn, W., Amorim, A., Paiva, R. C., & Ruhoff, A. (2024). ANADEM: A Digital Terrain Model for South America. Remote Sensing. https://doi.org/10.3390/rs16132321

Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., & Thieme, M. (2019). Global Hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0300-6 

MapBiomas. (2023). Coleção 8 da Série Anual de Mapas de Cobertura e Uso da Terra do Brasil. Retrieved from https://brasil.mapbiomas.org/colecoes-mapbiomas/ Last acess: 08/30/2024.

SGB - Geological Survey of Brazil [Serviço Geológico do Brasil]. (2024a). Projeto Rede Integrada de Monitoramento das Águas Subterrâneas. Retrieved from https://rimasweb.sgb.gov.br/layout/apresentacao.php. Last acess: 07/30/2024.

SGB - Geological Survey of Brazil [Serviço Geológico do Brasil]. (2024b). Sistema de Informações de Águas Subterrâneas. Retrieved from https://siagasweb.sgb.gov.br/layout/apresentacao.php. Last acess: 07/30/2024.

Xavier, A. C., Scanlon, B. R., King, C. W. & Alves, A. I. (2022) New improved Brazilian daily weather gridded data (1961–2020). International Journal of Climatology. 42, 8390–8404. https://doi.org/10.1002/joc.7731

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., & Bates, P. D. (2017). A high‐accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017gl072874