Mapa Do Aquifero Guarani

Getting your hands on the Guarani Aquifer map isn't as simple as downloading a single shapefile

The project was a multinational effort between Brazil, Argentina, Paraguay, and Uruguay, coordinated mainly through CPRM in Brazil. The final geospatial data covers roughly 1.2 million square kilometers and spans across those four countries. When I first needed the aquifer boundaries for a hydrogeological assessment in southern Mato Grosso do Sul, I went straight to the CPRM website looking for a clean shapefile. What I found was a portal that scattered the data across multiple layers, each with different coordinate systems and varying levels of projection accuracy.

Where to find the mapa do aquifero guarani

The official source is the CPRM (Companhia de Pesquisa de Recursos Minerais) geoservice platform. They have a specific page for the Projeto Aquífero Guarani. You can also access copies through the SUDHES (Sistema de Hidrogeologia do Sul da América do Sul) portal, though that one tends to be slower and occasionally has outdated metadata. Argentina has its own version hosted by the SEGEMAR, which sometimes shows slightly different boundary interpretations along the western edge near Corrientes. If you need the most complete dataset, stick with CPRM's original release. The data comes in several formats. There's a shapefile with the main aquifer extent, a separate layer for the effective porosity zones, another for recharge areas, and a DEM-derived layer showing the confining bed topology. Each one uses a different reference ellipsoid. The Brazilian dataset uses SAD69, while the Argentine supplementary data often references WGS84. Mixing them without reprojecting will quietly corrupt your spatial joins. I learned this the hard way when a client flagged that my boundary overlay was drifting roughly 300 meters against known survey points near Foz do Iguaçu.

The data you actually get and what it means

The Guarani map isn't a single-layer product. It's a multilayer geodatabase. The primary layer shows the areal extent of the aquifer system, which is bounded by the Eastern Brazilian Craton to the east and the Parana Basin structural limits to the west and south. The maximum saturated thickness reaches about 1,800 meters in the central depocenter near the border of Paraná and Santa Catarina. The average transmissivity across the main production zones sits somewhere between 5,000 and 15,000 m²/day, though individual test wells in the Tupi Formation have recorded values over 30,000 m²/day. One thing that trips up a lot of people is the distinction between the aquifer map and the groundwater flow model. The map shows geometry and static properties. It does not show flow velocity, residence time, or sustainable yield zones. There was a period around 2018 when several engineering firms used the aquifer extent layer directly to calculate withdrawal permits, assuming the boundaries implied uniform recharge conditions. That assumption is wrong. Recharge rates vary from under 100 mm/year in the confined central zones to over 800 mm/year in the outcrop areas near the Cape Saint Roch region. Using a single average for permit calculations will give you numbers that look precise but are structurally unsound.

Working with the file formats

The CPRM dataset ships as a ZIP archive containing shapefiles, a GeoTIFF DEM, and a PDF report. The shapefiles are attribute-rich. Each polygon has fields for lithology codes, porosity ranges, and hydrogeological unit classification. The problem is that the attribute table encoding sometimes uses Windows-1252 instead of UTF-8, which breaks QGIS field displays and causes garbled text in Portuguese diacritics. Open the project in QGIS or ArcGIS Pro, then run a field calculation to recode the text fields if you see characters like "çõ" appearing where they shouldn't. The DEM layer is at roughly 90-meter resolution, derived from SRTM data fused with local bathymetric surveys in the submerged portions. For most regional planning work that's sufficient. If you're doing wellhead protection zone modeling at the municipal level, you'll want to merge it with higher-resolution LiDAR data from IBGE or the state environmental agencies. The 90-meter grid will smooth out the karst features in the Ribeira do Ivaí sector, which matters a lot if you're delineating vulnerability zones for supply wells.

A specific problem I ran into and how I fixed it

Last year I was working on a contamination risk assessment for a municipality in the Paraná state portion of the aquifer. The client provided a set of monitoring well coordinates and wanted me to intersect them with the aquifer's recharge zone layer. The intersection returned zero matches for about forty percent of the wells. At first I thought the well data was wrong. After checking the original survey reports, the coordinates were fine. The issue was that the recharge zone layer in the CPRM dataset uses a generalized polygon boundary that doesn't capture the localized fissure-controlled recharge pathways in the fractured rock outcrops. Those pathways exist but weren't digitized at the resolution the project produced. The workaround was straightforward but required an extra step. I pulled the geological map of Paraná state from the geological survey at a 1:100,000 scale, identified the fracture density contours in the relevant sector, and created a buffer zone along the mapped fracture corridors that extended roughly 5 kilometers from the outcrop edges. Running the intersection against that buffered layer brought the match rate up to ninety-two percent. It's not perfect, but it's closer to reality than relying solely on the published recharge polygon.

Common pitfalls to avoid

Don't assume the aquifer boundaries are hydrogeologically consistent across the entire extent. The northern sector near the Amazonian basement contact has fundamentally different hydraulic behavior than the southern sector where the parana sedimentary sequences dominate. The confining layers vary in continuity too. In some areas the Botucato formation acts as a tight confining unit. In others it's laterally discontinuous and fractured enough to allow vertical connectivity. If you're modeling contaminant transport, treating the entire system as either fully confined or fully unconfined will give you inaccurate breakthrough curves. Another issue is temporal resolution. The Guarani aquifer project produced its best data between 2004 and 2012. Since then, extraction rates have increased substantially, particularly in the São Paulo and Paraná metropolitan peripheries. The static maps don't reflect current drawdown trends. If you need to assess current storage changes, you should combine the map data with INPE satellite gravimetry results from the GRACE-FO mission, which show measurable decline in certain sub-basins over the past decade.

What to do if the official source is down

The CPRM portal occasionally goes offline for maintenance, usually during the fiscal year closing in December. If that happens, the data has been mirrored on the GeoBrasil platform and occasionally appears in the repositories of state water agencies like SABESP in São Paulo or DAEE in Paraná. The mirrors aren't always current, so check the metadata timestamps before using them for anything beyond preliminary work. A shapefile labeled 2015 with no revision notes is likely incomplete. If you need the data quickly and can work with less detail, the WWF Brazil and UNEP have published simplified versions for educational and advocacy purposes. These strip out the attribute complexity and keep only the outline polygons. Useful for presentations or initial scoping. Not useful for anything requiring hydrogeological precision.

Bottom line on using this data

The Guarani aquifer map is a solid foundation layer for regional hydrogeological work. It covers a massive area with reasonable resolution and credible attribute data. But it was never intended to be a substitute for site-specific investigation. The boundaries are approximations. The property distributions are interpolated in places where borehole data was sparse. And the recharge and vulnerability layers carry the same uncertainty as any model built from decades-old field measurements. Use it as a starting point, not a conclusion. Always validate against local well data and, when possible, run your own calibration against observed head measurements before making decisions that affect water allocation or contamination risk assessments.