Getting started with potentiomap

potentiomap turns groundwater-level observations into mapped potentiometric surfaces and related review products. This article uses small synthetic data. It does not demonstrate a groundwater-flow model.

library(potentiomap)
data("synthetic_wells")

points <- ps_make_points(
  synthetic_wells,
  x = "x", y = "y", value = "gw_elevation", name_col = "well_id",
  crs = "EPSG:26916", head_unit = "m", output_unit = "m",
  vertical_datum = "synthetic example datum",
  surface_reference = "land_surface", metadata_mode = "strict"
)

result <- ps_interpolate(
  points, methods = "IDW", grid_res = 300,
  return = "result", support = TRUE, support_max_distance = 1000
)
result
#> <potentiomap_result>
#>   observations: 32 
#>   methods: IDW 
#>   status: IDW=success 
#>   prediction support: available
ps_diagnostics(result, "IDW")
#> $formula
#> [1] "Z ~ 1"
#> 
#> $observation_count
#> [1] 32
#> 
#> $idw_power
#> [1] 2
#> 
#> $idw_nmax
#> [1] 15
#> 
#> $return_status
#> [1] "success"
#> 
#> $requested_method
#> [1] "IDW"
#> 
#> $returned_method
#> [1] "IDW"
#> 
#> $finite_prediction_count
#> [1] 169
#> 
#> $nonfinite_prediction_count
#> [1] 0
result$support$summary
#>             support_class cells   percent
#> 1               supported    78 46.153846
#> 2   outside_training_hull    85 50.295858
#> 3 beyond_maximum_distance     0  0.000000
#> 4            outside_mask     0  0.000000
#> 5  prediction_unavailable     0  0.000000
#> 6    multiple_limitations     6  3.550296

surface <- result$surfaces$IDW
contours <- ps_contours(surface, interval = 1)

par(bg = "white")
terra::plot(
  surface, col = hcl.colors(64, "RdYlBu", rev = TRUE),
  main = "First potentiometric surface"
)
terra::plot(contours, add = TRUE, col = "#263845", lwd = 1)
terra::plot(points, add = TRUE, pch = 21, bg = "white", cex = 0.75)

IDW is deterministic and distance based. TPS produces a smooth penalized surface. Ordinary kriging assumes a constant unknown mean. Universal kriging uses a specified spatial trend. Method selection should consider hydrogeology, network geometry, sample density, model diagnostics, prediction support, validation design, and map purpose.

The raster grid controls both detail and resource use. A smaller cell size can greatly increase the number of predictions. For larger grids, use an on-disk terra raster when practical, point terraOptions(tempdir = ...) to storage with adequate free space, and review terra::tmpFiles() after interrupted work. Do not change global terra options inside reusable functions without restoring them. Remove only temporary files you have identified as safe to delete.

Export occurs only when an output directory is supplied. GeoPackage is the recommended vector format.

output_dir <- file.path(tempdir(), "potentiomap-getting-started")
exports <- ps_export_surfaces(
  result, output_dir, out_stub = "synthetic", points = points,
  vector_format = "gpkg"
)
knitr::kable(data.frame(
  method = exports$method,
  raster = basename(exports$raster)
))

A finite raster is not proof that every cell is well supported. Inspect the support classes and fitted-method diagnostics before using a mapped surface for interpretation or decision making.

method	raster
IDW	synthetic_IDW_surface.tif

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Getting started with potentiomap