Western Water Datahub collection field guide

The Western Water Datahub publishes many water-relevant datasets through an OGC API - Environmental Data Retrieval and OGC API Features interface. The Swagger/OpenAPI page is the authoritative endpoint reference. This page is the companion field guide: it is meant to help a technically comfortable water SME decide which collection to open, which query shape to try first, and how to turn the response into a table, plot, map, or CSV.

The examples below were checked against the live WWDH collection metadata on June 4, 2026. The Datahub changes over time, so treat the code in the first section as the durable part: re-run discovery before starting a new analysis.

1. The mental model

An EDR service has a few layers:

The service landing page tells you what top-level resources exist.
/collections lists datasets, called collections.
/collections/{id} describes one collection, including its parameters, extent, links, and supported EDR query types.
Query endpoints retrieve either station/location metadata, feature geometries, or sampled environmental values.

In practice, WWDH collections fall into three broad families.

Family	Typical collections	Best first query	What comes back
Station or site time series	`rise-edr`, `snotel-edr`, `awdb-forecasts-edr`, `usace-edr`, `resviz-edr`, `teacup-edr`, `resops-edr`	`edr_locations()` to find sites, then `edr_cube()` or `edr_location()` for values	GeoJSON for sites; CoverageJSON for time series
Gridded or point-sampled coverages	`usgs-prism`	`edr_cube()` for a small bbox, or `edr_position()` for one lon/lat	CoverageJSON with grid cells or a point series
Feature/catalog layers	`snotel-huc06-means`, drought monitor layers, many NOAA forecast layers, DOI regions	`edr_queryables()` and `edr_items()`	GeoJSON features and attributes

The most important distinction is this:

parameter_names are data variables you can ask for, such as reservoir storage, SWE, stage, precipitation, or maximum temperature. In edr4r, discover them with edr_parameters() and pass them to query helpers as parameter_name =.
queryables are filterable feature properties, such as id, name, valid_time, or basin_index. Discover them with edr_queryables(). They are most useful for /items collections and feature-style filtering.

2. Ten-minute discovery checklist

Start every WWDH session by listing collections and their advertised query types.

library(edr4r)

wwdh <- edr_client("https://api.wwdh.internetofwater.app")

collections <- edr_collections(wwdh)
collections[, c("id", "title", "data_queries")]
#> # A tibble: 35 x 3
#>    id                 title                                  data_queries
#>    <chr>              <chr>                                  <list>
#>  1 rise-edr           USBR Reclamation Information Sharing... <chr [4]>
#>  2 snotel-edr         USDA Snowpack Telemetry Network ...     <chr [4]>
#>  3 awdb-forecasts-edr USDA Air and Water Database Forecasts   <chr [4]>
#>  4 snotel-huc06-means USDA Snotel Snow Water Equivalent ...   <NULL>
#>  5 usace-edr          USACE Access2Water API                  <chr [4]>
#>  6 noaa-qpf-day-1     NOAA Weather Prediction Center ...      <NULL>
#>  7 noaa-qpf-day-2     NOAA Weather Prediction Center ...      <NULL>
#>  8 noaa-qpf-day-3     NOAA Weather Prediction Center ...      <NULL>
#>  9 noaa-qpf-day-4-5   NOAA Weather Prediction Center ...      <NULL>
#> 10 noaa-qpf-day-6-7   NOAA Weather Prediction Center ...      <NULL>
#> # i 25 more rows

The data_queries column is your route map. If it contains locations, the collection has a site index. If it contains cube, you can usually pull many sites or grid cells inside a bbox in one request. If it contains area, you can query a polygon. If it is empty, do not assume the collection is useless; it is often an OGC API Features layer, so use edr_queryables() and edr_items().

Then inspect one collection before retrieving data:

edr_collection(wwdh, "snotel-edr")$title
#> [1] "USDA Snowpack Telemetry Network (SNOTEL)"

names(edr_collection(wwdh, "snotel-edr")$data_queries)
#> [1] "locations" "cube" "area" "items"

params <- edr_parameters(wwdh, "snotel-edr")
params[params$id %in% c("WTEQ", "SNWD", "PREC"),
       c("id", "name", "unit_symbol")]
#> # A tibble: 3 x 3
#>   id    name                                unit_symbol
#>   <chr> <chr>                               <chr>
#> 1 PREC  Precipitation Accumulation (in)     in
#> 2 SNWD  Snow Depth (in)                     in
#> 3 WTEQ  Snow Water Equivalent               in

The same metadata is browsable without R:

https://api.wwdh.internetofwater.app/collections?f=html
https://api.wwdh.internetofwater.app/collections/snotel-edr?f=html
https://api.wwdh.internetofwater.app/collections/snotel-edr/queryables?f=html

3. Choosing a query type

Use the smallest query that answers the question.

Question	EDR helper	URL shape	Notes
What collections exist?	`edr_collections()`	`/collections`	First step for every session
What variables does this collection serve?	`edr_parameters()`	`/collections/{id}`	Use returned `id` values as `parameter_name`
What sites are in my study area?	`edr_locations()`	`/collections/{id}/locations`	Returns GeoJSON, usually promoted to `sf`
What features or polygons are in this layer?	`edr_items()`	`/collections/{id}/items`	Best for non-EDR feature collections
What is the value at one lon/lat?	`edr_position()`	`/collections/{id}/position`	Good for gridded coverages such as PRISM
What values fall in this bbox?	`edr_cube()`	`/collections/{id}/cube`	Best first bulk query for stations or grids
What values fall in this polygon?	`edr_area()`	`/collections/{id}/area`	Useful for watershed or management boundaries
What values are near this point?	`edr_radius()`	`/collections/{id}/radius`	Supported by the client, but not common on WWDH
What values follow a path?	`edr_trajectory()` / `edr_corridor()`	`/trajectory`, `/corridor`	More common in weather/ocean EDR than current WWDH use

In R, arguments use readable names such as parameter_name. In the raw URL, EDR uses kebab-case query parameters such as parameter-name. edr4r does that translation for you.

Always remember bbox order:

bbox <- c(min_lon, min_lat, max_lon, max_lat)

4. Station and reservoir time series

Station-style collections have two parts: a location index and sampled values. Start with a small bbox and one parameter.

co_front_range <- c(-107, 37, -105, 40)

snotel_sites <- edr_locations(
  wwdh, "snotel-edr",
  bbox  = co_front_range,
  limit = 5
)

nrow(snotel_sites)
#> [1] 51

snotel_sites[, c("stationTriplet", "name", "stateCode", "networkCode")]
#> Simple feature collection with 51 features and 4 fields
#> # A tibble: 51 x 5
#>   stationTriplet name              stateCode networkCode geometry
#>   <chr>          <chr>             <chr>     <chr>       <POINT [deg]>
#> 1 303:CO:SNTL    Apishapa          CO        SNTL        ...
#> 2 1041:CO:SNTL   Beaver Ck Village CO        SNTL        ...
#> 3 335:CO:SNTL    Berthoud Summit   CO        SNTL        ...
#> # i 48 more rows

Some providers honor limit on /locations, and some return all matching sites inside the spatial filter. That is normal provider variation. Keep the bbox small while exploring.

For more than one station, prefer edr_cube() when the collection advertises it. It is one server request across a bbox, rather than one request per station.

swe <- edr_cube(
  wwdh, "snotel-edr",
  bbox           = c(-106.2, 39.6, -105.8, 40.0),
  datetime       = "2024-02-01/2024-04-30",
  parameter_name = "WTEQ"
)

swe_tbl <- covjson_to_tibble(swe)
dim(swe_tbl)
#> [1] 540   9

head(swe_tbl[, c("coverage_id", "parameter", "datetime", "x", "y", "value")])
#> # A tibble: 6 x 6
#>   coverage_id parameter datetime                x     y value
#>   <chr>       <chr>     <dttm>              <dbl> <dbl> <dbl>
#> 1 1           WTEQ      2024-02-01 00:00:00 -106.  39.9  11.6
#> 2 1           WTEQ      2024-02-02 00:00:00 -106.  39.9  11.7
#> 3 1           WTEQ      2024-02-03 00:00:00 -106.  39.9  12.3
#> 4 1           WTEQ      2024-02-04 00:00:00 -106.  39.9  12.3
#> 5 1           WTEQ      2024-02-05 00:00:00 -106.  39.9  12.5
#> 6 1           WTEQ      2024-02-06 00:00:00 -106.  39.9  12.5

The long table returned by covjson_to_tibble() has the same core columns across station, grid, and profile data:

Column	Meaning
`coverage_id`	The server’s coverage identifier for a station, feature, grid, or sampled geometry
`parameter`	The requested parameter id, such as `WTEQ`, `raw`, `Stage`, or `ppt`
`parameter_label`	Human-readable label when the server provides one
`unit`	Unit label or symbol when available
`datetime`	Time coordinate, parsed to UTC `POSIXct` when possible
`x`, `y`, `z`	Longitude, latitude, and optional vertical coordinate
`value`	The observation, forecast, summary statistic, or modeled value

Reservoir-oriented collections follow the same pattern. A few useful parameter examples:

Collection	Parameter id	Meaning	Unit
`rise-edr`	`3`	Daily lake/reservoir storage	acre-ft
`rise-edr`	`1830`	Lake/reservoir release - total	cfs
`usace-edr`	`Stage`	Stage	ft
`usace-edr`	`Outflow`	Outflow	ft3/s
`resviz-edr`	`raw`	Daily lake/reservoir storage	acre-ft
`resviz-edr`	`p10`, `avg`, `p90`	Historical storage quantiles	acre-ft
`resops-edr`	`p10`, `avg`, `p90`	USACE 30-year storage summaries	million cubic meters

For fast exploratory products, edr_explore() chooses a bulk query when possible and returns either a leaflet map, a ggplot, or the tidy data.

edr_explore(
  wwdh, "usace-edr",
  bbox           = c(-98, 31, -94, 34),
  datetime       = "2024-01-01/2024-03-31",
  parameter_name = "Stage",
  label_col      = "name",
  popup          = "plot+csv"
)

5. Gridded climate data: PRISM

usgs-prism is different from the station collections. It advertises position and cube, and its parameters are gridded monthly climate variables.

edr_parameters(wwdh, "usgs-prism")[, c("id", "name", "unit_symbol")]
#> # A tibble: 3 x 3
#>   id    name                                  unit_symbol
#>   <chr> <chr>                                 <chr>
#> 1 ppt   Mean monthly precipitation            mm/month
#> 2 tmn   Minimum monthly temperature           deg C
#> 3 tmx   Maximum Monthly Temperature           deg C

Use edr_position() when you want the monthly series at one point:

prism_point <- edr_position(
  wwdh, "usgs-prism",
  coords         = c(-112.4, 36.9),
  datetime       = "2023-01-01/2023-02-28",
  parameter_name = "ppt"
)

covjson_to_tibble(prism_point)
#> # A tibble: 2 x 9
#>   coverage_id parameter parameter_label unit     datetime        x     y     z value
#>   <chr>       <chr>     <chr>           <chr>    <dttm>      <dbl> <dbl> <dbl> <dbl>
#> 1 1           ppt       Mean monthly... mm/month 2023-01-01 -112.  36.9    NA ...
#> 2 1           ppt       Mean monthly... mm/month 2023-02-01 -112.  36.9    NA ...

Use edr_cube() when you want a small grid:

prism_grid <- edr_cube(
  wwdh, "usgs-prism",
  bbox           = c(-112.6, 36.7, -112.2, 37.1),
  datetime       = "2023-01-01/2023-02-28",
  parameter_name = c("ppt", "tmx")
)

grid_tbl <- covjson_to_tibble(prism_grid)
grid_tbl[, c("parameter", "datetime", "x", "y", "value")]
#> # A tibble: 324 x 5
#>   parameter datetime                x     y value
#>   <chr>     <dttm>              <dbl> <dbl> <dbl>
#> 1 ppt       2023-01-01 00:00:00 -113.  37.1  116.
#> 2 ppt       2023-01-01 00:00:00 -113.  37.1  120.
#> 3 ppt       2023-01-01 00:00:00 -112.  37.1  131.
#> # i 321 more rows

edr_map(grid_tbl, initial = list(parameter = "ppt"))

For grid data, edr_map() builds an interactive cell map with selectors for parameter and time.

6. Feature and catalog layers

Some WWDH collections do not advertise EDR data queries. These are usually feature layers: polygons, lines, or points with attributes. They still matter for water analysis because they can carry forecast products, drought categories, basin summaries, boundaries, or current status attributes.

The workflow is:

q <- edr_queryables(wwdh, "snotel-huc06-means")
names(q$properties)
#> [1] "geometry"
#> [2] "name"
#> [3] "geoconnex_url"
#> [4] "id"
#> [5] "number_of_stations_used_for_basin_index_calculation"
#> [6] "current_snow_water_equivalent_relative_to_thirty_year_avg"
#> [7] "basin_index"

huc_swe <- edr_items(wwdh, "snotel-huc06-means", limit = 3)
names(huc_swe)
#>  [1] "id"
#>  [2] "name"
#>  [3] "station_list"
#>  [4] "current_snow_water_equivalent_relative_to_thirty_year_avg"
#>  [5] "basin_index"
#>  [6] "median_values"
#>  [7] "latest_values"
#>  [8] "number_of_stations_used_for_basin_index_calculation"
#>  [9] "geoconnex_url"
#> [10] "latest_full_day_of_data"
#> [11] "geometry"

For catalog layers, treat /items as a feature download, not as a time-series sampling query. If the layer is large or provider-backed, begin with a small bbox or a low limit. Some upstream wrappers are sensitive to particular filter combinations, so if a request fails, try these in order:

Open the HTML collection page with ?f=html.
Open the queryables page and check exact field names.
Try /items?f=json&limit=1.
Try /items?f=json&bbox=minx,miny,maxx,maxy.
Remove optional filters and add them back one at a time.

7. Browser and curl equivalents

You do not have to use R for the exploration step. Every helper above maps to a plain URL.

Collection metadata:

https://api.wwdh.internetofwater.app/collections/rise-edr?f=html
https://api.wwdh.internetofwater.app/collections/rise-edr?f=json

Find SNOTEL stations in a bbox:

https://api.wwdh.internetofwater.app/collections/snotel-edr/locations?f=json&bbox=-107,37,-105,40

Sample PRISM precipitation at one point:

https://api.wwdh.internetofwater.app/collections/usgs-prism/position?f=json&coords=POINT(-112.4%2036.9)&datetime=2023-01-01/2023-02-28&parameter-name=ppt

Fetch feature items from a catalog collection:

https://api.wwdh.internetofwater.app/collections/snotel-huc06-means/items?f=json&limit=3

With curl, quote URLs that contain ? or & so your shell does not interpret them:

curl -sS \
  'https://api.wwdh.internetofwater.app/collections/usgs-prism?f=json'

8. Practical troubleshooting

When a WWDH request fails, it is usually one of these:

Symptom	Likely cause	What to try
HTTP 404 on a query verb	The collection does not support that query type	Check `data_queries` and switch verbs
HTTP 400 or 500 on a large query	The request is too broad or a source wrapper failed	Reduce bbox, date range, and parameter count
Empty features	The bbox/date/parameter combination has no data, or the provider ignores one filter	Confirm with the HTML page and try a known active area
`edr_parameters()` is empty	The collection is likely feature/catalog style	Use `edr_queryables()` and `edr_items()`
Unexpected station IDs	Provider-specific IDs differ from names or display codes	Use the feature `id` or the id column returned by `edr_locations()`
Values are character instead of numeric	At least one returned parameter had non-numeric values	Query numeric and categorical parameters separately

Good habits:

Start with one parameter and one short time window.
Use a bbox even when the endpoint can technically return everything.
Prefer cube for bulk station/grid retrieval when it is advertised.
Prefer /items for feature/catalog layers with no EDR data queries.
Save intermediate results as CSV or RDS once you have a query you trust, rather than re-hitting the live service during every analysis step.