tidyhydat package
This vignette will outline a few key options that will hopefully make
tidyhydat useful.
HYDAT download
To use many of the functions in the tidyhydat package
you will need to download a version of the HYDAT database, Environment
and Climate Change Canada’s database of historical hydrometric data then
tell R where to find the database. Conveniently tidyhydat
does all this for you via:
This downloads the most recent version of HYDAT and then saves it in
a location on your computer where tidyhydat’s function will
look for it. Do be patient though as this takes a long time! To see
where HYDAT was saved you can run hy_dir(). Now that you
have HYDAT downloaded and ready to go, you are all set to begin some
hydrologic analysis.
Usage
Most functions in tidyhydat follow a common argument
structure. We will use the hy_daily_flows() function for
the following examples though the same approach applies to most
functions in the package (See ls("package:tidyhydat") for a
list of exported objects). Much of the functionality of
tidyhydat originates with the choice of hydrometric
stations that you are interested in. A user will often find themselves
creating vectors of station numbers. There are several ways to do
this.
The simplest case is if you would like to extract only station. You
can supply this directly to the station_number
argument:
## Queried from version of HYDAT released on 2025-10-14
## Observations: 32,081
## Measurement flags: 6,243
## Parameter(s): Flow
## Date range: 1914-01-01 to 2023-12-31
## Station(s) returned: 1
## Stations requested but not returned:
## All stations returned.
## # A tibble: 32,081 × 5
## STATION_NUMBER Date Parameter Value Symbol
## <chr> <date> <chr> <dbl> <chr>
## 1 08LA001 1914-01-01 Flow 144 <NA>
## 2 08LA001 1914-01-02 Flow 144 <NA>
## 3 08LA001 1914-01-03 Flow 144 <NA>
## 4 08LA001 1914-01-04 Flow 140 <NA>
## 5 08LA001 1914-01-05 Flow 140 <NA>
## 6 08LA001 1914-01-06 Flow 136 <NA>
## 7 08LA001 1914-01-07 Flow 136 <NA>
## 8 08LA001 1914-01-08 Flow 140 <NA>
## 9 08LA001 1914-01-09 Flow 140 <NA>
## 10 08LA001 1914-01-10 Flow 140 <NA>
## # ℹ 32,071 more rowsAnother method is to use hy_stations() to generate your
vector which is then given the station_number argument. For
example, we could take a subset for only those active stations within
Prince Edward Island (Province code:PE) and then create vector for
hy_daily_flows():
PEI_stns <- hy_stations() |>
filter(HYD_STATUS == "ACTIVE") |>
filter(PROV_TERR_STATE_LOC == "PE") |>
pull_station_number()
PEI_stns## [1] "01CA003" "01CB002" "01CB004" "01CB018" "01CC002" "01CC005" "01CC010" "01CC011" "01CD005"## Queried from version of HYDAT released on 2025-10-14
## Observations: 127,022
## Measurement flags: 22,288
## Parameter(s): Flow
## Date range: 1961-08-01 to 2024-12-31
## Station(s) returned: 9
## Stations requested but not returned:
## All stations returned.
## # A tibble: 127,022 × 5
## STATION_NUMBER Date Parameter Value Symbol
## <chr> <date> <chr> <dbl> <chr>
## 1 01CA003 1961-08-01 Flow NA <NA>
## 2 01CB002 1961-08-01 Flow NA <NA>
## 3 01CA003 1961-08-02 Flow NA <NA>
## 4 01CB002 1961-08-02 Flow NA <NA>
## 5 01CA003 1961-08-03 Flow NA <NA>
## 6 01CB002 1961-08-03 Flow NA <NA>
## 7 01CA003 1961-08-04 Flow NA <NA>
## 8 01CB002 1961-08-04 Flow NA <NA>
## 9 01CA003 1961-08-05 Flow NA <NA>
## 10 01CB002 1961-08-05 Flow NA <NA>
## # ℹ 127,012 more rowsWe can also merge our station choice and data extraction into one unified pipe which accomplishes a single goal. For example if for some reason we wanted all the stations in Canada that had the name “Canada” in them we unify that selection and data extraction process into a single pipe:
## Queried from version of HYDAT released on 2025-10-14
## Observations: 92,416
## Measurement flags: 28,256
## Parameter(s): Flow
## Date range: 1918-08-01 to 2024-12-31
## Station(s) returned: 7
## Stations requested but not returned:
## All stations returned.
## # A tibble: 92,416 × 5
## STATION_NUMBER Date Parameter Value Symbol
## <chr> <date> <chr> <dbl> <chr>
## 1 01AK001 1918-08-01 Flow NA <NA>
## 2 01AK001 1918-08-02 Flow NA <NA>
## 3 01AK001 1918-08-03 Flow NA <NA>
## 4 01AK001 1918-08-04 Flow NA <NA>
## 5 01AK001 1918-08-05 Flow NA <NA>
## 6 01AK001 1918-08-06 Flow NA <NA>
## 7 01AK001 1918-08-07 Flow 1.78 <NA>
## 8 01AK001 1918-08-08 Flow 1.78 <NA>
## 9 01AK001 1918-08-09 Flow 1.5 <NA>
## 10 01AK001 1918-08-10 Flow 1.78 <NA>
## # ℹ 92,406 more rowsWe saw above that if we were only interested in a subset of dates we
could use the start_date and end_date
arguments. A date must be supplied to both these arguments in the form
of YYYY-MM-DD. If you were interested in all daily flow data from
station number “08LA001” for 1981, you would specify all days in 1981
:
This generally outlines the usage of the HYDAT functions within
tidyhydat.
Real-time functions
In addition to the approved and vetted data in the HYDAT database
ECCC also offers unapproved data that is subject to revision.
tidyhydat provides three functions to access these data
sources. Remember these are unapproved data and should
treated as such:
realtime_stations()realtime_dd()
Not every stations is currently part of the real-time network.
Therefore realtime_stations() points to a (hopefully)
updated ECCC data file of active real-time stations. We can use the
realtime_stations() functionality to get a vector of
stations by jurisdiction. For example, we can choose all the stations in
Prince Edward Island using the following:
hy_stations() and realtime_stations()
perform similar tasks albeit on different data sources.
hy_stations() extracts directly from HYDAT. In addition to
real-time stations, hy_stations() outputs discontinued and
non-real-time stations:
This is contrast to realtime_stations() which downloads
all real-time stations. Though this is not always the case, it is best
to use realtime_stations() when dealing with real-time data
and hy_stations() when interacting with HYDAT. It is also
appropriate to filter the output of hy_stations() by the
REAL_TIME column.
Meterological Service of Canada datamart -
realtime_dd()
To download real-time data using the datamart we can use
approximately the same conventions discussed above. Using
realtime_dd() we can easily select specific stations by
supplying a station of interest:
Another option is to provide simply the province as an argument and download all stations from that province:
Search functions
You can also make use of auxiliary functions in
tidyhydat called search_stn_name() and
search_stn_number() to look for matches when you know part
of a name of a station. For example:
## # A tibble: 10 × 5
## STATION_NUMBER STATION_NAME PROV_TERR_STATE_LOC LATITUDE LONGITUDE
## <chr> <chr> <chr> <dbl> <dbl>
## 1 10AA001 LIARD RIVER AT UPPER CROSSING YT 60.1 -129.
## 2 10AA006 LIARD RIVER BELOW SCURVY CREEK YT 60.8 -131.
## 3 10BE001 LIARD RIVER AT LOWER CROSSING BC 59.4 -126.
## 4 10BE005 LIARD RIVER ABOVE BEAVER RIVER BC 59.7 -124.
## 5 10BE006 LIARD RIVER ABOVE KECHIKA RIVER BC 59.7 -127.
## 6 10EC004 SOUTH NAHANNI RIVER NEAR LIARD RIVER CONFLUENCE NT 61.0 -123.
## 7 10ED001 LIARD RIVER AT FORT LIARD NT 60.2 -123.
## 8 10ED002 LIARD RIVER NEAR THE MOUTH NT 61.7 -121.
## 9 10ED008 LIARD RIVER AT LINDBERG LANDING NT 61.1 -123.
## 10 10GC004 MACKENZIE RIVER ABOVE LIARD RIVER NT 61.9 -121.Similarly, search_stn_number() can be useful if you are
interested in all stations from the 08MF sub-sub-drainage:
## # A tibble: 54 × 5
## STATION_NUMBER STATION_NAME PROV_TERR_STATE_LOC LATITUDE LONGITUDE
## <chr> <chr> <chr> <dbl> <dbl>
## 1 08MF001 ANDERSON RIVER NEAR BOSTON BAR BC 49.8 -121.
## 2 08MF002 BOULDER CREEK NEAR LAIDLAW BC 49.3 -122.
## 3 08MF003 COQUIHALLA RIVER NEAR HOPE BC 49.4 -121.
## 4 08MF004 FRASER RIVER ABOVE THOMPSON RIVER BC 50.2 -122.
## 5 08MF005 FRASER RIVER AT HOPE BC 49.4 -121.
## 6 08MF006 WAHLEACH CREEK NEAR LAIDLAW (UPPER STATION) BC 49.3 -122.
## 7 08MF007 NAHATLATCH RIVER AT OUTLET OF FRANCES LAKE BC 50.0 -122.
## 8 08MF008 NAHATLATCH RIVER NEAR KEEFERS BC 50.0 -122.
## 9 08MF009 SILVERHOPE CREEK NEAR HOPE BC 49.4 -121.
## 10 08MF011 STEIN RIVER NEAR LYTTON BC 50.3 -122.
## # ℹ 44 more rowsUsing joins
Sometimes it is required to make use of information from two tables
from HYDAT. In some cases, we need to combine the information into one
table using a common column. Here we will illustrate calculating runoff
by combining the hy_stations tables with the
hy_daily_flows table by the STATION_NUMBER
column:
stns <- c("08NH130", "08NH005")
runoff_data <- hy_daily_flows(station_number = stns, start_date = "2000-01-01") |>
left_join(
hy_stations(station_number = stns) |>
select(STATION_NUMBER, STATION_NAME, DRAINAGE_AREA_GROSS),
by = "STATION_NUMBER") |>
## conversion to mm/d
mutate(runoff = Value / DRAINAGE_AREA_GROSS * 86400 / 1e6 * 1e3)
ggplot(runoff_data) +
geom_line(aes(x = Date, y = runoff, colour = STATION_NAME)) +
labs(y = "Mean daily runoff [mm/d]") +
scale_colour_viridis_d(option = "C") +
theme_minimal() +
theme(legend.position = "bottom")
This is an effective way to make
use of the variety of tables available in HYDAT.